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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Xiao, Hui-Min | Wu, Shou-Wen | Wang, Liu
Article Type: Research Article
Abstract: In the process of large-scale group decision making (LSGDM), probabilistic linguistic term set (PLTS) is an useful tool to represent the preferences of expert. There is a common case that experts tend to provide incomplete preferences due to various reasons. However, previous methods which cope with the missing values never took experts′ level of cognition over alternatives and attributes into account. In reality, because of limited knowledge reservation and the complexity of decision problem, experts have diverse familiarity with each scheme and attribute. For handling the defect, we propose a novel method to fill missing preference values, based on …the combination of knowledge-match degree and trust degree of experts providing reference information. We obtain the knowledge-match degree through the accuracy and reliability of preference as well as the trust degree through social network analysis technology (SNA), and use the probabilistic linguistic weighted average operator (PLWA) to integrate the referential values into preferences of the missing expert. Moreover, to solve the consensus problem at minimal cost, a consensus model based minimum adjust is developed in which the consensus degree of identified elements are all lowest at three aspects including decision matrix, internal experts and intra-group. On the basis of the trust relationship, revising the preference with low consensus guarantees regulated experts′ real aspiration. In addition, a new approach to measure the weight of sub-group is proposed in the light of trust in-degree which considers the reliability of experts in the same subgroup.The feasibility and validity of the LSGDM method are tested by using a numerical example and comparing with other methods. Show more
Keywords: Incomplete preference, knowledge-match degree, trust degree, social network analysis, probabilistic linguistic term set
DOI: 10.3233/JIFS-212569
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4037-4060, 2022
Authors: Younus, Awais | Ghaffar, Iram
Article Type: Research Article
Abstract: Optimal control is a very important field of study, not only in theory but in applications, and fractional optimal control is also a significant branch of research in theory and applications. Based on the concept of fuzzy process, a fuzzy fractional optimal control problem is presented. In this article, we derived the necessary and sufficient optimality conditions for a class of fuzzy-fractional optimal control problems (FFOCPs) with gH-Atangana-Baleanu fuzzy-fractional derivative expressed in Caputo sense. The main aim is to find the best possible control that minimizes the fuzzy performance index and satisfies the related ABC fuzzy-fractional dynamical systems. We also …presented some examples for more illustration of the subject. Show more
Keywords: Fuzzy-fractional derivative, Atangana-Baleanu fuzzy-fractional derivative, generalized Hakahara differentiability, 90C46, 34K36, 93C42
DOI: 10.3233/JIFS-213028
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4061-4070, 2022
Authors: Gnanavel, V. K. | Baskaran, J.
Article Type: Research Article
Abstract: Power quality disturbance (PQD) defines the presence of inconsistencies that occur in the usual wave shapes of voltage and current signals. Power quality is considered the main challenge for power industry with the increase in dynamic load and highly subtle electronic devices. Besides, the islanding events, particularly unintended islanding, grasp significant challenges and it needs to be identified at the early stage. Islanding is an anomalousstate in the power system, where the distributed generators (DGs) are placed on supplying electrical energy to the local load even after the shortage of the major grid. Therefore, it is essential to identify and …differentiate the PQ events and islanding events in ensuring pollution-free power, equipment, and labor safety. With this motivation, this paper presents an automated optimal deep learning based islanding detection (AODL-ID) technique. The proposed AODL-ID technique involves three major stages namely decomposition, classification, and hyperparameter tuning. Firstly, an empirical mode decomposition (EMD) approach is utilized to decompose the basic signals from the polluted signals. In addition, bidirectional gated recurrent neural network (BiGRNN) technique is employed for the classification of islanding and non-islanding PQ events in the wind energy penetrated DG systems by means of features (Voltage and current (RMS, half-cycle, peak and fundamental) Frequency. Power Factor / Cos Phi. Power and energy (active, reactive, harmonic, apparent)). Since the hyperparameters play a significant role in overall classification performance, the hyperparameter tuning of the BiGRNN model takes place using chaotic crow search algorithm (CCSA). To examine the enhanced classification outcome of the AODL-ID technique, a set of experimental analyses is carried out and the outcomes are investigated interms of various evaluation metrics. The simulation outcomes highlighted the supremacy of the AODL-ID technique over the compared techniques. Show more
Keywords: Distributed generation systems, Islanding detection, power quality, deep learning, parametertuning, electrical energy
DOI: 10.3233/JIFS-213129
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4071-4081, 2022
Authors: Zhang, Zhe | Zhang, Yiyang | Li, Xiang | Qian, Yurong | Zhang, Tao
Article Type: Research Article
Abstract: This paper proposes a multi-feature spatial convolutional semantic matching model (BMCSA) based on BERT by enriching different feature spatial information of semantic features. BMCSA employs the BERT model to extract the semantic features of the text, then uses the two-dimensional convolutional network to extract different feature spatial information, and finally combines the Attention mechanism to capture the global feature spatial information. We use two different semantic matching data sets and a text inference data set to verify the effectiveness of the proposed model. Experimental results prove that BMCSA is better than the baseline model.
Keywords: Semantic matching, BERT, CNN, Attention mechanism
DOI: 10.3233/JIFS-212624
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4083-4093, 2022
Authors: Musa, Sagvan Younis | Asaad, Baravan Abdulmuhsen
Article Type: Research Article
Abstract: The most significant and fundamental topological property is connectedness (resp. disconnectedness). This property highlights the most important characteristics of topological spaces and helps to distinguish one topology from another. Taking this into consideration, we investigate bipolar hypersoft connectedness (resp. bipolar hypersoft disconnectedness) for bipolar hypersoft topological spaces. With the help of an example, we show that if there exist a non-null, non-whole bipolar hypersoft sets which is both bipolar hypersoft open and bipolar hypersoft closed over 𝒰, then the bipolar hypersoft space need not be a bipolar hypersoft disconnected. Furthermore, we present the concepts of separated bipolar hypersoft sets …and bipolar hypersoft hereditary property. Show more
Keywords: Bipolar hypersoft connected (resp. bipolar hypersoft disconnected), bipolar hypersoft topology, bipolar hypersoft sets, separated bipolar hypersoft sets, bipolar hypersoft hereditary property
DOI: 10.3233/JIFS-213009
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4095-4105, 2022
Authors: Khurana, Khushboo | Deshpande, Umesh
Article Type: Research Article
Abstract: In this information age, there is exponential growth in visual content and video captioning can address many real-life applications. Automatic generation of video captions can be beneficial to comprehend a video in a short time, assist in faster information retrieval, video analysis, indexing, report generation, etc. Captioning of industrial videos is of importance to get a visual and textual summary of the work ongoing in the industry. The generated captioned summary of the video can assist in remote monitoring of industries and these captions can be utilized for video question-answering, video segment extraction, productivity analysis, etc. Due to the presence …of diverse events processing of industrial videos are more challenging compared to other domains. In this paper, we address the real-life application of generating the descriptions for the videos of a labor-intensive industry. We propose a keyframe-based approach for the generation of video captions. The framework produces a video summary by extraction of keyframes, thereby reducing the video captioning task to image captioning. These keyframes are passed to the image captioning model for description generation. Utilizing these individual frame captions, multi-caption descriptions of a video are generated with a unique start and end time of each caption. For image captioning, a merge encoder-decoder model with a stacked decoder for caption generation is used. We have performed experimentation on a dataset specifically created for the small-scale industry. We have also shown that data augmentation on the small dataset can greatly benefit the generation of remarkably good video descriptions. Results of extensive experimentation performed by utilizing different image encoders, language encoders, and decoders in the merge encoder-decoder model are reported. Apart from presenting the results on domain-specific data, results on domain-independent datasets are also presented to show the applicability of the technique in general. Performance comparison with existing datasets - OVSD and Flickr8k and Flickr30k are reported to demonstrate the scalability of our method. Show more
Keywords: Video Localized Captioning, Keyframe extraction, Video Segmentation, Image Captioning, Merge Encoder-Decoder models, Stacked Bi-LSTM Decoder, Deep Learning
DOI: 10.3233/JIFS-212381
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4107-4132, 2022
Authors: Wu, Zhao | Jiang, Feng | Cao, Rui
Article Type: Research Article
Abstract: The rapid and effective identification of leaf diseases of woody fruit plants can help fruit farmers prevent and cure diseases in time to improve fruit quality and minimize economic losses, which is of great significance to fruit planting. In recent years, deep learning has shown its unique advantages in image recognition. This paper proposes a new type of network based on deep learning image recognition method to recognize leaf diseases of woody fruit plants. The network merges the output of the convolutional layer of ResNet101 and VGG19 to improve the feature extraction ability of the entire model. It uses the …transfer learning method to partially load the trained network weights, reducing model training parameters and training time. In addition, an attention mechanism is added to improve the efficiency of network information acquisition. Meanwhile, dropout, L2 regularization, and LN are used to prevent over-fitting, accelerate convergence, and improve the network’s generalization ability. The experimental results show that the overall accuracy of woody fruit plant leaf diseases identification based on the model proposed in this paper is 86.41%. Compared with the classic ResNet101, the accuracy is improved by 1.71%, and the model parameters are reduced by 96.63%. Moreover, compared with the classic VGG19 network, the accuracy is improved by 2.08%, and the model parameters are reduced by 96.42%. After data set balancing, the overall identification accuracy of woody fruit plant leaf diseases based on the model proposed in this paper can reach 86.73%. Show more
Keywords: Model fusion, transfer learning, neural network, image recognition
DOI: 10.3233/JIFS-213388
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4133-4144, 2022
Authors: Li, Qi | Hong, Liang
Article Type: Research Article
Abstract: The process of urbanization has brought about prosperity in urban civilizations, causing a series of ecological and social problems. Therefore, in recent years, monitoring the process of urban expansion has become a hot spot in the field of geosciences. The 8 main urban agglomerations built-up areas from 1995 to 2015 were extracted by night light images. Based on the expansion speed and intensity index, center of gravity migration model, spatial correlation analysis and grey correlation analysis, the characteristics of the spatial and temporal variation were described. Based on it, a driving force model was established to explore the factors behind …its spatial and temporal expansion. The built-up areas of the Yangtze River Delta, the Pearl River Delta, the Beijing-Tianjin-Hebei Region, the Chengdu-Chongqing Economic Circle, Central Plains, the middle reaches of the Yangtze River, central Yunnan, and the Beibu Gulf have been increasing year by year, and reached 7671 km2 , 3926 km2 , 3729 km2 , 3025 km2 , 6649 km2 , 3172 km2 , 500 km2 , 1047 km2 in 2015, which are 5.0, 6.6, 2.6, 5.1, 3.1, 2.8, 3.5, 3.2 times more than that in 1995 respectively. There is an expansion trend of ‘point-block-surface’ from the overall perspective. The development of all eight urban agglomerations belongs to the spatial expansion mode under the guidance of agglomeration, the spatial distribution presents positive spatial autocorrelation, and the agglomeration degree manifests fluctuating changes. Socio-economic factors such as non-agricultural population, regional Gross Domestic Product, and total industrial output have a greater impact on the expansion of urban built-up areas, while the number of colleges and universities and the total investment in fixed assets have less impact with less synchronization. Show more
Keywords: Night light images, urban built-up area, spatiotemporal variation, driving factors
DOI: 10.3233/JIFS-220201
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4145-4159, 2022
Authors: Mohammed, Parves | Jabeen Begum, S.
Article Type: Research Article
Abstract: In present scenario, Heart Disease has become the vital cause of mortality and diagnosis of heart diseases is a great confrontation in the field of medical data analysis. Data Mining is an efficient technique for processing and analyzing larger databases for deriving hidden knowledge appropriately. Hence, it is incorporated in medical data analysis for assisting in effective decision making and disease predictions. With that concern, this paper concentrates on framing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using the advanced data mining conceits. The model considers the significant features of patient data that are available in benchmark datasets. Here, …the main objective of the proposed model is to enhance the classification accuracy of patient data on classes under NORMAL and ABNORMAL. For enhancing the classification accuracy, the proposed integrated model utilizes the algorithms such as Decision Tree Algorithm, Naive Baye’s Classification and Ensemble Classifiers called Random Forest and Bagging. Further, performance evaluation is performed for analyzing the proposed work. For that, images from UCI repository are utilized and the comparative analysis shows that the proposed work produces better results than the existing models compared. Show more
Keywords: Heart disease diagnosis, data mining, classification accuracy, decision tree algorithm, Naive Baye’s classification, random forest and bagging
DOI: 10.3233/JIFS-220306
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4161-4171, 2022
Authors: Gopinath, S. | Sakthivel, K. | Lalitha, S.
Article Type: Research Article
Abstract: The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. …The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%). Show more
Keywords: Big data technology, convolutional recurrent neural network, weighted naïve bayesian network classifier algorithm, plant disease classification
DOI: 10.3233/JIFS-220747
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4173-4186, 2022
Authors: Aarthi, R.J. | Vinayagasundaram, B.
Article Type: Research Article
Abstract: Climate change and its consequences for human life have emerged as the world’s most pressing challenge. Due to the complexity, veracity, and velocity of climate data, a traditional, simple, and single machine learning model will not be sufficient to perform effective and timely analysis. The climate data can be effectively analyzed, and climate models can be developed with the proposed hybrid model. The deep learning AutoEncoder (AE) is used for feature extraction, removal of redundant and noisy data. The Synthetic Minority class Oversampling (SMOTE) technique to generate samples in minority class to mitigate the imbalance in the sample distribution. Extreme …Learning Machine (ELM) is used for further feature classification. The proposed method exploits big data strategies and the results interpretation process to extract accurate insight from climate data. ELM handles the class imbalance problem to improve the performance of the Early Warning System (EWS) model and fine-tune it. The hybrid method drastically reduces the computation cost and improves the accuracy to 93%, 86%, 95%, and 98% of four different datasets against other machine learning models. The experimental results of the AE_SMOTE_ELM model, compared with other state-of-the-art deep learning methods, shows accuracy and an efficiency of 90.4% and 91.76%, respectively, for two climate datasets. Show more
Keywords: AutoEncoder, SMOTE, ELM, climate data, class imbalance
DOI: 10.3233/JIFS-210666
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4187-4199, 2022
Authors: Deva Hema, D. | Ashok Kumar, K.
