Journal of Intelligent & Fuzzy Systems - Volume 42, issue 6

Authors: Yu, Hui | Li, Jun-qing | Chen, Xiao-Long | Zhang, Wei-meng

Article Type: Research Article

Abstract:  During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global …search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency. Show more

Keywords: Flexible job shop scheduling, outpatient scheduling, hybrid imperialist competitive algorithm, neighborhood search strategies

DOI: 10.3233/JIFS-212024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5523-5536, 2022

Authors: Aggarwal, Eshika | Mohanty, B.K.

Article Type: Research Article

Abstract: An outranking procedure for Multi-Attribute Decision-Making (MADM) problems is introduced in our work that acts as a decision-aid in recommending the products to the buyers. The buyer’s product assessment is taken as Interval-Valued Intuitionistic Fuzzy Sets (IVIFS) in each attribute. The confidence level that is implicit in the buyer’s product rating is explicated in the proposed work using fuzzy entropy. As the confidence level of the buyer on the product assessment is for both satisfaction and reluctance, it is suitably distributed in membership and non-membership parts of IVIFS. Our work generates a dominance matrix that represents partial or full dominance …of one product over another after scoring the products that are unified with buyer’s confidence. The proposed work suggests the product ranking after ascertaining the buyer’s flexibility. An algorithm is written in our work to validate the procedure developed. We have compared our work with other similar works to highlight the benefits of the proposed work. A numerical example is illustrated to highlight the procedure developed. Show more

Keywords: Interval-valued Intuitionistic fuzzy sets, confidence, partial dominance matrix, outranking, flexibility behaviour

DOI: 10.3233/JIFS-212026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5537-5551, 2022

Authors: Zhang, Min | Yang, Haijie | Li, Pengfei | Jiang, Ming

Article Type: Research Article

Abstract: Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes …by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets. Show more

Keywords: Computer vision, human pose estimation, parallel atrous convolution, body structure constraints

DOI: 10.3233/JIFS-212061

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5553-5563, 2022

Authors: Huang, Rui-Lu | Deng, Min-hui | Li, Yong-yi | Wang, Jian-qiang | Li, Jun-Bo

Article Type: Research Article

Abstract: With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval …2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods. Show more

Keywords: Cloud model theory, decision support framework, interval 2-tuple linguistic information, health-care waste management

DOI: 10.3233/JIFS-212065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5565-5590, 2022

Authors: Priyadharshini, A. | Chitra, S.

Article Type: Research Article

Abstract: Lung cancer is one of the most commonly occurring diseases that ranked in the top of the present survey. Advancements in the medical field enable non-invasive methods of computerised diagnosis procedures and detection processes. Deep learning methods are already in evaluation by keeping the deep analysis on improving segmentation accuracy and prediction accuracy etc. The classification of tumour type depends on the quality of segmentation work and feature mappings. In this paper, we developed a robust model that classifies the types of tumours with improved accuracy but is also capable of detecting the early stages of cancer by detecting the …unique hidden points of the image intensity in the lung images, etc. The system is comprised of a novel relative convergence technique for feature extraction technique to extract the infected area and its characteristic pixels to evaluate a unique feature mapping vector. The MSB feature mapping vectors are analysed with Hybrid Regress Fuzzy Net. The final result on whether a tumour is present in the CT image or normal depends on the three individual decisions made by the three algorithms mentioned. The accuracy of each algorithm is also considered for the probable decision-making. The performance measure of the entire proposed Hybrid Regress Net is evaluated through Accuracy, Precision, Recall and F1Score etc. Show more

Keywords: Lung tumor detection, nero-fuzzy logic, Image processing, medical imaging, machine learning

DOI: 10.3233/JIFS-212071

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5591-5604, 2022

Authors: Sanjay, Chintakindi | Alsamhan, Ali | Abidi, Mustufa Haider

Article Type: Research Article

Abstract: Manufacturing companies are focusing on continuous process development to thrive in today’s quality-conscious market. It is particularly relevant to investigate machining processes for advanced materials such as superalloys. Drilling is a major operation that is used in the majority of manufacturing processes. Hence, this research work is focused on investigating the drilling performance of the Monel K500. The output responses under consideration are metal removal rate (MRR), surface roughness, and tool wear. Various contemporary techniques were utilized in this work, namely machine learning methods, artificial neural networks, principal component analysis, and grey relation analysis using uncoated, coated, and HSS (high-speed …steel) drills. After annealing, the softened material can be easily machined to increase the MRR and decrease tool wear and surface roughness. The experimental results show that, after annealing, the surface roughness values for HSS drills have been reduced by 23.86%, uncoated drills by 27.29%, and coated drills by 29.27%, respectively. Moreover, tool wear values for HSS drills decreased by 28.51%, uncoated drills by 34.7%, and coated drills by 33.71%, based on the relative error approach. MRR values for HSS drills increased by 20.51 %, uncoated drills by 23.08%, and coated drills by 23.5%, respectively. For PCA (principal component analysis), feed (47%), and for GRA (gray relation analysis), feed (40.1%) will be the significant parameter followed by speed, and both methods have identified the same experimental run values for optimization of cutting parameters. The theoretical values were predicted using machine learning methods, which utilized the Python language using the Google Colab and then validated with experimental values. The predicted values obtained by the decision tree are close to the measured values as compared to support vector regression and K-nearest neighbor based on relative error. The estimated values obtained by the ANN (artificial neural networks) approach, using Easy NN plus software, match well with the actual values, with a slight deviation. Show more

Keywords: Monel K500, principal component analysis, grey relation analysis with S/N ratio, machine learning methods, ANN

DOI: 10.3233/JIFS-212087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5605-5625, 2022

Authors: Pan, Lujia | Kalander, Marcus | Wang, Pinghui

Article Type: Research Article

Abstract: Classification algorithms are widely applied to predict failures and detect anomalies in various application areas. It is common to assume that the data and labels are correct when training, but this is challenging to guarantee in the real world. If there are erroneous labels in the training data, a model can easily overfit to these, resulting in poor performance. How to handle label noise has been previously researched, however, few works focus on label noise in anomaly detection. In this work, we propose LDAAD, a novel algorithm framework for label de-noising for anomaly detection that combines unsupervised learning and semi-supervised …learning methods. Specifically, we apply anomaly detection to partition the training data into low-risk and high-risk sets. We subsequently build upon ideas from cross-validation and train multiple classification models on segments of the low-risk data. The models are used both to relabel the samples in the high-risk set and to filter the low-risk samples. Finally, we merge the two sets to obtain a final sample set with more confident labels. We evaluate LDAAD on multiple real-world datasets and show that LDAAD achieves robust results that outperform the benchmark methods. Specifically, LDAAD achieves a 5% accuracy improvement over the second-best method for symmetric noise while having a minimal detrimental impact when no label noise is present. Show more

