Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Srivastava, Stuti | Bansal, Richa | Thapar, Antika

Article Type: Research Article

Abstract: Bandwidth consecutive multicoloring problem is also known as a b-coloring problem. Let G  = (V , E ) be a graph where V is the set of vertices and E is the set of edges. Let each vertex v of V has a positive integer weight b  (v ) and each edge (v , w ) of E has a non-negative integer weight b  (v , w ). A bandwidth consecutive multicoloring of a graph is a problem of assigning b  (v ) consecutive positive integers to each vertex v of V in such …a manner that the difference between all the integers of vertex v and all the integers of vertex w is greater than b  (v , w ). The maximum integer assigned in this coloring is called the span of the coloring. The b-coloring is the problem of minimizing this span. No metaheuristic is proposed for general graphs so far for this problem till date as it is strongly NP-hard. In this paper, we proposed three heuristics for the problem including a greedy randomized adaptive search procedure (GRASP). The efficiency of these algorithms is tested on benchmark graphs and their performance is compared among themselves. Experimental results showed that among all three proposed heuristics, GRASP performed well for the given problem. Show more

Keywords: Graph theory, bandwidth coloring, greedy coloring, GRASP

DOI: 10.3233/JIFS-224242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9991-10002, 2023

Authors: Weng, Zhi | Liu, Ke | Zheng, Zhiqiang

Article Type: Research Article

Abstract: The detection and identification of individual cattle plays an integral role in precision feeding and insurance claims, among others. Most current research is based on high-performance computing devices, which limits the application of deep learning techniques to mobile terminals. To this end, in this paper, we propose a channel-pruned YOLOv5 network-based method for cattle face detection on mobile terminals, referred to as NS-YOLO. First, the original model is sparsely trained and a sparse regularization penalty term is applied to the BN layers, then the corresponding mask values are labeled according to different weight thresholds, and the channels are pruned with …global thresholds. Second, the detection precision is recovered by fine-tuning the model. Finally, the NCNN forward inference framework is used to quantize the model and an Android-based cattle face detection application is developed. Experimental results show that the model size, number of parameters and FLOPs are reduced by 86.10%, 88.19% and 63.25%, respectively, and the inference time is reduced by 35.53% compared to the original model, while mAP0.5 is reduced by only 1.6%. In particular, the 16-bit quantized model reduces the model size by 93.97% and the inference time by 34.97% compared to the original model on the mobile side. The results show that the proposed method can be easily deployed in resource-constrained mobile devices and has great advantages in practical engineering applications. Show more

Keywords: Cattle face detection, channel pruning, YOLOv5, model quantization, mobile deployment

DOI: 10.3233/JIFS-232213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10003-10020, 2023

Authors: Tang, Bin | Xie, Kai | Tao, Wenbin | Liu, Zhenyu | Zhu, Chuanqi | Zhao, Neng | Wang, Yiyang

Article Type: Research Article

Abstract: In order to improve bearing capacity of rockbolts in deep-buried coal mine roadways, orthogonal tests were conducted to study influencing factors of rockbolt anchoring effect. Wavelet neural network model was introduced to predict the pull-out force of rockbolt. The activation and output functions of the wavelet neural network were improved, and the scaling and translation parameters were also modified by using the gradient descent method. These improvements enhanced the approximation rate of the wavelet neural network model, and solve the problem that the wavelet transform method is monotonous and difficult to adapt to the complex and variable engineering conditions. Research …results illustrated that The value of the ultimate pull-out force is positively correlated with the strength of the specimen and pre-tension value of the specimen. According to the test results, the coal mine roadway support scheme was optimized, and the high prestress full-length anchoring rockbolt support technology was proposed. The effectiveness of research was verified through the engineering applications and in-situ monitoring results. Show more

Keywords: Rockbolt, improved Wavelet Neural Network, high prestress, pull-out test, field performance

DOI: 10.3233/JIFS-232435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10021-10032, 2023

Authors: Li, Jing | Li, Chengyu | Meng, Lusha

Article Type: Research Article

Abstract: Global warming caused by excessive emissions of carbon dioxide (CO2 ) has become a hot topic globally in today’s society, and optimizing carbon emission performance (CEP) is an effective way to alleviate CO2 emissions. Many studies have explored CEP at the global, national, provincial and sector levels. However, due to the difficulty in obtaining energy consumption data, there is a lack of studies at the urban agglomeration and city levels. Taking the urban agglomeration dimension as the starting point, this paper constructs an improved epsilon-based measure (EBM) model to measure the CEP of the Yellow River Basin. A spatial …data analysis model was introduced to explore the regional spatial characteristics of CEP. The newly developed spatial statistical model was used to study the driving factors of CEP. The results showed that: (1) The overall CEP of the Yellow River Basin was relatively high, showing an upward trend of volatility. There were significant differences between the seven urban agglomerations and 69 cities. (2) The CEP of the Yellow River Basin showed a trend of spatial agglomeration. The urban agglomerations of the eastern region showed a low-value agglomeration phenomenon, and the urban agglomerations of the central and western regions showed a trend of high-value agglomeration. (3) Economic development level (PGGDP), technological progress (TP), industrialization level (IND) and human capital (HC) can play a positive role in promoting the improvement in CEP, and population structure (PD) and energy structure (ES) can play a negative role in promoting the improvement in CEP. Industrial agglomeration (IA) and CEP show a “U"-shaped relationship that first inhibits and then promotes. In addition, foreign direct investment (FDI), IND, and HC have significant spatial spillover effects on neighboring cities. Show more

Keywords: Yellow River Basin, urban agglomeration, carbon emission performance, spatial Durbin model, spatial agglomeration

DOI: 10.3233/JIFS-233246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10033-10052, 2023

Authors: Shu, Mingming | Liu, Xiaoyu | Zhou, Hongming

Article Type: Research Article

Abstract: In order to better realize the effective display of painting art, this paper puts forward an interactive modeling method of structural sense of painting art communication from the perspective of media integration. From the perspective of comprehensive media, the painting art is spread and displayed, and the interactive evaluation index of painting art communication structure sense is constructed, and the interactive behavior evaluation model of painting art communication structure sense is constructed to realize the interactive modeling of communication structure sense. The experimental results show that from the perspective of integrating media, the somatosensory interaction mode of the communication structure …of painting art is highly practical in the practical application process, which meets the research requirements and can realize the effective display of painting art in a modified way. Show more

Keywords: Integrated media perspective, painting art, somatosensory interaction

DOI: 10.3233/JIFS-234284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10053-10062, 2023

Authors: Zhang, Hu | Bai, Ping | Li, Ru

Article Type: Research Article

Abstract: Short text classification task is a special kind of text classification task in that the text to be classified is generally short, typically generating a sparse text representation that lacks rich semantic information. Given this shortcoming, scholars worldwide have explored improved short text classification methods based on deep learning. However, existing methods cannot effectively use concept knowledge and long-distance word dependencies. Therefore, based on graph neural networks from the perspective of text composition, we propose concept and dependencies enhanced graph convolutional networks for short text classification. First, the co-occurrence relationship between words is obtained by sliding window, the inclusion relationship …between documents and words is obtained by TF-IDF, long-distance word dependencies is obtained by Stanford CoreNLP, and the association relationship between concepts in the concept graph with entities in the text is obtained through Microsoft Concept Graph. Then, a text graph is constructed for an entire text corpus based on the four relationships. Finally, the text graph is input into graph convolutional neural networks, and the category of each document node is predicted after two layers of convolution. Experimental results demonstrate that our proposed method overall best on multiple classical English text classification datasets. Show more

Keywords: Short text classification, Knowledge graph, Graph convolutional neural networks, Long-distance dependency, Building graph for text

DOI: 10.3233/JIFS-222407

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10063-10075, 2023

Authors: Guo, Peng | Wang, Xiaonan | Zhang, Duo

Article Type: Research Article

Abstract: Punishment promotes cooperation among selfish agents. Unlike previous studies, we propose a new supervision (heterogeneous preference supervision, HPS) mechanism based on the original random supervision (ORS) mechanism considering regulators’ limited supervision ability and agents’ heterogeneous preferences. The concepts of exemption list capacity, observation period, and removal time are introduced as the variables under the HPS mechanism. A public goods game model is built to verify the supervision effects under the ORS and HPS mechanisms. The simulation results show that the HPS mechanism can promote cooperation more than the ORS mechanism. Increasing the exemption list capacity can make regulators pay more …attention to defectors and improve the cooperation level. Setting a relatively moderate observation period benefits a better supervision effect, while a too-small or too-large observation period leads to the collapse of cooperation. Shortening the removal time can increase the updating speed of the exemption list and enhance the role of the exemption list, resulting in improving the fraction of cooperators. Show more

Keywords: Public goods game, supervision mechanism, supervision ability, heterogeneous preference, exemption list

DOI: 10.3233/JIFS-230775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10077-10088, 2023

Authors: Bharathi, J. | Nandhini, S.

Article Type: Research Article

Abstract: This paper explores the behaviour of a Bulk Arrival Retrial Queue Model (BARQ) with two phases of service under the Bernoulli Vacation schedule and Breakdown (BVSB). Each batch of customers arriving the system finds if the server is available, instantly utilizes the service. If the server is busy, under breakdown, or taking a vacation, then the customers enter into the orbit. After completing both service stages, the server will either take a vacation with probability p or wait until the next customer arrives with probability 1 - p or q . Our approach considers the nature of the customer as …balking and also takes into account the breakdown of server, which may occur instantaneously during any stage of service. Significant performance measures have been derived and presented. A numerical study of the proposed model is carried out using MATLAB and results were reported. Show more

Keywords: Retrial Queues, two types of service, Bernoulli Vacation, steady-state, Fortuitous Breakdown, impatient customers

DOI: 10.3233/JIFS-231195

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10089-10098, 2023

Authors: Thilagavathy, A. | Mohanaselvi, S.

Article Type: Research Article

Abstract: Consolidating cubical fuzzy numbers (CFNs) is essential in an uncertain decision-making process. This study focuses on creating innovative cubical fuzzy aggregation operators based on the newly proposed Einstein operational laws, utilizing the Bonferroni mean function to capture the interrelationships among the aggregated CFNs. The first contribution of this paper is introducing a novel cubical fuzzy Einstein Bonferroni mean averaging operator. Building upon this operator, we extend our research to develop cubical fuzzy Einstein Bonferroni mean weighted, ordered weighted, and hybrid averaging operators, taking into account the weights of the aggregated CFNs. To ensure their effectiveness, we thoroughly investigate the desirable …properties of these proposed operators. Furthermore, we leverage the introduced operators to establish a new approach known as the cubical fuzzy linear assignment method, which proves valuable in resolving multiple criteria group decision-making problems. As a practical demonstration of the method’s utility, we apply it to address a real-life challenge: identifying the optimal location for constructing a wind power plant under a cubical fuzzy environment. To validate the effectiveness of our approach, we compare its results with those obtained using existing methods from the literature. Additionally, we conduct a statistical analysis to visualize the correlative conjunction between the ranking outcomes obtained by different operators Show more

Keywords: Cubical fuzzy set, Einstein operational laws, Bonferroni mean, averaging aggregation operators, linear assignment method, wind power plant location selection

DOI: 10.3233/JIFS-232252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10099-10125, 2023

Authors: Guo, Yijian | Sun, Kaiqiong | Luo, Gang | Wang, Meng

Article Type: Research Article

Abstract: Leaf segmentation is crucial for plant recognition, especially for tree species identification. In natural environments, leaf segmentation can be very challenging due to the lack of prior information about leaves and the variability of backgrounds. In typical applications, supervised algorithms often require pixel-level annotation of regions, which can be labour-intensive and limited to identifying plant species using pre-labelled samples. On the other hand, traditional unsupervised image segmentation algorithms require specialised parameter tuning for leaf images to achieve optimal results. Therefore, this paper proposes an unsupervised leaf segmentation method that combines mutual information with neural networks to better generalise to unknown …samples and adapt to variations in leaf shape and appearance to distinguish and identify different tree species. First, a model combining a Variational Autoencoder (VAE) and a segmentation network is used as a pre-segmenter to obtain dynamic masks. Secondly, the dynamic masks are combined with the segmentation masks generated by the mask generator module to construct the initial mask. Then, the patcher module uses the Mutual Information Minimum (MIM) loss as an optimisation objective to reconstruct independent regions based on this initial mask. The process of obtaining dynamic masks through pre-segmentation is unsupervised, and the entire experimental process does not involve any label information. The experimental method was performed on tree leaf images with a naturally complex background using the publicly available Pl@ntLeaves dataset. The results of the experiment showed that compared to existing excellent methods on this dataset, the IoU (Intersection over Union) index increased by 3.9%. Show more

Keywords: Leaf extraction, deep learning, unsupervised image segmentation, mutual information, VAE

DOI: 10.3233/JIFS-232696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10127-10139, 2023

Authors: Umamageswari, A. | Deepa, S. | Bhagyalakshmi, A. | Sangari, A. | Raja, K.

