Journal of Intelligent & Fuzzy Systems - Volume 44, issue 5

Authors: Wu, Yisheng | Jin, Xin | Huang, Haiping

Article Type: Research Article

Abstract: This paper focuses on the task of Point-of-interest (POI) recommendation whose goal is to generate a list of POIs for a target user based on his or her history check-in records. Different from the traditional recommendation tasks (e.g., movie recommendation), there are many factors, like temporal factor and geographical factor, which make a great influence on user preference. Though existing POI recommendation methods tend to model the user preference from temporal factor, geographical factor or social factor, they fail to model these factors into a jointly model, leading to learn the suboptimal user preference. To tackle this issue, we propose …a Muti-channel Graph Attention Network (MGAN) for POI recommendation which learns the user preference from multiple aspects in a unify model. Specifically, MGAN first constructs several graphs with corresponding contextual features to capture the user preference from temporal, geographical, semantic and social aspects. Then MGAN leverages the graph attention networks to learn the representations of POIs from these graphs. Finally, MGAN estimates the user preference from the history check-in records and other similar users via the learned POI representations. We conduct extensive experiments on real-world datasets. And the results indicate that our proposed MGAN outperforms mainstream POI recommendation methods. Show more

Keywords: Point-of-interest, recommendation, graph attention network, temporal, geographical

DOI: 10.3233/JIFS-222952

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8375-8385, 2023

Authors: Deng, Xue | Geng, Fengting | Fang, Wen | Huang, Cuirong | Liang, Yong

Article Type: Research Article

Abstract: By considering the stock market’s fuzzy uncertainty and investors’ psychological factors, this paper studies the portfolio performance evaluation problems with different risk attitudes (optimistic, pessimistic, and neutral) by the Data Envelopment Analysis (DEA) approach. In this work, the return rates of assets are characterized as trapezoidal fuzzy numbers, whose membership functions with risk attitude parameters are described by exponential expression. Firstly, these characteristics with risk attitude are strictly derived including the possibilistic mean, variance, semi-variance, and semi-absolute deviation based on possibility theory. Secondly, three portfolio models (mean-variance, mean-semi-variance, and mean-semi-absolute-deviation) with different risk attitudes are proposed. Thirdly, we prove the …real frontiers determined by our models are concave functions through mathematical theoretical derivation. In addition, two novel indicators are defined by difference and ratio formulas to characterize the correlation between DEA efficiency and portfolio efficiency. Finally, numerical examples are given to verify the feasibility and effectiveness of our model. No matter what risk attitude an investor holds, the DEA can generate approximate real frontiers. Correlation analysis indicates that our proposed approach outperforms in evaluating portfolios with risk attitudes. At the same time, our model is an improvement of Tsaur’s work (2013) which did not study the different risk measures, and an extension of Chen et al.’s work (2018) which only considered risk-neutral attitude. Show more

Keywords: Portfolio performance evaluation, risk attitude, data envelopment analysis (DEA), possibility theory, real frontier

DOI: 10.3233/JIFS-223543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8387-8411, 2023

Authors: Lin, Sihong | Zhang, Kunbin | Guan, Dun | He, Linjie | Chen, Yumin

Article Type: Research Article

Abstract: Intrusion detection systems have become one of the important tools for network security due to the frequent attacks brought about by the explosive growth of network traffic. Autoencoder is an unsupervised learning model with a neural network structure. It has a powerful feature learning capability and is effective in intrusion detection. However, its network construction suffers from overfitting and gradient disappearance problems. Traditional granular computing methods have advantages in solving such problems, but the process is relatively complex, the granularity dimension is high, and the computational cost is large, which is not suitable for application in intrusion detection systems. To …address these problems, we propose a novel autoencoder: Granular AutoEncoders (GAE). The granulation reference set is constructed by random sampling. The granulation of training samples is based on single-feature similarity in a reference set to form granules. The granulation of multiple features results in granular vectors. Some operations of granules are defined. Furthermore, we propose some granular measures, including granular norms and granular loss functions. The GAE is further applied to the field of intrusion detection by designing an anomaly detection algorithm based on the GAE. The algorithm determines whether the network flows are anomalous by comparing the difference between an input granular vector and its output granular vector that is reconstructed by the GAE. Finally, some experiments are conducted using an intrusion detection dataset, comparing multiple metrics in terms of precision, recall, and F1-Score. The experimental results validate the correctness and effectiveness of the intrusion detection method based on GAE. And contrast experiments show that the proposed method has stronger ability for detecting anomalies than the correlation algorithms. Show more