Keywords: Label noise, anomaly detection, ensemble learning, semi-supervised learning

DOI: 10.3233/JIFS-212096

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5627-5637, 2022

Authors: Borza, Mojtaba | Rambely, Azmin Sham

Article Type: Research Article

Abstract: In the multi-objective programming problem (MOPP), finding an efficient solution is challenging and partially encompasses some difficulties in practice. This paper presents an approach to address the multi-objective linear fractional programing problem with fuzzy coefficients (FMOLFPP). In the method, at first, the concept of α  - cuts is used to change the fuzzy numbers into intervals. Therefore, the fuzzy problem is further changed into an interval-valued linear fractional programming problem (IVLFPP). Afterward, this problem is transformed into a linear programming problem (LPP) using a parametric approach and the weighted sum method. It is proven that the solution resulted from the …LPP is at least a weakly ɛ  - efficient solution. Two examples are given to illustrate the method. Show more

Keywords: Efficient solution, fuzzy numbers, fuzzy programming, interval arithmetic, weighted sum approach

DOI: 10.3233/JIFS-212105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5639-5652, 2022

Authors: Zhao, Hang | Chu, Jianjie | Mo, Rong | Chen, Chen | Ding, Ning

Article Type: Research Article

Abstract: At present, high-speed trains have become popular modern transportation. As a significant part of the high-speed train riding activity, the stowing and unloading luggage task has its characteristics. To comprehensively and reasonably evaluate passenger comfort of the stowing and unloading luggage task in high-speed trains. In this paper, passenger behavior characteristics are firstly analyzed by the author, the theoretical architecture of passenger comfort evaluation is constructed with the perspective of product aesthetics and ergonomics, and then the process of the passenger comfort evaluation is put forward. Secondly, a combination of Rough Number (RN) and Decision Making Trial and Evaluation Laboratory …(DEMATEL) (i.e. R-DEMATEL) is utilized to solve the centrality degree of comfort influencing factors and determine comfort evaluation indexes. Furthermore, the passenger comfort evaluation model with Fuzzy Neural Network (FNN) is constructed and trained. After that, the sample data of the evaluation are collected through the simulated experiment of the stowing and unloading luggage task, and they are trained with FNN comparing to Back Propagation Neural Network (BPNN). Eventually, the result of examples testing is verified that the effectiveness of the proposed method. Show more

Keywords: Comfort evaluation, stowing and unloading luggage, Rough-DEMATEL (R-DEMATEL), FNN, high-speed trains

DOI: 10.3233/JIFS-212109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5653-5665, 2022

Authors: Senthilkumar, D. | George Washington, D. | Reshmy, A.K. | Noornisha, M.

Article Type: Research Article

Abstract: Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments …are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features. Show more

Keywords: Water quality prediction, multi-target, non-linear, MARS, CART

DOI: 10.3233/JIFS-212117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5667-5679, 2022

Authors: Tavares, Thiago Henrique Barbosa de Carvalho | Ferreira, Bruno Pérez | Mendes, Eduardo Mazoni Andrade Marçal

Article Type: Research Article

Abstract: In this work the relationship between the Selic rate and some bank parameters defined by the so-called Basel Accords is studied. The cross-correlation between the Selic rate and the parameters is used to explain how these parameters affect the Selic rate and vice-versa so as to define the predictability of the Selic rate using (some of) these parameters as inputs. A model is then proposed for predicting the Selic rate based on some specific parameters using fuzzy logic ideas, which dealt with a partitioning of the universe of discourse using clusters related to the output data distribution. The proposed model …is compared to four other known models in the literature and showed to have better performance in average compared to all other models. Show more

Keywords: Finance, basel, statistics, fuzzy logic

DOI: 10.3233/JIFS-212128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5681-5694, 2022

Authors: Xie, Rongjian | Jia, Yucai | Wu, Yuanmei | Zhang, Peiyun

Article Type: Research Article

Abstract: During major epidemics, monitoring vaccine quality can ensure the public health and social stability. Considering that social media has become an important way for the public to obtain external information during the epidemic. We developed a dual regulatory system of vaccine quality with the government in the leading role and the participation of We Media, and constructed a four-party evolutionary game model (government regulatory agency, We Media, vaccine industry groups, and the public) and analyzed the stability of each game player’s strategy choice. The system’s possible equilibrium points are identified using Lyapunov’s first law. Then the game trajectory between stakeholders …is simulated by MATLAB, the effects of initial intention and parameters on the evolution process and results are analyzed. The results show that to ensure the quality and safety of vaccines and stabilize network public opinion during epidemics, the government should invest in an effective supervision mechanism. By strengthening responsibility, increasing penalties, and reducing supervision costs, the probability of vaccine industry groups providing high-quality vaccines is effectively enhanced. Restricting the behavior of We Media and supervising vaccine industry groups to reduce speculation reduces the cost of government supervision and improves its efficiency. Show more

Keywords: Major epidemics, vaccine quality, dual regulatory system, four-party evolutionary game

DOI: 10.3233/JIFS-212146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5695-5714, 2022

Authors: Wang, Haolun | Zhang, Faming

Article Type: Research Article

Abstract: The interaction operation laws (IOLs) between membership functions can effectively avoid the emergence of counterintuitive situations. The power average (PA) operator can eliminate the negative effect of extremely or improperly assessments on the decision results. The Heronian mean (HM) operator is capable of examining the interrelationship between the two attributes. To synthesize the powers of the IOLs, PA and HM operators in this paper, the PA and HM operators are extended to process T-spherical fuzzy evaluation information perfectly based on the IOLs, and the T-spherical fuzzy interaction power Heronian mean (T-SFIPHM) operator and its weighted form are proposed. We further …present some properties of these proposed AOs and discuss several special cases. Moreover, a novel method to T-spherical fuzzy multiple attribute decision making (MADM) problems applying the proposed AO is developed. Lastly, we present a numerical example to validate its feasibility and reasonableness, and the superiority of the developed method is further illustrated by sensitivity analysis of parameters and comparison with existing methods. The results show that proposed AOs not only can capture the interactivity among membership degree (MD), abstinence degree (AD) and non-membership degree (NMD) of T-spherical fuzzy numbers (T-SFNs), bust also ensure the overall balance of variable values in the process of information fusion and realize the interrelationship between attribute variables, so the decision results can be closer to reality and more reliable. Show more

Keywords: Multiple attribute decision making, T-spherical fuzzy sets, Heronian mean operator, interaction operation laws, power average operator

DOI: 10.3233/JIFS-212149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5715-5739, 2022

Authors: Khan, Faiz Muhammad | Bibi, Naila | Xin, Xiao Long | Muhsina, | Alam, Aftab