Article Type: Research Article
Abstract: Multivariate Time Series Crash Risk Prediction is essential in the development of Collision Avoidance Systems (CASs), which are vital components of the Intelligent Transportation System. The crash risk prediction performance is degraded with high computational cost and low accuracy. To address this issue, Attention based CNN-LSTM Hybrid model is proposed for Multivariate time series crash risk prediction through the augmentation of Convolutional Neural Network (CNN) with Attention based Long Short Term Memory (ATT-LSTM). Attention mechanism is incorporated with LSTM to learn long-term dependencies of ultra-long sequences. Modified Crash Risk Index (MCRI) is developed to label the crash and non-crash events …considering Adaptive Perception Reaction Time (APRT) which enables the enhancement of the accuracy of the Multivariate time series crash risk prediction system. The problem is formulated as multivariate time series prediction and validity of proposed model is evaluated with Next Generation Simulation (NGSIM) dataset. The proposed model outperforms state-of-the-art models where MCRI and CNN-ATT-LSTM enhance accuracy of the crash risk prediction. 98.1% of accuracy has been achieved in the proposed model. The result demonstrates that the proposed model requires less computational cost, high accuracy and minimum preprocessing. The proposed model presents warning to the driver at the time of collision accurately and can be implemented in Collision Avoidance Systems. Show more
Keywords: Crash risk, time series, prediction, deep learning, LSTM, CNN
DOI: 10.3233/JIFS-211775
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4201-4213, 2022
Article Type: Research Article
Abstract: Purpose: The purpose of this paper is to propose a pseudo-grey metabolic grey Markov model to deal with the prediction issue in which the original sequences are oscillation sequences. Design/methodology/approach: First, the original sequences were processed with the accelerated advection transformation and the weighted mean generation transformation to make them smoother. Then, the mean GM (1, 1) model was applied to the multi-step prediction of the pre-processed data sequences. Finally, with the help of the optimal partitioning method, the pseudo-grey metabolic Markov model was used to correct the prediction results and determine the final prediction values. …Findings: The results demonstrate that the accuracy of this model is significantly higher than that of the traditional grey Markov model, which further verifies the rationality of the proposed model. Therefore, scientific and reasonable prediction of urban rainfall is of great theoretical significance and application value for the government and decision-making departments to formulate drought prevention and disaster mitigation measures. Originality/value: The model in this paper not only provides new ideas for the data preprocessing problem of the grey Markov model, but also solves the problem of errors due to individual subjectivity in state interval division. It provides a novel idea for the development of grey prediction models. The rationality and validity of the model are illustrated by taking the Zhengzhou City of Henan Province as examples. Show more
Keywords: Prediction, GM (1,1) model, Markov model, rainfall
DOI: 10.3233/JIFS-213137
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4215-4225, 2022
Authors: Li, Jian | Li, Meiyue | Lin, Hao
Article Type: Research Article
Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219328 .
DOI: 10.3233/JIFS-220301
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4227-4241, 2022
Authors: Suresh, R. | Helenprabha, K.
Article Type: Research Article
Abstract: Internet of Medical Things (IoMT) is the combination of medical devices and utilization by networking technologies. But, the response time and cost were not reduced. In order to address these issues, IoMT Aware Data Collective Quadratic Ensembled Cat Boost Module Classification (IoMT-DCQECBMC) Method is introduced. Initially, IoMT Aware Data Collection is used for gathering data from medical devices. After the data collection process, Quadratic Ensembled Cat Boost Module Classification (QECBM) is carried out in IoMT-DCQECBMC Method to design an efficient VLSI architecture with minimal cost and area. The quadratic classifier is considered the weak learner that categorizes the module for …efficient VLSI design. Finally, the weak learners are joined to form the strong classifier to perform non-invasive blood glucose monitoring efficiently. Experimental evaluation is carried out on the factors such as computation cost, area, and accuracy with respect to a number of modules in VLSI circuits. The accuracy of the IoMT-DCQECBMC method is increased by 4% than conventional methods. In addition, the area consumption and computation cost of the proposed IoMT-DCQECBMC method are reduced by 13% to 30% other than existing methods. Show more
Keywords: Very-large-scale integration, integrated circuit, healthcare information, diabetes mellitus, non-invasive blood glucose monitoring, weak learner, classification
DOI: 10.3233/JIFS-220315
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4243-4253, 2022
Authors: Zhao, Yifan
Article Type: Research Article
Abstract: Interval fuzzy implications play an important role in both theoretical and applied communities of interval-valued fuzzy sets and have been widely studied. Recently, Dimuro et al. analyzed the law of O -conditionality for fuzzy implications in general. However, there is no corresponding researches about the interval extension. To fill the gap, in this paper, we introduce the generalized law of O -conditionality 𝕆 ( X , 𝕀 ( X , Y ) ) ≤ Y (GOC), where 𝕀 is an interval fuzzy …implication and 𝕆 is an interval overlap function. Meanwhile, we discuss the advantages one may get using it. Moreover, we consider the conditional antecedent boundary condition (CABC) for interval fuzzy implications derived from interval overlap and grouping functions, including, interval R 𝕆 - , ( 𝔾 , ℕ ) - , ( 𝕆 , 𝔾 , ℕ ) - and ( 𝔾 , 𝕆 , ℕ ) - implications. Finally, we further analyze the generalized law of O -conditionality for these four classes of interval fuzzy implications. Show more
Keywords: Interval fuzzy implications, O-conditionality, interval overlap and grouping functions
DOI: 10.3233/JIFS-211477
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4255-4269, 2022
Authors: Jia, Qilong | Li, Ying | Liu, Zhichen
Article Type: Research Article
Abstract: To address the challenging fault isolation problem, this paper proposes a new fault isolation approach based on propagated nonnegative matrix factorizations (PNMFs). PNMFs make significant contributions to the theoretical research on nonnegative matrix factorizations (NMFs)-based fault isolation. Specifically, PNMFs provide a new way to improve the fault isolation performance of NMFs by training a matrix-factorization model using labeled and unlabeled samples where labeled samples are the samples whose categories are already known, and unlabeled samples are the samples whose categories are unknown. Moreover, PNMFs incorporate label propagation theory into NMFs for recognizing unlabeled samples based on labeled samples. As a …result, PNMFs change the learning mechanism of NMFs for improving fault isolation performance. To demonstrate the superiority of the PNMFs-based fault isolation approach, a case study on fault isolation for a penicillin fermentation process based on PNMFs and NMFs was implemented. The case study results demonstrate that the proposed PNMFs-based fault isolation approach outperforms the state-of-the-art fault isolation approaches. Show more
Keywords: Fault isolation, propagated nonnegative matrix factorizations, nonnegative matrix factorizations, label propagation, soft label
DOI: 10.3233/JIFS-212590
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4271-4284, 2022
Authors: Geetha, Selvaraj | Narayanamoorthy, Samayan | Kang, Daekook | Baleanu, Dumitru
Article Type: Research Article
Abstract: Nowadays, energy from renewable energy resources (RERs) partially satisfies society’s energy demands. Investment in the renewable energy system is an arduous task because of huge investments. Generally, RERs selection involves conflicting criteria. Hence there is necessary to evaluate the RERs alternatives in economic, technological, and environmental aspects. Here, DEMATEL (Decision Making Trial and Evaluation Laboratory) method has been utilized to assess the interrelationship among the criteria under hesitant Pythagorean fuzzy (HPF) information. The Pythagorean fuzzy set (PFS) has recently obtained enormous attention and is applied widely in decision-making. We have proposed an integrating model with DEMATEL and VIKOR (Vise Kriterijumska …Optimizacija Kompromisno Resenje) methods to identify and evaluate the criteria and alternatives in RERs selection. Within the proposed model, the HPF-DEMATEL method is utilized for weighting the criteria, and the HPF-VIKOR method is utilized for ranking. Finally, an illustrative example demonstrates the proposed method. Show more
Keywords: Hesitant pythagorean fuzzy, DEMATEL, VIKOR, decision, renewable energy
DOI: 10.3233/JIFS-201584
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4285-4302, 2022
Authors: Xue, Cuihong | Yu, Ming | Yan, Gang | Qin, Mengxian | Liu, Yuehao | Jia, Jingli
Article Type: Research Article
Abstract: Some of the existing continuous sign language recognition (CSLR) methods require alignment. However, this is time-consuming, and breaks the continuity of the frame sequence, and also affects the subsequent process of CSLR. In this paper, we propose a multi-modal network framework for CSLR based on a multi-layer self-attention mechanism. We propose a 3D convolution residual neural network (CR3D) and a multi-layer self-attention network (ML-SAN) for the feature extraction stage. The CR3D obtains the short-term spatiotemporal features of the RGB and optical flow image streams, whereas the ML-SAN uses a bi-gated recurrent unit (BGRU) to model the long-term sequence relationship and …a multi-layer self-attention mechanism to learn the internal relationships between sign language sequences. For the performance optimization stage, we propose a cross-modal spatial mapping loss function, which improves the precision of CSLR by studying the spatial similarity between the video and text domains. Experiments were conducted on two test datasets: the RWTH-PHOENIX-Weather multi-signer dataset, and a Chinese SL (CSL) dataset. The results show that the proposed method can obtain state-of-the-art recognition performance on the two datasets, with word error rate (WER) value of 24.4% and accuracy value of 14.42%, respectively. Show more
Keywords: CR3D, multi-modal fusion, self-attention mechanism, ML-SAN, cross-modal spatial mapping
DOI: 10.3233/JIFS-211697
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4303-4316, 2022
Authors: Zhang, Hui | Bian, Weixin | Jie, Biao | Sun, Shuwan
Article Type: Research Article
Abstract: We propose an efficient identity authentication protocol based on cancelable biometric and Physical Uncloable Function (PUF) namely BioP-TAP, which realizes the two-way authentication between the user and the server. Specially, the concept of biometric template protection is added to the proposed protocol to better protect user privacy. We use the properties of PUF to generate the cancelable biometric and adds it to the authentication protocol. Then, we design a complete authentication protocol combining the elliptic curve Pedersen commitment and Zero-knowledge proof. Finally, we adopt the method of combining formalization and non-formalization to carry out scientific evaluation from multiple perspectives. And …the performance analysis and comparison with existing schemes are employed to evaluate the proposed scheme, so as to ensure the effectiveness and security. The results show that the proposed method is more effective for security than existing methods, and more suitable for the user biometric authentication in a multi-server environment. Show more
Keywords: Biometric identification, Authentication, Privacy protection, Cancelable biometrics, Physical unclonable function
DOI: 10.3233/JIFS-212095
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4317-4333, 2022
Authors: Yang, Ge | Lai, Haijian | Zhou, Qifeng
Article Type: Research Article
Abstract: Aiming at the inconsistency of manual detection of mobile phone screen defects, the image feature extraction of traditional machine learning is often set based on experience, resulting in unsatisfactory detection results. Therefore, a mobile phone screen defect detection model (Ghostbackbone) which is proposed by this paper based on YOLOv5 s and Ghostbottleneck. The bottleneck of Ghostbackbone mainly uses and improves the Ghostbottleneck of GhostNet. The attention module of Ghostbackbone uses Coordinated Attention and Depthwise Separable Convolution for parameter reduction. Finally, Ghostbackbone uses YOLOv5 as the object detector to train the mobile phone screen defect dataset. The experimental results show that the …parameter quantity of Ghostbackbone is 24% of that of YOLOv5 s, the average time of detecting a single picture is only 2% lower than that of YOLOv5 s, and the mAP0.5 : 0.95 is 2% higher than that of MobilenetV3 s. Show more
Keywords: Defect detect, object detect, lightweight network application, GhostNet, deep learning
DOI: 10.3233/JIFS-212896
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4335-4349, 2022
Authors: Li, Hao
Article Type: Research Article
Abstract: With the aggravation of many social psychological and psychological stress factors, the high incidence rate of depression and depression has become a major problem which puzzles people’s health and even endangers life. Non drug therapy has become an effective alternative to drug therapy, which is in line with the new trend of natural medicine in the world. This paper will use the real world research method (RWS) to conduct a clinical trial of pop light music in the treatment of depression. Based on the fuzzy algorithm, a comprehensive evaluation system for the treatment of depression was established. By comparing and …analyzing the main efficacy indexes between music group and traditional medicine group, we found that the cure rate, clinical control rate, significant efficiency, effective rate and ineffective rate of music therapy group were significantly better than those of drug group. Through the analysis of seven factors of HAMD (Hamilton Depression Scale) scale, we found that pop light music can improve the sleep status and physical symptoms of patients, and the improvement degree of music therapy is significantly better than that of drug therapy. Show more
Keywords: Cure rate, depression, fuzzy algorithm, HAMD scale, pop light music
DOI: 10.3233/JIFS-213211
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4351-4362, 2022
Authors: Velayudhan, Jithin | Narayanan, M.D | Saha, Ashesh | Sikha, O.K.