Article Type: Research Article

Abstract: To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as …well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network’s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry. Show more

Keywords: Deep learning, R-CNN, emotion recognition, facial expression, computer vision

DOI: 10.3233/JIFS-233842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10141-10155, 2023

Authors: Liu, Jun

Article Type: Research Article

Abstract: Composite cylindrical shells play a crucial role in aerospace and marine structures. This study investigates the optimal structure for cylindrical multilayer composite shells under the effect of axial pressure using the finite element method and NSGA-II genetic algorithm to determine the maximum buckling load capacity. The critical buckling load of multilayer composite shells depends on various parameters, such as fiber angle, the number of layers, the material of the layers, and their thickness. The objective functions are used to increase the structure load capacity and reduce its weight. ABAQUS software was used to perform finite element analysis on the composite …cylindrical shell for determining the buckling load. Using the response surface model, the relationship between variables and objective functions has been determined. Results of the proposed response surface model for the training stages are evaluated using various statistical indices and the root mean square error for buckling load and shell weight variables is 0.065 and 0.140, respectively. In the next step, the NSGA-II genetic optimization algorithm was used to modify the layout and thickness of the composite layers to optimize the buckling strength and weight of the structure. A genetic algorithm based on NSGA-II was used to optimize the geometric characteristics. Show more

Keywords: Multi-objective optimization, NSGA-II, buckling load, genetic algorithm, cylindrical shell

DOI: 10.3233/JIFS-230826

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10157-10165, 2023

Authors: Sun, Tiantian

Article Type: Research Article

Abstract: A scientific financial transfer payment system is an important guarantee for promoting the modernization of the national governance system and governance capacity. The new development concept puts forward new requirements for fiscal governance at a new historical stage. The reform and improvement of the special transfer payment system match the responsibility and responsibility of fiscal governance, and better serve the national strategic pattern of rural revitalization and common prosperity. The performance evaluation of financial special poverty alleviation (SPA) development funds is conducive to improving the efficiency of fund utilization, achieving the radiation effect and multiple effect of financial SPA development …funds, improving the ability of financial transfer payments, and enhancing the modernization of national financial governance capabilities. The performance evaluation of financial SPA development funds under the background of rural revitalization is a multiple attribute group decision making (MAGDM). Based on the existing MABAC model, the MABAC model is extended to 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). Firstly, the definitions of 2TLPFSs, 2TLPFWA operator and 2TLPFWG operator is introduced. Then, the existing MABAC method is also introduced. The 2-tuple linguistic Pythagorean fuzzy number MABAC method (2TLPFN-MABAC) is constructed to cope with the MAGDM under 2TLPFSs. Finally, a case study for performance evaluation of financial SPA development funds under the background of rural revitalization is constructed and some comparative analyses is employed to verify the 2TLPFN-MABAC method. Show more

Keywords: Multiple attribute group decision making (MAGDM), MABAC method, 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), Financial SPA development funds

DOI: 10.3233/JIFS-232168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10167-10181, 2023

Authors: Sun, Yin-Kun | Hua, Jun | Li, Yan-Na | Chen, Guang-Wei

Article Type: Research Article

Abstract: With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating …process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent. Show more

Keywords: Woodworking, fuzzy control, thermal-mechanical coupling, thermodynamic coupling, delay effect

DOI: 10.3233/JIFS-232242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10183-10192, 2023

Authors: Sritha, P. | Valarmathi, R.S. | Poongodi, C.

Article Type: Research Article

Abstract: One of the best methods for assessing a baby’s health is foetal electrocardiography (FECG). Previously restricted to more widespread global disorders such as common ischemia, it is new way to investigating foetal heart rate irregularities. Current prenatal monitoring practices ignore critical FECG waveform elements that are the foundation of both pediatric, adult cardiac assessment, and instead of focusing solely on the foetal heart rate. In this paper we proposed Double Multiply-and-Accumulate (MAC) approach used for package operators into a single DSP block of commercial FPGAs, theoretically doubling the calculation speed for FECG monitoring. For a variety of technical reasons, they …were using the Space-Time Block Code (STBC) monitoring mode of operation. To strengthen the security of FECG monitoring, the Advanced Encryption Standard (AES) method may be used with the double MAC operators using STBC based FECG monitoring that has been developed. The solution was then assessed using state-of-the-art the Space-Time Block Code (STBC) based FECG techniques, and its validity was confirmed using Verilog simulation and FPGA synthesis. The calculation throughput of an STBC-based FECG monitoring system was found to be doubled using the Double MAC approach. Our implementation result demonstrates that keys are necessary for 128-bit AES encoding and decoding operations via VHDL-coded transformations. It is now more vital than ever to do a feasibility analysis of any hardware design due to the increase in the number of ways presented for minimizing noise. The efficiency increased (92%), and the delay was decreased to 19.35 ns by employing this double MAC architecture. The simulation results demonstrate that transformations for coding on an FPGA are implemented using the Xilinx VIVADO tool. Show more

Keywords: FECG monitoring, Double MAC, STBC based FECG, Verilog, AES, Xilinx

DOI: 10.3233/JIFS-234164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10193-10211, 2023

Authors: Wang, Hui

Article Type: Research Article

Abstract: In Visual Communication Design (VCD), noise data is easy to appear, which reduces image quality and affects the effect of VCD. The non local mean image denoising algorithm is a good filtering denoising algorithm, but there are still issues of information interference and missing. To improve the performance of noise recognition and image denoising technology, this study proposes a non local mean image denoising algorithm based on machine learning technology. The whale optimization algorithm, as a machine learning technique, has good performance in seeking optimal solutions. Therefore, it is applied to optimize the filtering parameters of non local mean image …denoising algorithms to improve the perforGAmance of non local mean image denoising algorithms. To address the shortcomings of the whale optimization algorithm, BP neural network is introduced for optimization. Finally, the experiment uses the improved particle swarm optimization algorithm to optimize the BPNN and applies it to the recognition and classification of noise data. Combining the above contents, the IBINLM image denoising algorithm is constructed experimentally. It is verified that the IPSO-BPNN model’s loss value is 0.12; The recognition accuracy of the model for noise pixels is 98.64%; F1 value reaches 96.32%; The fitting degree reaches 0.983. The PSNR of IBINLM algorithm is 35.86 dB; MSE is 0.29; AUC value reaches 0.903. The results show that the IPSO-BPNN model and IBINLM image denoising algorithm have better performance compared to other models, which can improve the quality of visual communication works, playing an essential role in image transmission and storage in visual communication design. Show more

Keywords: Machine learning, non-local mean, image denoising, whale optimization algorithm, visual communication design

DOI: 10.3233/JIFS-234632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10213-10225, 2023

Authors: Guo, Ying | Peng, Jinzhu | Ding, Shuai | Liang, Jing | Wang, Yaonan

Article Type: Research Article

Abstract: In this paper, a variable impedance control method is proposed for uncertain robotic systems based on a nonlinear force contact-based flexible environmental model. First, a nonlinear force contact model between the rigid manipulator and flexible environment is applied to the compliant control of the manipulator, which can avoid excessive force overshoot that usually exists in the traditional spring-damping environmental model. Then, to achieve better force/position tracking performances, a fuzzy-based adaptive variable impedance controller is designed based on the force contact-based flexible environmental model, where the impedance parameters are adjusted online through the force and position feedback of the robotic system, …and the fuzzy logic system is used to compensate the uncertainties. Moreover, the stability of the adaptive variable impedance control scheme is proved by the Routh stability criterion, and the boundness of all the signals in the closed-loop control system is guaranteed by the Lyapunov stability theorem. Finally, the effectiveness of the proposed method is verified by the simulation of a two-link manipulator, and the results demonstrate that the performances of position tracking are improved, while the force overshoot and oscillation time are reduced. Show more

Keywords: Variable impedance control, flexible environment, fuzzy logic system, force contact model, robotic system

DOI: 10.3233/JIFS-224250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10227-10241, 2023

Authors: Kayyidavazhiyil, Abhilash

Article Type: Research Article

Abstract: Prediction of malicious attacks and monitoring of network behaviour is significant for providing security and mitigating the loss of credential information. In order to monitor network traffic and identify different types of attacks in the network, numerous existing algorithms have been provided for classifying unauthorized access from the authorized access. However, the traditional techniques have faced complications in satisfying the accuracy while making predictions of malicious activities. Detection accuracy have been addressed as a drawback which hinders in making appropriate identification of threats. In order to overcome such challenges, the proposed work is designed with effective IDS mechanism for detecting …and classifying the attacks taken from the UNSW-NB15 and NSL-KDD dataset. IDS (Intrusion Detection System) implementation is accomplished with three stages such as pre-processing is the initial phase in which scaling re-sizing of all images to similar width and height. Process of checking missing values reduces the computational complexities and enhances accuracy. Second stage is the novel feature-selection process accomplished by E-GSS (Enhanced Genetic Sine Swarm Intelligence) for selecting significant and optimal features. Finally, classification is the final phase in which intrusion is classified using novel DMH-ANN (Deep Meta-Heuristics Artificial Neural Network) which is internally being compared to three classifiers such as RF (Random Forest), NB (Naïve Bayes) and XG-Boost (Extreme Gradient). Experimental evaluation is carried out with the performance metrics such as accuracy, precision and recall and compared with existing algorithms for exhibiting the effectiveness of the proposed model. The research outcome reveals its efficiency in detecting and classifying attacks with greater accuracy. Show more

Keywords: Intrusion detection, UNSW-NB15 dataset, NSL-KDD, Genetic Sine Swarm, Metaheuristic ANN, Naïve Bayes, XG-Boost, random forest

DOI: 10.3233/JIFS-224283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10243-10265, 2023

Authors: Vaishnavi, D. | Balaji, G.N.

Article Type: Research Article

Abstract: Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising …performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters. Show more

Keywords: Copy Move technique, image forgery, deep learning, hyperparameter tuning, metaheuristics

DOI: 10.3233/JIFS-230291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10267-10280, 2023

Authors: Sri Geetha, M. | Grace Selvarani, A.

Article Type: Research Article

Abstract: Breast cancer is responsible for the deaths of hundreds of women every year. The manual identification of breast cancer has more difficulties, and have the possibility of error. Many imaging approaches are being researched for their potential to identify breast cancer (BC). Incorrect identification might sometimes result in unneeded therapy and diagnosis. Because of this, accurate identification of breast cancer may save a great number of patients from needing unneeded surgery and biopsies. Deep learning’s (DL) performance in the processing of medical images has substantially increased as a result of recent breakthroughs in the sector. Because of their improved capacity …to anticipate outcomes, deep learning algorithms are able to reliably detect BC from ultrasound pictures. Transfer learning is a kind of machine learning that reuses knowledge representations from public models that were built with the use of large-scale datasets. Transfer learning has been shown to often result in overfitting. The primary purpose of this research is to develop and provide suggestions for a deep learning model that is effective and reliable in the detection and classification of breast cancer. A tissue biopsy is obtained from the suspicious region in order to ascertain the nature of a breast tumor and whether or not it is cancerous. Tumors may take any of these forms. When the images have been reconstructed with the help of a variational autoencoder (VAE) and a denoising variational autoencoder (DVAE), a convolutional neural network (CNN) model is used. This will be the case because it opens up a new area of the field to be investigated. The histological subtypes of breast cancer are used in conjunction with the degree of differentiation to execute the task of breast cancer categorization. Show more

Keywords: Medical image classification, disease detection, deep learning, breast cancer, convolutional neural network (CNN), variationalautoencoder, histopathology image

DOI: 10.3233/JIFS-231345

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10281-10294, 2023

Authors: Gao, Caiwen | Zhang, Zhiqiang | Liu, Baoliang

Article Type: Research Article

Abstract: This paper extends the traditional multifactor population model from a probabilistic background to an uncertain background, allowing us to better account for the fact that the birth and death rates are sometimes affected by different, independent uncertain noises. The theory of multifactor uncertain differential equation is applied to analyze the novel model. First, a multifactor uncertain population model is developed and the solution of the model is obtained. Secondly, the stability in measure and stability in distribution of the multifactor uncertain population model are studied respectively, and the relationship between the two kinds of stability is further discussed. Then, a …method of moment estimation based on residuals is proposed for the unknown parameters in the multifactor uncertain population model. Finally, an example is given to illustrate the validity and rationality of the parameter estimation method. Show more

Keywords: Parameter estimation, residual, multifactor differential equation, stability

DOI: 10.3233/JIFS-232020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10295-10303, 2023

Authors: Mu, Yongan | Liu, Wei | Lu, Tao | Li, Juan | Gao, Sheng | Wang, Zihao

Article Type: Research Article

Abstract: The self-adaptive multi-agent system requires adaptive adjustments based on the dynamic environment during its runtime. Heterogeneous agent can accomplish different task goals, enhance the efficiency of system operation, but its complex collaboration problem poses new challenges to the study of verification of adaptive policies for heterogeneous multi-agents. This paper proposes a runtime verification method for self-adaptive multi-agent systems using probabilistic timed automata. The method constructs a probabilistic timed automaton model by formally describing the functional characteristics of heterogeneous agents and integrating random factors in the environment to simulate the operation process of the self-adaptive multi-agent system. Regarding the collaboration logic …among heterogeneous agents, security constraints are established to ensure the security of state transition processes during system operation. Combining model checking with runtime quantitative verification methods to conduct experiment and applying it in the case of an intelligent unmanned parking system. Experimental results manifest the correctness of the cooperation logic between agents can effectively ensure the stability of the system at runtime. Significant improvement in system uptime and efficiency compared to the initial system without runtime quantitative validation. Show more

Keywords: Self-adaptive system, heterogeneous multi-agent, probabilistic timed automata, agent cooperation logic, runtime quantitative verification

DOI: 10.3233/JIFS-232397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10305-10322, 2023

Authors: Xu, Zan | Lu, TongWei

Article Type: Research Article

Abstract: Some anomaly detection methods are based on CNN to fuse spatial and channel-wise information together within local receptive fields. However, the correlation between feature channels has not been fully utilized. Channel attention has been shown to model the interdependence between convolution feature channels and improve network representation. It is possible to introduce channel attention into anomaly detection. We attempt to directly embed the SE(Squeeze and Excitation) module into the convolutional layer but reduced anomaly detection performance. Therefore, we propose a lightweight channel attention module C-SE(Current Squeeze and Excitation) suitable for anomaly detection. C-SE module not only improves the representation ability …of depth convolutional neural network but also has a significant effect on texture anomaly detection. C-SE module body is constructed by average pooling and maximum pooling branches, which ensure that local salient features of the image are not lost. Then reduce the negative impact of feature calibration through a long connection. In addition, the improvement of classifier plays an important role. Experimental results have shown that the proposed method outperforms the Patch SVDD methods by 3% in image-level AUROC and 0.7% in pixel-level AUROC on the MVTec benchmark. The higher AUROC score and the faster rate of convergence prove the effectiveness of the method. Show more

Keywords: Anomaly detection, channel attention, feature calibration, texture, MVTec

DOI: 10.3233/JIFS-232677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10323-10334, 2023

Authors: Manivannan, K. | Sathiamoorthy, S.