Article Type: Research Article

Abstract: In multiple criteria decision making (MCDM) problem, the decision maker deal with a situation where the sum of membership and non-membership grade of an attributes does not belongs to [0, 1]. To avoid such a situation, we proposed a new type of fuzzy system known as fermatean fuzzy system. More precisely, we presented the notion of fermatean fuzzy ideal theory and rough fermatean fuzzy sets in semigroups. The idea of lower and upper approximation in fermatean fuzzy sets has been initiated. The study has been further extended to rough fermatean fuzzy left(resp. right, interior) ideals in semigroup. Several results related …to this notion are determined. Show more

Keywords: Intuitionistic fuzzy set, pythagorean fuzzy sets, rough sets, rough fermatean fuzzy sets in semigroups, rough fermatean fuzzy ideals in semigroups

DOI: 10.3233/JIFS-212162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5741-5752, 2022

Authors: Meng, Mengjun | Lin, Qiuyun | Wang, Yingming

Article Type: Research Article

Abstract: The great changes in the external environment of the manufacturing supply chain make its demand more complex and difficult to control. This paper takes China as an example. According to questionnaire survey and principal component analysis, the risk indicators caused by uncertain demand are screened and classified to construct evaluation system and complete risk identification. The Bayesian network integrating fuzzy set theory and left and right fuzzy ranking is used to explore the relationship between risk indicators and supply chain to achieve risk evaluation. In view of the highest risk factors, an incentive mechanism model based on information sharing is …put forward to prove theoretically that information sharing is an important strategy to reduce risk. The results are as follows: The uncertain demand will lead to a high level of risk in China’s manufacturing supply chain, in which the level of information technology is the biggest cause. Only when manufacturing enterprises are willing to share information and other node enterprises join the information sharing team, can demand uncertainty be fundamentally reduced. The proposed risk assessment model realizes the method innovation and theoretical innovation. It can practical and effectively help relevant enterprises to determine and control risks. Show more

Keywords: Uncertainty of demand, manufacturing supply chain, Bayesian networks, model simulation, risk assessment

DOI: 10.3233/JIFS-212207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5753-5771, 2022

Authors: Zhai, Longzhen | Feng, Shaohong

Article Type: Research Article

Abstract: The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the …ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning. Show more

Keywords: Emergency evacuation, path planning, ant colony algorithm (ACO), quantum evolution theory

DOI: 10.3233/JIFS-212220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5773-5788, 2022

Authors: Cao, Rui | Jiang, Feng | Wu, Zhao | Ren, Jia

Article Type: Research Article

Abstract: With the advancement of computer performance, deep learning is playing a vital role on hardware platforms. Indoor scene segmentation is a challenging deep learning task because indoor objects tend to obscure each other, and the dense layout increases the difficulty of segmentation. Still, current networks pursue accuracy improvement, sacrifice speed, and augment memory resource usage. To solve this problem, achieve a compromise between accuracy, speed, and model size. This paper proposes Multichannel Fusion Network (MFNet) for indoor scene segmentation, which mainly consists of Dense Residual Module(DRM) and Multi-scale Feature Extraction Module(MFEM). MFEM uses depthwise separable convolution to cut the number …of parameters, matches different sizes of convolution kernels and dilation rates to achieve optimal receptive field; DRM fuses feature maps at several levels of resolution to optimize segmentation details. Experimental results on the NYU V2 dataset show that the proposed method achieves very competitive results compared with other advanced algorithms, with a segmentation speed of 38.47 fps, nearly twice that of Deeplab v3+, but only 1/5 of the number of parameters of Deeplab v3 + . Its segmentation results were close to those of advanced segmentation networks, making it beneficial for the real-time processing of images. Show more

Keywords: Deep learning, indoor scene segmentation, neural network, image processing, receptive field

DOI: 10.3233/JIFS-212275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5789-5798, 2022

Authors: Muhiuddin, G. | Catherine Grace John, J. | Elavarasan, B. | Porselvi, K. | Al-Kadi, D.

Article Type: Research Article

Abstract:  The notions of hybrid ideals and k -hybrid ideals in a ternary semiring are introduced in this paper, and a substantial amount of effort has been made to study some of their features. In terms of characteristic function, we show some properties of k -hybrid ideals and give some characterizations of hybrid intersection with respect to these k -hybrid ideals. Finally, results based on a k -hybrid ideal’s homomorphic hybrid preimage are provided. With respect to k-hybrid ideals, we give certain characterizations of hybrid intersection.

Keywords: Semirings, Ternary semirings, k-hybrid ideals, Homomorphism, ψ-invariant

DOI: 10.3233/JIFS-212311

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5799-5807, 2022

Authors: Xiao, Qingjiang | Du, Shiqiang | Yu, Yao | Huang, Yixuan | Song, Jinmei

Article Type: Research Article

Abstract: In recent years, tensor-Singular Value Decomposition (t-SVD) based tensor nuclear norm has achieved remarkable progress in multi-view subspace clustering. However, most existing clustering methods still have the following shortcomings: (a) It has no meaning in practical applications for singular values to be treated equally. (b) They often ignore that data samples in the real world usually exist in multiple nonlinear subspaces. In order to solve the above shortcomings, we propose a hyper-Laplacian regularized multi-view subspace clustering model that joints representation learning and weighted tensor nuclear norm constraint, namely JWHMSC. Specifically, in the JWHMSC model, firstly, in order to capture the …global structure between different views, the subspace representation matrices of all views are stacked into a low-rank constrained tensor. Secondly, hyper-Laplace graph regularization is adopted to preserve the local geometric structure embedded in the high-dimensional ambient space. Thirdly, considering the prior information of singular values, the weighted tensor nuclear norm (WTNN) based on t-SVD is introduced to treat singular values differently, which makes the JWHMSC more accurately obtain the sample distribution of classification information. Finally, representation learning, WTNN constraint and hyper-Laplacian graph regularization constraint are integrated into a framework to obtain the overall optimal solution of the algorithm. Compared with the state-of-the-art method, the experimental results on eight benchmark datasets show the good performance of the proposed method JWHMSC in multi-view clustering. Show more

Keywords: Multi-view subspace clustering, representation learning, hyper-Laplacian regularization, weighted tensor nuclear norm

DOI: 10.3233/JIFS-212316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5809-5822, 2022

Authors: Sha, Gang | Wu, Junsheng | Yu, Bin

Article Type: Research Article

Abstract: Purpose: Reading spinal CT (Computed Tomography) images is very important in the diagnosis of spondylosis, which is time-consuming and prones to make biases. In this paper, we propose a framework based on Faster-RCNN to improve detection performances of three spinal fracture lesions: cfracture (cervical fracture), tfracture (thoracic fracture) and lfracture (lumbar fracture). Methods: First, we use ResNet50 to replace VGG16 in backbone network in Faster-RCNN to increase depth of training network. Second, we utilize soft-NMS (Non-Maximum Suppression) instead of NMS to avoid missed detection of overlapped lesions. Third, we simplify RPN (Region Proposal Network) to accelerate training speed …and reduce missed detection. Finally, we modify the classifier layer in Faster-RCNN and choose appropriate length-width ratio by changing anchor sizes in sliding window, then adopt multi-scale strategy in training to improve efficiency and accuracy. Results: The experimental results show that the proposed scheme has a good performance, mAP (mean average precision) is 90.6%, IOU (Intersection of Union) is 88.5 and detection time is 0.053 second per CT image, which means our proposed method can accurately detect spinal fracture lesions. Conclusion: Our proposed method can provide assistance and scientific references for both doctors and patients in clinically. Show more

Keywords: Faster-RCNN, Detection, ResNet50, soft-NMS

DOI: 10.3233/JIFS-212389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5823-5837, 2022

Authors: Ameen, Zanyar A.