Article Type: Research Article
Abstract: The synchronization phenomenon in two linearly coupled friction-induced oscillators is analyzed in this paper using a data-driven approach based on dynamic mode decomposition (DMD). The tip mass at the end of a cantilever beam in each of the oscillators is in frictional contact with a rigid rotating disc. The cantilever beams are subjected to base excitation and a linear spring between the tip masses provides the coupling between the two oscillators. The partial differential equation governing the motion of the system is reduced to a set of ordinary differential equations employing the method of modal projection. The qualitative nature of …the coupled oscillations is determined by analyzing the time displacement response, Fast Fourier Transform (FFT), Poincaré map, and the phase plane diagrams. DMD approximates the dynamical system in terms of coherent structures known as spatiotemporal modes. The frequency information is captured in the corresponding spatiotemporal modes. The influence of each frequency component on the whole dynamics of the system is studied by reconstructing the motion of each subsystem using the corresponding spatiotemporal mode. The contribution of a single dynamic mode towards the overall synchronized motion of the coupled system is analyzed by evaluating the linear correlation between those modes. Evaluation of the similarity measures helps to unearth how far each spatial and temporal mode behaves similarly in time. Show more
Keywords: Synchronization, friction-induced oscillators, dynamic mode decomposition, method of modal projection
DOI: 10.3233/JIFS-213248
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4363-4378, 2022
Authors: Yu, Xiaobing | Luo, Wenguan | Rao, R.Venkata
Article Type: Research Article
Abstract: Jaya, a simple heuristic algorithm, has shown attractive features, especially parameter-free. However, the simple structure of Jaya algorithm may result in poor performances, to boost the performance, a multi-strategy Jaya (MJaya) algorithm based on multi-population has been proposed in this paper. Three strategies correspond to three groups of solutions. The first strategy based on the first population is to introduce an adaptive weight parameter to the position-updating equation to improve the local search. The second strategy is based on rank-based mutation to enhance the global search. The third strategy is to exploit around the best solution to reinforce the local …search. Three strategies cooperate well during the evolution process. The experimental results based on CEC 2014 have proven that the proposed MJaya is superior compared with Jaya and its latest variants. Then, the proposed MJaya algorithm is used to solve three industrial problems and the results have shown that the proposed MJaya algorithm can also solve complex industrial applications effectively. Show more
Keywords: Jaya algorithm, multi-strategy, optimization, adaptive
DOI: 10.3233/JIFS-213471
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4379-4393, 2022
Authors: Albert, Johny Renoald | Selvan, P. | Sivakumar, P. | Rajalakshmi, R.
Article Type: Research Article
Abstract: A proposed hybrid approaches are incorporated in Electric Vehicle (EV) fast charging station (FCS) using (RES). Hybrid approach is improved by Adaptive Hybrid Particle Swarm Optimization (AHPSO) named as AHWPSO, moreover the proposed work Grey Wolf Optimization (GWO) is assist with adaptive hybridize PSO algorithm. Therefore, an overall pricing cost should be reduced maximum Electric Vehicle Charging Station (EVCS) with minimal installation. This simulation work is verified an adaptive time varying weightage parameters to increase the AHWPSO particle diversity factor. Proposed algorithm is incorporated with improve the novelty, and compared the results are recent version of PSO used for EVCS. …Its increase the charging ability, energy loss minimization, voltage deviation reduction, and cost minimization. A distribution micro-grid capacity and demand are tested. Similarly, low to peak period energy variations are controlled by proposed algorithm with reduced capacitor bank. Overall control algorithm code is executed buy MATLAB/Simulink platform, the performance of this work listed, and compare to the existing approaches with achievement of maximum efficiency. Show more
Keywords: Electric vehicle, renewable energy sources, adaptive hybrid PSO, grey wolf optimization, grid
DOI: 10.3233/JIFS-220089
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4395-4407, 2022
Authors: Cui, Qianna | Pan, Haiwei | Li, Xiaokun | Zhang, Kejia | Chen, Weipeng
Article Type: Research Article
Abstract: During the last years, object-based image segmentation (OBIA) has seen a considerable increase in the image segmentation. OBIA is generally based on superpixel methods, in which the clustering-based method plays an increasingly important role. Most clustering methods for generating superpixels suffer from inaccurate classification points with inappropriate cluster centers. To solve the problem, we propose a competitive mechanism-based superpixel generation (CMSuG) method, which both accelerates convergence and promotes robustness for noise sensitivity. Then, image segmentation results will be obtained by a region adjacent graph (RAG)-based merging algorithm after constructing an RAG. However, high segmentation accuracy is customarily accompanied by expensive …time-consuming costs. To improve computational efficiency, we address a parallel CMSuG algorithm, the time of which is much less than the CMSuG method. In addition, we present a parallel RAG method to decrease the expensive time-consuming cost in serial RAG construction. By leveraging parallel techniques, the running time of the whole image segmentation method decline with the time complexity from O (N ) + O (K 2 ) to O (N /K ) or O (K 2 ), in which N is the size of an input image and K is the given number of the superpixel. In the experiments, both nature image and remote sensing image segmentation results demonstrate that our CMSuG method outperforms the state-of-the-art superpixel generation methods, and then performs well for image segmentation in turn. Compared with the serial segmentation method, our parallel techniques gain more than four times acceleration in both remote sensing image dataset and nature image dataset. Show more
Keywords: Superpixels, competitive mechanism, image segmentation, parallel, graph-based
DOI: 10.3233/JIFS-212967
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4409-4430, 2022
Authors: Senthilkumar, T. | Kumarganesh, S. | Sivakumar, P. | Periyarselvam, K.
Article Type: Research Article
Abstract: Alzheimer’s disease (A.D.) is the most widespread type of Dementia, and it is not a curable neurodegenerative disease that affects millions of older people. Researchers were able to use their understanding of Alzheimer’s disease risk variables to develop enrichment processes for longitudinal imaging studies. Using this method, they reduced their sample size and study time. This paper describes the primitive detective of Alzheimer’s diseases using Neuroimaging techniques. Several preprocessing methods were used to ensure that the dataset was ready for subsequent feature extraction and categorization. The noise was reduced by converting and averaging many scan frames from real to DCT …space. Both sides of the averaged image were filtered and combined into a single shot after being converted to real space. InceptionV3 and DenseNet201 are two pre-trained models used in the suggested model. The PCA approach was used to select the traits, and the resulting explained variance ratio was 0.99The Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC)—and UCI machine learning datasets showed that our method is faster and more successful at identifying complete long-risk patterns when compared to existing methods. Show more
Keywords: Alzheimer’s disease, machine learning, SVM, neuroimaging techniques, MRI
DOI: 10.3233/JIFS-220628
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4431-4444, 2022
Authors: Vasanthi, G. | Prabakaran, N.
Article Type: Research Article
Abstract: Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every …sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations. Show more
Keywords: Wireless sensor network, energy consumption minimization, average fitness based Harris hawks optimization, optimal cluster head
DOI: 10.3233/JIFS-213252
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4445-4456, 2022
Authors: Jin, LeSheng | Yager, Ronald R. | Chen, Zhen-Song | Mesiar, Mesiar | Bustince, Humberto
Article Type: Research Article
Abstract: Motivated by a specific decision-making situation, this work proposes the concept and definition of unsymmetrical basic uncertain information which is a further generalization of basic uncertain information and can model uncertainties in some new decision-making situations. We show that unsymmetrical basic uncertain information in some sense can model linguistic hedges such as “at least” and “at most”. Formative weighted arithmetic means and induced aggregations are defined for the proposed concept. Rules-based decision making and semi-copula based integral for this concept with some numerical examples are also presented.
Keywords: Aggregation operators, basic uncertain information, evaluation, information fusion, integral, uncertainty, unsymmetrical basic uncertain information
DOI: 10.3233/JIFS-220593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4457-4463, 2022
Authors: Zhang, Yiping | Wilker, Kolja
Article Type: Research Article
Abstract: Traditional digital media system can not complete the format conversion and video transcoding of massive image and video information at the same time, which leads to long time of information processing and loss of data storage. Therefore, a digital media system driven by artificial intelligence and big data is designed. Using FastDFS design of digital media data management module. Design digital media image, video conversion module and digital media resource data dictionary library. Develop image plug-ins based on the MapInfo platform. Design video plug-in, introduce virtual reality technology to retrieve image information, call video source, create CvCapture object. Design system …software functions and digital media information acquisition algorithm. Intelligent artificial pixel feature acquisition technology is used to collect 3D visual information of digital media and design its pseudo-code. Compared with the traditional system, the information processing time of the designed system is shorter, and it takes 11.555 ms when there are more information objects. The experimental results show that the designed system can complete more complete storage of data. Show more
Keywords: Digital media, big data, FastDFS, MapInfo platform, intelligent artificial pixel feature acquisition technology
DOI: 10.3233/JIFS-211561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4465-4475, 2022
Authors: Arya, R. | Vimina, E.R.
Article Type: Research Article
Abstract: Local feature descriptors are efficient encoders for capturing repeated local patterns in many of the computer vision applications. Majority of such descriptors consider only limited local neighborhood pixels to encode a pattern. One of the major issues while considering more number of neighborhood pixels is that it increases the dimensionality of the feature descriptor. The proposed descriptor addresses these issues by describing an effective encoding pattern with optimal feature vector length. In this paper, we have proposed Local Neighborhood Gradient Pattern (LNGP) for Content-Based Image Retrieval (CBIR) in which the relationship between a set of neighbours and the centre pixel …is considered to obtain a compact 8-bit pattern in the respective pixel position. The relationship of the gradient information of immediate, next-immediate, and diagonal neighbours with the centre pixel is considered for pattern formation, and thus the local information based on pixels in three directions are captured. The experiments are conducted on benchmarked image retrieval datasets such as Wang’s 1K, Corel 5K, Corel 10K, Salzburg (Stex), MIT-Vistex, AT & T, and FEI datasets and it is observed that the proposed descriptor yields average precision of 71.88%, 54.57%, 40.66%, 71.85%, 86.12%, 82.54%, and 68.54% respectively in the mentioned datasets. The comparative analysis of the recent descriptors indicates that the proposed descriptor performs efficiently in CBIR applications. Show more
Keywords: Local binary patterns, intensity gradient, feature extraction, image retrieval, image descriptor
DOI: 10.3233/JIFS-212604
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4477-4499, 2022
Authors: Saravanakumar, S. | Saravanan, T.