Keywords: Granular computing, intrusion detection, autoencoder, deep Learning, anomaly detection

DOI: 10.3233/JIFS-223649

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8413-8424, 2023

Authors: Shanmukha, M.C. | Lee, Sokjoon | Usha, A. | Shilpa, K.C. | Azeem, Muhammad

Article Type: Research Article

Abstract: Topological indices and coindices are numerical invariants that relate to quantitative structure property/activity connections. The purpose of topological indices and coindices were introduced to draw the data related to chemical graphs with respect to adjacent & non adjacent pairs of vertex degrees respectively. These indices equip the researchers with a lot of information related to the properties and structure of the chemical compound. In this article, CoM-polynomials for molecular graph of linear and multiple Anthracene are computed from which eleven degree based topological coindices are derived.

Keywords: CoM-polynomial, topological coindex, anthracene

DOI: 10.3233/JIFS-223947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8425-8436, 2023

Authors: Devi Sri Nandhini, M. | Pradeep, Gurunathan

Article Type: Research Article

Abstract: Sentiment analysis is the contextual analysis of words to retrieve the social opinion of a brand which aids the business firms/institutions to know the impact of their products/services. It is habitual that users may express different opinions regarding various aspects of the same entity. Therefore, there is a strong demand to extract all the opinion targets may those be explicitly mentioned aspects or implicit aspects which are not directly specified in the reviews. In this context, comparatively less amount of work has been carried out concerning implicit aspect detection. The proposed work has been dedicated solely to extracting the implicit …aspects using a dynamic approach based on the type of sentence containing the clues for implicit aspect. A novel aspect pointer compendium (APC) has been developed that catalyzes the task of finding implicit aspects to the maximum extent possible. The APC incorporates the usage of different types of clues such as synonym clues, context clues, phrase clues, and partially implicit aspects that aid in the detection of hidden aspects. Based on this idea, the proposed work classifies the implicit aspect sentences into six types and proceeds with the task in an efficient manner. To strengthen the task of implicit aspect detection, the proposed work utilizes a hybrid technique encompassing APC, domain-specific adjective-noun collocation list (DSANCL), and the explicit aspect-opinion word pairs extracted from the reviews. The experimentation and results reveal that the proposed hybrid approach shows a good improvement in terms of the efficacy of extracting the implicit aspects as compared to the existing baseline models. Show more

Keywords: Implicit aspect detection, aspect pointer compendium, partially implicit aspects

DOI: 10.3233/JIFS-222927

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8437-8450, 2023

Authors: Yang, Yali | Zhang, Tianwei

Article Type: Research Article

Abstract: This paper firstly establishes the discrete-time lattice networks for nonlocal stochastic competitive neural networks with reaction diffusions and fuzzy logic by employing a mix techniques of finite difference to space variables and Mittag-Leffler time Euler difference to time variable. The proposed networks consider both the effects of spatial diffusion and fuzzy logic, whereas most of the existing literatures focus only on discrete-time networks without spatial diffusion. Firstly, the existence of a unique ω-anti-periodic in distribution to the networks is addressed by employing Banach contractive mapping principle and the theory of stochastic calculus. Secondly, global exponential convergence in mean-square sense to …the networks is discussed on the basis of constant variation formulas for sequences. Finally, an illustrative example is used to show the feasible of the works in the current paper with the help of MATLAB Toolbox. The work in this paper is pioneering in this regard and it has created a certain research foundations for future studies in this area. Show more

Keywords: Lattice, reaction diffusion, stochastic, finite difference method

DOI: 10.3233/JIFS-223495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8451-8470, 2023

Authors: Comakli Sokmen, Özlem | yılmaz, Mustafa

Article Type: Research Article

Abstract: The hierarchical Chinese postman problem (HCPP) aims to find the shortest tour or tours by passing through the arcs classified according to precedence relationship. HCPP, which has a wide application area in real-life problems such as shovel snow and routing patrol vehicles where precedence relations are important, belongs to the NP-hard problem class. In real-life problems, travel time between the two locations in city traffic varies due to reasons such as traffic jam, weather conditions, etc. Therefore, travel times are uncertain. In this study, HCPP was handled with the chance-constrained stochastic programming approach, and a new type of problem, the …hierarchical Chinese postman problem with stochastic travel times, was introduced. Due to the NP-hard nature of the problem, the developed mathematical model with stochastic parameter values cannot find proper solutions in large-size problems within the appropriate time interval. Therefore, two new solution approaches, a heuristic method based on the Greedy Search algorithm and a meta-heuristic method based on ant colony optimization were proposed in this study. These new algorithms were tested on modified benchmark instances and randomly generated problem instances with 817 edges. The performance of algorithms was compared in terms of solution quality and computational time. Show more