Article Type: Research Article

Abstract: As everyday problems contain a lot of data and ambiguity, it has become necessary to develop new mathematical approaches to address them and soft set theory is the best tool to deal with such problems. Hence, in this article, we introduce a non-continuous mapping in soft settings called soft U -continuous. We mainly focus on studying soft U -continuity and its connection to soft continuity. We further show that soft U -continuity preserves soft compact sets and soft connected sets. The later sets have various applications in computing …science and decision making theory. In the end, we show that if each soft U -continuous mapping f from a soft space X into a soft T 0 -space Y is soft continuous, then Y is soft T 1 . Show more

Keywords: soft continuous, soft compact, soft connected, soft separable, soft T1-space

DOI: 10.3233/JIFS-212410

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5839-5845, 2022

Authors: Wang, Xiaohan | Wang, Pei | Chen, Weilong | Hu, Wangwu | Yang, Long

Article Type: Research Article

Abstract: Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, …the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective. Show more

Keywords: Map matching, continuous window direction, road network topology, trajectory clustering

DOI: 10.3233/JIFS-212417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5847-5862, 2022

Authors: Li, Xin | Li, Xiaoli | Wang, Kang

Article Type: Research Article

Abstract: The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the …the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs. Show more

Keywords: Niche selection, multi-objective optimization, diversity, irregular Pareto front

DOI: 10.3233/JIFS-212426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5863-5883, 2022

Authors: He, Xiaorong

Article Type: Research Article

Abstract: Earthquake prediction is one of the important themes of earthquake research, and it is also a very difficult scientific problem in the world. In this study, a bibliometric analysis is conducted on the scientific publications about earthquake prediction indexed in SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) databases during the past two decades (1998–2017). The subject categories, annual and journal distributions, leading countries/regions and institutions are investigated in this field. The main research topics are identified through text mining method. The research trends are explored by keyword co-occurrence analysis and bursting keywords detection techniques. The results …of this study are helpful for scholars in this field to find the knowledge structure and important participants. It is also helpful for scholars to seize the current research hotspots and future development trends in this field. Show more

Keywords: Earthquake prediction, visualization, bibliometric analysis, citation structure, research trends

DOI: 10.3233/JIFS-212442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5885-5901, 2022

Authors: Chu, Yongjie | Ahmad, Touqeer | Zhao, Lindu

Article Type: Research Article

Abstract: Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain …Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot. Show more

Keywords: Domain Adaptation, discriminative learning, low-resolution face recognition, one-shot

DOI: 10.3233/JIFS-212454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5903-5917, 2022

Authors: Li, Yang | Chen, Simeng | Bai, Ke | Wang, Hao

Article Type: Research Article

Abstract: Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with …the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots. Show more

Keywords: Safety, fire risk level, path optimisation, discrete electrostatic discharge algorithm, improved discrete electrostatic discharge algorithm

DOI: 10.3233/JIFS-212464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5919-5930, 2022

Authors: Yang, Qingbo | Xu, Fangzhou | Leng, Jiancai

Article Type: Research Article

Abstract: Robotic arms are powerful assistants in many industrial production environments, and they run periodically in accordance with preset actions to complete specified operations. However, they may act abnormally when encountering unexpected situation and then lead to unnecessary loss. Recognizing the abnormal actions of robotic arms through surveillance video can automatically help us to understand their operating status and discover possible abnormalities in time. We designed a deep learning architecture based on 3D convolution for abnormal action recognition. The 3D convolutional layer can extract the spatial and temporal features of the robotic arm movements from the video frame difference sequence. The …features are compressed and streamlined by the maximum pooling layer to obtain concise and effective robotic arm action features. Finally, the fully connected layer is used to classify the features to recognize the abnormal robotic arm tasks. Support vector data description (SVDD) model is employed to detect abnormal actions of the robotic arm, and the well-trained SVDD model can distinguish the normal actions from the three kinds of abnormal actions with the Area Under Curve (AUC) 99.17%. Show more

Keywords: Robotic arm, action recognition, anomaly detection, 3D convolution neural network, support vector data description

DOI: 10.3233/JIFS-212468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5931-5937, 2022

Authors: Ramaraju, Satish Kumar | Kaliannan, Thenmalar | Androse Joseph, Sheela | Kumaravel, Umadevi | Albert, Johny Renoald | Natarajan, Arun Vignesh | Chellakutty, Gokul Prasad

Article Type: Research Article

Abstract: A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilevel inverter (MLI). In this method, the Modified Particle Swarm Optimization (MPSO) conveys a primary task for the VL-MLI using Half Height (H-H) method, it determine the required optimum switching angles to eliminate desired value of harmonics. The simulation circuit for fifteen level output uses single switch voltage-lift inverter fed with resistive and inductive loads (R & L load). The power quality is developed by voltage-lift multilevel inverter with minimized …harmonics under the various Modulation Index (MI) while varied from 0.1 up to 1. The circuit is designed in a Field Programmable Gate Array (FPGA), which includes the MPSO rules for fast convergence to reduce the lower order harmonics and finds the best optimum switching angle values. To report this problem the H-H has implemented with MPSO to reduce minimum Total Harmonic Distortion (THD) for simulation circuit using Proteus 7.7 simulink tool. Due to the absence of multiple switches, filter and inductor element exposes for novelty of the proposed system. The comparative analysis has been carried-out with existing optimization and modulation methods. Show more

Keywords: Solar-Photovoltaic, voltage lift-multilevel inverter, particle swarm optimization algorithm, half height, field program gate array

DOI: 10.3233/JIFS-212583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5939-5956, 2022

Authors: Wang, Chunye | Sun, Jian | Xu, Xiaoxin | Zou, Bin | Zhang, Min | Tang, Yang | Zeng, Min