Article Type: Research Article
Abstract: In today’s world, Alzheimer’s Disease (AD) is one of the prevalent neurological diseases where early disease prediction can significantly enhance the compatibility of patient treatment. Nevertheless, accurate diagnosis and optimal feature selection play a vital challenge in AD detection. Most of the existing diagnosis systems failed to attain superior prediction accuracy and precision rate. In order to mitigate these constraints, a new efficient Convolutional Neural Network-based Stacked Long Short-Term Memory (CNN-SLSTM) methodology has been proposed in this paper. The key objective of the proposed model is to examine the brain’s condition and evaluate the changes that occur throughout the interracial …period. The proposed model includes multi-feature learning and categorization in which the raw Electroencephalography (EEG) data will be passed via the feature extractor to decrease the computing complexity and execution time. Afterward, the SLSTM network is constructed with completely linked layer and activation layers to record the temporal relationship between features and the next stage of AD. The proposed CNN-SLSTM model can be trained using real-time EEG sensor data. The performance results clearly apparent that the proposed model can efficiently predict the AD with superior accuracy of 98.67% and precision of 98.86% when compared with existing state-of-the-art techniques. Show more
Keywords: Alzheimer’s disease prediction, convolutional neural network, diagnosis, EEG data, multi-feature extraction
DOI: 10.3233/JIFS-212797
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4501-4516, 2022
Authors: Xiao, Yaning | Sun, Xue | Guo, Yanling | Cui, Hao | Wang, Yangwei | Li, Jian | Li, Sanping
Article Type: Research Article
Abstract: Honey badger algorithm (HBA) is a recently developed meta-heuristic algorithm, which mainly simulates the dynamic search behavior of honey badger in wild nature. Similar to other basic algorithms, HBA may suffer from the weakness of poor convergence accuracy, inadequate balance between exploration and exploitation, and ease of getting trapped into the local optima. In order to address these drawbacks, this paper proposes an enhanced honey badger algorithm (EHBA) to improve the search quality of the basic method from three aspects. First, we introduce the highly disruptive polynomial mutation to initialize the population. This is considered from increasing the population diversity. …Second, Lévy flight is integrated into the position update formula to boost search efficiency and balance exploration and exploitation capabilities of the algorithm. Furthermore, the refraction opposition-based learning is applied to the current global optimum of the swarm to help the population jump out of the local optima. To validate the function optimization performance, the proposed EHBA is comprehensively analyzed on 18 standard benchmark functions and IEEE CEC2017 test suite. Compared with the basic HBA and seven state-of-the-art algorithms, the experimental results demonstrate that EHBA can outperform other competitors on most of the test functions with superior solution accuracy, local optima avoidance, and stability. Additionally, the applicability of the proposed method is further highlighted by solving four engineering design problems. The results indicate that EHBA also has competitive performance and promising prospects for real-world optimization tasks. Show more
Keywords: Honey badger algorithm, highly disruptive polynomial mutation, Lévy flight, refraction opposition-based learning, engineering design problems
DOI: 10.3233/JIFS-213206
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4517-4540, 2022
Authors: Abiyev, Rahib H. | Sadikoglu, Gunay | Alsalihi, Adnan | Abizada, Rufat
Article Type: Research Article
Abstract: Sensory experiences that include vision, hearing, touching, smelling and tasting are important parameters that enable people to trade effectively in retail stores. In this study, based on multisensory attributes the evaluation of customer satisfaction is considered using fuzzy set theory and conjoint analysis. Fuzzy set theory is one of the best methodologies for describing the meaning of linguistic values that express customer preferences. However, there may be different customer and expert opinions in the evaluation of preferences by expressing linguistic values. In the paper, a type-2 fuzzy set is used to handle these uncertainties. This paper proposes the combination of …type-2 fuzzy sets and conjoint analysis in order to evaluate customer satisfaction using customer opinions about sensory variables such as sight, sound, taste, touch and smell when purchasing goods in retail stores. For this purpose, using statistical survey results and type-2 fuzzy sets the customer satisfaction degrees were determined. The methodology used for the determination of customer satisfaction is based on conjoint analysis that uses the similarity measure to determine the closest opinions of the customers and experts for the evaluation of customer satisfaction degrees. The obtained experimental results indicate the efficiency of the presented approach in the determination of customer satisfaction in retail markets. Show more
Keywords: Sensory attributes, customer satisfaction, fuzzy sensory evaluation, conjoint analysis
DOI: 10.3233/JIFS-213218
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4541-4554, 2022
Authors: Basumatary, Bhimraj | Wary, Nijwm | Khaklary, Jeevan Krishna | Garg, Harish
Article Type: Research Article
Abstract: These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of COVID-19. To study and to have comments, a committee of five experts has been …formed from a different region of Assam to observe and comment to identify Coronavirus’s weakest factors. For a better survey, we have divided the state into four areas namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique. Show more
Keywords: Assam, COVID-19, trapezoidal fuzzy number, fuzzy VIKOR, vulnerability region
DOI: 10.3233/JIFS-213279
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4555-4564, 2022
Authors: Prabhakaran, Priyanka | Subbaiyan, Anandakumar | Bhaskaran, Priyanka | Velusamy, Sampathkumar
Article Type: Research Article
Abstract: In India, Rail mode of transport serves as frequently preferrable transit systems operating with the optimal cost. Typically, the Indian Railways transport thousands of people on a day-to-day basis in addition to transporting large consignment of goods. Therefore, it is important for the trains to ensure that they run on quality tracks. At times, these tracks are challenged by the friction generated by continuous passage of trains in addition to over corrosions that occur due to their environmental imbalance. Preventive Track Maintenances (PTMs) have been recently introduced by railways for enhancing the quality of railway tracks, but on the contrary, …it was failed to focus on the actual needs or emergencies of railway tracks. Moreover, none of the existing methods have been tested with real time datasets. Specifically, holding only two class labels are being considered resulting in the reduction of classification performances. But the major challenging task is that the real-time datasets fall under the category of multi-variant data. Hence, this study aims to provide a Decision Support System (DSS) that predicts the Railway Track Quality (RTQ) from the real time datasets available on the track inspection data of the Indian metro rail system. The proposed research uses clustering and classification processes for achieving Predictive Track maintenance (PTM). Furthermore, the proposed method of RPTMs includes five steps namely data collection, data transformations, clustering of data, preventive maintenances, and evaluations. The undertaken datasets are transformed into numeric formats for the creation of clusters using Kernel Mean Weight Fuzzy Local Information C Means (KMWFLICMs). The resultant clusters from the data have five major types of clusters such as Normal, Low risks, Medium, High, and Emergency Risks based on the parameters of gauge, cross level attributes, turnouts and versine of mainline. From the inferred cluster results, the dataset was further classified to choose maintenance status from four major classes namely No Actions, Fixed Maintenances, Investigate Maintenances, and Emergency Maintenance pertinent to the outcomes of FWCNNs (Fuzzy Weight Convolution Neural Networks). The proposed system was experimented on MATLAB and evaluated against various machine learning approaches. Therefore, the obtained statistical results confirmed that the proposed FWCNN model had afforded higher accuracy in predicting the maintenance interventions based on relevant risk category. Show more
Keywords: Track quality, railway track maintenance, railroad tracks, preventive maintenance, kernel mean weight fuzzy local information C means (KMWFLICM), clustering algorithm, fuzzy weight convolution neural network (FWCNN), metro rail
DOI: 10.3233/JIFS-213439
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4565-4586, 2022
Authors: ShirMohammadi, Mohammad Mehdi | Esmaeilpour, Mansour
Article Type: Research Article
Abstract: Traffic control prediction is one of the important issues of smart cities in that, by studying traffic parameters, there can be provided more peace and comfort in appropriate traffic routes. Combination of new and different technologies and scientific technical models for this complex prediction has always been paid attention to by researchers. In this paper, by presenting and improving one of the new methods of data collection with traffic congestion index, the appropriate models for predicting traffic control have been compared. Rapid and inexpensive collection of information and, the dynamics and momentary changes of traffic flows showed that the use …of wavelet neural network was more accurate than other models of traffic control prediction. The application of combined Wavelet Neural Network with Complete Ensemble Empirical Mode Decompositionin traffic control prediction in this paper as CEEMD & WNN showed that the prediction accuracy increased compared to ARIMA, WNN, HYBRID ARIMA & WNN, TN methods and this new method has reasonable performance against the evaluation criteria to predict traffic control. Show more
Keywords: Wavelet neural network, artificial neural network, prediction, traffic control, complete ensemble empirical mode decomposition
DOI: 10.3233/JIFS-213557
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4587-4599, 2022
Authors: Okuwobi, Idowu Paul | Ding, Zhixiang | Wan, Jifeng | Ding, Shuxue
Article Type: Research Article
Abstract: Artificial intelligent (AI) systems for clinical-decision support are an important tool in clinical routine. It has become a crucial diagnostic tool with adequate reliability and interpretability in disease diagnosis and monitoring. Undoubtedly, these models are faced with insufficient data challenges for training, which often directly determines the model’s performance. In order word, insufficient data for model training leads to inefficiency in the model built. To overcome this problem, we propose an AI-driven model by transfer learning in accurate diagnosis for medical decision support. Our approach leverages the shortage of data with a pretrained model by training the neural network with …a fraction of the new dataset. For this purpose, we utilized the VGG19 network as the backbone network to support our model in integrating known features with the newly learned features for accurate diagnosis and decision making. Integrating this trained model speeds up the training phase and improve the performance of the proposed model. Experimental results show that the proposed model is effective and efficient in diagnosing different medical diseases. As such, we anticipated that this diagnosis tool will ultimately aid in facilitating early treatment of these treatable diseases, which will improve clinical out-comes. Show more
Keywords: Optical coherence tomography (OCT), choroidal neovascularization (CNV), diabetic macular edema (DME), age-related macular degeneration (AMD), convolutional neural networks (CNN), artificial intelligence (AI)
DOI: 10.3233/JIFS-220066
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4601-4612, 2022
Authors: Kotteswari, K. | Bharathi, A.
Article Type: Research Article
Abstract: Cloud computing is an on-demand model that computes shared and dynamic resource availability in a remote or independent location. Cloud computing provides many services online to clients in a pay-as-you-go manner. Nowadays, many organizations use cloud computing techniques with the prime motive that cost can be reduced, and resources are dynamically allocated. Performance evaluation and measurement approaches for cloud computing help the cloud services consumer to evaluate their cloud system based on performance attributes. Although the researchers have proposed many techniques and approaches in this direction in past decades, none of them has attained widespread industrial benefit. This paper proposes …a novel quality evaluation methodology named Stochastic Neural Net (SNN) to evaluate the cloud quality of Infrastructure as a Service (IaaS). This model deeply measures the performance by considering every activity of the IaaS system. Based on their characteristics, these works suggest key QoS factors for individual parts and activities. The individual QoS metric makes the SNN methodology acquire accurate results regarding performance measurement. The performance evaluation result can be used to improve the cloud computing system. The proposed model is compared with other standard models. The experimental comparison shows that the proposed model is more efficient than other standard models. Show more
Keywords: IaaS, stochastic model, performance measure, neural network, availability
DOI: 10.3233/JIFS-220501
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4613-4628, 2022
Authors: IssanRaj, R. | Visalakshi, S.
Article Type: Research Article
Abstract: The behaviour and effective performance of solar cell is represented by Triple Diode Solar Cell Module (TDSCM) circuit with five parameters for different environmental conditions. The equations described the solar modules behaviour are usually implicit in nature and the parameter extraction was very complexity. From the Photovoltaic (PV) module data sheet, one can identify the four equations applying to single, double, and triple diode parameters. For getting fifth equation researchers have gone with several approximations, which concludes the computation complexity, convergence problem, and low accuracy issues. In the proposed work the fifth equation are framed under the area characteristics curve …(V-I & P-V) concept using Simpson’s approximation. To find which PV module is less accuracy and non-linearity consideration for the performance level. Therefore, to overcome these issues the multi-objective Genetic Algorithm (GA) optimization method are prescribed to frame the fifth equation of the Simpson’s rules. This works improved non-linearity performance and gives the high accuracy modelling compare to other single, double diode methods. Show more
Keywords: Photovoltaic cell model, solar cell modeling, genetic algorithm, triple diode model, Simpson’s rule
DOI: 10.3233/JIFS-220561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4629-4643, 2022
Authors: Lian, Wenwu
Article Type: Research Article
Abstract: The uncertainty of information plays an important role in practical applications. Uncertainty measurement (UM) can help us in disclosing the substantive characteristics of information. Probabilistic set-valued data is an important class of data in machine learning. UM for probabilistic set-valued data is worth studying. This paper measures the uncertainty of a probability set-valued information system (PSVIS) by means of its information structures based on Gaussian kernel method. According to Bhattacharyya distance, the distance between objects in each subsystem of a PSVIS is first built. Then, the fuzzy T cos -equivalence relations in a PSVIS by using Gaussian kernel method …are obtained. Next, information structures in a PSVIS are defined. Moreover, dependence between information structures is investigated by using the inclusion degree. As an application for the information structures, UM in a PSVIS is investigated. Finally, to evaluate the performance of the investigated measures, effectiveness analysis is performed from dispersion analysis, correlation analysis, and analysis of variance and post-hoc test. Show more
Keywords: GrC, PSVIS, Gaussian kernel method, Bhattacharyya distance, Information structure, Uncertainty, Measurement
DOI: 10.3233/JIFS-210460
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4645-4668, 2022
Authors: Dhurkari, Ram Kumar
Article Type: Research Article
Abstract: The Analytic Hierarchy Process (AHP) is a popular Multi-Criteria Decision Making (MCDM) method. The workability of AHP made it suitable for solving complicated and elusive decision problems that subsequently led to its widespread applications in highly diverse fields. However, AHP has also received criticisms on various fronts, one of which is the rank reversal problem. When a replica of an existing alternative is introduced in the Multi-Criteria Decision (MCD) setting, it sometimes causes rank order reversal among alternatives. However, the addition of a replica of an alternative in the MCD setting is not limited to the rank reversal problem, but …it also affects the inconsistency measure computed for the decision-maker (DM). An empirical study was conducted using AHP to measure the changes in the inconsistency of the DM on a well-defined and familiar MCD problem. The results indicate that when a replica is added to a pair-wise comparison matrix, the inconsistency of the DM reduces. It is found that there are two sources of inconstancy in a pair-wise preference matrix. One is intransitivity and another is the limitation of the 1–9 ratio scale. It is found that an inconsistency up to 50% is purely because of limitations of the ratio scale and higher inconsistencies are purely because of intransitivity in preferences defined by the DM. Therefore, the DMs should review and revise their preferences when their inconsistency exceeds 50%. This 50% threshold is also useful in deciding whether to apply a prediction algorithm to identify near consistent matrices. If the inconsistency of a matrix is above 50%, the prediction algorithms used to improve the consistency cannot be applied on the original inconsistent matrix because the source of inconsistency is intransitivity which means that the DM either does not have complete information about the problem or has not attended to the problem carefully. Show more
Keywords: Analytic Hierarchy Process, inconsistency, transitivity
DOI: 10.3233/JIFS-212041
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4669-4679, 2022
Authors: Chakaravarthy, Sankar | Chandran, Kalaivani | Mariappan, Saravanan | Ramalingam, Sujatha
Article Type: Research Article
Abstract: Transport network is the backbone of economy. Every path has some positive and negative attributes such as transportation cost, road condition, traveling time etc., These attribute values are taken as fuzzy membership value with either positive or negative sign when modeling the transport network as signed fuzzy graph. The stability of these type of signed fuzzy graphs are discussed with the help of vulnerability parameters and edge integrity. In this paper, we have introduced complete signed fuzzy graph, signed fuzzy star graph, complement of a signed fuzzy graph, union of two signed fuzzy graph, join of two signed fuzzy graph …and cartesian product of two signed fuzzy graphs. For some standard signed fuzzy graph edge integrity value is calculated. Further this concept is applied in supply chain network with three layers, to study its stability and optimum path. Show more
Keywords: Vulnerability parameters, edge integrity, signed fuzzy graph
DOI: 10.3233/JIFS-220314
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4681-4690, 2022
Authors: Wang, Guan | Wang, Jie-Sheng | Wang, Hong-Yu | Liu, Jia-Xu
Article Type: Research Article
Abstract: Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering …validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets. Show more
Keywords: Data mining, fuzzy c-means clustering algorithm, clustering validity function, ratio component-wise design
DOI: 10.3233/JIFS-213481
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4691-4707, 2022
Authors: Gökalp, Yaşar | Yüksel, Serhat | Dinçer, Hasan
Article Type: Research Article
Abstract: This study aims to create a strategy for reducing energy costs in hospitals to ensure the sustainability of health services. In this framework, a novel hybrid decision making approach is generated based on golden cut-oriented bipolar and q-rung orthopair fuzzy sets (q-ROFs). Firstly, balanced scorecard (BSC)-based criteria are evaluated by using multi stepwise weight assessment ratio analysis (M-SWARA) approach. Secondly, alternatives are examined with the help of technique for order preference by similarity to ideal solution (TOPSIS) technique. The novelty of this study is to find critical factors that affect the energy costs of health institutions with an original fuzzy …decision-making model. This proposed model has also some superiorities by comparing with previous models in the literature. First, SWARA method is improved, and this technique is generated with the name of M-SWARA. Hence, the relationship between the criteria can be examined owing to this issue. Additionally, golden cut is taken into consideration to compute the degrees in bipolar q-ROFSs to achieve more accurate results. These two issues have an important impact on the originality of the proposed model. The findings demonstrate that consciousness level of employees has the highest weight with respect to the energy costs in hospitals. Additionally, the type of energy used also plays a significant role for this issue. Thus, renewable energy sources should be considered in meeting the energy needs of hospitals. Although the installation costs of these energy types are higher, it will be possible to significantly reduce energy costs in the long run. Show more
Keywords: q-rung orthopair fuzzy sets, M-SWARA, bipolar fuzzy sets, golden cut, SWARA, TOPSIS
DOI: 10.3233/JIFS-220126
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4709-4722, 2022
Authors: Zhou, Xiaoguang | He, Xin | Huang, Xiaoxia
Article Type: Research Article
Abstract: Traditionally, the return on investment has been described as either a random variable or a fuzzy variable, while this paper discusses the uncertain portfolio selection in which each security return is assumed to be an uncertain variable. To better optimize the return and risk of a portfolio, we propose two models: uncertain minimax mean-variance (UM-EV) model and uncertain minimax mean-semivariance (UM-SVE) model. The crisp equivalents of the UM-EV model that regard the security return as a normal and linear uncertain variable are derived, and the optimization problem is solved using linear programming. For the UM-SVE model, the crisp equivalent of …a zigzag uncertain variable is introduced, and the optimization solution is calculated using hybrid intelligent algorithm. Finally, the effectiveness of the proposed models is illustrated using numerical examples. Show more
Keywords: Uncertain theory, minimax model, portfolio selection, mean-variance model, mean-semivariance model
DOI: 10.3233/JIFS-211766
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4723-4740, 2022
Authors: Fathabadi, Fatemeh Rashidi | Grantner, Janos L. | Shebrain, Saad A. | Abdel-Qader, Ikhlas
Article Type: Research Article
Abstract: Recent developments in deep learning can be used in skill assessments for laparoscopic surgeons. In Minimally Invasive Surgery (MIS), surgeons should acquire many skills before carrying out a real operation. The Laparoscopic Surgical Box-Trainer allows surgery residents to train on specific skills that are not traditionally taught to them. This study aims to automatically detect the tips of laparoscopic instruments, localize a point, evaluate the detection accuracy to provide valuable assessment and expedite the development of surgery skills and assess the trainees’ performance using a Multi-Input-Single-Output Fuzzy Logic Supervisor system. The output of the fuzzy logic assessment is the performance …evaluation for the surgeon, and it is quantified in percentages. Based on the experimental results, the trained SSD Mobilenet V2 FPN can identify each instrument at a score of 70% fidelity. On the other hand, the trained SSD ResNet50 V1 FPN can detect each instrument at the score of 90% fidelity, in each location within a region of interest, and determine their relative distance with over 65% and 80% reliability, respectively. This method can be applied in different types of laparoscopic tooltip detection. Because there were a few instances when the detection failed, and the system was designed to generate pass-fail assessment, we recommend improving the measurement algorithm and the performance assessment by adding a camera to the system and measuring the distance from multiple perspectives. Show more
Keywords: Deep learning, laparoscopic surgical box-trainer, laparoscopic surgical instrument detection, fuzzy logic-based performance assessment, minimally invasive surgery, CNN
DOI: 10.3233/JIFS-213243
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4741-4756, 2022
Authors: Pande, Sandeep Dwarkanath | Rathod, Suresh Baliram | Chetty, Manna Sheela Rani | Pathak, Shantanu | Jadhav, Pramod Pandurang | Godse, Sachin P.
