Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2024: 1.7
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
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl