Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Tekin, Özlem

Article Type: Research Article

Abstract: Spherical fuzzy sets are an advanced tool of three-dimensional membership functions which consist of membership, non-membership and hesitancy degrees. In this paper, it is introduced a new approach via proximal spaces for spherical fuzzy sets. To do this, the spherical fuzzy proximity axioms are defined on proximal relator spaces. Also, spherical fuzzy spatial Lodato proximity relation is studied. By using spherical fuzzy proximity relation, it is defined that descriptive proximity relation. An example is given how people are proximal(near) to each other via their description features.

Keywords: Proximity space, relator space, fuzzy relation, fuzzy proximity, spherical fuzzy sets, spherical fuzzy proximity

DOI: 10.3233/JIFS-230314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6875-6886, 2023

Authors: Guo, Aiyin | Xu, Yunjian | Li, Gang

Article Type: Research Article

Abstract: In order to simultaneously calculate the temporal and spatial characteristics of behavior sequence samples, a convolutional neural network recognition model based on a multi-scale convolutional operator is proposed. Firstly, the skeleton vector information in the sequence samples is integrated into a behavior matrix by superposition, and then the matrix is input into the recognition model. In order to explore the role of bone points with different adjacencies in describing human behavior, the convolutional operator in each layer of the convolutional neural network is extended to a multi-scale convolutional operator, and the features obtained by the network are used for classification. …Good recognition rates were obtained in the MSR-Action3D dataset and HDM05 dataset. Show more

Keywords: Behavior recognition, spatiotemporal characteristics, deep convolutional neural network, deep learning, behavior matrix

DOI: 10.3233/JIFS-231220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6887-6896, 2023

Authors: Prabhu, S. | Mary Anita, E.A. | Mohanageetha, D.

Article Type: Research Article

Abstract: Wireless sensor nodes (WSN) combine sensing and communication capabilities in the smallest sensor network component. Sensor nodes have basic networking capabilities, such as wireless connection with other nodes, data storage, and a microcontroller to do basic processing. The intrusion detection problem is well analyzed and there exist numerous techniques to solve this issue but suffer will poor intrusion detection accuracy and a higher false alarm ratio. To overcome this challenge, a novel Intrusion Detection via Salp Swarm Optimization based Deep Learning Algorithm (ID-SODA) has been proposed which classifies intrusion node and non-intrusion node. The proposed ID-SODA technique uses the k-means …clustering algorithm to perform clustering. The Salp Swarm Optimization (SSO) technique takes into residual energy, distance, and cost while choosing the cluster head selection (CHS). The CHS is given the input to a multi-head convolutional neural network (MHCNN), which will classify into intrusion node and non-intrusion node. The performance analysis of the suggested ID-SODA is evaluated based on the parameters like accuracy, precision, F1 score, detection rate, recall, false alarm rate, and false negative rate. The suggested ID-SODA achieves an accuracy range of 98.95%. The result shows that the suggested ID-SODA improves the overall accuracy better than 6.56%, 2.94%, and 2.95% in SMOTE, SLGBM, and GWOSVM-IDS respectively. Show more

Keywords: Wireless sensor nodes, k-means clustering, Salp Swarm optimization, multi-head convolutional neural network

DOI: 10.3233/JIFS-231756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6897-6909, 2023

Authors: Durgam, Revathi | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: In machine learning, a crucial task is feature selection in that the computational cost will be increased exponentially with increases in problem complexity. To reduce the dimensionality of medical datasets and reduce the computational cost, multi-objective optimization approaches are mainly utilized by researchers. Similarly, for improving the population diversity of the Flamingo Search Algorithm, the neighbourhood centroid opposition-based learning mutation is employed. In this paper, to improve the classification accuracy, enhance their exploration capability in the search space and reduce the computational cost while increasing the size of dataset, neighbourhood centroid opposition-based learning (NCOBL) is integrated into the multi-objective optimization …based Flamingo Search Algorithm (MOFSA). The optimal selected datasets are classified by using the weighted K-Nearest Neighbour classifier. With the use of fifteen benchmark medical datasets, the efficacy of the suggested strategy is assessed in terms of recall, precision, accuracy, running time, F-measure, hamming loss, ranking loss, standard deviation, mean value error, and size of the selected features. Then the performance of the suggested feature selection technique is compared to that of the existing approaches. The suggested method produced a minimum mean value, standard deviation, mean hamming loss, and maximum accuracy of about 99%. The experimental findings demonstrate that the suggested method may enhance classification accuracy and also eliminate redundancy in huge datasets. Show more