Article Type: Research Article
Abstract: Due to the evolution in the digital domain limitless multimedia is generated daily. It creates a necessity of potential and appealing image resuscitation system. In this paper, a shape and texture-based image retrieval system is proposed that estimates the resemblances of each query image with the images stored in the repository in the form of shape and textural facets and retrieves the images within an expected range of resemblance. The proposed approach employs a statistical approach for image retrieval. The proposed approach takes into account discriminative features of the input image for generating the shape and texture descriptors that produce …outstanding results for image databases of restricted variety, which merely includes homogeneous patterns, this approach yielded satisfactory results. For texture images it uses the spatial gray level dependency matrix (SGLDM) and proposes an algorithm to compute the the inverse difference moment (IDM) as the optimal image representative feature. It further employs K-Nearest Neighbour (KNN) classifier for the classification and retrieval tasks. The proposed system outperforms the various other ultra-modern content-based image retrieval (CBIR) systems in many respects. Show more
Keywords: CBIR, shape, texture, fourier descriptors, IDM, retrieval, KNN
DOI: 10.3233/JIFS-213355
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4757-4768, 2022
Authors: An, Qing | Tang, Ruoli | Hu, Qiqi
Article Type: Research Article
Abstract: Under the background of smart city, the concepts of “green building” and “net-zero energy building” become more and more popular for reducing the building power consumption. As a result, the technologies related to the design and intelligent control of building integrated green energy system develop rapidly in recent years. In this study, the topological structure of large-scale building integrated photovoltaic (BIPV) system is analyzed, and a novel data-driven maximum power point tracking (MPPT) methodology is developed. To be specific, several characteristic-variables for achieving efficient MPPT of large-scale BIPV system are proposed, and the data-driven MPPT model based on deep neural …network (DNN) is developed. Then, the developed characteristic-variables and DNN model are verified by a comprehensive set of numerical experiments. The optimal DNN structure is also verified in detail in this study. In addition, in order to dynamically track the degradation of photovoltaic module and overcome its influence on DNN model, the time-window mechanism of BIPV knowledge-base is introduced, and the optimal length of time-window for different DNN structures is verified by numerical experiments. Experimental results show that the DNN model with developed characteristic-variables and time-window mechanism achieves accurate and robust forecasting performance on the MPPT of large-scale BIPV system. Show more
Keywords: Deep neural network, building integrated photovoltaic system, large-scale PV system, maximum power point tracking, Date-driven MPPT
DOI: 10.3233/JIFS-213513
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4769-4787, 2022
Authors: Suriya, N. | Vijay Shankar, S.
Article Type: Research Article
Abstract: The usage of Electric vehicle (EVs) has been exponentially growing due to its focus on eco-friendly means of transport, distributed charging platform and user dictated supporting infrastructures. The EVs are charged by the charging stations which equipped with Electric Vehicle Supply Equipment (EVSE) that contains Internet enabled computers. These systems are considered to be more important for controlling the function such as charging electric vehicles, authorization and smart connection to the local power grid using different wireless technologies such as green WIFI, Bluetooth and even 5 G. The cyber-attacks such as DoS and DDoS attacks can violate integrity, confidentiality and availability …of the EVSE resources. Hence the intelligent Intrusion Detection System (IDS) is required to ensure the system for the robust and trustworthy deployment of EVSE resources. To meet the above challenge, this paper proposes new composite and intelligent system which contains the deep learning based IDS and high random chaotic generators to safeguard the data against the different cyber-attacks. The proposed IDS has been modelled based on Gated Recurrent Units (GRU) and counter measures are performed by adopting the Enhanced Chaotic Scroll attractor keys (ECSA). The contribution of this research paper is as follows: Novel Dataset Preparation for EVSE under different attack scenarios, Implementation of high accurate multi-objective accurate GRU based IDSs, Design of Enhanced Chaotic Countermeasure Encryption Schemes for the counterfeiting the attacks in Internet Enabled EVSE system. The extensive experimentation has been carried out into two important phases. In first phase algorithm centric metrics such as prediction accuracy, time of detection, whereas in second phase key centric metrics such as Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Key sensitivity and entropy are calculated and compared with the other existing methodologies. Results demonstrates that the proposed ensemble system has outperformed than the other methodologies and proves its strong place in designing the more secured Internet Enabled EVSE systems. Show more
Keywords: Cyber-attacks, gate current units, enhanced chaotic scroll attractors, npcr, uaci, entropy
DOI: 10.3233/JIFS-220310
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4789-4801, 2022
Authors: Zhang, Wei | Wang, Zhiming
Article Type: Research Article
Abstract: Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their outstanding performance. However, they simultaneously bring enormous computational overhead, making it difficult to deploy to mobile and edge devices. Therefore, researchers use network compression techniques such as quantization, knowledge distillation and neural network pruning to alleviate this problem. Among network pruning, filter pruning has received broad attention. At present, most of the filter pruning methods need to define pruning rates manually, which is a trial-and-error process and requires rich experimental experience. Some methods obtain global optimal network parameters by Neural Architecture Search (NAS) or Evolutionary …Algorithms (EA) to overcome this difficulty. However, they also introduce huge computational burden. To mitigate the above problems, this study proposes a pruning strategy based on Principal Component Analysis (PCA) called PCA-Pruner. Filter weights of a layer is regarded as a set of features, and the number of filters responding to feature dimension. Then, the number of reserved filters in each layer can be determined by PCA which is a classical dimensionality reduction technology. After that, we calculate the L1 norm of each filter in each layer and use it as an importance measurement to prune filters. Experimental results show that PCA-Pruner achieve performance improvements over the state-of-the-arts algorithms. For example, we compress the FLOPs and parameters of ResNet-56 on CIFA-10 by 45.8% and 47.1%, with an increase in accuracy of 0.27%. For ResNet-110 on CIFAR-10, we improve the accuracy by 0.58% and reduce the FLOPs and Params of the model by 58.3% and 56.2%, respectively. Towards ResNet-56 on CIFAR-100 dataset, we achieve a 38.8% FLOPs decrease and 38.0% Params reduction with only 0.69% accuracy loss. Show more
Keywords: Network compression, neural network pruning, dimensionality reduction, PCA
DOI: 10.3233/JIFS-211555
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4803-4813, 2022
Authors: Xuan, Cho Do | Huong, DT | Duong, Duc
Article Type: Research Article
Abstract: The Advanced Persistent Threat (APT) attack is a form of dangerous, intentionally and clearly targeted attack. Currently, the APT attack trend is through the end-users and then escalating privileges in the system by spreading malware which is widely used by attackers. Therefore, the problem of early detection and warning of the APT attack malware on workstations is urgent. In this paper, we propose a new approach to APT malware detection on workstations based on the technique of analyzing and evaluating process profiles. The characteristics and principles of our proposed method are as follows: Firstly, processes are collected and aggregated into …process profiles of APT malware; Secondly, these process profiles are used by Graph2Vec graph analysis algorithm to extract the characteristics of the process profile. Finally, in order to conclude about the sign of malicious APT, this paper proposes to use Long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) algorithm. With the proposed approach in the paper, we have not only succeeded in building and synthesizing APT malware behavior on Workstations as a basis to improve the efficiency of predicting APT malware, but also have opened up a new approach to the task of synthesizing and analyzing anomalous behavior of malware. Show more
Keywords: APT, APT malware detection on Workstation, Event ID, deeplearning, process profile, Graph2Vec
DOI: 10.3233/JIFS-212880
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4815-4834, 2022
Authors: Wang, Yongqiao | Ni, He
Article Type: Research Article
Abstract: This paper studies nonparametric estimation of the discount curve, which should be decreasing and positive over the entire maturity domain. Very few papers explicitly impose these shape requirements for removing the possibility of obtaining a shape-violating estimation. No matter how small the approximating error is, a shape-violating discount curve can never be accepted by the financial industry. Since these shape requirements are continuously constrained and involve an infinite number of inequality constraints, it is hard to provide a necessary and sufficient implementation that is computationally tractable. Existing parametric and nonparametric methods fail to achieve universal flexibility and shape compliance simultaneously. …This paper proposes a nonparametric method that approximates the discount curve with algebraic polynomials and ensures the discount function is decreasing and positive over the entire domain. This estimation problem can be reformulated equivalently as a semidefinite program that is convex and computationally tractable. The proposed method is the first one which not only has asymptotic universal fitting flexibility, but also fully complies with shape requirements. Experimental results on one artificial data, one US Gilt STRIPS data, and one US Treasury bonds data demonstrate its superiority over state-of-the-art methods in terms of both the compliance of shape requirements and out-of-sample fitting measures. Show more
Keywords: Curve fitting, term structure of interest rates, shape restriction, nonparametric regression, function approximation
DOI: 10.3233/JIFS-213432
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4835-4847, 2022
Authors: Yu, Chun-Min | Chen, Kuen-Suan
Article Type: Research Article
Abstract: As the Internet of Things (IoT) becomes more and more popular and full-grown, diverse technologies for measurement and collection of business data continually improve as well. Effective data analysis of and applications can be helpful to stores to make smart and quick decisions in a jiffy, so that the percentage of customer satisfaction and in-store shopping can increase to raise the total revenue. Some researchers have suggested that the number of customers who enter a store refers to a Poisson process. Based on previous research, an attribute service performance index was proposed in this paper. This paper reviewed the fuzzy …one-tailed testing model of the attribute service performance index and put forward a fuzzy two-tailed testing model of two indices based on the confidence interval to verify whether the improvement had a significant effect. Now that this fuzzy evaluation model is built on the confidence interval of the index, we can diminish the chance of misjudgment caused by sampling error. Its design can incorporate the past data or expert experience. Thus, the evaluation accuracy can be retained in the case of small-sized samples. Show more
Keywords: Attribute service performance index, Poisson process, confidence interval, membership function of fuzzy number, fuzzy testing
DOI: 10.3233/JIFS-220090
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4849-4857, 2022
Authors: Xu, Chang | Li, Bo | Zhang, Lingxian
Article Type: Research Article
Abstract: Asymmetric ν -twin Support vector regression (Asy-ν -TSVR) is an effective regression model in price prediction. However, there is a matrix inverse operation when solving its dual problem. It is well known that it may be not reversible, therefore a regularized asymmetric ν -TSVR (RAsy-ν -TSVR) is proposed in this paper to avoid above problem. Numerical experiments on eight Benchmark datasets are conducted to demonstrate the validity of our proposed RAsy-ν -TSVR. Moreover, a statistical test is to further show the effectiveness. Before we apply it to Chinese soybean price forecasting, we firstly employ the Lasso to analyze the influence …factors of soybean price, and select 21 important factors from the original 25 factors. And then RAsy-ν -TSVR is used to forecast the Chinese soybean price. It yields the lowest prediction error compared with other four models in both the training and testing phases. Meanwhile it produces lower prediction error after the feature selection than before. So the combined Lasso and RAsy-ν -TSVR model is effective for the Chinese soybean price. Show more
Keywords: Soybean, price forecast, TSVR, pinball loss, lasso
DOI: 10.3233/JIFS-212525
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4859-4872, 2022
Authors: Shunmuga Priya, M.C. | Karthika Renuka, D. | Ashok Kumar, L.