Keywords: Optimization, arc routing problems, chance-constrained stochastic programming, new efficient algorithm, hierarchical Chinese postman problem

DOI: 10.3233/JIFS-222097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8471-8492, 2023

Authors: Zhong, Cheng | Wang, Jie-Sheng | Liu, Yu

Article Type: Research Article

Abstract: The rolling bearing fault diagnosis is affected by industrial environmental noise and other factors, leading to the existence of some redundant components after signal decomposition. At the same time, the existence of the modal aliasing phenomenon in empirical mode decomposition (EMD) and the relevant improved algorithms also leads to the existence of many invalid features in the components. These phenomena have great influence on the bearing fault diagnosis. So a rolling bearing bidirectional-long short term memory (Bi-LSTM) fault diagnosis method was proposed based on segmented interception auto regressive (SIAR) spectrum analysis and information fusion. The ensemble empirical mode decomposition (EEMD), …the complementary ensemble empirical mode decomposition (CEEMD) and the robust EMD (REMD) algorithms decompose the rolling bearing fault signals, and AR spectrum analysis is performed on the obtained components respectively. By comparing the AR spectra of the components corresponding to different fault locations, the effective AR spectral values are intercepted as the eigenvalues of the data, and finally all the eigenvalues are fused to achieve the purpose of screening effective features more efficiently so as to reduce the impact of feature redundancy caused by mode aliasing on neural network training. Then the Bi-LSTM neural network was used as a rolling bearing fault diagnosis classifier, and the simulation experiments were conducted based on the rolling bearing fault signal data from Case Western Reserve University to verify the effectiveness of the proposed feature extraction and fault diagnosis method. Show more

Keywords: Rolling bearing, fault diagnosis, AR spectrum analysis, information fusion, empirical mode decomposition, LSTM

DOI: 10.3233/JIFS-222476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8493-8519, 2023

Authors: Rajesh, Thota Radha | Rajendran, Surendran | Alharbi, Meshal

Article Type: Research Article

Abstract: Multi-agent reinforcement learning (MARL) is a generally researched approach for decentralized controlling in difficult large-scale autonomous methods. Typical features create RL system as an appropriate candidate to develop powerful solutions in variation of healthcare fields, whereas analyzing decision or treatment systems can be commonly considered by a prolonged and sequential process. This study develops a new Penguin Search Optimization Algorithm with Multi-agent Reinforcement Learning for Disease Prediction and Recommendation (PSOAMRL-DPR) model. This research aimed to use a unique PSOAMRL-DPR algorithm to forecast diseases based on data collected from networks and the cloud by a mobile agent. The major intention of …the proposed PSOAMRL-DPR algorithm is to identify the presence of disease and recommend treatment to the patient. The model manages the agent container with different mobile agents and fetched data from dissimilar locations of the network as well as cloud. For disease detection and prediction, the PSOAMRL-DPR technique exploits deep Q-network (DQN) technique. In order to tune the hyperparameters related to the DQN technique, the PSOA technique is used. The experimental result analysis of the PSOAMRL-DPR technique is validated on heart disease dataset. The simulation values demonstrate that the PSOAMRL-DPR technique outperforms the other existing methods. Show more

Keywords: Multi-agent reinforcement learning, penguin search optimization, deep Q-learning, disease prediction, treatment recommendation