Keywords: Flamingo search algorithm, K-Nearest Neighbour, feature selection, multi-objective optimization, disease classification

DOI: 10.3233/JIFS-232128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6911-6922, 2023

Authors: Lin, Youping | Wang, Wenxin | Chen, Yanling | Li, Feng

Article Type: Research Article

Abstract: The evaluation of teaching quality plays a crucial role in promoting the improvement of education quality and ensuring the healthy development of education. This study presents a novel teaching quality evaluation model based on improved interval-valued intuitionistic fuzzy Best-Worst method (IVIF-BWM) and interval-valued intuitionistic fuzzy weighted Maclaurin symmetric mean operators (IVIFWMSM). The study is divided into three parts. Firstly, to derive the optimal interval-valued intuitionistic fuzzy weights of criteria, we develop an improved IVIF-BWM by establishing a goal programming model based on the multiplicative consistent interval-valued intuitionistic fuzzy preference relation(IVIFPR), and then we propose the new consistency index (CI) and …the consistency ratio (CR) under interval-valued intuitionistic fuzzy environment to verify the reliability of the derived results. Secondly, with regard to the importance and interaction relationships among criteria, IVIFWMSM is used to aggregate evaluation values of alternatives on each evaluation criteria in multi-criteria decision making process. Finally, the proposed teaching quality evaluation model is applied to a case of teaching quality evaluation in higher education and a comparison study with other existing methods are performed. The results demonstrate that the proposed teaching quality evaluation model not only overcomes the shortcomings of previous methods, but also is more accuracy, effective and reasonable for dealing with the teaching quality evaluation under the intuitionistic fuzzy environments. Show more

Keywords: Teaching quality evaluation model, interval-valued intuitionistic fuzzy Best-Worst method, interval-valued intuitionistic fuzzy preference relation, interval-valued intuitionistic fuzzy weighted Maclaurin symmetric mean operator

DOI: 10.3233/JIFS-232272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6923-6941, 2023

Authors: Ashwini, A. | Purushothaman, K.E. | Rosi, A. | Vaishnavi, T.

Article Type: Research Article

Abstract: The most common challenge faced by dermoscopy images is the automatic detection of lesion features. All the existing solutions focus on complex algorithms to provide accurate detections. In this research work, proposed Online Tigerclaw Fuzzy Region Segmentation with Deep Learning Classification model, an intellectual model is proposed that provides discrimination of features with classification even in fine-grained samples. This model works on four different stages, which include the Boosted Anisotropic Diffusion filter with Recursive Pixel Histogram Equalization (BADF-RPHE) in the preprocessing stage. The next step is the proposed Online Tigerclaw Fuzzy Region Segmentation (OTFRS) algorithm for lesion area segmentation of …dermoscopic images, which can achieve 98.9% and 97.4% accuracy for benign and malignant lesions, respectively. In the proposed OTFRS, an accuracy improvement of 1.4% is achieved when compared with previous methods. Finally, the increased robustness of lesion classification is achieved using Deep Learning Classification –DenseNet 169 with 500 images. The proposed approach was evaluated with accuracy classifications of 100% and 98.86% for benign and malignant lesions, respectively, and a processing time of less than 18 sec. In the proposed DensetNet-169 classification technique, an accuracy improvement of 3% is achieved when compared with other state-of-art methods. A higher range of true positive values is obtained for the Region of Convergence (ROC) curve, which indicates that the proposed work ensures better performance in clinical diagnosis for accurate feature visualization analysis. The methodology has been validated to prove its effectiveness and throw light on the lives of affected patients so they can resume normalcy and live long. The research work was tested in real-time clinical samples, which delivered promising and encouraging results in skin cell detection procedures. Show more

Keywords: Boosted Anisotropic Diffusion filter with Recursive Pixel Histogram Equalization (BADF-RPHE), Deep learning Classification - DenseNet 169, Proposed Online Tigerclaw fuzzy Region Segmentation (OTFRS), Skin tumor

DOI: 10.3233/JIFS-233024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6943-6958, 2023

Authors: Jyothi, Kilari | Dubey, R.B.