Article Type: Research Article
Abstract: Speech recognition has now become ubiquitous and plays an inevitable role in almost all sectors. Numerous works have been proposed on speech recognition; however, more accurate transcriptions are not possible. Exploration of various studies related to spell correction implies that several kinds of research have been carried out in this field but still it is a very challenging problem. This led to the need for a new spell corrector framework capable of leveraging the performance of the automatic speech recognition (ASR) system. The proposed work unveils state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) based spell correction module developed on top …of the deep recurrent neural network (RNN) based ASR system. The impact of BERT-based spell correction on the ASR system is evaluated on three different accent datasets in the perspective of word error rate (WER), character error rate (CER), and Bilingual evaluation understudy (BLEU) score. The experimental results inferred that the enhanced spell correction module is efficacious in detecting and correcting spell errors, by achieving the WER of 5.025% on librispeech corpus, 6.35% on voxforge, and 7.05% on NPTEL corpus. Show more
Keywords: Deep learning, natural language processing, spell error correction, word error rate, BERT
DOI: 10.3233/JIFS-213332
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4873-4882, 2022
Authors: Işık, Gürkan | Kaya, İhsan
Article Type: Research Article
Abstract: As a combining concept of Pythagorean fuzzy sets (PFSs) and linguistic fuzzy sets (FSs), linguistic PFSs (LPFSs) has been suggested in the literature to deal with the uncertain and inconsistent information in multi-criteria decision making (MCDM) process. The LPFSs based procedure has been built by assuming that the experts make assessments suitable with PFS. It does not provide a mechanism to ensure the suitability of the assessments with theory of PFSs but there are other type of non-standard fuzzy sets such as Neutrosophic sets (NSs) used for modeling with inconsistent information. The main motivation of this study is to offer …an assessment collection method to guarantee that the input statements will be Pythagorean fuzzy linguistic expressions. As a second motivation, it is aimed to extend the PFS method for the fuzzy modeling of the other type of decision-making problems apart from MCDM which do not require aggregation and comparison operations and continue with precise fuzzy modeling (PFM). The third motivation of this study is to offer enhancements on the LPFSs method to increase the sensitivity of the modeling while protecting the interpretability. For these purposes, a new methodology based on LPFSs has been proposed and applied on a decision-making problem in a comparative way. Show more
Keywords: Fuzzy modifiers, fuzzy sets, linguistic terms, linguistic 2-tuple statements, pythagorean fuzzy sets
DOI: 10.3233/JIFS-213384
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4883-4894, 2022
Authors: Dobrović, Željko | Tomičić-Pupek, Katarina
Article Type: Research Article
Abstract: Defense organizations, like the ministry of defense, the armed forces, the general staff of the armed forces, the army, the navy, or the air force units, use a specific business technology. What makes their business technology specific is a predictable changeability of their business processes. Namely, these organizations function in more than one state, each having its own business processes. An organization transits from one state to another in a predictable manner, thus changing its business processes. This kind of business technology is not exclusively restricted to defense organizations, as it also applies to police as well as crisis management …organizations. In order to develop information systems (IS) supporting these organizations properly, the complexity of their future IS should be assessed first. This assessment can be performed by relying on existing genetic taxonomies, i.e., by situating the planned defense IS inside the IS genetic taxonomy space, with regard of relevant characteristics of organizational processes supported by the IS. A behavioral dimension described in this paper addresses the dynamics of states defense systems operate in, offering to contribute to the understanding of defense systems’ response to changes in dynamic ecosystems, assisting thereby researchers and practitioners in describing dynamic properties of investigated systems. Show more
Keywords: Genetic taxonomy space, information systems, complexity, defense organizations
DOI: 10.3233/JIFS-220370
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4895-4909, 2022
Authors: Yang, Yaliu | Wu, Xue | Liu, Fan | Zhang, Yingyan | Liu, Conghu
Article Type: Research Article
Abstract: With the increasing severity of the global energy crisis and environmental pollution, there is an urgent need to change the economic development model driven by certain factors and the investment scale and pursue science- and technology-driven innovative development. This study aims to improve the efficiency of scientific and technological innovation and promote the high-quality development of regional industrial enterprises. It constructs a data-driven DEA-Malmquist evaluation model to evaluate and optimize regional industrial enterprises’ scientific and technological innovation efficiency. First, we collect the panel data of regional industrial enterprises’ scientific and technological innovation input-output indexes. Second, we use the Pearson correlation …coefficient method to identify and construct the evaluation index system of regional industrial enterprises’ scientific and technological innovation efficiency. Third, we build a DEA-Malmquist evaluation model to quantitatively evaluate regional industrial enterprises’ scientific and technological innovation efficiency from static and dynamic aspects. Finally, we verify the feasibility and effectiveness of the method using statistical data on scientific and technological innovation and development of Anhui industrial enterprises from 2011 to 2019 and put forth targeted countermeasures and suggestions. This study provides theoretical and methodological support for the sustainable development of industrial enterprises. Show more
Keywords: Data-driven, DEA-Malmquist evaluation model, Anhui Province, industrial enterprise, scientific and technological innovation efficiency
DOI: 10.3233/JIFS-220491
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4911-4928, 2022
Authors: Wang, Yufei | Dong, Xiaoshe | Wang, Longxiang | Chen, Weiduo | Chen, Heng
Article Type: Research Article
Abstract: In recent years, with the development of flash memory technology, storage systems in large data centers are typically built upon thousands or even millions of solid-state drives (SSDs). Therefore, the failure of SSDs is inevitable. An SSD failure may cause unrecoverable data loss or unavailable system service, resulting in catastrophic results. Active fault detection technologies are able to detect device problems in advance, so it is gaining popularity. Recent trends have turned toward applying AI algorithms based on SSD SMART data for fault detection. However, SMART data of new SSDs contains a large number of features, and the high dimension …of data features results in poor accuracy of AI algorithms for fault detection. To tackle the above problems, we improve the structure of traditional Auto-Encoder (AE) based on GRU and propose an SSD fault detection method – GAL based on dimensionality reduction with Gated Recurrent Unit (GRU) sparse autoencoder (GRUAE) by combining the temporal characteristics of SSD SMART data. The proposed method trains the GRUAE model with SSD SMART data firstly, and then adopts the encoder of GRUAE model as the dimensionality reduction tool to reduce the original high-dimensional SSD SMART data, aiming at reducing the influence of noise features in original SSD SAMRT data and highlight the features more relevant to data characteristics to improve the accuracy of fault detection. Finally, LSTM is adopted for fault detection with low-dimensional SSD SMART data. Experimental results on real SSD dataset from Alibaba show that the fault detection accuracy of various AI algorithms can be improved by varying degrees after dimensionality reduction with the proposed method, and GAL performs best among all methods. Show more
Keywords: Fault detection, dimensionality reduction, sparse auto-encoder, solid state drives, gated recurrent unit
DOI: 10.3233/JIFS-220590
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4929-4946, 2022
Authors: Chang, Te-Min | Lin, Sin-Jin | Hsu, Ming-Fu | Yang, Min-Lang
Article Type: Research Article
Abstract: Because the nature of numerical information is intuitive and comprehensible, it has been widely used to form a basis for decision making, yet numerical information based on historical principle does not reflect messages about future corporate performance. To confront this issue, one may consider textual information that can transmit future corporate potential without any hysteresis. The key point is how to digest an extensive amount of textual information and identify those topics most likely to precede changes in operation status. Topic modeling can categorize these textual disclosures based on their underlying content and help examine which topics have a strong …relevance to corporate operations. To extract decisive words from textual information, we set up a statistical-based approach with objectivity as opposed to frequently used heuristics (i.e., dictionary-based approaches with human involvement). Joint utilization of topic modelling and a statistical-based approach can compress an excessive amount of textual information into a manageable size in a timely manner and further realize a discrepancy among various topics in terms of relevance and influence on corporate operations. Our results benefit managers and current and future investors in how to structure regulatory filings and how word choices are decisive to them in their decision judgments. Show more
Keywords: Management decision, textual information, financial news media, risk management
DOI: 10.3233/JIFS-211732
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4947-4960, 2022
Authors: Baskaran, P. | Karuppasamy, K.
Article Type: Research Article
Abstract: The advancement of the Internet of Things (IoT) technologies will play a significant role in the evolution of the smart city, smart healthcare, and smart grid applications. The key objective of IoT is to allow the autonomous exchange of valuable data between invisibly embedded devices with the help of some prominent technologies. Wireless Sensor Network (WSN) is one of the emerged technologies used for sensing and data exchange processes in IoT-based applications. Network sustainability and energy stability are the most significant multi-objectives to attain an energy-efficient IoT-based WSN (IWSN). Consequently, in order to handle these multi-objectives, a novel Adaptive Regional …Clustering (ARC) scheme has been proposed in this paper by exploiting two appropriate methodologies. Primarily, location-based modelling is employed to gather the location information from each sensor node in the IWSN environment. Thereafter, an effective hierarchical clustering can be carried out with the assist of the ARC algorithm. The cluster head will be chosen based on node capacity and node trust value by implementing the Enhanced Monkey Inspired Optimization (EMIO) algorithm. Finally, the optimal cluster head node acts as an energy-efficient local director for conducting inter-cluster connectivity, data transmission, and other duties. The effectiveness of the proposed ARC-EMIO scheme has been assessed using the NS-3 simulator and the results evident that the proposed scheme guarantees better performance with an improved network lifetime of 35% and energy efficiency of 22% when compared with the existing state-of-the-art clustering techniques. Show more
Keywords: Internet of things, wireless sensor networks, multi-objective, regional clustering, monkey inspired optimization
DOI: 10.3233/JIFS-213017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4961-4974, 2022
Authors: Chithra, K. | Shunmughanaathan, V.K. | Karthik, S. | Srihari, K.