Article Type: Research Article

Abstract:  The denial-of-service (DoS) attacks block the communications of the power grids, which affects the availability of the measurement data for monitoring and control. In order to reduce the impact of DoS attacks on measurement data, it is essential to predict missing measurement data. Predicting technique with measurement data depends on the correlation between measurement data. However, it is impractical to install phasor measurement units (PMUs) on all buses owing to the high cost of PMU installment. This paper initializes the study on the impact of PMU placement on predicting measurement data. Considering the data availability, this paper proposes a scheme …for predicting states using the LSTM network while ensuring system observability by optimizing phasor measurement unit (PMU) placement. The optimized PMU placement is obtained by integer programming with the criterion of the node importance and the cost of PMU deployment. There is a strong correlation between the measurement data corresponding to the optimal PMU placement. A Long-Short Term Memory neural network (LSTM) is proposed to learn the strong correlation among PMUs, which is utilized to predict the unavailable measured data of the attacked PMUs. The proposed method is verified on an IEEE 118-bus system, and the advantages compared with some conventional methods are also illustrated. Show more

Keywords: Integer linear programming, DoS attacks, deep learning, state prediction

DOI: 10.3233/JIFS-212593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5957-5971, 2022

Authors: Lv, Zhaoming | Peng, Rong

Article Type: Research Article

Abstract: The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious …population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems. Show more

Keywords: Meta-heuristic algorithms, grasshopper optimization algorithm, solitarious and gregarious states, chemotaxis operator, ontology matching

DOI: 10.3233/JIFS-212633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5973-5986, 2022

Authors: Cheng, Ru | Wang, Lukun | Wei, Mingrun

Article Type: Research Article

Abstract: Finer-grained local features play a supplementary role in the description of pedestrian global features, and the combination of them has been an essential solution to improve discriminative performances in person re-identification (PReID) tasks. The existing part-based methods mostly extract representational semantic parts according to human visual habits or some prior knowledge and focus on spatial partition strategies but ignore the significant influence of channel information on PReID task. So, we proposed an end-to-end multi-branch network architecture (MCSN) jointing multi-level global fusion features, channel features and spatial features in this paper to better learn more diverse and discriminative pedestrian features. It …is worth noting that the effect of multi-level fusion features on the performance of the model is taken into account when extracting global features. In addition, to enhance the stability of model training and the generalization ability of the model, the BNNeck and the joint loss function strategy are applied to all vector representation branches. Extensive comparative evaluations are conducted on three mainstream image-based evaluation protocols, including Market-1501, DukeMTMC-ReID and MSMT17, to validate the advantages of our proposed model, which outperforms previous state-of-the-art in ReID tasks. Show more

Keywords: Person re-identification, multi-branch deep network, multi-level global fusion feature, spatial-channel partition

DOI: 10.3233/JIFS-212656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5987-6001, 2022

Authors: Qiu, Chenye | Liu, Ning

Article Type: Research Article

Abstract: This paper proposes a novel two layer differential evolutionary algorithm with multi-mutation strategy (TLDE) for solving the economic emission dispatch (EED) problem involving random wind power. In recent years, renewable energy such as wind power is more and more participated in the power systems to address the problems of fossil energy shortage and environmental pollution. Hence, the EED problem with the availability of random wind power is investigated in this paper. Due to the uncertain nature of wind speed, the Weibull probability distribution function is used to model the random wind power. In order to improve the search ability, TLDE …divides the population into two layers according to the fitness ranking, and individuals in the two layers are treated differently to fully investigate their own potential. The two layers can cooperate with each other to further enhance the search performance by utilizing an information sharing strategy. Also, an adaptive restart scheme is introduced to avoid falling into stagnation. The performance of the proposed TLDE is testified on the 40 units system with 2 modified wind turbines. The experimental results demonstrate that the TLDE method can achieve precise dispatch strategy in EED problem with random wind power. Show more

Keywords: Economic emission dispatch, wind power, differential evolution, mutation operator, two layer structure

DOI: 10.3233/JIFS-212735

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6003-6016, 2022

Authors: Wang, Zhi | Song, Shufang | Wei, Hongkui

Article Type: Research Article

Abstract: When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution of solution set simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed …for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils. Show more

Keywords: Teaching-learning-based optimization (TLBO), multi-objective optimization, convergence, distribution, airfoils

DOI: 10.3233/JIFS-212743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6017-6026, 2022

Authors: Shen, Yonghong

Article Type: Research Article

Abstract: In the present paper, the notion of the linearly correlated difference for linearly correlated fuzzy numbers is introduced. Especially, the linearly correlated difference and the generalized Hukuhara difference are coincident for interval numbers or even symmetric fuzzy numbers. Accordingly, an appropriate metric is induced by using the norm and the linearly correlated difference in the set of linearly correlated fuzzy numbers. Based on the symmetry of the basic fuzzy number, the linearly correlated derivative is proposed by the linearly correlated difference of linearly correlated fuzzy number-valued functions. In both non-symmetric and symmetric cases, the equivalent characterizations of the linearly correlated …differentiability of a linearly correlated fuzzy number-valued function are established, respectively. Moreover, it is shown that the linearly correlated derivative is consistent with the generalized Hukuhara derivative for interval-valued functions. Show more

Keywords: Fuzzy numbers, Linearly correlated difference, Linearly correlated derivative, Canonical form, Linearly correlated fuzzy number-valued functions

DOI: 10.3233/JIFS-212908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6027-6043, 2022

Authors: Lu, Haishu | Li, Rong

Article Type: Research Article

Abstract: In this paper, based on the KKM method, we prove a new fuzzy fixed-point theorem in noncompact CAT(0) spaces. As applications of this fixed-point theorem, we obtain some existence theorems of fuzzy maximal element points. Finally, we utilize these fuzzy maximal element theorems to establish some new existence theorems of Nash equilibrium points for generalized fuzzy noncooperative games and fuzzy noncooperative qualitative games in noncompact CAT(0) spaces. The results obtained in this paper generalize and extend many known results in the existing literature.

Keywords: CAT(0) space, fuzzy fixed point, fuzzy maximal element, fuzzy noncooperative game, Nash equilibrium point

DOI: 10.3233/JIFS-212194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6045-6062, 2022

Authors: Sindhiya Devi, R. | Perumal, B. | Pallikonda Rajasekaran, M.