DOI: 10.3233/JIFS-223933

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8521-8533, 2023

Authors: Wang, Jing | Cai, Qiang | Wang, Hongjun | Wei, Guiwu | Liao, Ningna

Article Type: Research Article

Abstract: Green supply chain management attaches great importance to the coordinated development of social economy and ecological environment, and requires enterprises to consider environmental protection factors in product design, packaging, procurement, production, sales, logistics, waste and recycling. Suppliers are the “source” of the entire supply chain, and the choice of green suppliers is the basis of green supply chain management, and their quality will directly affect the environmental performance of enterprises. The green supplier selection is a classical multiple attribute group decision making (MAGDM) problems. Interval-valued intuitionistic fuzzy sets (IVIFSs) are the extension of intuitionistic fuzzy sets (IFSs), and are utilized …to depict the complex and changeable circumstance. To better adapt to complex environment, the purpose of this paper is to construct a new method to solve the MAGDM problems for green supplier selection. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original MABAC method based on the cumulative prospect theory (CPT) is extended into IVIFSs (IVIF-CPT-MABAC) method for MAGDM issues. Meanwhile, the method to evaluate the attribute weighting vector is calculated by CRITIC method. Finally, a numerical example for green supplier selection has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new method’s effectiveness and stability. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval-valued intuitionistic fuzzy sets (IVIFSs), MABAC, cumulative prospect theory (CPT), CRITIC, green supplier selection

DOI: 10.3233/JIFS-224206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8535-8560, 2023

Authors: Gokul Pran, S. | Raja, Sivakami

Article Type: Research Article

Abstract: Network flaws are used by hackers to get access to private systems and data. This data and system access may be extremely destructive with losses. Therefore, this network intrusions detection is utmost significance. While investigating every feature set in the network, deep learning-based algorithms require certain inputs. That’s why, an Adaptive Artificial Neural Network Optimized with Oppositional Crow Search Algorithm is proposed for network intrusions detection (IDS-AANN-OCSA). The proposed method includes several phases, including feature selection, preprocessing, data acquisition, and classification. Here, the datas are gathered via CICIDS 2017 dataset. The datas are fed to pre-processing. During pre-processing, redundancy eradication …and missing value replacement is carried out with the help of random forest along Local least squares for removing uncertainties. The pre-processed datas are fed to feature selection to select better features. The feature selection is accomplished under hybrid genetic algorithm together with particle swarm optimization technique (GPSO). The selected features are fed to adaptive artificial neural network (AANN) for categorization which categorizes the data as BENIGN, DOS Hulk, PortScan, DDoS, DoS Golden Eye. Finally, the hyper parameter of adaptive artificial neural network is tuned with Oppositional Crow Search Algorithm (OCSA) helps to gain better classification of network intrusions. The proposed approach is activated in Python, and its efficiency is evaluated with certain performance metrics, like accuracy, recall, specificity, precision, F score, sensitivity. The performance of proposed approach achieves better accuracy 99.75%, 97.85%, 95.13%, 98.79, better sensitivity 96.34%, 91.23%, 89.12%, 87.25%, compared with existing methods, like One-Dimensional Convolutional Neural Network Based Deep Learning for Network Intrusion Detection (IDS-CNN-GPSO), An innovative network intrusion detection scheme (IDS-CNN-LSTM) and Application of deep learning to real-time Web intrusion detection (IDS-CNN-ML-AIDS) methods respectively. Show more

Keywords: Adaptive artificial neural network, feature selection, genetic particle swarm optimization, intrusion detection systems

DOI: 10.3233/JIFS-222120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8561-8571, 2023

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

Article Type: Research Article

Abstract: In many multi criteria group decision making problems, the individual evaluation values offered by experts are with uncertainties. Therefore, when assigning weights to those experts using preferences induced weights allocation, we can have two types of bi-polar preferences. The first one is the optimism-pessimism preference over evaluation values; the second one is the uncertainty aversion preference over the attached numerical certainty/uncertainty degrees. When performing preferences induced weights allocation, the certainty/uncertainty degrees will affect the optimism-pessimism preference induced weights allocation because the magnitudes of those evaluation values might not be the exact ones. Moreover, the importance of those experts in multi …criteria group decision making can also have influence over the two types of preference induced weights allocation processes, and the importance can also be with uncertainties and can be expressed using basic uncertain information. Therefore, to handle this situation with multiple inducing variables and uncertainties, we simultaneously consider the influence of the uncertainties attached to evaluation values and the influence of uncertain importance of experts, and thus we at the same time adopt the method of confidence threshold and the method of uncertain importance level function to propose some synthesized method to adjust the induced weights allocation processes. We also propose a complete multi criteria group decision making problems to show the feasibility and reasonability of the proposed decision model for the complex situation where both evaluation values and expert importance are expressed by basic uncertain information. Show more

