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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: Yu, Ming | Jia, Jingli | Xue, Cuihong | Yan, Gang | Guo, Yingchun | Liu, Yuehao
Article Type: Research Article
Abstract: Sign language is the primary way of communication between hard-of-hearing and hearing people. Sign language recognition helps promote the better integration of deaf and hard-of-hearing people into society. We reviewed 95 types of research on sign language recognition technology from 1993 to 2021, analyzing and comparing algorithms from three aspects of gesture, isolated word, and continuous sentence recognition, elaborating the evolution of sign language acquisition equipment and we summarized the datasets of sign language recognition research and evaluation criteria. Finally, the main technology trends are discussed, and future challenges are analyzed.
Keywords: Sign language recognition, convolutional neural network, encoder-decoder, dataset
DOI: 10.3233/JIFS-210050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3879-3898, 2022
Authors: Tran, Duc Quynh | Nguyen, Xuan Thao | Nguyen, Doan Dong | Nguyen, Quang Thuan
Article Type: Research Article
Abstract: In this paper, we propose a new formula for the entropy based on similarity measures of intuitionistic fuzzy sets (IFS). The contribution of this work is the proof that the new formula satisfies all the conditions of entropy. The experimentation on some examples shows that the new entropy is useful. Besides, we use the new entropy and similarity measures to design an algorithm for ranking assets in stock markets. The numerical results on 5 benchmark data sets were reported. It points out that the entropy and the similarity measures of IFS may provide an alternative tool for solving portfolio selection …problems. Show more
Keywords: Intuitionistic fuzzy set, similarity measures, stock markets, assets ranking, entropy
DOI: 10.3233/JIFS-211563
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3899-3909, 2022
Authors: Peng, Jiangang | Cai, Ya | Xia, Guang | Hao, Ming
Article Type: Research Article
Abstract: This study examines decision theory based on interval type-2 fuzzy sets with linguistic information for the three-way decision approach by addressing the challenge of uncertainty for information analysis and fusion in subjective decision-making processes. First, the interval type-2 fuzzy linguistic term sets (IT2 FLTSs) are defined to represent and normalize the uncertain preference information in linguistic decision-making. Subsequently, perception computing based on computing with words paradigm is introduced to implement information fusion among different decision-makers in the linguistic information-based fuzzy logic reasoning process. Then, a three-way decision (3WD) theory based on IT2 FLTSs with fuzzy neighborhood covering is proposed, and …the corresponded tri-partitioning strategies that satisfy Jaccard similarity of membership distributions are given. Finally, 3WD theory is applied to multi-criteria group decision-making with linguistic terms, and the algorithm steps are illustrated by a promising application under the background of coronavirus disease 2019 to reveal the feasibility and practicability of the proposed approach. Show more
Keywords: Three-way decision, interval type-2 fuzzy set, linguistic term sets, multi-criteria group decision-making
DOI: 10.3233/JIFS-213236
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3911-3932, 2022
Authors: Vanitha, K. | Satyanarayana, D. | Giri Prasad, M.N.