Article Type: Research Article

Abstract: In today’s world, Brain Tumor diagnosis plays a significant role in the field of Oncology. The earlier identification of brain tumors increases the compatibility of treatment of patients and offers an efficient diagnostic recommendation from medical practitioners. Nevertheless, accurate segmentation and feature extraction are the vital challenges in brain tumor diagnosis where the handling of higher resolution images increases the processing time of existing classifiers. In this paper, a new robust weighted hybrid fusion classifier has been proposed to identify and classify the tumefaction in the brain which is of the hybridized form of SVM, NB, and KNN (SNK) classifiers. …Primarily, the proposed methodology initiates the preprocessing technique such as adaptive fuzzy filtration and skull stripping in order to remove the noises as well as unwanted regions. Subsequently, an automated hybrid segmentation strategy can be carried out to acquire the initial segmentation results, and then their outcomes are compiled together using fusion rules to accurately localize the tumor region. Finally, a Hybrid SNK classifier is implemented in the proposed methodology for categorizing the type of tumefaction in the brain. The hybrid classifier has been compared with the existing state-of-the-art classifier which shows a higher accuracy result of 99.18% while distinguishing the benign and malignant tumors from brain Magnetic Resonance (MR) images. Show more

Keywords: Adaptive fuzzy filter, brain MR images, tumor diagnosis, hybrid classifier, segmentation

DOI: 10.3233/JIFS-212200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6063-6078, 2022

Authors: Cao, Yukun | Miao, Zeyu

Article Type: Research Article

Abstract: Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the …antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE . Show more

DOI: 10.3233/JIFS-212374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6079-6089, 2022

Authors: Gayathri, R. | Babitha Lincy, R.

Article Type: Research Article

Abstract: The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable …arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version. Show more

Keywords: Handwritten character recognition, inception-v3, tamil language, transfer learning

DOI: 10.3233/JIFS-212378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6091-6102, 2022

Authors: Vaishnavi, V. | Suveetha Dhanaselvam, P.

Article Type: Research Article

Abstract: The study of neonatal cry signals is always an interesting topic and still researcher works interminably to develop some module to predict the actual reason for the baby cry. It is really hard to predict the reason for their cry. The main focus of this paper is to develop a Dense Convolution Neural network (DCNN) to predict the cry. The target cry signal is categorized into five class based on their sound as “Eair”, “Eh”, “Neh”, “Heh” and “Owh”. Prediction of these signals helps in the detection of infant cry reason. The audio and speech features (AS Features) were exacted …using Mel-Bark frequency cepstral coefficient from the spectrogram cry signal and fed into DCNN network. The systematic DCNN architecture is modelled with modified activation layer to classify the cry signal. The cry signal is collected in different growth phase of the infants and tested in proposed DCNN architecture. The performance of the system is calculated through parameters accuracy, specificity and sensitivity are calculated. The output of proposed system yielded a balanced accuracy of 92.31%. The highest accuracy level 95.31%, highest specificity level 94.58% and highest sensitivity level 93% attain through proposed technique. From this study, it is concluded that the proposed technique is more efficient in detecting cry signal compared to the existing techniques. Show more

Keywords: Infant cry signal, spectrogram images, audio and speech features, mel-bark frequency cepstral domain, dense convolution neural network

DOI: 10.3233/JIFS-212473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6103-6116, 2022

Authors: Fan, Jianping | Zhou, Wei | Wu, Meiqin

Article Type: Research Article

Abstract: Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the …degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811. Show more

Keywords: Dempster-Shafer evidence theory, conflicting evidences, information fusion, Lance distance, non-extensive entropy

DOI: 10.3233/JIFS-212489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6117-6129, 2022

Authors: Lingaraj, Vanitha | Kaliannan, Kalaiselvi | Rohini, Venmathi Asirvatham | Thevasigamani, Rajesh Kumar | Chinnasamy, Karthikeyan | Durairaj, Vijendra Babu | Periasamy, Keerthika

Article Type: Research Article

Abstract: Flow state assessment is essential to understand the involvement of an individual in a particular task assigned. If there is no involvement in the task assigned then the individual in due course of time gets affected either by psychological or physiological illnesses. The National Crime Records Bureau (NCRB) statistics show that non-involvement in the task drive the individual to a depression state and subsequently attempt for suicide. Therefore, it is essential to determine the decrease in flow level at an earlier stage and take remedial steps to recover them. There are many invasive methods to determine the flow state, which …is not preferred and the commonly used non-invasive method is the questionnaire and interview method, which is the subjective and retroactive method, and hence chance to fake the result is more. Hence, the main objective of our work is to design an efficient flow level measurement system that measures flow in an objective method and also determines real-time flow classification. The accuracy of classification is achieved by designing an Expert Active k-Nearest Neighbour (EAkNN) which can classify the individual flow state towards the task assigned into nine states using non-invasive physiological Electrocardiogram (ECG) signals. The ECG parameters are obtained during the performance of FSCWT. Thus this work is a combination of psychological theory, physiological signals and machine learning concepts. The classifier is designed with a modified voting rule instead of the default majority voting rule, in which the contribution probability of nearest points to new data is considered. The dataset is divided into two sets, training dataset 75%and testing dataset 25%. The classifier is trained and tested with the dataset and the classification efficiency is 95%. Show more

Keywords: Stroop colour test, Flow Stroop Colour Word Test, expert active k-Nearest neighbour, flow state, electrocardiogram

DOI: 10.3233/JIFS-212504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6131-6144, 2022

Authors: Abdur Rahman, Usama | Jayakumar, C.

Article Type: Research Article

Abstract: Wireless visual sensor networks (WVSNs) have emerged as a strategic inter disciplinary category of WSN with its visual sensor based intelligence that has garnered considerable attention. The growing demand for energy efficient and maximized life time networks in highly critical applications like surveillance, military and medicine has opened up more prospects as well as challenges in the deployment of WVSNs. Multi-hop communication in WVSN results in overloading of intermediate sensor nodes due to its dual function in the network which results in hotspot effect. This can be mitigated with the help of mobile sinks and rendezvous points based route design. …But mobile sinks has to visit every cluster head to gather data which results in longer traversal path and higher latency and power consumption related issues if not addressed properly will impact the performance of the network. Our objective is to analyze and determine the optimal trajectory for mobile sink node traversal with the help of a high quality transmission architecture integrated with reinforcement learning and isolation forest based anomaly detection to propose an energy efficient meta-heuristic approach to enhance the performance of network by reducing the latency and securing the network against possible attacks. Show more

Keywords: Wireless visual sensor networks, mobile sinks, hotspot, reinforcement learning, isolation forest, anomaly detection, applications of WVSN

DOI: 10.3233/JIFS-212557

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6145-6157, 2022

Authors: Jayaseelan, Samuel Manoharan | Gopal, Sakthivel Thirumalai | Muthu, Sangeetha | Selvaraju, Sivamani | Patel, Md Saad

Article Type: Research Article

Abstract: Image enhancement is one of the most critical stages towards any image processing application. The outcome of image enhancement determines the accuracy and precise nature of the overall output from the image processing under interest. This research paper has shown specific interests towards enhancement of Scanned Electron Microscopic (SEM) images which are primarily concerned with projection of fine details exist in internal details of surfaces, metals, fine powders, fibers etc. These fine details play a dominant role in detection of minute cracks, artifacts, progressing faults, texture of powders, their coarseness or fineness, internal details of fibers in forensics. However, due …to the image capturing process which is through conventional camera-based models, noise tends to be a major source in degrading or blurring the underlying vital information. A cross neighbor fuzzy filter is a hybrid combination called Hybrid Fuzzy Based Cross Neighbor Filtering (HF-CNF) which is proposed in this research paper in order to minimize impulse and random noise to a great extent also to fine tune the further processing stages. The proposed method has been subjected to extensive analysis by comparison with state of art and recent benchmark methods and superior performance justified in terms of several validation metrics. Show more

Keywords: Image enhancement, scanned electronic microscopic, images, fuzzy filter, morphological processing, peak signal to noise ratio

DOI: 10.3233/JIFS-212561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6159-6169, 2022

Authors: Vijayanand, S. | Saravanan, S.