Article Type: Research Article
Abstract: Mobile Ad hoc Networks (MANETs) are independent of central administration or any infrastructure, hence it is flexible in nature. Though, the network is self organizing, the mobile nodes have some resource constraints. There is always a requirement forefficient routing protocol to manage the energy consumption and reduce the energy wastages in MANETWith that in mind, this article proposes Ant Colony Optimization for Enhanced Energy Efficient Routing (EEER-ACO). Furthermore, the network design balances the Transition Probability Standard (TPS)offset Coefficient to maximize navigation processing effectiveness and decrease path finding packets. Furthermore, the Surviving route lifespan is calculated based upon that node’s position …and speed rate. Through incorporating the Residual Node Lifetime (RNL) and the Residual Link Lifetime (RLL), the Residual Node Lifetime (RNL), the ACO based pheromone has been designed. Further, the algorithm involves in choosing an optimal quality route for assuring continuous and efficient data packet transmission over the defined MANET. The investigation took into account the energy consumption of nodes as well as associated motility. Furthermore, the results indicate that the EEER-ACO algorithm improves network durability by reducing end-to-end delay, data packet loss, and path discovery rate. In comparison to previous algorithms, the proposed study has demonstrated that it achieves a 35 percent better performance than traditional protocols. Show more
Keywords: Network lifetime, path discovery, Ant Colony Optimization (ACO), energy utilization, routing
DOI: 10.3233/JIFS-212913
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4975-4985, 2022
Authors: Phatai, Gawalee | Chiewchanwattana, Sirapat | Sunat, Khamron
Article Type: Research Article
Abstract: In the business sector, predicting the movement of the Stock Exchange of Thailand (SET) index is challenging. Due to worldwide stock market fluctuations, investors commonly invest in price-changing businesses solely in the long term. Therefore, an accurate SET index movement prediction method is significant for investment purposes and has been the goal of many previous studies. Some studies have indicated that neural network (NN) models perform more effectively and accurately than traditional statistical models; accordingly, NNs employing backpropagation (BP) with sigmoid and smooth adaptive activation functions (SAAFs) and 10 metaheuristic algorithms to determine the initial prediction weights were developed in …this study. An experiment was conducted using a Thailand SET50 index dataset, and the results revealed that the model utilizing SAAFs with a cultural algorithm (CA) for weight initialization yielded more precise and efficient predictions than those of other competing models. This finding indicated the possibility of applying the proposed method for SET index movement prediction in the future. Show more
Keywords: Smooth adaptive neural network, weight initialization, cultural algorithm, SET index movement prediction
DOI: 10.3233/JIFS-213233
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4987-5000, 2022
Authors: Liu, Jianhua | Wang, Zhiheng
Article Type: Research Article
Abstract: In order to solve the problem that the population diversity of sparrow search algorithm (SSA) decreases and easily falls into the local optimal solution when it approaches the global optimal, an artificial immune algorithm-sparrow search algorithm (AIA-SSA) is proposed in this paper by combining artificial immune algorithm and sparrow search algorithm. This paper uses 10 benchmark functions for experimental simulation of AIA-SSA algorithm, and compares it with five widely used intelligent algorithms and SSA. Experimental results show that AIA-SSA overcomes the deficiency of SSA and improves the search accuracy, convergence speed and stability of the algorithm. Meanwhile, this paper applies …AIA-SSA to network intrusion detection and constructs a network intrusion detection model based on support vector machine (SVM). After testing, the accuracy of AIA-SSA-SVM prediction for various network attacks has been greatly improved. It not only shows that AIA-SSA-SVM has a broad application prospect in the field of network security, but also verifies the feasibility and advanced nature of AIA-SSA in solving practical engineering problems. Show more
Keywords: Intrusion detection, SSA, intelligence algorithm, adaptive search, differential evolution algorithm
DOI: 10.3233/JIFS-210813
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5001-5011, 2022
Authors: Wang, Chuantao | Feng, Fan
Article Type: Research Article
Abstract: With the development of Internet+medicine, online medical treatment has gradually become the new development direction of medical industry. Many hospitals provide online registration services to the public, and due to the lack of professional medical knowledge of patients, the problem of wrong registration often occurs. How to use deep learning technology to provide professional help to patients and reduce the waste of medical resources has become an urgent problem. To address the above problems, this paper proposes an ERNIE-based text classification model for intelligent triage. The model consists of two parts, ERNIE and BiGRU. The pre-training model ERNIE is used …to extract the feature representation of the text, and then input to the BiGRU neural network to get the text classification results. Compared with different models on 2 datasets, the experimental results show that the model proposed in this paper has better accuracy and recall than other models. Show more
Keywords: Text classification, ERNIE, deep learning, intelligent triage
DOI: 10.3233/JIFS-212140
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5013-5022, 2022
Authors: Idrees, Ammara | Gilani, S.A.M. | Younas, Irfan
Article Type: Research Article
Abstract: Coronary artery disease (CAD) is a common heart disease that causes the blockage of coronary arteries. To reduce fatality, an accurate diagnosis of this disease is very important. Angiography is one of the most trustworthy and conventional methods for CAD diagnosis however, it is risky, expensive, and time-consuming. Therefore in this study, we proposed a differential evolution-based support vector machine (SVM) for early and accurate detection of CAD. To improve the accuracy, different data preprocessing techniques such as one-hot encoding and normalization are also used with differential evolution for feature selection before performing classification. The proposed approach is benchmarked with …the Z-Alizadeh Sani and Cleveland datasets against four state-of-the-art machine learning algorithms, and a highly cited genetic algorithm-based SVM (N2GC-nuSVM). The experimental results show that our proposed differential evolution-based SVM outperforms all the compared algorithms. The proposed method provides accuracies of 95±1% and 86.22% for predicting CAD on the benchmark datasets. Show more
Keywords: Coronary Artery Disease (CAD), Machine Learning (ML), Differential Evolution (DE), Genetic Algorithm (GA), Support Vector Machine (SVM), Naïve Bayes (NB), Multilayer perceptron (MLP), Classification, True positive rate (TRP), False positive rate (FPR)
DOI: 10.3233/JIFS-213130
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5023-5034, 2022
Authors: Lee, Dohyun | Kim, Kyoungok
Article Type: Research Article
Abstract: Boosting methods are known to increase performance outcomes by using multiple learners connected sequentially. In particular, Adaptive boosting (AdaBoost) has been widely used owing to its comparatively improved predictive results for hard-to-learn samples based on misclassification costs. Each weak learner minimizes the expected risk by assigning high misclassification costs to suspect samples. The performance of AdaBoost depends on the distribution of noise samples because the algorithm tends to overfit noisy samples. Various studies have been conducted to address the noise sensitivity issue. Noise-filtering methods used in AdaBoost remove samples defined as noise based on the degree of misclassification to prevent …overfitting to noisy samples. However, if the difference in the classification difficulty between classes is considerable, it is easy for samples from classes that are difficult to classify to be defined as noise. This situation is common with imbalanced datasets and can adversely affect performance outcomes. To solve this problem, this study proposes a new noise detection algorithm for AdaBoost that considers differences in the classification difficulty of classes and the characteristics of iteratively recalculated sample weight distributions. Experimental results on ten imbalanced datasets with various degrees of imbalanced ratios demonstrate that the proposed method defines noisy samples properly and improves the overall performance of AdaBoost. Show more
Keywords: AdaBoost, noise-robust learning, noise-filtering, class imbalance, class separation
DOI: 10.3233/JIFS-213244
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5035-5051, 2022
Authors: Banitalebi, Sadegh | Ahn, Sun Shin | Jun, Young Bae | Borzooei, Rajab Ali
Article Type: Research Article
Abstract: In this paper, the notions of normal m -dominating set, normal m -domination number, inverse normal domination set (number) and inverse normal m -domination number are introduced, and some the related results are investigated. Finally, a utilization relevant to decision-making based on influencing factors the company’s efficiency is presented.
Keywords: Pythagorean fuzzy graph, normal m-dominating set, normal m-domination number, inverse normal domination set (number), inverse normal m-domination number
DOI: 10.3233/JIFS-220319
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5053-5062, 2022
Authors: Poongodi, J. | Kavitha, K. | Sathish, S.
Article Type: Research Article
Abstract: In recent years, the Internet of Things (IoT) has attracted more attention after the integration of IoT devices with the cloud for data management. IoT is used for sharing data in healthcare services. However, security and privacy vulnerabilities still exist in data transfers to and from cloud environments. Due to memory limitations, it is difficult to implement security protocols in IoT devices. So an intermediate secure node is established to enhance data security and privacy while handling healthcare data. Blockchain is a renowned technology that is adopted in various data management and security applications, but its potential is not unleashed …effectively in healthcare data management. Therefore in this research work, firstly, the data is pre-processed and then the Ant colony optimization method is used to find the shortest path for efficient data delivery. And, a blockchain based data security approach for healthcare data management is presented. The main objective is to enhance security against healthcare data threats. In the IoTs fog layer, a public-permissioned blockchain security process with elliptical curve cryptography and digital signature is presented as a distributed ledger database that provides immutable security, transparency in transactions, and prevents data tampering. The performance of the proposed approach is verified through various parameters like certificate generation time and size, data retrieval time, and size for better validation. Show more
Keywords: Blockchain, healthcare IoT, data security, cryptography
DOI: 10.3233/JIFS-220797
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5063-5073, 2022
Authors: Sahu, Preeti Ranjan | Hota, Prakash Kumar | Panda, Sidhartha | Long, Hoang Viet | Allahviranloo, Tofigh
Article Type: Research Article
Abstract: This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization …and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority. Show more
Keywords: Modified grasshopper optimization algorithm, static synchronous series compensator, adaptive fuzzy lead-lag controller, power system stability
DOI: 10.3233/JIFS-212716
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5075-5094, 2022
Authors: Urquiza-Yllescas, José Fidel | Mendoza, Sonia | Rodríguez, José | Sánchez-Adame, Luis Martín
Article Type: Research Article
Abstract: Nowadays, chatbots have become popular tools in such a way that they are used in different sectors like commercial, elderly care, tourism, and education. The COVID-19 pandemic has forced many students and teachers to suspend face-to-face classes. Therefore, schools and governments have found it necessary to continue education remotely, using the resources provided by the Internet. This fact has created a greater interest in educational chatbots, so several projects have been proposed to develop these academic tools, each following its way of implementation and addressing issues from different points of view. This paper presents a proposal for chatbot classification, following …the Systematic Mapping Study and an iterative method to review and classify educational chatbots. We also discuss the resulting categories and their characteristics and limitations and possible uses by developers and researchers. Show more
Keywords: Chatbot, classification, chatbot-user interaction, education, scholar tools
DOI: 10.3233/JIFS-213275
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5095-5107, 2022
Authors: Tan, Fuxiang | Qian, Yurong | Kong, Yuting | Zhang, Hao | Zhou, Daxin | Fan, Yingying | Chen, Long | Xiao, Zhengqing
Article Type: Research Article
Abstract: Rain streaks severely affect the perception of the content and structure of an image so high-performance deraining algorithms are needed in order to eliminate the effects of various rain streaks for high-level computer vision tasks. Although much progress has been made with existing deraining methods, the task of single image deraining remains challenging. In this paper, we first point out that existing Transformers lack sufficient ability to capture channel attention which restricted the ability of models in deraining. To improve the performance of deraining model, we propose a dual branch deraining network based on Transformer. One branch uses dense connections …to connect Transformer modules which embed the attention of a composite channel. This branch captures channel attention more finely to learn the representation of rain streaks features. The other branch first obtains features at different scales by gradually expanding the receptive field, then uses these features to obtain attention for regional features, and finally uses the attention to guide the model to focus on areas of high rain streaks density and large scales. By fusing these two branches, the model is able to capture channel attention more finely and to focus on regions of high rain streaks density and large scales. The extensive experimental results on synthetic and real datasets demonstrate that the proposed method outperforms most advanced deraining methods. Show more
Keywords: Deraining, transformer, deep learning, image processing, channel attention
DOI: 10.3233/JIFS-220055
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5109-5123, 2022
Authors: Ming-Hui, Liao | Ge, You | Yuchen, Wei
Article Type: Research Article
Abstract: Small and medium-sized enterprises (SMEs) have been suffering from the problem of “difficult and expensive financing.” The few loans available are allocated unevenly to SMEs, resulting in a financing mismatch. To address this problem, this paper constructs a system dynamics model to simulate and calculate the theoretical loan amounts of SMEs. Compares the values between the theoretical and actual loan amount to find out the enterprises with extreme values of the difference. Then, it analyzes the characteristics of the index data of these enterprises to explore feasible solutions to solve the financing mismatch problem. The CRITIC and AHP weight determination …methods will be used to calculate weights and screen indicators. The data will be quantified and calculated according to the standardized scoring table of enterprise borrowing factor indicators. The results show that enterprises with actual borrowing amounts higher than the theoretical borrowing amount perform better than other sample enterprises in three indicators: growth rate of primary business income, the upper limit of the loan amount, and subsidy amount. The actual loan amount can be increased by improving these indicators. This research can help solve the problem of financing mismatch to realize effective resource allocation and provide significant guidance for the research on enterprises to obtain more loans. Show more
Keywords: Dual-Cycle, SME financing, system dynamics, borrowing amount, simulation analysis
DOI: 10.3233/JIFS-220091
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5125-5146, 2022
Authors: Wang, Xiaohan | Zhang, Zepei | Wang, Pei | Chen, Jiaqing | Wu, Junze
Article Type: Research Article
Abstract: Density peak clustering can be used in identifying high-density regions for urban hot spots detection. The distance matrix of each two position points needs to be calculated in the existing density peak clustering methods which causes inefficient clustering when processing large-scale data, and the traditional two-dimensional decision map cannot identify the coincident points. Thus, characteristic density peak clustering algorithm is proposed to avoid the influence of noise. At first, the location feature points and support index are defined to simulate the original locations. The number of feature points is adjusted by parameters to make density peak clustering no longer sensitive …to the amount of data to simplify the complexity to be solved. And then, the local density and the distance between the clustering centers of the feature points are proposed to construct three-dimensional decision map. Finally, the clustering center, basic clustering points, and noise data points are determined using the three-dimensional decision map combined with the support index of the feature points. Experiments are performed on real data set and the prototype system to verify that the method can significantly improve time efficiency while clustering accuracy is maintained. Show more
Keywords: Density peak clustering, three-dimensional decision map, location characterization, taxi trajectory, hot spots detection
DOI: 10.3233/JIFS-220327
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5147-5164, 2022
Authors: Liu, Siliang | Zhang, Wenyu | Yang, Song | Shi, Jiaxuan
Article Type: Research Article
Abstract: With the wide recognition of the potential commercial value of drone technology, the delivery method of trucks with drones has gradually been applied to the logistics field. This paper proposed a novel truck-drone collaborative service network for the special case of poor ground transportation. The proposed model uses the truck as an auxiliary tool at fixed non-customer locations to support drone deliveries, and develops the potential of drones to carry multiple commodities and undertake wide-range delivery missions in each flight. A modified variable neighborhood search algorithm with a new threedimensional coding scheme and five new neighborhood operators is presented to …represent the routes of multiple drones in collaborative service networks and solve the proposed model. The experiment was conducted under three types of customer distribution scenarios and experimental results illustrate that the presented algorithm effectively solves the proposed model compared with other hybrid heuristic algorithms. Show more
Keywords: Truck-drone, collaborative service networks, wide-range drone delivery, route optimization, Heuristic algorithms
DOI: 10.3233/JIFS-220378
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5165-5184, 2022
Authors: Arun, R. Arumuga | Umamaheswari, S.