Article Type: Research Article

Abstract: In the last decades, Tuberculosis (TB) can be considered a serious illness affecting people over the globe and it leads to mortality when left untreated. Chest X-Ray (CXR) is the topmost selection for the recognition of pulmonary diseases in hospitals since it can be cost-efficient and easily available in many nations. But, manual CXR image screening is a huge load for radiologists, which results in a maximum inter-observer discrepancy rate. At present, Computer-Aided Detection (CAD) is a powerful imaging equipment for detecting and screening dangerous ailments. In recent times, Deep Learning (DL) based CAD schemes have demonstrated positive outcomes in …the recognition of TB diseases. This study introduces an Egret Swarm Optimization Algorithm with Deep Feature Fusion based Tuberculosis Classification (ESOA-DFFTC) technique on CXR Images. The presented ESOA-DFFTC technique utilizes feature fusion and tuning processes for the classification of TB. To accomplish this, the ESOA-DFFTC model first exploits the Gaussian Filtering (GF) approach for image denoising purposes. Next, the ESOA-DFFTC model performs a feature fusion process using three DL models namely ResNeXt-50, MobileNetv2, and Xception. To enhance the achievement of the DL models, the ESOA-based hyperparameter optimizer is implemented in the study. For TB classification, the ESOA-DFFTC methodology uses an Arithmetic Optimization Algorithm (AOA) with Weight-Dropped Long Short-Term Memory (WDLSTM) methodology. The investigational output of the ESOA-DFFTC system was examined on a benchmark medical imaging dataset. A wide comparative investigation stated the greater achievement of the ESOA-DFFTC system over other current algorithms. Show more

Keywords: Computer-aided diagnosis, machine learning, Tuberculosis, chest X-Ray images, feature fusion, Metaheuristics

DOI: 10.3233/JIFS-233975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10335-10347, 2023

Authors: Fang, Yuhua | Xie, Zhijun | Chen, Kewei | Huang, Guangyan | Zarei, Roozbeh | Xie, Yuntao

Article Type: Research Article

Abstract: Traditional Simultaneous Localization and Mapping application in dynamic situations is constrained by static assumptions. However, the majority of well-known dynamic SLAM systems use deep learning to identify dynamic objects, which creates the issue of trade-offs between accuracy and real-time. To tackle this issue, this work suggests a unique dynamic semantics method(DYS-SLAM) for semantic simultaneous localization and mapping that strikes a trade-off between high accuracy and high real-time performance. We propose M-LK, an enhanced Lucas-Kanade(LK) optical flow method. This technique keeps the continuous motion and greyscale consistency assumptions from the original method while switching out the spatial consistency assumption for a …motion consistency assumption, reducing sensitivity to image gradients to identify dynamic feature points and regions efficiently. In order to increase segmentation accuracy while maintaining real-time performance, we develop a segmentation refinement scheme that projects 3D point cloud segmentation results into 2D object detection zones. A dense semantic octree graph is built in the interim to expedite the high-level process. Compared to the four equivalent dynamic SLAM approaches, experiments on the publicly available TUM RGB-D dataset demonstrate that the DYS-SLAM method offers competitive localization accuracy and satisfactory real-time performance in both high and low-dynamic scenarios. Show more

Keywords: Visual SLAM, object detection, dynamic environment, deep learning for visual perception

DOI: 10.3233/JIFS-234235

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10349-10367, 2023

Authors: Mao, Yaxin

Article Type: Research Article

Abstract: The process of attempting to estimate the future prices of particular stocks by utilizing historical data and various analytical tools, including deep learning algorithms, is called stock price prediction. Insurance providers’ overall approach and decisions to manage their risks, enhance their profitability, and give value to their policyholders are referred to as the insurance strategy. It requires various things to be considered, including underwriting procedures, pricing strategies, product creation, risk analysis, claims administration, and investment choices. This study proposed optimizing an insurance strategy and predicting securities prices using a deep learning algorithm. Initially, the real stock data sources for Microsoft …Corporation (MSFT) were gathered from Ping An Insurance Company of China (PAICC) and the Shanghai-based National Association of Securities Dealers Automated Quotation (NASDAQ). Normalization is the procedure used to preprocess data for the raw data. We suggest an Enhanced dragonfly-optimized deep neural network (EDODNN) with stock price forecasting and insurance. The outcomes demonstrate that the proposed model outperforms the current methodology and achieves accuracy, precision, recall, F1 score, R 2 , and RMSE. To display the effectiveness of the suggested system, its performance is compared to more established methods to obtain the highest level of efficiency for the research. Show more

Keywords: Deep learning, stock price prediction, insurance strategy, microsoft corporation (MSFT), enhanced dragonfly optimized deep neural network (EDODNN)

DOI: 10.3233/JIFS-234292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10369-10379, 2023

Authors: Yuan, Jinhai | Li, Sisi | Fan, Xin

Article Type: Research Article

Abstract: Educators across different fields disseminate their knowledge and utilize digital technologies to improve student skills for their careers and sustainability. Students’ skills that are improved are verified based on assessment and knowledge application over different circumstances. The article investigates the impact of the effective educator’s knowledge assessment and their role in student skill development. The motivation for the research arises from the realization that teachers’ knowledge and their capacity for transferring skills and information to students successfully play a significant role in the quality of education. The goal of the study is to develop a GA2M that has been verified …and can be used by educators to improve their performance, enhance student’s results, and eventually progress educational practices to use Fuzzy methods for reasoning and to include new rules for improving knowledge to bridge the knowledge gap between educators and students’ skill growth. It requires a great effect by the educator to enhance their ability over successive performance improvement. This article analyzes the ability for better improvement using the proposed Guided Ability Assessment Model (GA2M). The proposed model discards the negative impact of the ability on students’ skill deterioration. Besides, the ratio of skill improvement across various new abilities and exposures is analyzed using Fuzzy inference. This analysis frames the interference using knowledge rules required for different circumstances. These rules are framed using existing skill implications and problem-solving ability. This proposed model proposes new rules for development of various abilities of educators. Based on their current ability, further training process for the educator’s skill development is prescribed. Therefore, the inference for fuzzification is performed for the positive impact on students’ skill development. If the inference succeeds, the assessment is leveraged between skill guidance and ability. Show more

Keywords: Digital assessment, educator ability, fuzzy model, student skill

DOI: 10.3233/JIFS-231074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10381-10395, 2023

Authors: Luo, Zhi-Yong | Chen, Ya-Nan | Liu, Xin-Tong

Article Type: Research Article

Abstract: In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, …BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling. Show more

Keywords: Cloud computing, task scheduling, multi-objective optimization, sparrow search algorithm

DOI: 10.3233/JIFS-232527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10397-10409, 2023

Authors: Khan, Muhammad Ishfaq | Almazrooei, Abdullah Eqal | Yanhong, Li | Ibrar, Muhammad | Nazif, Fatima | Latif, Abdul

Article Type: Research Article

Abstract: Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating …its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies. Show more

Keywords: Picture fuzzy set, complex picture fuzzy set, Hamy mean operator, multi-attribute group decision making, evaluation the disaster management program

DOI: 10.3233/JIFS-232529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10411-10436, 2023

Authors: Feng, Qingyuan

Article Type: Research Article

Abstract: China’s education has entered the era of quality, and in order for higher vocational colleges to develop better, effective measures must be taken to improve the quality of education. Facing the continuous innovation of education in higher vocational colleges, it is necessary to strengthen institutional construction and constrain educational management with institutional constraints, which is the key to ensuring the education quality. The higher vocational education quality evaluation in the new era is regarded as multiple attribute decision-making (MADM). Recently, the EDAS and CRITIC model has been employed to solve MAGDM. The triangular fuzzy neutrosophic sets (TFNSs) are constructed as …an efficient tool for portraying the uncertain information during the higher vocational education quality evaluation in the new era. In this paper, the triangular fuzzy neutrosophic number EDAS (TFNN-EDAS) model based on the Hamming distance and Euclid distance is constructed to solve the MADM under TFNSs. The CRITIC method is utilized to obtain the weight information based on the Hamming distance and Euclid distance under INNSs. Finally, a numerical example of higher vocational education quality evaluation in the new era is constructed and some efficient comparisons are founded to verify the TFNN-EDAS method. Show more

Keywords: Multiple attribute decision making (MADM), triangular fuzzy neutrosophic sets (TFNSs), EDAS method, higher vocational education quality evaluation

DOI: 10.3233/JIFS-234044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10437-10450, 2023

Authors: Tan, Lirui | Zheng, Qiuju | Chen, Junji

Article Type: Research Article

Abstract: Network security is one of the key concerns with wireless sensor networks. Because sensor nodes employ radio channel frequencies, which are riskier than traditional networks, the attacker may be able to force the node to compromise, disrupt data integrity, eavesdrop, or insert false data into the network, wasting network resources. Creating dependable sensor networks that offer the highest level of security while using the fewest resources is thus one of the problems. The designers of wireless sensor networks can take into account providing an efficient key management system that can enhance any efficiency features such as communication overhead, calculation rate, …memory demand, and energy consumption rate. The energy level of nodes is determined in this article using a novel approach based on fuzzy systems and simple to implement in both hardware and software. In this study, the memory needed to carry out the plan was decreased, and the search performance was raised by integrating elliptic curve cryptography with an AVL search tree and a LEACH model. Also, the frequency range of radio channels in this study is 2.4 GHz. Based on the theoretical analysis as well as the outcomes of the experiments, the suggested key management strategy for wireless sensor networks improves security while also reducing computational overhead by 23%, energy consumption by 14%, and memory consumption parameters by 14%. 18% of people have used the network. Additionally, it was demonstrated that the suggested approach is scalable and extendable. Because of this, the suggested technique has a wide range of applications in massive wireless sensor networks. Show more

Keywords: Effective key management, transaction security, homogeneous mobile wireless sensor networks

DOI: 10.3233/JIFS-233476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10451-10466, 2023

Authors: Dong, Yanfeng | Wang, Meng

Article Type: Research Article

Abstract: At present, China’s football sports are relatively backward in training theory and practice. If you want to break out of Asia and enter the world, and gain a firm foothold in the international football arena, you must use a scientific and realistic attitude, absorb the successful experience of advanced football countries, reflect on our training concepts and practices, and deeply study the training laws of football, in order to find a way suitable for our development. Athletes’ competitive ability is the core issue of sports training. The failure of our football level is directly related to our systematic understanding of …athletes’ competitive ability. This problem has led to the separation of our training practice from the actual competition, making training unable to meet the needs of the competition. Only by solving this problem, can we improve the level of football in China. The football players’ competitive ability evaluation is affirmed as multiple attribute decision making (MADM). In such paper, motivated by the idea of cotangent similarity measure (CSM), the CSMs are extended to DVNSs and four CSMs are created under DVNSs. Then, two weighted CSMs are built for MADM under DVNSs. Finally, a numerical example for Football players’ competitive ability evaluation is affirmed and some comparative algorithms are produced to affirm the built method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), cotangent similarity measure (CSM), football players’ competitive ability evaluation

DOI: 10.3233/JIFS-231194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10467-10476, 2023

Authors: Kolla, Bhavannarayanna | Venugopal, P.

Article Type: Research Article

Abstract: Breast cancer is a widespread and significant health concern among women globally. Accurately categorizing breast cancer is essential for effective treatment, ultimately improving survival rates. Moreover, deep learning (DL) has emerged as a widely adopted approach for precise medical image classification in recent years, showing promise in this domain. However, despite the availability of DL models proposed in the literature for automated classification of breast cancer histopathology images, achieving high accuracy remains challenging. A minor modification to pre-trained models and simple training strategies can further enhance model accuracy. Based on the approach, this paper proposed an anti-aliased filter in a …pre-trained ResNet-34 and a novel three-step training process to improve BC histopathology image classification accuracy. The training involves systematically unfreezing layers and imposing additional constraints on the rate of change of learnable parameters. In addition, four-fold on-the-fly data augmentation enhances model generalization. The Ada-Hessian optimizer adjusts learning rates based on first and second-order gradients to improve convergence speed. The training process utilizes a large batch size to minimize the training loss associated with batch normalization layers. Even with the limited GPU size, the gradient accumulation technique achieves a large batch size. Collectively, these strategies minimize training time while maintaining or improving the accuracy of BC histopathology image classification models. In the experimental implementation, the proposed architecture achieves superior results compared to recent existing models, with an accuracy of 98.64%, recall (98.98%), precision (99.35%), F1-Score (99.17%), and MCC (97.36%) for binary classification. Similarly, the model achieves an accuracy of 95.01%, recall (95.01%), precision (94.95%), F1-Score (94.94%), and MCC (93.42%) for the eight-class category of BC images. Show more

Keywords: Deep learning, anti-aliased ResNet, BreakHis, breast cancer, fine-tuning, transfer learning

DOI: 10.3233/JIFS-231563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10477-10495, 2023

Authors: Sowmya, S. | Jose, Deepa

Article Type: Research Article

Abstract: In order to assess the fetus health and make timely decisions throughout pregnancy, Fetal Electrocardiography (FECG) monitoring is essential. Huge datasets for electrocardiograms are freely accessible from Physionet ATM Dataset1- Abdominal and Direct Fetal ECG Database (adfecgdb), Dataset2- Fetal ECG Synthetic Database (fecgsyndb), Dataset3- Non-Invasive Fetal ECG Database(nifecgdb). In this study, categorization is done based on normal and abnormal (Atrial fibrillation) FECG from three online dataset which contains FECG recordings as major details. Deep learning models like Transfer Learning (TL) and Convolutional Neural Networks (CNN) are being investigated. The composite abdominal signal and the FECG are separated using a wavelet …transform approach. The best model for categorizing the parameters of the FECG is determined through a comparative analysis and performance is improved using Continuous Wavelet Transform (CWT). The accuracy of the CNN-based technique is found to be 98.59%, whereas the accuracy of the transfer learning model is 99.01% for FECG classification. The computation of metric parameters for all the datasets is done. The classification of normal and abnormal (Atrial fibrillation) is best performed in TL model compared to CNN. Real-time data analysis is done for PQRST plotting and comparative study is done using Net Reclassification Improvement (NRI) and obtained NRI = 13%, z static 0f 3.7641, p -Value of 0.00016721. Acute Myocardial Infraction (AMI) identification is done based on ST segment of Maternal ECG (MECG) images to analyze the heart attack risk. The proposed work can be utilized to track FECG waveforms in real-time for wearable technology because of its end-to-end properties and expandable intrinsic for diagnosing multi-lead heart disorders. Show more

Keywords: Fetal electrocardiogram, convolutional neural networks, transfer learning, physio net ATM, deep learning models

DOI: 10.3233/JIFS-231681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10497-10514, 2023

Authors: Deepika, S. | Anandakumar, S. | Bhuvanesh Kumar, M. | Baskar, C.