Keywords: Aggregation operators, basic uncertain information, induced ordered weighted averaging operators, information fusion, multi criteria group decision making, uncertain decision making

DOI: 10.3233/JIFS-222590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8573-8583, 2023

Authors: Wang, Chong | Yang, Gongping | Huang, Yuwen | Liu, Yikun | Zhang, Yan

Article Type: Research Article

Abstract: Fruit detection is essential for harvesting robot platforms. However, complicated environmental attributes such as illumination variation and occlusion have made fruit detection a challenging task. In this study, a Transformer-based mask region-based convolution neural network (R-CNN) model for tomato detection and segmentation is proposed to address these difficulties. Swin Transformer is used as the backbone network for better feature extraction. Multi-scale training techniques are shown to yield significant performance gains. Apart from accurately detecting and segmenting tomatoes, the method effectively identifies tomato cultivars (normal-size and cherry tomatoes) and tomato maturity stages (fully-ripened, half-ripened, and green). Compared with existing work, the …method has the best detection and segmentation performance for these tomatoes, with mean average precision (mAP) results of 89.4% and 89.2%, respectively. Show more

Keywords: Mask R-CNN, Swin Transformer, tomato detection, instance segmentation

DOI: 10.3233/JIFS-222954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8585-8595, 2023

Authors: Amanulla Khan, M. | Sithi Shameem Fathima, S.M.H.

Article Type: Research Article

Abstract: Gait recognition is the process of recognizing a person based on their walking style. Each person’s walking gait is distinctive and cannot be imitated by others. However, the walking motion of a person will be changed based on their behaviour but their walking pattern doesn’t change. In this paper, a novel Clustering based Faster RCNN has been proposed to identify the single, double and multi-gait. The gait images from the publicly available dataset are pre-processed using Multi scale Retinex (MSR) to reduce the noise artifacts. The Faster RCNN is used for extracting the relevant features from the gait images via …the two modules namely CNN and RPN. The CNN layers extract the most relevant features as feature maps and RPN is used for creating the bounding boxes for the extracted features. Fuzzy K-means clustering is used to group the features based on their labels, and it specifies the features acquired using CNN and RPN as input. Finally, the Fast RCNN is employed for classifying the gait images into suspicious and non-suspicious walking pattern. The proposed Clustering based Faster RCNN net achieves the high accuracy rate of 98.74% and 99.19% for suspicious and non-suspicious walking pattern respectively. The proposed Clustering based Faster RCNN model was compared with other traditional models like CNN, U-net, Fab net and Fast R-CNN. The proposed Clustering based Faster RCNN model improves the overall accuracy of 8.86% 33.77% 3.12% and 5.48% better than mmGait, LSTM Net, STDNN and RNN respectively. Show more

Keywords: Gait recognition, deep learning, faster R-CNN, fuzzy K-means clustering, multi scale Retinex

DOI: 10.3233/JIFS-224114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8597-8606, 2023

Authors: Chen, Kejia | Wu, Qianqian | Yan, Minru | Li, Xuannan

Article Type: Research Article

Abstract: The purpose of this paper is to explore how port enterprises can scientifically select a better logistic service provider (LSP) to achieve a high efficiency. An empirical study is conducted to verify the effectiveness of the combination weighting-grey synthetic decision-making method by helping the LSP selection of a port enterprise in China. Data are collected from questionnaires administered to port logistics’ industry professionals. The method is proposed, which associates the analysis network process method with the entropy method to determine the combined weights of the evaluation indexes. The improved centre-point triangular whitenization weight function is introduced to cluster the alternative …port LSPs and judge the corresponding grey classes. Subsequently, the synthetic weighted decision-making vectors are used to determine the grey synthetic decision-making coefficient vectors. The grey synthetic clustering decision-making coefficients are calculated to establish a synthetic decision-making rank of the alternative plans. The combined method can help the port enterprises realize the selection of better LSPs in a scientific manner. Show more

Keywords: Combination weighting, grey synthetic decision-making, logistics service, provider selection

DOI: 10.3233/JIFS-222156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8607-8626, 2023