Article Type: Research Article

Abstract: Due to the growth of Big Data (BD) storage and access in cloud computing infrastructure, the detection of anomalies for Cloud Servers (CSs) is essential to ensure data confidentiality. Over the past decades, different security systems have been designed based on various methods like encryption, Access Policy (AP) control schemes, signcryption and so on. Among many security systems, a new Improved NTRU (INTRU) decryption based on the AP strategy has been suggested to secure the BD processed by the CSs. Also, the shared secret data was authenticated to defend the clients from anomalies in the cloud. But, the AP upgrade …must not degrade the confidentiality of storing information, reveal trust in the CS or cause any different security challenges. It is not considered that such security challenges occur when the data owner shares its data with many CSs. Hence in this article, an INTRU with Detecting Anomalous in CS (INTRU-DACS) system is proposed that employs a deep learning-based Anomaly Detection System (ADS) to handle and secure the BD stored in the CSs. The main goal of this method is to effectively identify the abnormalities in the real world by the conduct utilization, i.e., the System Call Identifier Sequences (SCISs) created from CSs in which these conducts are associated with BD. Initially, effective data summarization is constructed via different feature states to analyze the SCISs of specific durations. After that, an anomaly identification algorithm is proposed to train and test the streaming of raw SC sequences. This observable SCs execution task of CSs is gathered from log files. The variations of such SCISs having a specified duration are random for usual and unusual sequences. So, the fact of current normal and abnormal services is recognized regarding their SCISs. Such normal and abnormal behavioral states are learned from Convolutional Neural Network-Hidden Markov Model (CNNHMM) classifier to identify the anomalies in CSs. But, it is still a challenging process because of the patterns of usual and unusual events. The performance is not effective since it models only the conduct of a huge number of SCISs created from a single CS. As a result, a Secure Access Control Scheme with DACS (SACS-DACS) system is proposed in which a Multidimensional Feature Misbehavior Server Detection method (MFMSD) is introduced for detecting anomalies in multiple CSs. In this method, large-scale SCISs of multiple CSs are extracted, including different features such as network traffic sequence features, CPU energy usage and memory usage from host logs. These extracted multidimensional features are fed to the CNNHMM that identifies the anomalies and maximizes the detection accuracy. At last, the simulation results demonstrate the effectiveness of the SACS-DACS and INTRU-DACS as compared to the INTRU. Show more

Keywords: Big data, cloud computing, access control, improved NTRU, anomaly detection, CNN, HMM

DOI: 10.3233/JIFS-212572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6171-6181, 2022

Authors: Hamdi, Mohammed

Article Type: Research Article

Abstract: With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning …techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, We have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error. Show more

Keywords: Machine learning, software classification, software sustainability, data analytics

DOI: 10.3233/JIFS-212600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6183-6194, 2022

Authors: George, Sophia Jasmine | Ramaraju, Satish Kumar | Venkataraman, Vanitha | Kaliannan, Thenmalar | Kumaravel, Umadevi | Veerasundaram, M.

Article Type: Research Article

Abstract: Conventionally in many countries, electrical power industry is organized as vertically integrated system. Under this system, large utilities are authoritative for the generation, transmission and distribution of electrical power. Such utilities are governed by the rules and regulations of the government and are forced to operate within the prescribed guidelines with minimal profit. This confirmation causes an ineffective and sluggish perspective in power industry with a lack of technical innovation, competent management and customer satisfaction. To overcome these deficiencies, power sector around the globe is getting restructured. This paper addresses an inevitable technical disputes occurring in deregulated environment i.e., transmission …congestion which has an adverse effect on system security, increase in electricity pricing and line losses. Flexible AC Transmission System (FACTS) is a boon to the power sector which helps in a better and reliable power flow through the transmission lines. The problem is articulated as a multi objective function satisfying all the operational and security limits. Three heuristic algorithms namely Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS) and hybrid Quantum based PSO-Bio-geography based krill herd optimization (Q-PSOBBKH) algorithms were applied in finding solution to this complex congestion problem. To study the effectiveness of the proposed objective, IEEE 14 bus system was considered as the test system. In order to validate the proposed methodology three congestion cases i.e. bilateral transaction, multilateral transaction and overloading were imposed on the test bus system. Simulation was carried out in MATLAB. Show more

Keywords: Deregulated power system, particle swarm optimization, symbiotic organism search algorithm, hybrid quantum based PSO, bio-geography based Krill Herd Algorithm

DOI: 10.3233/JIFS-212717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6195-6208, 2022

Authors: Susmi, S. Jacophine

Article Type: Research Article

Abstract: Gene expression profiles are sequences of numbers, and the need to analyze them has now increased significantly. Gene expression data contain a large number of genes and models used for cancer classification. As the wealth of these data being produced, new prediction, classification and clustering techniques are applied to the analysis of the data. Although there are a number of proposed methods with good results, there is still limited diagnostics and a lot of problems still to be solved. To solve the difficulty, in this paper, an efficient gene expression data classification is proposed. To predict the cancer class of …patients from the gene expression profile, this paper presents a novel classification framework in the manner of three steps namely, Pre-processing, feature selection and classification. In pre-processing, missing value is filled and redundant data are removed. To attain the enhanced classification outcomes, the important features are selected from the database with the help of Adaptive Salp Swarm Optimization (ASSO) algorithm. Then, the selected features are given to the multi kernel SVM (MKSVM) to classify the gene expression data namely, BRCA, KIRC, COAD, LUAD and PRAD. The performance of proposed methodology is analyzed in terms of different metrics namely, accuracy, sensitivity and specificity. The performance of proposed methodology is 4.5% better than existing method in terms of accuracy. Show more

Keywords: Adaptive salp swarm optimization, gene expression data, multi kernel SVM, feature selection

DOI: 10.3233/JIFS-212733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6209-6220, 2022

Authors: Fernandes, Filipe | Stefenon, Stéfano Frizzo | Seman, Laio Oriel | Nied, Ademir | Ferreira, Fernanda Cristina Silva | Subtil, Maria Cristina Mazzetti | Klaar, Anne Carolina Rodrigues | Leithardt, Valderi Reis Quietinho