Article Type: Research Article

Abstract: In the present marketing environment, choosing the right suppliers is very difficult for any construction company. Current supplier selection models in the construction industry often suffer from limitations such as incomplete criteria coverage, inadequate handling of uncertainties, and oversimplification of decision-making, leading to sub-optimal supplier choices and project risks. This paper aims in selecting the best suppliers among the different M-Sand environment suppliers. In this study 13 qualitative criterions are selected by the expert team. For handling the attributes, uncertainties, vagueness associated with supplier selection problems the Fuzzy Delphi, Fuzzy Analytical hierarchal Process (AHP) and Fuzzy Technique for order preference …by similarity to ideal solution (TOPSIS) methods were chosen. In the first phase of this study, Fuzzy Delphi Method is employed to select the 5 significant criterions. These criterions can be used to help the construction company in the direction to choose the right suppliers at the end. During the second phase, one of the significant Multi-criteria Decision Making Method called AHP is employed with extended support of fuzzy logic to evaluate the weightage of each criterion. Further ranking of various alternative suppliers are done by Fuzzy TOPSIS model. The ranking results indicate that A2 is the best supplier followed by A1 and A2. The third phase of this study deals with analyzing both the qualitative and quantitative criteria, hence Data Envelopment Analysis (DEA) is adopted to correlate the criteria. This is done to select efficient suppliers. The develop model is demonstrated in the construction industry. Show more

Keywords: Supplier selection, multi-criteria decision making method, fuzzy delphi method, fuzzy AHP, Fuzzy TOPSIS and DEA model.

DOI: 10.3233/JIFS-231790

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10515-10528, 2023

Authors: Wang, Liyang | Yang, Xiao

Article Type: Research Article

Abstract: Evaluating English teaching quality is vital for improving knowledge-based developments through communication for different aged students. Teaching quality assessment relies on the teachers’ and students’ features for constructive progression. With the development of computational intelligence, optimization and machine learning techniques are widely adapted for teaching quality assessment. In this article, a Quality-centric Assessment Model aided by Fuzzy Optimization (QAM-FO) is designed. This optimization approach validates the student-teacher features for a balanced model assessment. The distinguishable features for improving students’ oral and verbal communication from different teaching levels (basic, intermediate, and proficient) are extracted. The extracted features are the crisp input …for the fuzzy optimization such that the recurring fuzzification detains the least fit feature. Such features are replaced by the level-based teaching and performance feature that differs from the previous fuzzy input. This replacement is pursued until a maximum recommendable feature (performance/ learning) is identified. The identified feature is applicable for different teaching levels for improving the quality assessment. Therefore, the proposed optimization approach provides different feasible recommendations for teaching improvements. Show more

Keywords: English teaching, fuzzy optimization, quality assessment, recommendation model

DOI: 10.3233/JIFS-232034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10529-10543, 2023

Authors: Bai, Xuemei | Tan, Jiaqi | Hu, Hanping | Zhang, Chenjie | Gu, Dongbing

Article Type: Research Article

Abstract: The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence …of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios. Show more

Keywords: Electroencephalogram, emotion recognition, chebyshev network gated recurrent units, spectral graph convolution recurrent neural network, adjacency matrix

DOI: 10.3233/JIFS-232465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10545-10561, 2023

Authors: Xian, Luo | Tian, Lan

Article Type: Research Article

Abstract: In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction …model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field. Show more

Keywords: Intelligent transportation system, passenger flow prediction, stacked denoising autoencoder, bidirectional long short-term memory network, attention mechanism introduction

DOI: 10.3233/JIFS-232979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10563-10577, 2023

Authors: Fan, Kun | Hu, Yanrong | Liu, Hongjiu | Liu, Qingyang

Article Type: Research Article

Abstract: Accurately predicting soybean futures fluctuations can benefit various market participants such as farmers, policymakers, and speculators. This paper presents a novel approach for predicting soybean futures price that involves adding sequence decomposition and feature expansion to an Long Short-Term Memory (LSTM) model with dual-stage attention. Sequence decomposition is based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, a technique for extracting sequence patterns and eliminating noise. The technical indicators generated enrich the input features of the model. Dual-stage attention are finally employed to learn the spatio-temporal relationships between the input features and the target sequence. The …research is founded on data related to soybean contract trading from the Dalian Commodity Exchange. The suggested method surpasses the comparison models and establishes a fresh benchmark for future price forecasting research in China’s agricultural futures market. Show more

Keywords: Soybean futures, time series forecasting, attention mechanism, sequence decomposition, technical indicator

DOI: 10.3233/JIFS-233060

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10579-10602, 2023

Authors: Jingjing, Huang | Xu, Zhang

Article Type: Research Article

Abstract: In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of …offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better. Show more

Keywords: Chaotic mapping, particle swarm, optimization algorithm, offline courses of tax law, resource intelligence recommendation

DOI: 10.3233/JIFS-233095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10603-10617, 2023

Authors: Alsuwat, Emad

Article Type: Research Article

Abstract: Machine learning (ML) techniques play a crucial role in producing precise predictions without the use of explicit programming by utilizing representative and unbiased data. These methods, which are a subset of artificial intelligence (AI), are used in a variety of settings, including recommendation engines, spam filtering, malware detection, classification, and predictive maintenance. While ML algorithms improve results, they also present security and privacy threats, especially in the face of adversarial ML attacks such as data poisoning assaults that can undermine data modeling applications. This study introduces SecK2, a cutting-edge ML method developed to stop dangerous input from entering ML models. …The scalability of SecK2 is proved through meticulous experimental research, revealing its astonishing capacity to identify data poisoning attacks at a previously unheard-of pace. As a result, SecK2 becomes a valuable tool for guaranteeing the reliability and security of ML models. Our suggested method produces outstanding results by a variety of criteria. Notably, it achieves a noteworthy 61% convergence rate and an exceptional 89% attack detection rate. Additionally, it offers a phenomenal 96% throughput while protecting data integrity at 53%. The technique also boasts impressive Validation accuracy of 96% and Training accuracy of 92%. The suggested technology offers a strong and reliable barrier against the rising danger of data poisoning attacks. ML practitioners can have more faith in their models, thanks to SecK2’s capabilities, protecting against potential adversarial assaults and preserving the dependability of ML-based applications. Show more

Keywords: Data poisoning attacks, machine learning, privacy prediction, malicious data

DOI: 10.3233/JIFS-233942

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10619-10633, 2023

Authors: Nie, Kuang | Langari, Reza

Article Type: Research Article

Abstract: Surface electromyography (sEMG) signals have great potential for predicting upper limb motion. Although prior investigations have explored diverse applications of sEMG signal analysis, but few studies have focused on real-time motion prediction within the context of upper limb configuration space. Additionally, previous research has not adequately considered individual variability in sEMG features. This study aims to accomplish two main objectives. Firstly, it seeks to examine the dissimilarities in signal distribution across different subjects when employing various features. Additionally, the study aims to establish a correlation between signal distribution patterns and the model’s predictive accuracy. Secondly, the study introduced a personalized …standardization (PSD) technique, which will serve to normalize the shape of the signal distribution across different subjects, thereby addressing the inter-individual differences in sEMG features. A bi-directional long short-term memory (Bi-LSTM) network is employed to estimate the real-time moving intention of the upper limb after applying the PSD technique. The analysis of signal distribution involved nine combinations of features, encompassing six features, namely mean absolute value (MAV), wave length (WL), variance (VAR), root mean square (RMS), mean frequency (MNF) and median frequency (MDF). To assess predictive capabilities, several models were evaluated. Remarkably, the distribution analysis clearly demonstrated that the shape of the signal distribution notably influences the model’s performance. Accroding to results, the incorporation of the PSD technique resulted in a notable improvement in the accuracy of the Bi-LSTM model, which leds to an enhancement of up to 2.8 percentage points in predictive accuracy. Additionally, the Bi-LSTM model emerged as the highest-performing model among all the compared models during the analysis. These findings underscore the importance of considering individual variability in sEMG features when developing predictive models for upper limb motion and highlight the potential benefits of employing the PSD technique to enhance model performance. Show more

Keywords: Surface electromyography, Real-time motion prediction, Deep Learning, Signal pre-processing

DOI: 10.3233/JIFS-234018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10635-10648, 2023

Authors: Gui, Zhen

Article Type: Research Article

Abstract: The task of multi-label text classification involves assigning a set of related labels to a given document. However, there are three main problems with this task. Firstly, the joint modeling of label-text and label-label relationships is inadequate. Secondly, the semantic mining of the label itself is insufficient. Lastly, the utilization of the internal structure information of the label is ignored. To address these issues, a new multi-label text classification method has been proposed. This method is based on joint attention and shared semantic space. The joint multi-head attention mechanism models the relationship between labels and documents as well as the …relationship between labels simultaneously. This helps to avoid error transmission and utilizes the interaction information between them. The decouple shared semantic space embedding method improves the method of using labels semantic information and reduces deviation in the phase of modeling correlation. The hierarchical hinting method based on prior knowledge relies on the prior knowledge in the pre-trained model to exploit the labels hierarchy information. Experimental results have shown that this proposed method is superior to existing multi-label text classification methods in public datasets. Show more

Keywords: Multi-label text classification, attention mechanism, label representation, semantic embedding, pre-trained model

DOI: 10.3233/JIFS-234151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10649-10659, 2023

Authors: Deng, Yu | Zhang, Wenxia

Article Type: Research Article

Abstract: In recent years, due to the rapid development of internet technology, the integration process of digital technology and financial services has accelerated. Digital Financial inclusion has emerged as the times require, becoming an important force to promote private enterprises to get out of financing difficulties. The development level evaluation of digital inclusive finance is a classical multiple attribute group decision making (MAGDM) problems. Recently, Recently, the Exponential TODIM(ExpTODIM) and (grey relational analysis) GRA method has been used to cope with MAGDM issues. The intuitionistic fuzzy sets (IFSs) are used as a tool for characterizing uncertain information during the development level …evaluation of digital inclusive finance. In this paper, the intuitionistic fuzzy Exponential TODIM-GRA (IF-ExpTODIM-GRA) method is built to solve the MAGDM under IFSs. In the end, a numerical case study for development level evaluation of digital inclusive finance is supplied to validate the proposed method. The main contributions of this paper are outlined: (1) the ExpTODIM and GRA method has been extended to IFSs; (2) Information Entropy is used to derive weight under IFSs. (3) the IF-ExpTODIM-GRA method is founded to solve the MAGDM under IFSs; (4) a numerical case study for development level evaluation of digital inclusive finance and some comparative analysis are supplied to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), intuitionistic fuzzy sets (IFSs), ExpTODIM, GRA, digital inclusive finance

DOI: 10.3233/JIFS-234827

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10661-10673, 2023

Authors: Chen, Ze | Lu, Ning | Hou, Botao | Liu, Xin | Zuo, Xiaojun

Article Type: Research Article

Abstract: In order to improve the effect and accuracy of risk source identification, this paper studied the network security risk source identification model of power CPS system based on fuzzy artificial neural network. The network security risk source index system of power CPS system was constructed, and the dimension of index data was reduced by principal component analysis. Fuzzy theory is used to process the index data after dimension reduction, and the comprehensive membership vector of each index is obtained. The dynamic clustering algorithm is used to determine the number of hidden layer units of radial basis function neural network, and …the network security risk source identification model is established. Finally, the quantitative value of risk source identification is output. The experimental results show that the model can effectively reduce the dimension of the network security risk source index data of the power CPS system. The optimal distance threshold of the hidden layer is 4.2, and the optimal number of units is 6. In the final identification results, four severe risk sources and five moderate risk sources were obtained, and the quantitative values of risk source identification of each index were 63, 70, 71, 77, 65, 89 and 96, respectively, indicating that the model can effectively identify network security risk sources of power CPS systems. With the increase of the proportion of communication nodes removed, when there are various types of security vulnerability information, the mean square error value of the model is always lower than the set threshold, indicating that the model has high recognition accuracy. Show more

Keywords: Fuzzy theory, artificial neural network, power CPS system, network security, risk sources, identification model

DOI: 10.3233/JIFS-224090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10675-10691, 2023

Authors: Kamran, Muhammad | Ashraf, Shahzaib | Salamat, Nadeem | Naeem, Muhammad | Hameed, Muhammad Shazib

Article Type: Research Article

Abstract: One of the hottest areas for applying the solutions currently available is the internet of things-based smart housing society architecture and its uncertainty analysis. When intelligent parking, waste management, public transportation, public safety, and other automatic methods for housing society’s growth were implemented, it became even more crucial. An intelligent, smart system is necessary to manage these problems and provide smooth services. Additionally, it will be helpful in reducing issues with time waste and societal safety. However, the issue comes up when describing accurate, approximate, or questionable parking, transit, safety, and waste management areas. This paper discusses several mathematical solutions …for the smart housing society that use fuzzy rough sets, probabilistic hesitant fuzzy sets, and their extensions with neutrosophic sets. For further growth, a few studies on the graphic display of the evolution of the smart housing society are also considered. The rough set theory can be useful when dealing with imprecise, incomplete, or indeterminate data sets. The core contribution of this work is the construction of a novel generalized notion of a single-valued neutrosophic probabilistic hesitant fuzzy rough set (SV-NPHFRS), which is a hybrid structure of the single-valued neutrosophic set, the probabilistic hesitant fuzzy set, and the rough set. In contrast to the present literature, the underlying idea of SV-NPHFRS is that it is a powerful mathematical tool for managing uncertainty and imperfect information. This method is particularly beneficial when there are a number of competing criteria to consider. The aggregation technique plays an important role in decision-making concerns, especially when more competing criteria are present. In the study’s comparison phase, the suggested decision support system is compared to relevant existing approaches. The results suggest that, in terms of choice flexibility, the suggested technique has the potential to outperform the drawbacks of the current decision-making tools. The proposed study is expected to be useful for a number of researchers conducting future work on housing societies, waste management, public safety diagnostics, and hybridization. Show more

Keywords: Single-valued neutrosophic probabilistic hesitant fuzzy rough sets, aggregation operators, decision making

DOI: 10.3233/JIFS-224364

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10693-10737, 2023

Authors: Suo, Chunfeng | Li, Yongming | Guo, Li

Article Type: Research Article

Abstract: The polygonal interval-valued fuzzy number is constructed based on the polygonal fuzzy number and the interval-valued fuzzy number. Its main feature is that the linear operation of finite ordered points reduces the complexity of traditional interval-valued fuzzy number operations. This research presents a generalized distance formula between two polygonal interval-valued fuzzy numbers and explores topological properties under the distance of polygonal interval-valued fuzzy numbers. In addition, we adopt the TOPSIS (technique for order preference by similarity to an ideal solution) and prospect theory approach for the multi-attribute decision-making problem. The information of attributes describes with polygonal interval-valued fuzzy numbers, and …we then implement optimized ranking on the alternatives according to the profit and loss ratio. Finally, we verify the effectiveness and practicability of the decision-making method and fuzzy numbers at polygonal interval-valued fuzzy numbers in e-commerce risk assessment. Show more

Keywords: Polygonal interval-valued fuzzy number, human resource recruitment, generalized distance, arithmetic operation

DOI: 10.3233/JIFS-230040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10739-10755, 2023

