Journal of Intelligent & Fuzzy Systems - Volume 44, issue 4

Authors: Li, Jingyi | Chao, Shiwei

Article Type: Research Article

Abstract: Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have …more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel. Show more

Keywords: Binary classification, imbalanced data, support vector machine

DOI: 10.3233/JIFS-222501

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6901-6910, 2023

Authors: Yagoub, Imam | Lou, Zhengzheng | Qiu, Baozhi | Abdul Wahid, Junaid | Saad, Tahir

Article Type: Research Article

Abstract: In a real-world, networked system, the ability to detect communities or clusters has piqued the concern of researchers in a wide range of fields. Many existing methods are simply meant to detect the membership of communities, not the structures of those groups, which is a limitation. We contend that community structures at the local level can also provide valuable insight into their detection. In this study, we developed a simple yet prosperous way of uncovering communities and their cores at the same time while keeping things simple. Essentially, the concept is founded on the theory that the structure of a …community may be thought of as a high-density node surrounded by neighbors of minor densities and that community centers are located at a significant distance from one another. We propose a concept termed “community centrality” based on finding motifs to measure the probability of a node becoming the community center in a setting like this and then disseminate multiple, substantial center probabilities all over the network through a node closeness score mechanism. The experimental results show that the proposed method is more efficient than many other already used methods. Show more

Keywords: Community detection, node density, node closeness, motifs, community center

DOI: 10.3233/JIFS-220224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6911-6924, 2023

Authors: Geepthi, D. | Columbus, C. Christopher | Jeyanthi, C.

Article Type: Research Article

Abstract: P2P networks are particularly vulnerable to Sybil and Eclipse attacks, especially those based on Distributed Hash Tables (DHT). However, detecting Sybil and Eclipse attacks is a challenging task, and existing methods are ineffective due to unequal sample distribution, incomplete definitions of discriminating features, and weak feature perception. This paper proposes a Fuzzy Secure Kademlia (FSK) that detects and mitigates the Sybil and Eclipse attack. At first, a node requests authentication by providing its MAC address, location, Node Angle (NA), and Node Residual Energy (NRE) to an infrastructure server. As long as the packet’s ID, location, NA, and NRE match the …packet’s received ID, it can be recognized as normal. The incoming packet, however, is detected as Sybil or Eclipse attack packets if copies are made in locations other than those specified. When the Sybil or Eclipse attack has been detected, locate the multiplied nodes. By using the FSK, the malicious node can be removed, preventing it from causing any harm to the network. The suggested framework is compared with existing methods in terms of detection time, and energy consumption. Experimental results indicate that the suggested FSK technique achieves a better detection time of 29.4%, 25.5%, 22.6%, and 18.1% than CSI, DHT, CMA, and EDA methods. Show more

Keywords: To-peer, sybil attack, eclipse attack, fuzzy secure kademlia, distributed hash table, detection, mitigate

DOI: 10.3233/JIFS-222802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6925-6937, 2023

Authors: Zhang, Zhenyu | Guo, Jian | Zhang, Huirong | Qin, Yong

Article Type: Research Article

Abstract: Preference relations have been extended to q-rung orthopair fuzzy environment, and the q-rung orthopair fuzzy preference relations (q-ROFPRs) with additive consistency are defined. Then, the concept of normalized q-rung orthopair fuzzy weight vector (q-ROFWV) is proposed, and the transformation method of constructing q-ROFPR with additive consistency is given. To obtain the weight vector of any q-ROFPRs, a goal programming model to minimize the deviation of the q-ROFPRs from the constructed additive consistent q-ROFPRs is established. The q-rung orthopair fuzzy weighted quadratic (q-ROFWQ) operator is selected to aggregate multiple q-ROFPRs, efficiently handling extreme values and satisfying monotonicity about the order relation. …Further, a group decision-making (GDM) method is developed by combining the q-ROFWQ operator and the goal programming model. Finally, the practicality and feasibility of the developed GDM method are demonstrated by an example of rail bogie crucial component identification. Show more