Article Type: Research Article
Abstract: Traditional machine learning-based pest classification methods are a tedious and time-consuming process A method of multi-class pest detection based on deep learning and convolutional neural networks could be the solution. It automatically extracts the complex features of different pests from the crop pest images. In this paper, various significant deep learning-based object detection models like SSD, EfficientDet, Faster R-CNN, and CenterNet are implemented based on the Tensorflow Object Detection framework. Several significant networks like MobileNet_V2, ResNet101_V1, Inception_ResNet_V2, EfficientNet, and HourGlass104 are employed as backbone networks for these models to extract the different features of the pests. Object detection models are …capable of identifying and locating pests in crops. Initially, these models are pre-trained with the COCO dataset and later be fine-tuned to the target pest dataset of 20 different pest classes. After conducting experiments on these models using the pest dataset, we demonstrate that Faster R-CNN_ResNet101_V1 outperformed every other model and achieved mAP of 74.77%. Additionally, it is developed as a lightweight model, whose size is ∼9 MB, and can detect pest objects in 130 milliseconds per image, allowing it to be used on resources-constrained devices commonly used by farmers. Show more
Keywords: Deep learning, Convolutional Neutral Network, object detection, pest detection, transfer learning
DOI: 10.3233/JIFS-220595
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5185-5203, 2022
Authors: Hou, Yingan | Su, Junguang | Liang, Jun | Chen, Xiwen | Liu, Qin | Deng, Liang | Liao, Jiyuan
Article Type: Research Article
Abstract: In recent years, the number of stroke patients in China has been increasing and the development trend is not optimistic. In order to reduce the burden of doctors, improve the efficiency of clinical diagnosis and reduce the medical cost, the development of cerebral apoplexy imaging diagnosis is an inevitable trend. Taking stroke lesions in medical images as the object, a deep learning model 3D-SE ResNet10 is proposed which can distinguish whether stroke lesions are included in a given medical image with high accuracy. This model combines the attention mechanism with the residual learning network, and uses 3D convolution kernel to …utilize the continuous information between slices in the medical image sequence. The model achieves an average accuracy of 88.69%, an average sensitivity of 87.58% and an average specificity of 90.26% in multiple experiments based on the realistic dataset. Its classification effect is significantly higher than that of 2D convolutional neural networks and 3D convolutional neural networks without attention mechanism. The experimental results show that our model is effective and feasible, and has certain practical value. Show more
Keywords: Stroke, deep learning, medical image, 3D convolution, attention
DOI: 10.3233/JIFS-212511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5205-5214, 2022
Authors: Mobini, M. | Ahn, S.S. | Borzooei, R.A.
Article Type: Research Article
Abstract: Molodtsov, initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. In the paper entitled “The Category of Soft Sets”, we studied the category of soft sets, and we proved that the category of soft sets does not necessarily have products. But, there is a mistake in the proof of this result. In this short note, we give a correct proof for this result.
Keywords: Soft sets, category, product
DOI: 10.3233/JIFS-220762
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5215-5219, 2022
Authors: Kalaiarasan, D. | Ahilan, A.
Article Type: Research Article
Abstract: Securing image data from prying hackers is crucial in safeguarding the secrecy of data. Over the years, this was done by encrypting the image using an algorithm and a key, where the visible image was converted into a meaningless object. It is a difficult problem to design an image encryption technique based on chaotic systems with predictable cryptographic features. In this paper, the Quaternion, along with the Rossler attractor, was used to generate the key combination. The ciphering was done using the Least Square Approximation Algorithm (LSA). The algorithm was tested on a grayscale image database. The algorithm was initially …tested in software using MATLAB R2018b, and was implemented in the Cyclone II EP2C35F672C6 device FPGA. On average, for a cipher image, the Peak Signal to Noise ratio (PSNR) was 9.09303 dB and the entropy was 7.9990 bits. For the cipher image, the Number of Pixels Change Rate (NPCR) and Unified Average Change Intensity (UACI) were 99.6039 and 33.4980, respectively. This proved that the algorithm could effectively mitigate the statistical and differential attacks. The key space was 2 (M ×N ×7 ×8) , which was sufficiently high and mitigated the brute force attacks. The obtained results confirm that the cipher images resulting from the proposed ciphering scheme possess good cryptographic properties in terms of entropy, PSNR, UACI, NPCR, and keyspace analysis. Furthermore, the strength of the key is evaluated by the NIST test suite. Show more
Keywords: Quaternion, rossler attractor, Least Square Approximation, Pseudo random number generation, image encryption
DOI: 10.3233/JIFS-213600
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5221-5236, 2022
Authors: Ziquan, Xiang | Jiaqi, Yang | Naseem, Muhammad Hamza | Zuquan, Xiang
Article Type: Research Article
Abstract: In view of the extremely complex logistics level, long logistics cycle, high-risk coefficient, extremely fuzzy and uncertain evaluation information of cruise construction logistics allocation, an improved intuitionistic fuzzy TOPSIS method is proposed to evaluate the risk of cruise construction logistics allocation. Firstly, on the basis of empirical data analysis and research interviews, risk sources and risk assessment criteria are determined, and different weights are given to experts according to their importance. Then, in order to reduce the fuzziness and uncertainty of risk source information, an intuitionistic fuzzy weighted arithmetic average (IFWAA) operator is used to aggregate the experts’ opinions, and …the intuitionistic fuzzy aggregation decision risk matrix is obtained. The objective weight of risk assessment criteria is introduced to balance the impact of subjective weight on the final results, to improve the accuracy of the evaluation results. Finally, according to the relative closeness coefficient between each risk source and the positive-ideal solution, the priority of the risk sources of cruise construction logistics allocation is obtained, and the corresponding risk control measures are put forward. Compared with other methods and sensitivity analysis, the effectiveness and applicability of this method are verified. Show more
Keywords: Cruise construction, intuitionistic fuzzy number, TOPSIS method, risk assessment, logistics allocation
DOI: 10.3233/JIFS-211163
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5237-5250, 2022
Authors: Akila, K.
Article Type: Research Article
Abstract: Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category. This paper focuses on a specific issue related to inter-class variation in Human Action Recognition. To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features …are analyzed and discriminated by proposed by Deterministic Normalized –Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set. With a trained dataset and rules framed with the end user, our intelligent HAR system is capable of achieving the accuracy rate of 97.4% which is higher than the other state of art approaches. Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation. Show more
Keywords: Human action recognition, wavelet packet, video processing, feature extraction
DOI: 10.3233/JIFS-212088
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5251-5262, 2022
Authors: Geetha, M.P. | Karthika Renuka, D.
Article Type: Research Article
Abstract: In recent years, E-Commerce is globally increasing among online purchaser, in which customer post product related queries for finding the best product in online shopping. Manually answering the product related queries in real-time, cause online traffic and practically not possible. So, automatic answering system is helpful for answering product related queries. But, the product queries are always in product-explicit, so discovering related product queries and recovering its responds is distinctly be impractical. Accordingly, we propose Hierarchical Deep Neural Network (HiDeNN) model using MOQA framework to discern the appropriate reviews based on Mixtures of Opinions Question Answering (MOQA). The Hierarchical Deep …Neural Network provides discerning the most relevant review for queries and it also provides the relevant answer for specific product category queries. The proposed method is executed on Python and it provides 9.594% and 7.574% higher accuracy value for Discerning Appropriate Reviews compared with the existing method like Relevant Reviews for Answering Product-related Queries (MOQA-BERTQA+FLTR+PT) and IQA: Interactive Query Construction on Semantic Question Answering Systems (IQC-SQA). The experimental result indicates that the proposed MOQA- HiDeNN method can efficiently and accurately get the optimal global solutions for recognizing the appropriate discerning of most relevant review for queries and it also provides the relevant answer for specific product category queries. Show more
Keywords: E-Commerce, product-related queries, product, online shopping, mixtures of opinions, hierarchical deep neural network, accuracy.
DOI: 10.3233/JIFS-212485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5263-5277, 2022
Authors: Prabaharan, L. | Raghunathan, A.
Article Type: Research Article
Abstract: The assisted method of fertilization has required an identification of sperm cells with normal morphological structure. The abnormal sperm cells cannot provide successful result in artificial fertilization. Nowadays the assessment of morphology of sperm cells is subjective and error prone hence creating automatic evaluation method for morphology assessment, it will improve the success ratio in infertility treatment. The first step in our proposed system is pre-processing where noise removal process is applied on microscopic medical images. In second step, adaptive alpha valued Havrda-Chavrat entropy-based threshold technique is proposed where the maximum probability distribution of foreground pixels or background pixels is …assigned to alpha value. Further, existing state-of-art threshold-based segmentation methods are implemented and obtained results on the input images. These segmentation results are compared with the proposed method in terms of supervised and unsupervised evaluation metrics, in which our proposed thresholding method has given optimum threshold value for the segmentation of spermatozoa cells. The outcome of the segmented images and their metric values are indicating better segmentation by our proposed method. Furthermore, this proposed method can be implemented in the mobile applications for diagnosis with artificial intelligence techniques. Show more
Keywords: Spermatozoa, spermatozoa segmentation, image processing, artificial intelligence, computer aided diagnosis
DOI: 10.3233/JIFS-213478
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5279-5292, 2022
Authors: Preethi Saroj, S. | Gurunathan, Pradeep
Article Type: Research Article
Abstract: Accurate segmentation of brain tumor regions from magnetic resonance images continues to be one of the active topics of research due to the high usability levels of the automation process. Faster processing helps clinicians in identification at initial stage of tumor and hence saves valuable time taken for manual image analysis. This work proposes a Cascaded Layer-Coalescing (CLC) model using convolution neural networks for brain tumor segmentation. The process includes three layers of convolution networks, each with cascading inputs from the previous layer and provides multiple outputs segmenting complete, core and enhancing tumor regions. The initial layer identifies complete tumor, …coalesces the discriminative features and the input data, and passes it to the core tumor detection layer. The core tumor detection layer in- turn passes discriminative features to the enhancing tumor identification layer. The information injection through data coalescing voxels results in enhanced predictions and also in effective handling of data imbalance, which is a major contributor in model viewpoint. Experiments were performed with Brain Tumor Segmentation (BraTS) 2015 data. A comparison with existing literature works indicate improvements up to35% in sensitivity, 27% in PPV and 28% in Dice Score, indicating improvement in the segmentation process. Show more
Keywords: Brain tumor segmentation, deep learning, CNN, cascaded layer-coalescing, auxiliary networks
DOI: 10.3233/JIFS-220167
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5293-5308, 2022
Authors: Qiao, Ruixiu | Guo, Xiaozhou | Mao, Wenyu | Li, Jixing | Lu, Huaxiang
Article Type: Research Article
Abstract: The location attention mechanism has been widely applied in deep neural networks. However, as the mechanism entails heavy computing workload, significant memories consumed for weights storage, and shows poor parallelism in some calculations, it is hard to achieve high efficiency deployment. In this paper, the field-programmable gate array (FPGA) is employed to implement the location attention mechanism in hardware, and a novel fusion approach is proposed to connect the convolutional layer with the fully connected layer, which not only improves the parallelism of both the algorithm and the hardware pipeline, but also reduces the computation cost for such operations as …multiplication and addition. Meanwhile, the shared computing architecture is used to reduce the demand for hardware resources. Parallel computing arrays are utilized to time-multiplex a single computing array, which can speed up the pipeline parallel computing of the attention mechanism. Experimental results show that for the location attention mechanism, the FPGA’s inference speed is 0.010 ms, which is around a quarter of the speed achieved by running it with GPU, and its power consumption is 1.73 W, which is about 2.89% of the power consumed by running it with CPU. Compared with other FPGA implementation methods of attention mechanism, it has less hardware resource consumption and less inference time. When applied to speech recognition tasks, the trained attention model is symmetrically quantized and deployed on the FPGA. The result shows that the word error rate is only 0.79% higher than that before quantization, which proves the effectiveness and correctness of the hardware circuit. Show more
Keywords: Attention mechanism, neural networks, FPGA, deep learning, hardware implementation
DOI: 10.3233/JIFS-212273
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5309-5323, 2022
Authors: Ali Abdu, Nail Adeeb | Wang, Zhaoshun
Article Type: Research Article
Abstract: Numerous academic and industrial research studies have focused on the feasibility of implementing blockchain-based healthcare systems data management that can transform the way data systems functioning, usage, and development can be effective. In this manuscript, the focus is on the development of a Grid-based blockchain structure formanaging the surgical process-related information in integrated systems. Considering the implications, scope of human error in the surgical processes, and the quantum of records integral to such process, the need for having robust records of surgical data is imperative. Aiming at improving the security of the information, in this manuscript, the Grid-block structure constituting …multiple grids framework is proposed, which can be optimal for transaction query processing, responsiveness for triggers, and security inheritance. The proposed system, if chosen for real-time implementation, can support interoperability of data among the distinct healthcare systems and support in minimizing the issues of human error inspections, prevention scope for errors whilst improving the quality of healthcare solutions. Show more
Keywords: Surgical procedure data, grid-framework blockchain system, enhanced surgical process data management system
DOI: 10.3233/JIFS-213414
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5325-5335, 2022
Authors: Dong, Xiaoqin | Sun, Xianbin
Article Type: Research Article
Abstract: In multi-attribute large group decision-making (MALGDM), the ideal state indicates a high degree of consensus among a set of decision-makers (DMs). It is complex to reach consensus because the number of decision attributes and DMs increases. Thus, we developed a novel consensus model to manage the decision-making in large group based on the non-cooperative behavior. The improved clustering method takes account of the similarities among different DMs. Similar DMs will be grouped into the same group. The consensus threshold is determined from an objective and subjective aspect to judge whether the consensus reaching process continues. With the introduction of three …non-cooperative behaviors, we investigated a non-cooperative behavior detection method under the change of consensus level. Base on the number of DMs who are willing to change their preliminary views and the change value of consensus level, the non-cooperative degree of subgroup can be computed. According to the non-cooperative degree, the subgroups’ weight can be modified to raise the consensus level. Meanwhile, the subgroup is allowed to change. Based on the adjustment amount of DMs’ opinions, whether decision maker (DM) belongs to this subgroup is recalculated. Finally, an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method. Show more
Keywords: Large group consensus, comprehensive consensus threshold, non-cooperative subgroup, consensus level, consensus reaching model
DOI: 10.3233/JIFS-201805
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5337-5351, 2022
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