Authors: Cao, Xianghong | Wu, Kunning | Geng, Xin | Wang, Yongdong

Article Type: Research Article

Abstract: With the acceleration of urbanization, the frequency of building fire incidents has been increasing year by year. Therefore, rapid, efficient, and safe evacuation from buildings has become an urgent and important task. A construction fire escape path planning method based on an improved NavMesh algorithm is proposed in this paper. Firstly, by using the method of local updates in the navigation grid, redundant computation is reduced, and the update time of the improved algorithm is about 6.8% of that of the original algorithm, immediate generation of navigation is achieved. Secondly, the heuristic function of the pathfinding algorithm is improved, and …a multi-exit path planning mechanism is proposed to achieve more efficient, which can quickly plan a safe evacuation path away from the spreading fire and smoke in the event of a fire. Finally, a new evaluation index called Navigation Grid Complexity (NGC) is proposed and demonstrated to measure the quality of navigation grids. The feasibility and effectiveness of the proposed method are validated through simulation experiments on actual building models, which can provide real-time, efficient, intelligent, and safe path planning for rapid evacuation of evacuees in the fire scene. Show more

Keywords: NavMesh, path planning, fire emergency evacuation, dynamic environments

DOI: 10.3233/JIFS-232681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10757-10768, 2023

Authors: Ran, Lang | Hong, Chaoqun | Zhang, Xuebai | Tang, Chaohui | Xie, Yuhong

Article Type: Research Article

Abstract: Human pose estimation is a challenging visual task that relies on spatial location information. To improve the performance of human pose estimation, it is important to accurately determine the constraint relationship among keypoints. To address this, we propose MfvPose, a novel hybrid model that leverages rich multi-scale information. The proposed model incorporates the HRFOV module, which uses cascaded atrous convolution to maintain high-resolution representations of the backbone extractor and enrich the multi-scale information. In addition, we introduce learnable scalar weights to the Transformer encoder. In detail, it involves a multiplication by a diagonal matrix with learnable scalar weights on output …of each residual block, which improves the dynamics of model training and enhances the accuracy of human pose estimation. It is experimentally shown that our proposed MfvPose achieves promising results on various benchmarks. Show more

Keywords: Receptive field, multi-head self-attention, atrous convolution, human pose estimation

DOI: 10.3233/JIFS-233375

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10769-10778, 2023

Authors: Baskar, A. | Rajaram, A.

Article Type: Research Article

Abstract: Mobile Adhoc Network (MANET) is a dynamic network with mobility nodes. Emerging applications for MANETs in real-time present numerous research challenges. Specifically, the mobile nodes’ dynamic character hinders the routing efficacy in MANET. Previous algorithms for routing like DSDV DSR, AODV, and are inefficient due to an ineffective route discovery method. Route selection becomes more complex and energy-intensive for large-scale applications, such as air pollution monitoring. For air pollution monitoring applications, this research seeks to improve data delivery while reducing energy consumption. In this work, we proposed DeepOptimizer for achieving optimal data transmission. First, the network is segregated into multiple …clusters using the Rough set theory. In the all clusters, Cluster Head is accountable for split a data into normal and emergency. This process is performed by grouping data by K++ means algorithm. For emergency data, Graph-based Route Selection (GRS) algorithm. This is the fast algorithm that selects the optimal route. On the other hand, the normal data transmission route is selected by the Deep-SpikeQNetwok-based Whale Optimization (WO) algorithm. Finally, the network is tested through simulations made in ns-3 based on network lifetime, throughput, energy level, delay and packet delivery ratio. Show more

Keywords: Deep routing, emergency data transmission, spiking networks, MANET

DOI: 10.3233/JIFS-233425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10779-10797, 2023

Authors: Xia, Wenxin | Che, Jinxing

Article Type: Research Article

Abstract: Wind energy needs to be used efficiently, which depends heavily on the accuracy and reliability of wind speed forecasting. However, the volatility and nonlinearity of wind speed make this difficult. In volatility and nonlinearity reduction, we sequentially apply complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to secondarily decompose the wind speed data. This framework, however, requires effectively modeling multiple uncertainty components. Eliminating this limitation, we integrate crow search algorithm (CSA) with deep belief network (DBN) to generate a unified optimal deep learning system, which not only eliminates the influence of multiple uncertainties, but …also only adopts DBN as a predictor to realize parsimonious ensemble. Two experiments demonstrate the superiority of this system. Show more

Keywords: Parsimonious ensemble, secondary decomposition, optimal deep learning, crow search algorithm

DOI: 10.3233/JIFS-233782

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10799-10822, 2023

Authors: Kumari, Ritika | Singh, Jaspreeti | Gosain, Anjana

Article Type: Research Article

Abstract: Class imbalance problem (CIP) exists when the class distribution is not uniform. Many real-world scenarios face CIP which attracted the researcher’s attention to this problem. Training machine learning (ML) models with class imbalanced datasets is a challenging problem. Ensemble methods in ML involve training multiple classifiers, combining or averaging their predictions to come to a final prediction. Specifically designed ensemble-based methods can overcome the difficulty faced by traditional classifiers and can handle the CIP. The performance of 19 ensemble methods for 44 unbalanced datasets is assessed in this paper in order to observe the effects of the class imbalance ratio …(CIR). For performance evaluation, we divide these datasets into three categories, i.e., Slightly Imbalance (SI), Moderately Imbalance (MI) and Highly Imbalance (HI) based on CIR. With the proposed perspective, we observe that different ensemble methods perform well in different categories suggesting that the percentage of minority or majority class could be a criterion for the selection of ensemble methods for class imbalance datasets. Moreover, visual representations and different non-parametric statistical tests are also used to have more reliable results. Show more

Keywords: Ensemble methods, boosting, bagging, hybrid approaches, classification

DOI: 10.3233/JIFS-223333

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10823-10834, 2023

Authors: Koshti, Dipali | Gupta, Ashutosh | Kalla, Mukesh

Article Type: Research Article

Abstract: Visual question Answering (VQA) is a computer vision task that requires a system to infer an answer to a text-based question about an image. Prior approaches did not take into account an image’s positional information or the questions’ grammatical and semantic relationships during image and question processing. Featurization, which leads to the false answering of the question. Hence to overcome this issue CNN –Graph based LSTM with optimized BP Featurization technique is introduced for feature extraction of image as well as question. The position of the subjects in the image has been determined using CNN with a dropout layer and …the optimized momentum backpropagation during the extraction of image features without losing any image data. Then, using a graph-based LSTM with loopy backpropagation, the questions’ syntactic and semantic dependencies are retrieved. However, due to their lack of external knowledge about the input image, the existing approaches are unable to respond to common sense knowledge-based questions (open domain). As a result, the proposed Spatial GCNN knowledge retrieval with PDB Model and Spatial Graph Convolutional Neural Network, which recovers external data from Wikidata, have been used to address the open domain problems. Then the Probabilistic Discriminative Bayesian model, based Attention mechanism predicts the answer by referring to all concepts in question. Thus, the proposed method answers the open domain question with high accuracy of 88.30%. Show more

Keywords: Visual Question Answering, graph-based LSTM, SVO triples sentence, Discriminative Bayesian model, dynamic memory network

DOI: 10.3233/JIFS-230198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10835-10852, 2023

Authors: My, Bui T.T. | Ta, Bao Q.

Article Type: Research Article

Abstract: Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data.

Keywords: Classifiers, credit scoring, decision tree, ensemble classifiers, imbalanced data

DOI: 10.3233/JIFS-230825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10853-10864, 2023

Authors: Catherine Grace John, J. | Deepika, M. | Elavarasan, B.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10865-10872, 2023

Authors: Thumilvannan, S. | Balamanigandan, R.

Article Type: Research Article

Abstract: The survival of patients’ deaths owing to Heart Disease (HD) could be improved with the assistance of an enhanced approach for predicting the risk of diabetes and HD. Nevertheless, such schemes are developed rarely. Thus, this paper proposes a new Power Lognormal Distribution-Semi-Supervised Learning-centric Restricted Boltzmann Machine (PLD-SSL-RBM) diabetes and HD risk level prediction model for IoT data. The missing data are removed by partial Derivation of the Hamilton-Cluster Centered-K-means Clustering (DH-CC-KC) to efficiently train the classifier and then, the data are aggregated. Next, to reduce the dataset size, the features are reduced with Shell Sort-Principal Component Analysis (SS-PCA). Then, …the fuzzy rule-based decisions are created with the T -test-centric Uniform Distribution-Elephant Herd Optimization Algorithm (T -test-UDEHOA) Correlated Features (CF) to classify the risk levels accurately. Lastly, the risk levels of HD and diabetes are predicted; in addition, by employing the Elliptic Curve Cryptography (ECC)7encryption technique, the data is securely stored on the medical database. The proposed risk prediction model’s performance is analyzed on the Framingham dataset. As per the experimental outcomes, when analogized to the prevailing methodologies, the proposed technique attained a higher accuracy of 99.55%. Show more

Keywords: Internet of Things (IoT), heart disease and diabetes risk, Restricted Boltzmann Machine (RBM), correlated features, Elephant Herd Optimization Algorithm (EHOA), Correlated Feature (CF)

DOI: 10.3233/JIFS-232851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10873-10886, 2023

Authors: Thakur, Divya | Lalwani, Praveen

Article Type: Research Article

Abstract: The use of mobile devices has contributed greatly to the expansion of Human Activity Recognition (HAR) studies in recent years. Researchers find it attractive because of its versatility, low cost, compact size, ease of usage, and wide range of possible applications. Conventional, biological, and control-based systems are just some of the methods that have been created for humanoid robot movement in recent years. This article specifically targeted improvement in the proposed method, which is different from previous papers. This is being done with the use of the publicly available Human Activity Gait (HAG) data set, which documents a wide range …of different types of activities. IMU sensors were used to collect this data set. Several experiments were conducted using different machine-learning strategies, each with its own set of hyper-parameters, to determine how best to utilize these data. In our proposed model Cuckoo Search Optimization is being used for optimum feature selection. On this data set, we have tested a number of machine learning models, including LR, KNN, DT, and proposed CSOEM (Cuckoo Search-Based Optimized Ensemble Model). The simulation suggests that the proposed model CSOEM achieves an impressive accuracy of 98%. This CSOEM is built by combining the feature selection strategy of Cuckoo Search Optimizations with the ensembling of the LR, KNN, and DT. Show more

Keywords: Bipedal robot locomotion, CSO: cuckoo search optimization, HAG: human activity gait, HAR: human activity recognition

DOI: 10.3233/JIFS-232986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10887-10900, 2023

Authors: Lian, Jing | Chen, Shi | Pi, Jiahao | Li, Linhui | Li, Qingfeng

Article Type: Research Article

Abstract: Localization through intricate traffic scenes poses challenges due to their dynamic, light-variable, and low-textured nature. Existing visual Simultaneous Localization and Mapping (SLAM) methods, which are based on static and texture-rich assumptions, struggle with drift and tracking failures in such complex environments. To address this, we propose a visual SLAM algorithm based on semantic information and geometric consistency in order to solve the above issues and further realize autonomous driving applications in road environments. In dynamic traffic scenes, we employ an object detection network to identify moving objects and further classify them based on geometric consistency as dynamic objects or potential …dynamic objects. This method permits us to preserve more reliable static feature points. In low-texture environments, we propose a method that employs key object categories and geometric parameters of static scene objects for object matching between consecutive frames, effectively resolving the problem of tracking failure in such scenarios. We conducted experiments on the KITTI and ApolloScape datasets for autonomous driving and compared them to current representative algorithms. The results indicate that in the dynamic environment of the KITTI dataset, our algorithm improves the compared metrics by an average of 29.68%. In the static environment of the KITTI dataset, our algorithm’s performance is comparable to that of the other compared algorithms. In the complex traffic scenario R11R003 from the ApolloScape dataset, our algorithm improves the compared metrics by an average of 25.27%. These results establish the algorithm’s exceptional localization accuracy in dynamic environments and its robust localization capabilities in environments with low texture. It provides development and support for the implementation of autonomous driving technology applications. Show more

Keywords: Autonomous vehicles, SLAM, traffic environments, object detection

DOI: 10.3233/JIFS-233068

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10901-10919, 2023

Authors: Liu, Zhichao | Wang, Yachao | Ma, Zhiyuan | Cao, Mengnan | Liu, Mingda | Yang, Xiaochu

Article Type: Research Article

Abstract: Real-time monitoring of electricity usage details through load monitoring techniques is a crucial aspect of smart power grid management and monitoring, allowing for the acquisition of information on the electricity usage of individual appliances for power users. Accurate detection of electricity load is essential for refined load management and monitoring of power supply quality, facilitating the improvement of power management at the user side and enhancing power operation efficiency. Non-intrusive load monitoring (NILM) techniques require only the analysis of total load data to achieve load monitoring of electricity usage details, and offer advantages such as low cost, easy implementation, high …reliability, and user acceptance. However, with the increasing number of distributed new load devices on the user side and the diversification of device development, simple load recognition algorithms are insufficient to meet the identification needs of multiple devices and achieve high recognition accuracy. To address this issue, a non-intrusive load recognition (NILR) model that combines an adaptive particle swarm optimization algorithm (PSO) and convolutional neural network (CNN) has been proposed. In this model, pixelated images of different electrical V-I trajectories are used as inputs for the CNN, and the optimal network layer and convolutional kernel size are determined by the adaptive PSO optimization algorithm during the CNN training process. The proposed model has been validated on the public dataset PLAID, and experimental results demonstrate that it has achieved a overall recognition accuracy of 97.26% and F-1 score of 96.92%, significantly better than other comparison models. The proposed model effectively reduces the confusion between various devices, exhibiting good recognition and generalization capabilities. Show more

Keywords: Smart grid, non-intrusive load recognition, DL, Convolutional Neural Network, adaptive Particle Swarm Optimization

DOI: 10.3233/JIFS-233813

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10921-10935, 2023

Authors: Afzali, Parvaneh | Rezapour, Abdoreza | Rezaee Jordehi, Ahmad

Article Type: Research Article

Abstract: Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of uncertainties such as high intra-class variations and noises, we leverage the …fuzzy logic in the design of a custom Convolutional Neural Network (CNN) with a type-2 fuzzy activation function for the first path. Additionally, the second path utilizes the transfer learning-based CNN to enhance the discriminability of the learned features. Our method allows for text-independent writer identification, eliminating the need for identical handwriting samples to train and test the model. Considering that various factors can influence the handwriting style, a dataset containing right-to-left handwriting samples is assembled. The proposed method is evaluated on our developed dataset and four widely-known public datasets, namely KHATT, CVL, Firemaker, and IAM. High accuracy values are achieved, with results of 99.85%, 99.83%, 99.79%, 99.64%, and 98.17% for each dataset, respectively. One noteworthy aspect of this study is that the evaluation results on diverse datasets demonstrate the applicability of the proposed model to various languages. Moreover, the model performs effectively in real-world scenarios with limited handwritten data. Show more

Keywords: Writer identification, convolutional neural networks, Type-2 fuzzy logic, deep feature concatenation

DOI: 10.3233/JIFS-231889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10937-10949, 2023

Authors: Mishra, Rajiv Kumar | Yadav, Rajesh Kumar | Nath, Prem

Article Type: Research Article

Abstract: The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate …at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture. Show more

Keywords: IoT, secure data sharing, unauthorized access, blockchain, IPFS, etc.