Article Type: Research Article
Abstract: This paper addresses a novel neuro-fuzzy-based approach to set the weighted linking strength of parameter - adaptive reduced pulse coupled neural networks. In reduced PCNN based medical image fusion algorithms, it is quite essential to evaluate the prominence of each pixel in an image. The fusion performance in turn depends on the linking factor, internal activity. Thus, we need to set these values of reduced PCNN in a more adaptive manner with fewer complications and uncertainties. For this, the weighted linking strength i.e., lambda of the reduced PCNN neurons is attentively set by a fuzzy-based approach. Here, lambda of neurons …is represented as fuzzy membership values using the activity level measures such as local information entropy and energy. Finally, a new model called-Fuzzy adaptive reduced pulse coupled neural networks is developed by reducing the number of parameters and fuzzy adaptive settings of them. This leads to a very less complicated network and more computational efficacy, which is a prominent part of health care requirements. The proposed scheme is free from the shortcomings such as loss of boundaries, structural details, unwanted artifacts, degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques. Show more
Keywords: Magnetic resonance imaging, computed tomography, SPECT, discrete wavelet transform
DOI: 10.3233/JIFS-213416
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3933-3946, 2022
Authors: Baffour, Adu Asare | Qin, Zhen | Zhu, Guobin | Ding, Yi | Qin, Zhiguang
Article Type: Research Article
Abstract: Recognizing facial expressions rely on facial parts’ movement (action units) such as eyes, mouth, and nose. Existing methods utilize complex subnetworks to learn part-based facial features or train neural networks with an extensively perturbed dataset. Different from existing methods, we propose a trainable end-to-end convolutional neural network for facial expression recognition. First, we propose a Local Prediction Penalty to stimulate facial expression recognition research with no part-based learning. It is a technique to punish the feature extractor’s local predictive power to coerce it to learn coarse-grained features (general facial expression). The Local Prediction Penalty forces the network to disregard predictive …local signals learned from local receptive fields and instead depend on the global facial region. Second, we propose a Spatial Self-Attention method for fine-grained feature representation to learn distinct face features from pixel positions. The Spatial Self-Attention accumulates attention features at privileged positions without changing the spatial feature dimension. Lastly, we leverage a classifier to carefully combine all learned features (coarse-grained and fine-grained) for better feature representation. Extensive experiments demonstrate that our proposed methods significantly improve facial expression recognition performance. Show more
Keywords: Facial expression recognition, spatial self-attention, coarse-grained, fine-grained, convolutional neural network
DOI: 10.3233/JIFS-212022
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3947-3959, 2022
Authors: Cid-Galiot, Jonathan J. | Aguilar-Lasserre, Alberto A. | Grande-Ramírez, José Roberto | Juárez-Martínez, Ulises | Posada-Gómez, Rubén | Calderón-Palomares, Luis A.
Article Type: Research Article
Abstract: This research is carried out in the Mexican oil and gas industry. An Intelligent Decision Support System (IDSS) is proposed, through support modules for the human operator (fuzzy expert system and artificial neural network) that simulate, forecast and standardize operational decision criteria of a sequential pipeline pumping system, with problems of vandalism, mechanical deterioration in the face of a complex topographic profile, in order to minimize operational subjectivity and prevent contingencies. The research provides new control and monitoring alternatives that guarantee the operational reliability of a pumping station, minimizing the effects of risk by managing the knowledge of the experts …involved in the problem, data mining and association of results, which allow to unify criteria decision. The originality of the work focuses on the ability to model, identify and adapt variables to current international parameters, considering previous works through a comprehensive perspective. Show more
Keywords: Hydrocarbon, pipeline transport system (PTS), fuzzy expert system (FES), artificial neural network (ANN), intelligent decision support system (IDSS)
DOI: 10.3233/JIFS-212411
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3961-3981, 2022
Authors: Tao, Yuwen | Jiang, Yizhang | Dong, Xuan | Zhou, Leyuan | Ding, Yang | Qian, Pengjiang
Article Type: Research Article