Keywords: q-rung orthopair fuzzy preference relation (q-ROFPR), goal programming model, q-rung orthopair fuzzy weighted quadratic (q-ROFWQ) operator, group decision making

DOI: 10.3233/JIFS-221859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6939-6955, 2023

Authors: Saravanabhavan, C. | Kirubakaran, S. | Premkumar, R. | Joyce, V. Jemmy

Article Type: Research Article

Abstract: One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify …the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory. Show more

Keywords: Service mining, reduction in dimensions, high-useful set-ups, recurring patterns, graphs, support and fuzzy both count

DOI: 10.3233/JIFS-221672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6957-6971, 2023

Authors: Amutha, S.

Article Type: Research Article

Abstract: White blood cell (WBC) leukemia is caused by an excess of leukocytes in the bone marrow, and image-based identification of malignant WBCs is important for its detection. This research describes a new hybrid technique for accurate classification of WBC leukemia. To increase the image quality, the preprocessing is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). The images are then segmented using Hidden Markov Random Fields (HMRF). To extract features from WBC images, Visual Geometry Group Network (VGGNet), a powerful Convolutional Neural Network (CNN) architecture, is used After that, an Efficient Salp Swarm Algorithm (ESSA) is used to optimize the …extracted features. The proposed method is tested on two Acute Lymphoblastic Leukemia Image Databases, yielding good accuracy of 98.1% for dataset 1 and 98.8% for dataset 2. While enhancing accuracy, the ESSA optimization picked just 1K out of 25K features retrieved with VGGNet. The combination of CNN feature extraction with ESSA feature optimization could be effective for a variety of additional image classification tasks. Show more

Keywords: WBC leukemia, VGGNet-CNN, ALLIDB, efficient scalp swarm algorithm

DOI: 10.3233/JIFS-221302

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6973-6989, 2023

Authors: Liu, Weiling | Liu, Ping | Han, Furong | Xiao, Yanjun

Article Type: Research Article

Abstract: The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the …recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods. Show more

Keywords: Electronic nose, Malodorous gas detection, BP neural network

DOI: 10.3233/JIFS-222539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6991-7008, 2023

Authors: Yu, Qingying | Xiao, Zhenxing | Yang, Feng | Gong, Shan | Shi, Gege | Chen, Chuanming

Article Type: Research Article

Abstract: With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the …reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q -value table search mechanism, the proposed algorithm can generate the recommended route almost in real time. Show more

Keywords: Route recommendation, multi-objective, user preferences, reinforcement learning, dynamic exploration factor

DOI: 10.3233/JIFS-222932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7009-7025, 2023

Authors: Karthikeyan, G. | Komarasamy, G. | Daniel Madan Raja, S.

Article Type: Research Article

Abstract: With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing healthcare quality and making better decisions during tough times. This research concentrates on modelling and automated disease prediction model to offer an earlier prediction model for heart disease and the risk factors. This work considers a standard UCI machine learning-based benchmark dataset for model validation and extracts the risk factors related to the disease. The outliers and imbalanced datasets are pre-processed using data normalization to enhance the classification performance. Here, feature selection is performed using non-linear Particle Swarm Optimization (NL  - PSO ). …Finally, classification is done with the Improved Deep Evolutionary model with Feed Forward Neural Networks (IDEBDFN). The algorithm’s learning nature is used to evaluate the nature of the hidden layers to produce the optimal results. The outcomes demonstrate that the anticipated model provides superior prediction accuracy. The simulation is carried out in a MATLAB environment, and metrics like accuracy, F-measure, precision, recall, and so on are evaluated. The accuracy (without features) of the evolutionary model in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, specificity is 95%, specificity is 2% higher than both the datasets, F1-score is 40%, execution time (min) is 0.04 min, and the AUROC is 96.85% which is substantially higher than other datasets. The proposed model works efficiently compared to various prevailing standards and individual approaches. Show more

Keywords: Heart disease prediction, pre-processing, feature selection, classification, evolutionary model, feed-forward neural network

DOI: 10.3233/JIFS-220912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7027-7042, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7043-7043, 2023