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

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