Abstract: Epilepsy is a common brain disease, caused by abnormal discharge of human brain neurons, resulting in brain dysfunction syndrome. Although epilepsy does not have much impact on patients in the short term, but long-term frequent seizures can lead to physical and mental impact of patients. At present, the method used to detect epilepsy is to make a comprehensive judgment by EEG examination combined with clinical symptoms. With the application of AI technology, some advanced algorithms have been used to assist medical diagnosis. In this trend, we use extreme learning machine to observe and detect patients with epilepsy. ELM has the …characteristics of high efficiency and high precision, so it is often used in regression and classification problems. However, in the face of different data sets, ELM structure is not enough to achieve good performance. This is caused by the uneven distribution of data in different data sets. To solve this problem, we add the transfer learning module to the basic ELM structure. The purpose of adding transfer learning is to divide the disordered data in the domain space and construct a data set suitable for ELM learning. Specifically, the raw data are mapped to high-dimensional space by kernel method through domain adaptive method. Secondly, in high-dimensional space, the distance between different domains should be reduced appropriately. Finally, ELM method is used to analyze and predict the changed data set. In the whole algorithm process, due to the characteristics of ELM updating weight, only a certain amount of hidden nodes are needed, and the training process is very fast. At the same time, after adding the transfer learning function module, the accuracy of ELM is also satisfactory. In this paper, the epilepsy data of patients were used for comparative experiments. The experimental results show that the method can maintain high efficiency and satisfactory accuracy. Show more
Keywords: Extreme learning machine, domain adaptation, signal classification, Epileptic EEG
DOI: 10.3233/JIFS-212068
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3983-3992, 2022
Authors: Wei, Lixin | Wang, Yexian | Fan, Rui | Hu, Ziyu
Article Type: Research Article
Abstract: In order to solve the premature convergence of multi-objective evolutionary algorithm, a two-stage diversity enhancement differential evolution algorithm for multi-objective optimization problem(TSDE) is proposed. The offspring with better performance needs the generation of high-quality parent generation. In this paper, an improved cell density method is used to screen for the high quality parents by estimating the global distribution of the objective space. Moreover, Principal Component Analysis operator is introduced to the external archive to perturb the non-dominated solution, which not only ensures the convergence but also improves the diversity. In order to verify the effectiveness of the algorithm, TSDE and …other advanced methods are run on 19 test functions. The results show that TSDE performs better than other algorithms. Show more
Keywords: Multi-objective optimization, differential evolution, evolutionary algorithms, principal component analysis
DOI: 10.3233/JIFS-202645
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3993-4010, 2022
Authors: Joseph, Abin John | Asaletha, R.
Article Type: Research Article
Abstract: WCSN is one of the most significant research areas in the terrestrial networking field due to its wide range of applications. One of the most difficult challenges is expanding the overall running time without attaching any new batteries or hardware. Using a novel EDT (Energy, Distance, Time) driven strategy, this paper proposes a clustering algorithm to solve the problems of the hotspot as well as reduce battery energy loss. The CH rotation method was then described in detail. This paper will introduce a new function called SCH (Substitute cluster head), which has replaced CH. The main aim of this research …is to improve energy reliability and network lifetime. Finally, the presented EDT approach can be comparable to current algorithms, where MFSTERP’s network lifetime is 15.4%, EECHS’s is 23.2%, and ABC-DE’s is 11.4%, but our proposed EDT methodology extends network lifetime by 40% as well as decreases energy usage by 7% as compared to LEACH when determining the SCH. Show more
Keywords: Wireless chemical sensor network, EDT strategy, cluster head, substitute cluster head, residual energy
DOI: 10.3233/JIFS-212912
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4011-4021, 2022
Authors: Guang, Jinzheng | Xi, Zhenghao
Article Type: Research Article
Abstract: It is an essential and challenging task to accurately identify unknown plants from images without professional knowledge due to the large intra-class variance and small inter-class variance. Aiming at the problem of low accuracy and model complexity, a lightweight plant species recognition algorithm using EfficientNet with Efficient Channel Attention (ECAENet) is proposed. The proposed approach is based on EfficientNet, which used neural architecture search to gain a baseline network and uniformly scales all dimensions of depth, width, and resolution using a compound coefficient. To overcome Squeeze-and-Excitation block complexity, the proposed method replaces all the two fully-connected layers in the channel …attention modules with a fast one-dimensional convolution with an adaptive kernel, which avoids dimensionality reduction and effectively learns the discriminative features. The experimental results demonstrate that our ECAENet achieves 99.56%, 99.75%, 98.40%, and 93.79% accuracy on the well-known Swedish Leaf, Flavia Leaf, Oxford Flowers, and Leafsnap datasets, respectively. In particular, our method achieves 3.6x fewer network parameters and 8.4x FLOPs than others with similar accuracy. Therefore, our method achieves better recognition performance compared to most of the existing plant recognition methods. Show more
Keywords: Plant species recognition, efficientNet, image Classification, channel attention, convolutional neural networks
DOI: 10.3233/JIFS-213314
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4023-4035, 2022
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
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