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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: 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: Approximate multipliers are a trending digital design that was specially developed for the implementation of low power and high-speed circuits. The main purpose of this design is to trade the necessity of accurate multipliers. In this work, a novel imprecise compressor was designed to develop the Hazy Multipliers for low-error resilient applications. These imprecise compressors are synthesized using a 40 nm CMOS technology. When compared with the previous approximate multiplier design the proposed Hazy Multipliers are can reduce the error up to 96%, 98.9%, 99.5% respectively than the existing methods. Finally, the proposed design is investigated on the image smoothening application …to show the performance metrics of Hazy Multipliers. Show more
Keywords: Approximate multipliers, accurate multipliers, imprecise compressors, image smoothening
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: Technological development in biomedical procedures has given an upper understanding of the ease of evaluating and handling critical scenarios and diseases. A sustainable model design is required for the post-medical procedures to maintain the consistency of medical treatment. In this article, a telerobotic-based stroke rehabilitation optimization and recommendation technique cum framework is proposed and evaluated. Selecting optimal features for training deep neural networks can help in optimizing the training time and also improve the performance of the model. To achieve this, we have used Whale Optimization Algorithm (WOA) due to its higher convergence accuracy, better stability, stronger global search ability, …and faster convergence speed to streamline the dependency matrix of each attribute associated with post-stroke rehabilitation. Deep Neural Networking assures the selection of datasets from training and testing validation. The proposed framework is developed on providing decision support with a recommendation of activities and task flow, these recommendations are independent and have higher feasibility with the scenario of evaluation. The proposed model achieved a precision of 99.6%, recall of 99.5 %, F1-score of 99.7%, and accuracy of 99.9%, which outperform the other considered optimization algorithms such as antlion and gravitational search algorithms. The proposed technique has provided an efficient recommendation model compared to the trivial SVM-based models and techniques. Show more
Keywords: Exoskeleton robotic framework, whale-optimization algorithm, deep neural networks, stroke disease, Industry 4.0
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
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