DOI: 10.3233/JIFS-232483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10951-10966, 2023

Authors: Rubia J, Jency | Lincy R, Babitha

Article Type: Research Article

Abstract: Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image …processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method. Show more

Keywords: Plant disease detection, deep learning, meta-heuristic optimizers, Grey Wolf optimization algorithms, Whales optimization algorithms

DOI: 10.3233/JIFS-213423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10967-10983, 2023

Authors: Wang, Ming

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-224523

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10985-10996, 2023

Authors: Ren, Zenggen | Guo, Fu | Hu, Mingcai | Qu, Qingxing | Li, Fengxiang

Article Type: Research Article

Abstract: Generating kansei profiles for products represent fundamental aspects of kansei engineering (KE). Conventionally, the semantic differential (SD) method has been extensively employed to construct product kansei profiles, aiming to delve into consumers’ perceptions of products. However, this approach is associated with significant time consumption and inefficiency. In light of this, we introduce an innovative kansei evaluation approach that incorporates consumers’ kansei preferences, thereby enhancing the efficiency of the evaluation process. This approach comprises three integral modules: Firstly, the generation of product kansei profiles and the construction of a kansei database for decision alternatives are achieved through the analysis of online …reviews. Subsequently, the kansei data is adjusted based on consumers’ kansei preferences. Finally, the rank correlation analysis (RCA) is conducted to establish the prioritization of decision alternatives. Notably, this method facilitates the ranking of products in accordance with consumers’ kansei preferences, thereby assisting consumers in navigating through an array of functionally similar products to identify their preferred choices. A comprehensive case study illustrates the implementation procedure and validates the practicality of our proposed method. Show more

Keywords: Kansei evaluation, kansei preferences, kansei profiles, online reviews, rank correlation analysis

DOI: 10.3233/JIFS-230654

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10997-11012, 2023

Authors: Shafi, Muhammad Ammar | Rusiman, Mohd Saifullah | Jacob, Kavikumar | Musa, Aisya Natasya

Article Type: Research Article

Abstract: With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression …with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model’s predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer. Show more

Keywords: Intelligent modelling, fuzzy hybrid, Prediction data, computation software, statistical error

DOI: 10.3233/JIFS-231814

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11013-11019, 2023

Authors: Sun, Peixi | Wang, Yixuan | Song, Jaehoon

Article Type: Research Article

Abstract: A brand is an enterprise’s market image and huge intangible assets. A brand is an enterprise’s market image and huge intangible assets, and it is also a comprehensive embodiment of an enterprise’s core competitiveness. Therefore, continuous improvement of brand competitiveness undoubtedly has far-reaching significance for manufacturing enterprises. Using the brand competitiveness evaluation index system and selected evaluation methods of manufacturing enterprises constructed in this article, the brand competitiveness evaluation index system and selected evaluation methods can not only study the overall brand competitiveness of the participating enterprises, but also understand the advantages and disadvantages of the brand competitiveness of the …participating enterprises from different perspectives, To help and guide manufacturing enterprises to strengthen brand building in a targeted manner and continuously improve the brand competitiveness of manufacturing enterprises. The brand competitiveness evaluation of manufacturing enterprises is a classical MAGDM problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The interval neutrosophic sets (INSs) are used as a tool for characterizing uncertain information during the brand competitiveness evaluation of manufacturing enterprises. In this manuscript, the interval neutrosophic number TODIM-VIKOR (INN-TODIM-VIKOR) method is built to solve the MAGDM under INSs. In the end, a numerical case study for brand competitiveness evaluation of manufacturing enterprises is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), Interval neutrosophic sets (INSs), TODIM, VIKOR, Brand competitiveness evaluation

DOI: 10.3233/JIFS-232001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11021-11034, 2023

Authors: Kalaiarasan, D. | Ahilan, A. | Ramalingam, S.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-213337

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11035-11057, 2023

Authors: Zhang, Shiguang | Guo, Di | Zhou, Ting

Article Type: Research Article

Abstract: Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments …were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model. Show more

Keywords: Extreme learning machine, heteroscedastic Gaussian noise, least squares support vector regression, twin hyperplanes, wind-speed forecasting

DOI: 10.3233/JIFS-232121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11059-11073, 2023

Authors: Vidyabharathi, D. | Sivanesh, S. | Theetchenya, S. | Vidhya, G.

Article Type: Research Article

Abstract: Detecting of cracks and damages, especially in multi storied buildings is a crucial aspect of infrastructure and building maintenance, as it ensures safety and reliability. An enhanced framework for the crack detection is proposed to identify the fine cracks which are present at greater heights and not captured to the human vision from the ground. The cracks are identified and classified by the deep convolutional neural network model. The Oriented Non-Maximal Suppression module reduces the false positives to improve the classification accuracy and reliability. The proposed method O-CNN(CNN with ONMS)can be used in real-world for the infrastructure inspection and potential …applications in civil engineering construction. The ability to input different types of data, including images and videos, makes the proposed system user-friendly and easy to use. Furthermore the system reduces the risk of human error and prevents the huge damages caused to the building. Also, it prevents the major loss which may be caused to the lives. Overall, the proposed system contributes to the field of deep learning and computer vision by providing an effective and better solution for crack detection in real-world scenarios. Show more

Keywords: Deep learning, convolutional neural networks, oriented non-maximal suppression, O-CNN

DOI: 10.3233/JIFS-232793

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11075-11091, 2023

Authors: Alphy, Anna | Rajamohamed, | Velusamy, Jayaraj | Vidhya, K. | Ravi, G. | Rajasekaran, Arun Sekar

Article Type: Research Article

Abstract: Age-Related Macular Degeneration is a progressive, irreversible eye condition that causes vision loss and impairs quality of life. The lost potential of the optic nerve cannot be regained, but a patient with Age-Related Macular Degeneration must have early diagnosis and treatment in order to prevent visual loss. The diagnosis of Age-Related Macular Degeneration is based on visual field loss tests, a patient’s medical history, intraocular pressure, and a physical fundus evaluation. Age-Related Macular Degeneration must be diagnosed early in order to avoid irreparable structural damage and vision loss. The objective of the proposed study is to develop a new optimization-driven …strategy-based recurrent neural network using the Internet of Things for the identification of age-related macular degeneration. The Recurrent Neural Network (RNN) classifier is trained using the Particle Swarm Optimization (PSO) technique included into the RNN-IoMT. Initially, the input picture is sent through pre-processing in order to remove noise and artefacts. The generated preprocessed picture is simultaneously sent to optical disc detection and blood vessel detection. In addition, picture level characteristics are extracted from the image that has been preprocessed. Finally, the image-level, optic disc-level, and blood vessel-level features are retrieved and compiled into a feature vector. The acquired feature vector is fed into the RNN classifier, with the suggested PSO used to train the RNN for Age-Related Macular Degeneration detection via the Internet of Medical Things. The suggested PSO+RNN exhibits better performance with enhanced precision of 97.194%, sensitivity of 97.184%, and specificity of 97.2044%, respectively. Show more

Keywords: Wearables, internet of things, teleophthalmology, deep learning, fundus images

DOI: 10.3233/JIFS-233044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11093-11105, 2023

Authors: Nguyen, Vy Duong Kim | Do, Phuc

Article Type: Research Article

Abstract: People will increasingly get expedited and diverse means of accessing news as societies progress. Furthermore, there is a noticeable increase in the prevalence of incorrect and misleading information. Our research is motivated by the significant concerns regarding the detrimental impacts of disinformation on the general public, political stability, and trust in the media. The scarcity of Vietnamese-language datasets can be attributed to the predominant focus of false news detection studies on datasets only in English. Detection investigations of fake news have predominantly relied on supervised machine learning algorithms, which possess notable limitations when confronted with unclassified news articles that are …either authentic or untrue. The utilization of Knowledge Graphs (KG) and Graph Convolutional Networks (GCN) holds promise in addressing the constraints of supervised machine learning algorithms. To address these problems, we propose an approach that integrates KG)into the procedure for detecting fake news. We utilize the Vietnamese Fake News Detection dataset (VFND-vietnamese-fake-news), comprising authentic and deceptive news articles from reputable Vietnamese newspapers such as vnexpress, tuoitre, and have been collected from 2018 to 2023. News articles are only labeled as real or fake after experiencing independent verification. The Glove embedding (Global Vectors for Word Representation) is employed to establish a knowledge network for the given dataset. This knowledge graph’s construction is accomplished using the Word Mover’s Distance (WMD) algorithm in conjunction with the K-nearest neighbor approach; GCN approach and the input KG train models to discern between real and fake news. With labeling half of the input dataset, the experimental findings indicate a notable level of accuracy, reaching up to 85%. Our research holds significant importance in identifying fake news, particularly within the context of the Vietnamese language. Show more

Keywords: Fake news detection, graph convolutional network, semi-supervised, K-nearest neighbor, word mover’s distance

DOI: 10.3233/JIFS-233260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11107-11119, 2023

Authors: Bo, Lina

Article Type: Research Article

Abstract: In college education, English is a required course for every college student, and undergraduate colleges have certain requirements for college English proficiency. At the same time, English is directly related to its learning, so improving the quality of college English teaching (CET) is very important. Teaching quality is a key indicator for measuring the effectiveness of English teaching. Learning quality evaluation is a very complex process that involves many factors, such as evaluation indicators, evaluation methods, etc. Therefore, establishing an objective and scientific English quality evaluation system is a challenging issue. The CET quality evaluation is a MAGDM. Then, the …TODIM and VIKOR was used to set up MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a tool for depicting uncertain information during the CET quality evaluation. In this study, the entropy and score values are employed to establish the objective weights. Then, an integrated interval-valued intuitionistic fuzzy TODIM-VIKOR (IVIF-TODIM-VIKOR) is developed to cope with the MAGDM problem. An illustrative example for CET quality evaluation and some comparative analysis are developed to demonstrate the validity and reliability of IVIF-TODIM-VIKOR method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2 interval-valued intuitionistic fuzzy sets (IVIFSs), TODIM, VIKOR, CET quality evaluation

DOI: 10.3233/JIFS-234149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11121-11133, 2023

Authors: Sreelatha, Tammineni | Maheswari, M. | Ravi, G. | Manikanda Devarajan, N. | Arun, M.

Article Type: Research Article

Abstract: Data compression is the ancestor of image compression, which uses fewer bits to represent the same picture. It is categorised as lossy or lossless depending on the quality required. In a lossless compression situation, no information is lost during the decompression process. Data loss is possible with the lossy technique since it is not reversible. In an effort to boost compression efficiency while maintaining a high xiv reconstruction quality of picture, near lossless approaches have evolved. The medical pictures consist of a large number of items, each of which may be described in detail and utilised for a variety of …purposes. The clinically relevant item in 2D medical pictures is referred to as the Region of Interest (ROI), whereas in 3D images, it is referred to as the Volume of Interest (VOI). Saving energy is crucial since it is one of the most limited resources in these networks. However, DTN has an additional difficulty since communication between nodes is maintained so long as they are in physical proximity to one another. However, because to the nodes’ mobility, this may not be long enough to provide the necessary multimedia data transmission. Wireless networks are susceptible to security assaults, and traditional security solutions are computationally demanding, making them unsuitable for networks that constantly need to recharge their batteries. All of these are reasons for tackling the problems of multimedia data processing and transmission via wireless networks in this dissertation. With this in mind, it has been attempted to investigate low-overhead and safe multimedia data compression as a solution to the issue that energy-constrained nodes in these networks limit complex multimedia processing while keeping at least basic security features. LZW-OMCA compression using the Octagonal Multimedia Compression Algorithm is part of the suggested method. The purpose of this is to improve the compression ratio. The proposed approach uses a little bit of crypt to compress data, which makes the data unreadable to anybody except the intended receiver, hence providing network security. The previous proposed works analysed the performance of several compression algorithms applied to multimedia material. Performance assessment utilising MSE, SSIM, and other metrics are used to show the pros and cons of each segment. Show more

Keywords: Octagonal multimedia compression algorithm, data compression, LZW-OMCA compression, MSE, SSIM

DOI: 10.3233/JIFS-234314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11135-11147, 2023

Authors: Shi, Jianzhong

Article Type: Research Article

Abstract: Compared with type-2 fuzzy sets, the secondary membership degree of interval type-3 fuzzy sets is an interval rather than crisp value, which makes interval type-3 fuzzy sets can obtain more degree of freedoms. This article studies an interval type-3 fuzzy PID controller based on interval type-3 fuzz sets. The framework of interval type-3 fuzzy PID controller is identical with type-2 fuzzy PID controller, but it contains more adjustment controller parameters and its type reduction procedure is more complex. In this paper, type reduction of interval type-3 fuzzy sets is derived from general type-2 fuzzy sets represented by α-plane and a …direct NT type reduction algorithm is applied. The control effects of interval type-3 fuzzy PID controller are firstly tested by 2 nonlinear plants, the simulation results show that interval type-3 fuzzy PID controller has better control performance indexes than PID controller, type-1 fuzzy PID controller, interval type-2 fuzzy PID controller and general type-2 fuzzy PID controller. Furthermore, the interval type-3 fuzzy PID controller will be applied in rated voltage control of solid oxide fuel cells (SOFC) power plant. The output voltage control of SOFC is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. The simulation results demonstrate the advantages and robustness of proposed interval type-3 fuzzy PID controller. Show more

Keywords: Type-2 fuzzy sets, interval type-3 fuzzy sets, interval type-3 fuzzy control system, type reduction, solid oxide fuel cells

DOI: 10.3233/JIFS-231460

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11149-11162, 2023

Authors: Chandra, Harshit | Bajpai, Shrish | Alam, Monauwer | Chandel, Vishal Singh | Pandey, Amit Kumar | Pandey, Digvijay