Authors: Tao, Zhifu | Wang, Xinyu | Zhu, Benji | Wu, Peng

Article Type: Research Article

Abstract: The aim of this paper is to introduce a combination of Basic Uncertain Information (BUI) and a Bag Based Technique for Order Preference by Similarity to Ideal Solution (BBTOPSIS), which is further applied to multi-attribute decision making (MADM) with BUI. To realize the decision process, a novel comparison law is developed to derive the superiority, inferiority and noninferiority multi-attribute canonical fuzzy bags. Mathematical properties of the developed comparison law is discussed. Besides, to extend traditional TOPSIS method in BUI, a novel distance measure between BUI is also introduced, which is composed by distance between transformed intervals and similarity between BUI. …Superiority of the developed distance measure is illustrated. Finally, a decision algorithm is presented to solve MADM with BUI by using the developed BBTOPSIS under BUI. A numerical example on location of medical warehouse is presented to illustrate the feasibility and validity of the developed decision method. Show more

Keywords: Basic uncertain information, BBTOPSIS, multi-attribute decision making, medical warehouse, location

DOI: 10.3233/JIFS-223835

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8627-8636, 2023

Authors: Liu, Chang

Article Type: Research Article

Abstract: The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed …as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic simplified neutrosophic sets (PSNSs), GRA method, talent training quality evaluation

DOI: 10.3233/JIFS-224494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8637-8647, 2023

Authors: Fang, Jian | Lin, Xiaomei | Liu, Weida | An, Yi | Sun, Haoran

Article Type: Research Article

Abstract: The purpose of facial expression recognition is to capture facial expression features from static pictures or videos and to provide the most intuitive information about human emotion changes for artificial intelligence devices to use effectively for human-computer interaction. Among the factors, the excessive loss of locally valid information and the irreversible degradation trend of the information at different expression semantic scales with increasing network depth are the main challenges faced currently. To address such problems, an enhanced pyramidal network model combining with triple attention mechanisms is designed in this paper. Firstly, three attention mechanism modules, i.e. CBAM, SK, and SE, …are embedded into the backbone network model in stages, and the key features are sensed by using spatial or channel information mining, which effectively reduces the effective information loss caused by the network depth. Then, the pyramid network is used as an extension of the backbone network to obtain the semantic information of expression features across scales. The recognition accuracy reaches 96.25% and 73.61% in the CK+ and Fer2013 expression change datasets, respectively. Furthermore, by comparing with other current advanced methods, it is shown that the proposed network architecture combining with the triple attention mechanism and multi-scale cross-information fusion can simultaneously maintain and improve the information mining ability and recognition accuracy of the facial expression recognition model. Show more

Keywords: Facial expression recognition, attention mechanism, Resnet-50, pyramid network

DOI: 10.3233/JIFS-222252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8649-8661, 2023

Authors: Zuo, Haichun

Article Type: Research Article

Abstract: The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating …Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends. Show more

Keywords: Cloud computing, virtual machine allocation, virtualization, resource utilization, review

DOI: 10.3233/JIFS-222896

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8663-8696, 2023

Authors: Navaneethan, M. | Janakiraman, S.

Article Type: Research Article

Abstract: E-commerce, often known as electronic commerce, is the purchasing and selling of goods over the internet using electronic devices to share data. Banks and other financial institutions are frequently added as third-party platforms to traditional e-commerce platforms. As a result, it raises issues with integrity and cyber security. We suggest a deep learning-based strategy called the Hybrid Interactive Autodidactic School-Based Teaching-Learning Optimization (HIASTLO) algorithm to address these issues. The IoT-based e-commerce blockchain is used to extract and reject the various cyberattacks in the network, and deep learning is utilised to improve the weight and bias of the neural networks. We …used a variety of performance indicators, including accuracy, precision, and recall, to identify cyberattacks. We also evaluated how well our work performed when compared to previous BSIoTNET, BCFC, DRNN, DNN-KNN, MOO-FS, LRNN, and HDLM efforts. Furthermore, MudraChain and NormaChain are used to examine the transaction time of our suggested task. The results show that our suggested work performs better than any other methods and offers highly secure internet services. Show more

Keywords: E-commerce, blockchain, deep learning, cyber attacks, IoT

DOI: 10.3233/JIFS-220743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8697-8709, 2023

Authors: Weng, Peng | Xie, JingJing | Zou, Yang

Article Type: Research Article

Abstract: The estimation of compressive strength includes time-consuming, finance-wasting, and laboring approaches to undertaking High-performance concrete (HPC) production. On the other side, a vast volume of concrete consumption in industrial construction requires an optimal mix design with different percentages to reach the highest compressive strength. The present study considered two deep learning approaches to handle compressive strength prediction. The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total …aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices. Show more

Keywords: HPC concrete, compressive strength, deep learning, arithmetic optimization algorithm, grasshopper optimization algorithm

DOI: 10.3233/JIFS-221714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8711-8724, 2023

Authors: Nishanth, R. | Sulochana, C. Helen | Radhamani, A.S. | Ahilan, A.