Article Type: Research Article

Abstract: This manuscript proposes a hybrid method to solve the job shop scheduling problem (JSP). Here, the machine consumes different amounts of energy for processing the tasks. The proposed method is the joint execution of Feedback Artificial Tree (FAT) and Atomic Orbital Search (AOS), hence it is called the FAT-AOS method. The aim of the proposed multi-objective method is to lessen the non-processing energy consumption (NEC), total weighted tardiness and earliness (TWET), and makespan (Cmax). Depending on the machine’s operating status, such as working, standby, off, or idle, the energy-consumption model of the machine is constructed. The NEC is the essential …metric and the Cmax and TWET are the classical performance metrics used to predict the effects of energy effectiveness in JSP. The proposed AOS technique optimizes the objective of the system and FAT is used to predict the optimal outcome. The proposed method’s performance is implemented in MATLAB and is compared with various existing methods. From this simulation, under the 15x15_1 instance, the proposed method makes the span the best value of 1370, the median is 1720, and the worst value become 2268 is obtained. Show more

Keywords: Hybrid approach, total weighted tardiness and earliness, job shop scheduling, machine status, non-processing energy consumption, makespan

DOI: 10.3233/JIFS-222362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6959-6981, 2023

Authors: Fan, Jianping | Wang, Min | Wu, Meiqin

Article Type: Research Article

Abstract: Virtual teams (VT) have become increasingly popular due to modern technology. VT allows talented people from different places with different skills to work towards a common goal through network media. In order to form a more versatile VT, selecting VT members becomes a critical step. Based on the linguistic Pythagorean fuzzy sets (LPFS), this paper proposes an integrated approach to select VT members by means of the method based on standard removal effects (MEREC) and the method based on the mean solution distance of direct and indirect uncertainty (DIUEDAS). Firstly, decision information is described by LPFS. Secondly, MEREC is used …to determine the criteria weights. Finally, the decision-making and evaluation laboratory (DEMATEL), failure mode and effects analysis (FMEA), and EDAS are combined to select the optimal VT members under the premise of evaluating the uncertainty in selecting VT members. In addition, this paper proposes a new method for determining expert weights. At the end of the paper, the model and the expert weight determination method are applied to the case of a port selecting VT members, and the effectiveness of the model proposed is demonstrated by comparative analysis in this paper. Show more

Keywords: VT members, LPFS, MEREC, DEMATEL, FMEA, EDAS

DOI: 10.3233/JIFS-232494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6983-7003, 2023

Authors: Ramachandran, Dhanagopal | Venkatesh, J. | Jothilakshmi, R. | Gugapriya, G.

Article Type: Research Article

Abstract: Since there is no central controller, preserving the security and energy efficiency of wireless sensor networks (WSN) is challenging. They also have a flexible configuration. A network of this type is vulnerable to several attacks. The main goal of this paper is to focus on a well-known attack known as the sinkhole attack. Sensors are installed and positioned equally in a WSN to communicate sensed data to a centralized station regularly. So, the sinkhole attack is a big danger to the WSN network layer, and it is still a difficult issue on sensor networks, where even the malicious node collects …packets from other regular sensor nodes and dumps them. To maintain the integrity and authentication of data during its travel in wireless sensor networks overcoming sinkhole attacks we propose a novel approach. In our approach besides overcoming sinkhole attack using a threshold-based method, authentication, and data integrity is maintained using a watermarking-based technique. Show more

Keywords: Ad-hoc On-demand Distance Vector (AODV), Binary Logistic Regression (BLR), Intrusion Detection System (IDS), Low Energy Adaptive Clustering Hierarchy (LEACH), Machine Learning, Wireless Sensor Network (WSN), Network Simulator (NS), Statistical Analysis (SA), Support Vector Machine (SVM)

DOI: 10.3233/JIFS-224463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7005-7023, 2023

Authors: Zuo, Yandi | Wang, Pan | Fan, Zhun | Li, Ming | Guo, Xinhua | Gao, Shijie

Article Type: Research Article

Abstract: Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades; however, the distributed AFSP with DPm  → 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented; a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed; the …historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances. Show more

Keywords: distributed assembly flow shop scheduling, uncertainty, artificial bee colony algorithm, fuzzy makespan

DOI: 10.3233/JIFS-230592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7025-7046, 2023