Article Type: Research Article

Abstract: Hyperspectral (HS) images contain rich spatial and spectral information. Due to its large size, it is difficult to store, process, analyze, or transmit the critical information contained in it. The compression of hyperspectral images is inevitable. Many transform based Hyper Spectral Image Compression Algorithms (HSICAs) have been proposed in the past that work for both lossy and lossless compression processes. The transform based HSICA uses linked lists or dedicated markers or array structure to keep track of significant and insignificant sets or coefficients of a transformed HS image. However, these algorithms either suffered from low coding efficiency, high memory requirements, …or high coding complexity. This work proposes a transform based HSICA using a curvelet transform to improve the directional elements and the ability to represent edges and other singularities along curves. The proposed HSICA aims to provide superior quality compressed HS images by representing HS images at different scales and directions and to achieve a high compression ratio. Experimental results show that the proposed algorithm has a low coding memory requirement with a 2% to 5% increase in coding gain compared to the other state of art compression algorithms. Show more

Keywords: Complexity theory, curvelet transform, hyperspectral image, hyperspectral image compression, transform coding

DOI: 10.3233/JIFS-231684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11163-11187, 2023

Authors: Yang, Lei | Gao, Ge

Article Type: Research Article

Abstract: As China’s economic and social development enters a new stage, the role of innovation and entrepreneurship is becoming increasingly prominent, and its importance is also being emphasized. As the main force for future employment and national economic construction, college students naturally become the new force for innovation and entrepreneurship. Therefore, it is imperative for universities to carry out in-depth innovation and entrepreneurship education (IEE) for college students. Then, with the continuous development of social development needs and the professional growth needs of college students, the “innovation and entrepreneurship” education for college students should also be adjusted in a timely manner …in terms of educational concepts, models, and methods. The IEE environment evaluation in universities under the background of “ Double Innovation” is looked as multiple attribute decision-making (MADM). In this paper, the information entropy model is employed to calculate the objective weight of the evaluation attribute. Then, interval-valued intuitionistic fuzzy Combined Compromise Solution (IVIF-CoCoSo) is built based on the Hamming distance and Euclid distance to cope with MADM under interval-valued intuitionistic fuzzy sets (IVIFSs). The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation”. Finally, the IVIF-CoCoSo approach is compared with existing methods to verify the effectiveness of IVIF-CoCoSo algorithm. The main contributions of this constructed paper are: (1) the IVIF-CoCoSo method is built based on the Hamming distance and Euclid distance. (2) the information entropy model is employed to calculate the objective weight of the evaluation attribute. (3) The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation” based on IVIF-CoCoSo. (4) The IVIF-CoCoSo model is compared with existing methods to verify the effectiveness of the IVIF-CoCoSo algorithm. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-CoCoSo method, information entropy method; IEE environment

DOI: 10.3233/JIFS-232151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11189-11201, 2023

Authors: Abiyev, Rahib H. | Aliev, Rafik | Kaynak, Okyay

Article Type: Research Article

Abstract: In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) based on the integration of Z-valued fuzzy logic and neural networks is proposed. Z-valued fuzzy rule base is presented and its inference process is described using interpolative approximate reasoning. Accordingly, the structure of the Z-FNN is proposed using a distance measure and interpolative approximate reasoning scheme. Based on presented architecture the learning algorithm of Z-FNN is designed. The updating of the unknown parameters of the network is carried out using Genetic Algorithms (GA). The proposed Z-FNN system is utilized for dynamic plant identification. The effectiveness of Z-FNN has been …tested by comparing its performance with the performances of other fuzzy systems available in the literature. The proposed approach has been proven to be a suitable alternative for the identification of nonlinear systems characterized by uncertain and imprecise information. Show more

Keywords: Fuzzy neural networks, Z-number, fuzzy rule, learning

DOI: 10.3233/JIFS-232741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11203-11216, 2023

Authors: Ashwin Shenoy, M. | Thillaiarasu, N.

Article Type: Research Article

Abstract: Automated identification of human activities remains a complex endeavor, particularly in unique settings like temple environments. This study focuses on employing machine learning and deep learning techniques to analyze human activities for intelligent temple surveillance. However, due to the scarcity of standardized datasets tailored for temple surveillance, there is a need for specialized data. In response, this research introduces a pioneering dataset featuring Eight distinct classes of human activities, predominantly centered on hand gestures and body postures. To identify the most effective solution for Human Activity Recognition (HAR), a comprehensive ablation study is conducted, involving a variety of conventional machine …learning and deep learning models. By integrating YOLOv4’s robust object detection capabilities with ConvLSTM’s ability to model both spatial and temporal dependencies in spatio-temporal data, the approach becomes capable of recognizing and understanding human activities in sequences of images or video frames. Notably, the proposed YOLOv4-ConvLSTM approach emerges as the optimal choice, showcasing a remarkable accuracy of 93.68%. This outcome underscores the suitability of the outlined methodology for diverse HAR applications in temple environments. Show more

Keywords: Dataset, machine learning, deep learning, YOLOv4, ConvLSTM, Human Activity Recognition

DOI: 10.3233/JIFS-233919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11217-11232, 2023

Authors: Zhou, Jiwen

Article Type: Research Article

Abstract: With the proposal of the “Double First Class” construction concept in Chinese universities, cultivating high-quality talents has become the main direction of university education. At present, local universities in China are actively adapting to the changes of the times, taking the construction of the Double First Class as an opportunity, continuously adjusting and deepening the reform of the double innovation and innovation talent cultivation mode, further improving the curriculum system, increasing investment in professional practical teaching, stimulating students’ enthusiasm for innovation and entrepreneurship (IAE), and focusing on improving students’ comprehensive abilities, achieving significant results in talent cultivation. The “Entrepreneurship and …Entrepreneurship” career ability evaluation of local college students could be considered as multiple attribute decision-making (MADM). Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method was employed to deal with MADM. The triangular fuzzy neutrosophic sets (TFNSs) are employed as a better tool for expressing uncertain information during the “Entrepreneurship and Entrepreneurship” career ability evaluation of local college students. In this paper, the triangular fuzzy neutrosophic number CoCoSo (TFNN-CoCoSo) based on the Hamming distance and Euclid distance is constructed to cope with the MADM under TFNSs. The information entropy method is employed to compute the weight values based on the Hamming distance and Euclid distance under TFNSs. Finally, a numerical example of “Entrepreneurship and Entrepreneurship” career ability evaluation of local college students is constructed and some decision comparisons are constructed to verify the TFNN-CoCoSo method. Show more

Keywords: Multiple attribute decision making (MADM), TFNSs, CoCoSo model, information entropy, career education quality evaluation

DOI: 10.3233/JIFS-234138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11233-11246, 2023

Authors: Tan, Ruipu | Chen, Chong | Zhang, Wende | Yang, Lehua | Ma, Hangfei

Article Type: Research Article

Abstract: With the rising incidences of emergencies, it is both challenging and meaningful to study how to make decisions quickly and take appropriate measures to control the spread and evolution of the situation. However, most current emergency decision-making focuses on mathematical model construction, whereas fuzzy decision-making is biased towards subjective assumptions, which are both insufficient for practicability. We studied the intelligent acquisition of single-valued neutrosophic numbers based on emotional tendency analysis and applied them to emergency decision-making. First, Python programming technology was used to crawl, preprocess, and statistically analyse the network comment data of emergencies, and a quantised single-value neutrosophic number …was obtained. Second, the attribute values, represented as neutrosophic numbers, were uniformly converted into cloud droplets, and the weight of the attribute values was objectively determined according to the digital feature entropy in the cloud droplets. Subsequently, a case-based reasoning approach was used to calculate the combined weighted similarity between the alternatives and ideal solution (target case) to obtain a ranking of the alternatives and historical cases in the case base that best matches the target case. Finally, a typhoon disaster assessment was considered as an example to verify the feasibility and effectiveness of the proposed method, and the advantages of the proposed method were emphasised through multi-aspect and multi-angle comparative analyses. The relevant research can be used for public opinion monitoring during emergencies and emergency handling. Show more

Keywords: Emergency decision-making, intelligent acquisition of single-valued neutrosophic numbers, emotional tendency analysis, cloud model, case-based reasoning (CBR)

DOI: 10.3233/JIFS-231039

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11247-11268, 2023

Authors: Deng, Ming | Zhou, Zhiheng | Liu, Guoqi | Zeng, Delu | Zhang, Mingyue

Article Type: Research Article

Abstract: Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired …target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively. Show more

Keywords: Active contour model, intensity inhomogeneity, bias field, saliency map

DOI: 10.3233/JIFS-231741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11269-11283, 2023

Authors: Shahi, Samira | Navidi, Hamidreza

Article Type: Research Article

Abstract: This paper proposes an efficient interval solidarity value that operates well for interval cooperative games. In addition to the axioms of symmetry, efficiency, and additivity, this value also satisfies two new axioms, namely, interval-egalitarian A-null player and interval differential marginality. The interval-egalitarian A-null player axiom equally divides the result of the difference between the grand coalition value and the sum of the solidarity value of players in the degenerate interval game among A-null players. The interval differential marginality axiom is an interval version of the Casajus differential marginality axiom. This property states that the difference in the interval solidarity value …of two players is determined by the difference between their average marginal contributions in the degenerate interval game. Eventually, the efficiency results and applicability of the proposed approach are compared with those of the other methods. Show more

Keywords: Interval cooperative games, solidarity value, efficiency, uncertainty

DOI: 10.3233/JIFS-223736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11285-11293, 2023

Authors: Mostafa, Ayman Mohamed | Rushdy, Ehab | Medhat, Reham | Hanafy, Asmaa

Article Type: Research Article

Abstract: Cloud computing is a cost-effective way for organizations to access and use IT resources. However, it also exposes data to security threats. Authentication and authorization are crucial components of access control that prevent unauthorized access to cloud services. Organizations are turning to identity management solutions to help IT administrators face and mitigate security concerns. Identity management (IDM) has been recognized as a more robust solution for validating and maintaining digital identities. Identity management (IDM) is a key security mechanism for cloud computing that helps to ensure that only authorized users have access to data and resources. Traditional IDM solutions are …centralized and rely on a single authority to manage user identities, which makes them vulnerable to attack. However, existing identity management solutions need to be more secure and trustworthy. Blockchain technology can create a more secure and trustworthy cloud transaction environment. Purpose: This paper investigates the security and trustworthiness of existing identity management solutions in cloud computing. Comparative results: We compared 14 traditional IDM schemes in cloud systems to explore contributions and limitations. This paper also compared 17 centralized, decentralized, and federated IDM models to explain their functions, roles, performance, contribution, primary metrics, and target attacks. About 17 IDM models have also been compared to explore their efficiency, overhead consumption, effectiveness to malicious users, trustworthiness, throughput, and privacy. Major conclusions: Blockchain technology has the potential to make cloud transactions more secure and reliable. It featured strong authentication and authorization mechanisms based on smart contracts on the Ethereum platform. As a result, it is still regarded as a reliable and immutable solution for protecting data sharing between entities in peer-to-peer networks. However, there is still a large gap between the theoretical method and its practical application. This paper also helps other scholars in the field discover issues and solutions and make suggestions for future research. Show more

Keywords: Cloud computing, identity management, blockchain, security-as-a-service, single-sign-on model

DOI: 10.3233/JIFS-231911

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11295-11317, 2023

Authors: Yu, Xulong | Yu, Qiancheng | Zhang, Yue | Wang, Aoqiang | Wang, Jinyun

Article Type: Research Article

Abstract: Traditional methods for detecting surface defects on ceramic tiles result in misdetection and missed detection, which makes it difficult to guarantee product stability and consistency within the same batch. Therefore, this article proposes an improved YOLOv5 algorithm for detecting surface defects on ceramic tiles. Firstly, the Res2Net module is combined with self-attention to fully utilize local and global information and improve the feature extraction effect of defects. Secondly, the GS-BiFPN neck network is designed to enhance the fusion capability of shallow detail and deep semantic information and alleviate ambiguity and redundancy on the feature map. Then, a lightweight attention module …is introduced to improve the detection capability of difficult-to-recognize defects and anti-background interference. Finally, the SIoU loss function improves the model’s convergence speed and accuracy. Experimental results demonstrate that the improved algorithm’s mean average precision (mAP) reaches 73.3%, 6.3% higher than the baseline model. Even when compared with YOLOv7-tiny, the mAP of the improved algorithm has increased by 8.7%. Additionally, the detection speed of the model can reach 92 frames per second, which can meet the requirements of ceramic tile surface defect detection in industrial scenarios. Show more

Keywords: Defect detection, YOLO, Attention mechanism, multi-scale feature fusion, SIoU

DOI: 10.3233/JIFS-231991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11319-11331, 2023

Authors: Ji, Bin | Zhou, Chuhao | Chen, Ze | Zheng, Shuai

Article Type: Research Article

Abstract: The use of similarity measures in interval-valued neutrosophic sets (IVNSs) theory is essential for comparing and assessing the degree of IVNSs difference. However, existing similarity measures for IVNSs suffer from several issues such as lacking precise axiomatic definitions, counterintuitive results, division by zero errors, inability to distinguish between positive and negative differences, and failure to satisfy the ranking definition. To address these limitations, we propose a novel multi-parameter similarity measure for IVNSs based on the tangent function. We demonstrate that our measure satisfies the axiomatic definition and apply it to medical diagnosis, achieving accurate diagnostic results. Additionally, we consider the …interactions between symptoms, adjust the proposed similarity measure using Choquet integrals, and provide analytical comparisons to demonstrate the advantages of our improved similarity measure, highlighting its stability and high confidence in the field of medical diagnosis.This study contributes to the advancement of similarity measures in IVNSs theory and provides valuable insights for the field of medical diagnosis. Show more

Keywords: Interval-valued neutrosophic sets, similarity measures, medical diagnosis, tangent function, choquet integrals

DOI: 10.3233/JIFS-232444

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11333-11351, 2023

Authors: Lv, Zhaoming

Article Type: Research Article

Abstract: Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning …scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems. Show more

Keywords: Meta-heuristic algorithms, flow regime algorithm, opposition-based learning, benchmark functions

DOI: 10.3233/JIFS-233329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11353-11368, 2023

Authors: Saraswathi, C. | Pushpa, B.