Article Type: Research Article

Abstract: The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the …contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models. Show more

Keywords: Long short-term memory, COVID-19, spreading viruses

DOI: 10.3233/JIFS-212788

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6221-6234, 2022

Authors: Davids, D. Minola | Christopher, C. Seldev

Article Type: Research Article

Abstract: The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method’s five steps. Finally a post-processing step is utilized. For recognizing the proposed method’s effectiveness, the results are then contrasted with the existent …methods. Show more

Keywords: Video summarization, Levy Flight (LF) and opposition-based learning, Coyote Optimization Algorithm (LFOB-COA), Bi-directional Long Short-term Memory (BLSTM), Jaccard Similarity-centered Fuzzy C-Means (JSFCM)

DOI: 10.3233/JIFS-212800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6235-6243, 2022

Authors: Ajam, Leila | Nodehi, Ali | Mohamadi, Hosein

Article Type: Research Article

Abstract: Literature in recent years has introduced several studies conducted to solve the target coverage problem in wireless sensor networks (WSNs). Sensors are conventionally assumed as devices with only a single power level. However, real applications may involve sensors with multiple power levels (i.e., multiple sensing ranges each of which possesses a unique power consumption). Consequently, one of the key problems in WSNs is how to provide a full coverage on all targets distributed in a network containing sensors with multiple power levels and simultaneously prolong the network lifetime as much as possible. This problem is known as Maximum Network Lifetime …With Adjustable Ranges (MNLAR) and its NP-completeness has been already proved. To solve this problem, we proposed an efficient hybrid algorithm containing Genetic Algorithm (GA) and Tabu Search (TS) aiming at constructing cover sets that consist of sensors with appropriate sensing ranges to provide a desirable coverage for all the targets in the network. In our hybrid model, GA as a robust global searching algorithm is used for exploration purposes, while TS with its already-proved local searching ability is utilized for exploitation purposes. As a result, the proposed algorithm is capable of creating a balance between intensification and diversification. To solve the MNLR problem in an efficient way, the proposed model was also enriched with an effective encoding method, genetic operators, and neighboring structure. In the present paper, different experiments were performed for the purpose of evaluating how the proposed algorithm performs the tasks defined. The results clearly confirmed the superiority of the proposed algorithm over the greedy-based algorithm and learning automata-based algorithm in terms of extending the network lifetime. Moreover, it was found that the use of multiple power levels altogether caused the extension of the network lifetime. Show more

Keywords: Wireless sensor networks, cover set formation, scheduling algorithms, genetic algorithm, Tabu search

DOI: 10.3233/JIFS-202736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6245-6255, 2022

Authors: Pankajashan, Savaridassan | Maragatham, G. | Kirthiga Devi, T.

Article Type: Research Article

Abstract: Anomaly-based detection is coupled with recognizing the uncommon, to catch the unusual activity, and to find the strange action behind that activity. Anomaly-based detection has a wide scope of critical applications, from bank application security to regular sciences to medical systems to marketing apps. Anomaly-based detection adopted by various Machine Learning techniques is really a type of system that consists of artificial intelligence. With the ever-expanding volume and new sorts of information, for example, sensor information from an incontestably enormous amount of IoT devices and from network flow data from cloud computing, it is implicitly understood without surprise that there …is a developing enthusiasm for having the option to deal with more conclusions automatically by means of AI and ML applications. But with respect to anomaly detection, many applications of the scheme are simply the passion for detection. In this paper, Machine Learning (ML) techniques, namely the SVM, Isolation forest classifiers experimented and with reference to Deep Learning (DL) techniques, the proposed DA-LSTM (Deep Auto-Encoder LSTM) model are adopted for preprocessing of log data and anomaly-based detection to get better performance measures of detection. An enhanced LSTM (long-short-term memory) model, optimizing for the suitable parameter using a genetic algorithm (GA), is utilized to recognize better the anomaly from the log data that is filtered, adopting a Deep Auto-Encoder (DA). The Deep Neural network models are utilized to change over unstructured log information to training ready features, which are reasonable for log classification in detecting anomalies. These models are assessed, utilizing two benchmark datasets, the Openstack logs, and CIDDS-001 intrusion detection OpenStack server dataset. The outcomes acquired show that the DA-LSTM model performs better than other notable ML techniques. We further investigated the performance metrics of the ML and DL models through the well-known indicator measurements, specifically, the F-measure, Accuracy, Recall, and Precision. The exploratory conclusion shows that the Isolation Forest, and Support vector machine classifiers perform roughly 81%and 79%accuracy with respect to the performance metrics measurement on the CIDDS-001 OpenStack server dataset while the proposed DA-LSTM classifier performs around 99.1%of improved accuracy than the familiar ML algorithms. Further, the DA-LSTM outcomes on the OpenStack log data-sets show better anomaly detection compared with other notable machine learning models. Show more

Keywords: Anomaly detection, classification, deep learning, hyperparameter optimization, long short term memory model, artificial neural networks, openstack cloud

DOI: 10.3233/JIFS-201707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6257-6271, 2022

Authors: Zhou, Ya | Gao, Jinding

Article Type: Research Article

Abstract: In order to solve some optimization problems with multi-local optimal solutions, a plague infectious disease optimization (PIDO) algorithm is proposed by the dynamic model of plague infectious disease with pulse vaccination and time delay. In this algorithm, it is assumed that there are several villagers living in a village, each villager is characterized by some characteristics. The plague virus is prevalent in the village, and the villagers contract the infectious disease through effective contact with sick rats. The plague virus attacks is the few characteristics of the human body. Under the action of the plague virus, the growth status of …each villager will be randomly transformed among 4 states of susceptibility, exposure, morbidity and recovery, thus a random search is achieved for the global optimal solution. The physical strength degree of villagers is described by the human health index (HHI). The higher the villager’s HHI index, the stronger the physique and the higher the surviving likelihood. 9 operators (S_S, S_E, E_E, E_I, E_R, I_I, I_R, R_R, R_S) are designed in the PIDO algorithm, and each operator only deals with the 1/1000∼1/100 of the total number of variables each time. The case study results show that PIDO algorithm has the characteristics of fast search speed and global convergence, and it is suitable for solving global optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization, global optimization, plague transmission dynamic model, random search

DOI: 10.3233/JIFS-211092

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6273-6291, 2022

Authors: Karthika, A. | Subramanian, R. | Karthik, S.

Article Type: Research Article

Abstract: Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. …In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM. Show more

Keywords: Focal cortical dysplasia, T1- MPRAGE and T2- FLAIR, S2 feature extraction, lesion segmentation, recurrent neural network

DOI: 10.3233/JIFS-212463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6293-6306, 2022