Article Type: Research Article

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

DOI: 10.3233/JIFS-220418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8725-8741, 2023

Authors: Zhang, Huiyuan | Wang, Hongjun | Cai, Qiang | Wei, Guiwu

Article Type: Research Article

Abstract: As an improved form of fuzzy sets (FSs), spherical fuzzy sets (SFSs) could provide decision makers (DMs) with more free space to express their preference information. In this article, we first develop some Hamacher power aggregation operators under SFSs by power operators and Hamacher operators, including spherical fuzzy Hamacher power average (SFHPA) operator, spherical fuzzy Hamacher power geometric (SFHPG) operator, spherical fuzzy Hamacher power weighted average (SFHPWA) operator, spherical fuzzy Hamacher power weighted geometric (SFHPWG) operator, spherical fuzzy Hamacher power ordered weighted average (SFHPOWA) operator, spherical fuzzy Hamacher power ordered weighted geometric (SFHPOWG) operator, spherical fuzzy Hamacher power hybrid average …(SFHPHA) operator and spherical fuzzy Hamacher power hybrid geometric (SFHPHG) operator. At the same time, some properties of the proposed operators are investigated, and the relationships between these operators and existing operators are discussed. Furthermore, a novel spherical fuzzy entropy measure is introduced to calculate unknown attribute weights. Then, some novel multiple attribute group decision making (MAGDM) methods are established by the proposed operators as well as entropy measure under SFSs. Lastly, the practicability of the presented methods is verified with a numerical case. Moreover, the robustness, availability and superiority for the developed methods are demonstrated via sensitivity analysis and further comparation with the existing methods. Show more

Keywords: Spherical fuzzy sets, Hamacher operators, power operators, spherical fuzzy Hamacher power aggregation operators, entropy measure, multiple attribute group decision making

DOI: 10.3233/JIFS-224468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8743-8771, 2023

Authors: AlShammari, Naif Khalaf | Qazi, Emad Ul Haq | Gabr, Ahmed Maher | Alzamil, Ahmed A. | Alshammari, Ahmed S. | Albadran, Saleh Mohammad | Reddy, G. Thippa

Article Type: Research Article

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

DOI: 10.3233/JIFS-221295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8773-8783, 2023

Authors: Hemanand, D. | Sridhar, P. | Priya, C. | Sathish Kumar, P.J.

Article Type: Research Article

Abstract: Wireless Sensor Networks are becoming increasingly popular in everyday life since they offer a variety of network structures for developing cutting-edge real-time applications. Wireless sensor devices have high energy consumption limitations, so it is necessary to handle excessive energy consumption by malicious nodes properly to improve network performance. Even though numerous studies have been conducted to increase the dependability of routing in WSNs, the existing routing strategies do not meet the required security constraints by using intelligent methods to protect the sensor nodes from malicious attack. To overcome this challenge a novel Trust Aware Clustering based Secure Routing Techniques (TAC-SRT) …has been proposed to minimize the overall energy consumption, improved security to nodes and to maximize the network lifetime. The proposed method is carried out in three phases. In the first phase, the cluster head is selected by using K mean clustering. In the second phase, the trust value of each node is evaluated by using Mamdani fuzzy inference rule. In the third phase, the Tversky similarity index is used to find the normal or malicious node and establishes the shortest route. The Fully Homomorphic Elliptic Curve Cryptography technique is then used to perform secure data transmission. The effectiveness of the proposed strategy is examined using several parameters, such as the lifetime of the network, data confidentiality, active nodes, and energy consumption. The proposed technique improves the network lifetime by 23.01%, 17.4%, and 13.2% better than MOSFA, SecDL, and CAR-MOSOA respectively. Finally, the proposed method demonstrated superior performance in terms of delay, throughput, encryption time, network lifetime, and packet delivery ratio compared with existing techniques. Show more

Keywords: Secure routing, fuzzy inference system, wireless sensor network, tversky similarity index, cluster head selection

DOI: 10.3233/JIFS-223197

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8785-8800, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8801-8801, 2023