Article Type: Research Article

Abstract: Alopecia Areata (AA) is one of the most widespread diseases, which is generally classified and diagnosed by the Computer Aided Diagnosis (CAD) models. Though it improves AA diagnosis, it has limited interoperability and needs skilled radiologists in medical image interpretation. This problem can be solved by developing Deep Learning (DL) models with CAD for accurately diagnosing AA patients. Many studies engaged only in specific DL models such as Convolutional Neural Network (CNN) in medical imaging, which provides different independent results and many parameters, which limits their generalizability for different datasets. To combat this limitation, this work proposes an Ensemble Pre-Learned …DL and an Optimized Long Short-Term Memory (EPL-OLSTM) model for AA classification. Initially, many healthy and AA scalp hair images are separately fed to the pre-learned CNN structures, i.e. AlexNet, ResNet, and InceptionNet to extract the deep features. Then, these features are passed to the OLSTM, in which the Battle Royale Optimization (BRO) algorithm is applied to optimize the LSTM’s hyperparameters. Moreover, the output of the LSTM is classified by the fuzzy-softmax into the associated AA classes, including mild, moderate, and severe. Thus, this model can increase the accuracy of differentiating between healthy and multiple AA scalp hair classes. Finally, an extensive experiment using the Figaro1k (for healthy scalp hair images) and DermNet (for different AA scalp hair images) datasets demonstrates that the EPL-OLSTM achieves 93.1% accuracy compared to the state-of-the-art DL models. Show more

Keywords: Alopecia areata, computer-aided diagnosis, deep learning, pre-learned CNN, LSTM, battle royale optimizer, fuzzy-softmax

DOI: 10.3233/JIFS-232172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11369-11380, 2023

Authors: Yu, Shanshan | Wang, Yajun

Article Type: Research Article

Abstract: The street design and landscape in China include cultural elements representing the Heritage and history of this generation. Such designs are planned, fabricated, and implemented based on previous elements and novel findings from the past. The novel findings are identified using sophisticated technologies like the Internet of Things (IoT). Therefore, this article introduces a Cultural Design Planning Method (CDPM) for Street Landscape (SL) in maintaining the renowned Heritage of Chinese roads. The proposed method relies on IoT-based data and cultural elements from the previous design and its impact on society. In this case, the impact is computed using attraction and …cultural progression from the tourists and location. The cultural element’s connectivity and resemblance to the current location display the cultural progression. Such progression and impacts are recurrently validated using deep learning; the learning process identifies the elements and their associated impact on society. The previous and current street designs are augmented in the learning process to leverage placement and street design precision. The landscapes are periodically validated based on the current trends and associations. Show more

Keywords: Chinese cultural elements, deep learning, IoT, street landscape

DOI: 10.3233/JIFS-232292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11381-11395, 2023

Authors: Karthikeyan, P. | Brindha, K.

Article Type: Research Article

Abstract: Decentralised fog computing can provide real-time interaction, minimize latency, heterogeneity, and provide networking services between edge devices and cloud data centers. One of the biggest challenges in the fog layer network is finding a trustworthy fog node. Trust management encompasses the process of being trustworthy and the act of assessing the reliability of other nodes. It is essential to carry out a comprehensive review using a systematic approach in this field to advance our understanding, address emerging challenges, and foster secure and efficient trust management practices. This research paper considers a comprehensive analysis of high-quality fog computing trust management literature …from 2018 to 2022. A variety of distinct approaches have been chosen by fog computing-based trust management and these techniques are classified into three categories: algorithms, challenges, and limitations. Further, it reviews the various trust attacks in fog environments, details the solutions proposed in the current literature, and concludes with a discussion of the open challenges and potential future research directions in fog computing. Show more

Keywords: Trust management, fog computing, cloud computing, edge devices, security

DOI: 10.3233/JIFS-232892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11397-11423, 2023

Authors: Ma, Junpeng | Liu, Feiyan | Xiao, Chenggang | Wang, Kairan | Liu, Zirui

Article Type: Research Article

Abstract: The wake effect of wind farm can reduce the incoming wind speed at the wind turbine located in the downstream direction, resulting in the decrease of global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate the upper atmospheric circulation, obtain wind speed, wind direction and other data that can truly reproduce the fluid characteristics of the regional wind farm group. The boundary conditions and solution conditions of CFD model are set, and the computational fluid dynamics model of the region is obtained. WRF is coupled with CFD, and Fitch wake model is introduced into it. …By introducing the drag coefficient of wind turbine into the calculation of wind speed and turbulent kinetic energy in CFD-WRF coupling model, the wind field characteristics and wake effect of wind farm are simulated online. Monte Carlo sampling method is used to obtain random wind resource data in CFD-WRF coupling model, and then the sampled data is used to calculate the group output of wind farms, and evaluate the impact of wake effect on wind farm treatment. The experimental results show that this method can effectively analyze the characteristic data of regional wind field, and the calculation time of RANS method is about 3 s. Due to the wake effect, the overall output and efficiency of wind field will be significantly reduced. Show more

Keywords: CFD-WRF coupling, wind resource map, wind farm group, wake effect evaluation, wind speed and direction data, fitch wake model

DOI: 10.3233/JIFS-233273

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11425-11437, 2023

Authors: Pi, Feng | Tian, Shengwei | Pei, Xinjun | Chen, Peng | Wang, Xin | Wang, Xiaowei

Article Type: Research Article

Abstract: With the development of the Internet of Things (IoT), mobile devices are playing an increasingly important role in our daily lives. There are various malware threats present in these mobile devices, which can steal users’ personal information. Some malware exploits Inter-Component Communication (ICC) to execute malicious activities for unauthorized data access and system control, enabling communication between different components within an app and between different apps. In this paper, we propose an Adaptive Transformer-based malware framework (named AdaTrans) that combines sensitive Application Programming Interface (API)- and ICC-related features. The framework first extracts sensitive function call subgraphs (SFCS) to reflect the …caller-callee relationships, and then utilizes ICC interactions to reveal hidden communication patterns in malicious activities. Moreover, we propose a novel adaptive Transformer model to detect malicious behaviors. We evaluate our framework on real-world datasets and demonstrate that AdaTrans consistently outperforms other existing state-of-the-art systems. Show more

Keywords: Internet of things, ICC, Malware detection, transformer

DOI: 10.3233/JIFS-233556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11439-11452, 2023

Authors: Lyu, Yucheng | Mo, Yuanbin | Yue, Songqing | Hong, Lila

Article Type: Research Article

Abstract: Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is …improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles’ position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment. Show more

Keywords: Path planning, beetle antennae search algorithm, Levy flight, artificial bee colony algorithm

DOI: 10.3233/JIFS-224163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11453-11479, 2023

Authors: Zhang, Lei | Gu, Yue | Xia, Pengfei | Wei, Chuyuan | Yang, Chengwei

Article Type: Research Article

Abstract: Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all …three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results. Show more

Keywords: Knowledge graph embedding, multi-feature, multi-fold relation, hierarchical relation, self-attention mechanism

DOI: 10.3233/JIFS-231774

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11481-11493, 2023

Authors: Zhou, Xueling | Sun, Lei | Wei, Cuiping

Article Type: Research Article

Abstract: With the advancement of technology and growing social demand, large scale group decision making has gained significant importance in the field of decision making. Clustering analysis plays a crucial role in enhancing the efficiency of large scale group decision making processes. Linguistic evaluation is more in line with people’s cognitive and expressive habits. The hesitant fuzzy linguistic term set (HFLTS) offers more flexibility in expressing evaluation information. This paper is dedicated to designing a fuzzy C -means clustering model that is specifically applicable for the hesitant fuzzy linguistic preference relation (HFLPR). The objective function of the model is built based …on the dissimilarity between HFLPRs and the initial cluster centers to obtain the fuzzy membership matrix and cluster centers. Since initializing cluster centers is a crucial step to produce a reasonable cluster result, three methods are proposed for generating initial centers for HFLPRs. The first and second methods are improvements over existing approaches that dealt with the clustering problem with numerical values. The third approach considers both the preference relation of preferring an alternative and the distribution of the actual preference relations. Based on this, a fuzzy C -means clustering algorithm with HFLPR is designed to obtain the cluster centers and membership matrix for there types of initializing clustering centers. Finally, based on the quality and speed of clustering, a numerical example and comparative analyses illustrate that the proposed clustering algorithm is efficient and effective. Show more

Keywords: Large scale group decision making, Fuzzy C-means clustering, Hesitant fuzzy linguistic term set, Probabilistic linguistic term set

DOI: 10.3233/JIFS-224098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11495-11514, 2023

Authors: Muthulakshmi, V. | Hemapriya, N.

Article Type: Research Article

Abstract: The advent of deep learning techniques has ignited interest in medical image processing. The proposed work in this paper suggests one of the edge technologies in deep learning, which is recommended, based on a Radiomics feature extraction model for the effective detection of Kaposi sarcoma, a vascular skin lesion expression that indicates the most prevalent cancer in AIDS patients. This work investigates the role and impact of medical image fusion on deep feature learning based on ensemble learning in the medical domain. The model is crafted wherein the pre-built ResNet50 (Residual network) and Visual Geometry Group (VGG16) are fine-tuned and …an ensemble learning approach is applied. The pre-defined CNN was incrementally regulated to determine the appropriate standards for classification efficiency improvements. Our findings show that layer-by-layer fine-tuning can improve the performance of middle and deep layers. This work would serve the purpose of masking and classification of skin lesion images, primarily sarcoma using an ensemble approach. Our proposed assisted framework could be deployed in assisting radiologists by classifying Kaposi sarcoma as well as other related skin lesion diseases, based on the positive classification findings. Show more

Keywords: Kaposi sarcoma, vascular skin lesions, ensemble learning, ResNet50, VGG16, radiomics

DOI: 10.3233/JIFS-230426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11515-11534, 2023

Authors: Ma, Chao | Yager, Ronald R. | Liu, Jing | Yatsalo, Boris | Garg, Harish | Senapati, Tapan | Jin, LeSheng

Article Type: Research Article

Abstract: Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for …simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions. Show more

Keywords: Basic uncertain information, decision making, information fusion, relative basic uncertain information, uncertain decision making, Yager induced weights allocation

DOI: 10.3233/JIFS-231395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11535-11547, 2023

Authors: Gong, Kaixin | Ma, Weimin | Ren, Zitong | Wang, Jia

Article Type: Research Article

Abstract: Large-scale group decision-making (LSGDM) issues are increasingly prevalent in modern society across various domains. The preference information has emerged as a widely adopted approach to tackle LSGDM problems. However, a significant challenge lies in facilitating consensus among decision-makers (DMs) with diverse backgrounds while considering their hesitation and psychological behavior. Consequently, there is a pressing need to establish a novel model that enables DMs to evaluate alternatives with heterogeneous preference relations (HPRs). To this end, this research presents a new consensus-building method to address LSGDM problems with HPRs. First, a novel approach for solving collective priority weight is introduced based on …cosine similarity and prospect theory. In particular, a new cosine similarity measure is defined for HPRs. Subsequently, a consensus index is provided to gauge the consensus level among DMs by considering their psychological behavior and risk attitudes. Further, a consensus-reaching model is developed to address LSGDM with HPRs. Finally, an instance of supplier selection is presented to demonstrate the practicality and efficacy of the proposed method. Show more

Keywords: Large-scale group decision-making, prospect theory, heterogeneous preference relations, consensus reaching, risk attitudes

DOI: 10.3233/JIFS-231456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11549-11566, 2023

Authors: Huang, Feidan | Deng, Zexi | Cao, Fasheng

Article Type: Research Article

Abstract: Concepts of generalized commutative intuitionistic fuzzy finite state machines and generalized switching intuitionistic fuzzy finite state machines are proposed in this paper, and properties of these two kinds of intuitionistic fuzzy finite state machines are investigated. By using the conditions associated with generalized commutativity, a kind of congruences of intuitionistic fuzzy finite state machines is defined. Homomorphic properties of generalized commutative intuitionistic fuzzy finite state machine are also discussed. Moreover, products of generalized commutative intuitionistic fuzzy finite state machines and products of generalized switching intuitionistic fuzzy finite state machines are studied, and some related properties are proved.

Keywords: Intuitionistic fuzzy finite state machine, generalized commutativity, congruence, homomorphism, product

DOI: 10.3233/JIFS-231549

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11567-11583, 2023

Authors: Wang, Liming | Liu, Yingming | Pang, Xinfu | Wang, Qimin | Wang, Xiaodong

Article Type: Research Article

Abstract: A low-carbon economic scheduling method based on a Q-learning-based multiobjective memetic algorithm (Q-MOMA) is proposed to improve the economy of cogeneration system scheduling and reduce carbon emission. First, the model incorporates a carbon capture device, a heat storage device, and a demand response mechanism to enhance the system’s flexibility and wind power consumption. In addition, the Q-MOMA algorithm combines global and local search and uses a Q-learning algorithm to dynamically adjust the crossover and mutation probabilities to improve the algorithm’s searchability. Finally, the fuzzy membership function method is used to make a multiobjective decision, which balances the economy and low …carbon of the system, and a compromise scheduling scheme is given. The effectiveness of the proposed model and solution method is verified through the simulation calculation of the improved system and compared with the simulation results of various optimization algorithms. The simulation results show that the proposed model can improve the wind power consumption space and the system’s economy and reduce carbon emissions. The Q-MOMA algorithm has a relatively better optimization ability in the low-carbon economic scheduling of the cogeneration system. Show more

Keywords: Bi-objective optimization, carbon capture, demand response, memetic algorithm, Q-learning

DOI: 10.3233/JIFS-231824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11585-11600, 2023

Authors: Wenjun, Zhou | Jianmin, Ma

Article Type: Research Article

Abstract: In the speaker verification task based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), by constructing the UBM as a tree structure, the kernel Gaussians suitable for different speakers can be quickly selected, which speeds up the modeling of speaker acoustic space by GMM. The tree-based kernel selection algorithm (TBKS) introduces a beam-width, which increases the candidate range of kernels and improves the kernel selection accuracy. In this paper, we improve the TBKS algorithm by introducing a recall rate to adjust the number of nodes recalled in each layer of the tree structure. This adjustment refines the quantity and resolution of …Gaussian distributions in various subspaces within the acoustic space, compensating for the loss caused by discarding some significant Gaussians erroneously. Speaker verification experiments are carried out based on the Aishell2 dataset. The results reveal that the modified TBKS algorithm reduces EER by 7.5% relatively and increses computational reduction factor to 42.93, enhancing both recognition accuracy and speed. In addition, the test speech is spliced into different lengths and common environmental noise is added to verify the universality of the improved algorithm. Show more

Keywords: Speaker verification, fast scoring, gaussian mixture model, tree-based kernel selection

DOI: 10.3233/JIFS-232304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11601-11611, 2023