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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: Zhang, Kun | Zhou, Yu | Long, Haixia | Wu, Shulei | Wang, Chaoyang | Hong, Haizhuang | Fu, Xixi | Wang, Haifeng
Article Type: Research Article
Abstract: The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a …marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M. Show more
Keywords: Artificial intelligence, K-means clustering algorithm, marine information management, network security
DOI: 10.3233/JIFS-236018
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4817-4827, 2024
Authors: Haripriya, V. | Vishal Gupta, Mohan | Nadkarni, Nikita | Malik, Suraj | Yadav, Aditya | Joshi, Apoorva
Article Type: Research Article
Abstract: From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for …normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments. Show more
Keywords: Multimedia, security issues, ensemble whale optimized recurrent neural network (EWO-RNN), min-max normalization, kernel principle component analysis (K-PCA)
DOI: 10.3233/JIFS-237041
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4829-4840, 2024
Authors: Jayachandran, A. | Ganesh, S.
Article Type: Research Article
Abstract: Microaneurysms, tiny, circular red dots that occur in retinal fundus images, are one of the earliest symptoms of diabetic retinopathy. Because microaneurysms are small and delicate, detecting them can be difficult. Their small size and cunning character make automatic detection of them difficult. In this study, a novel encoder-decoder network is proposed to segment the MAs automatically and accurately. The encoder part mainly consists of three parts: a low-level feature extraction module composed of a dense connectivity block (Dense Block), a High-resolution Block (HR Block), and an Atrous Spatial Pyramid Pooling (ASPP) module, of which the latter two modules are …used to extract high-level information. Therefore, the network is named a Multi-Level Features based Deep Convolutional Neural Network (MF-DCNN). The proposed decoder takes advantage of the multi-scale features from the encoder to predict MA regions. Compared with the existing methods on three datasets, it is proved that the proposed method is better than the current excellent methods in the segmentation results of the normal and abnormal fundus. In the case of fewer network parameters, MF-DCNN achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC), and other evaluation metrics. MF-DCNN is lightweight and able to use multi-scale features to predict MA regions. It can be used to automatically segment the MA and assist in computer-aided diagnosis. Show more
Keywords: Microaneurysm detection, fundus images, segmentation, features
DOI: 10.3233/JIFS-230154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4841-4857, 2024
Authors: Long, Zuqiang | Luo, Zelong | Wang, Yunmeng
Article Type: Research Article
Abstract: The analytical structure plays an important role in system design and stability analysis of FLC. Structure analysis of traditional IT2 TS FLCs using Zadeh min operator and KM algorithm requires multiple IC dividing, which results in complex calculation and cumbersome parameter adjustment. This article proposes a new IT2 TS FLC by adopting product-type operator and NT algorithm. The proposed controller has such advantages: 1)use product-type operator to skip the partitioning in fuzzy inference process;2) use NT algorithm to avoid determining switching points and sorting rule consequents in type-reduction process. Then, the controller is proved to be universal approximator and sufficient …condition is deduced. Finally, we derive the analytical structure of the controller by substituting the parameters, and study the relationship between the uncertainty parameter θ and the analytical structure when the rule consequents are symmetric or asymmetric. Both the computational costs during operation and the computational workload for structural analysis can be reduced significantly by using the new FLC. Show more
Keywords: NT algorithm, IT2 TS FLC, analytical structure, universal approximation
DOI: 10.3233/JIFS-231866
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4859-4867, 2024
Authors: Yang, Hui | Wang, Yi-Na
Article Type: Research Article
Abstract: In this paper, we provide some new characterizations of L -convex systems. For this purpose, we first introduce the concept of partial hull operators and establish the categorical relationship between partial hull operators and convex systems. Then we abstract the relationship between a subset and its partially convex hull in convex system to a binary relation, called enclosed relation. Moreover, we prove that the enclosed relations are equivalent to convex systems. Subsequently, we generalize the concept of partial hull operators and enclosed relations to the fuzzy case, which will be called L -partial hull operators and L -enclosed relations respectively. …Finally we explore the categorical isomorphisms between them. Show more
Keywords: L-convex systems, partially L-convex hull operators, L-partial hull operators, L-enclosed relations
DOI: 10.3233/JIFS-232243
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4869-4879, 2024
Authors: dos Santos Lima, Matheus | Kich, Victor Augusto | Steinmetz, Raul | Tello Gamarra, Daniel Fernando
Article Type: Research Article
Abstract: The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot’s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved …into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper. Show more
Keywords: Deep reinforcement learning, parallel robots, delta robot
DOI: 10.3233/JIFS-232795
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4881-4894, 2024
Authors: Gao, Kaihan | Ju, Yiwei | Li, Shuai | Yang, Xuebing | Zhang, Wensheng | Li, Guoqing
Article Type: Research Article
Abstract: Recent advances in high-throughput electron microscopy (EM) have revolutionized the examination of microstructures by enabling fast EM image generation. However, accurately segmenting EM images remains challenging due to inherent characteristics, including low contrast and subtle grayscale variations. Moreover, as manually annotated EM images are limited, it is usually impractical to utilize deep learning techniques for EM image segmentation. To address these challenges, the pyramid multiscale channel attention network (PmcaNet) is specifically designed. PmcaNet employs a convolutional neural network-based architecture and a multiscale feature pyramid to effectively capture global context information, enhancing its ability to comprehend the intricate structures within EM …images. To enable the rapid extraction of channel-wise dependencies, a novel attention module is introduced to enhance the representation of intricate nonlinear features within the images. The performance of PmcaNet is evaluated on two general EM image segmentation datasets as well as a homemade dataset of superalloy materials, regarding pixel-wise accuracy and mean intersection over union (mIoU) as evaluation metrics. Extensive experiments demonstrate that PmcaNet outperforms other models on the ISBI 2012 dataset, achieving 87.85% pixel-wise accuracy and 73.11% mean intersection over union (mIoU), while also advancing results on the Kathuri and SEM-material datasets. Show more
Keywords: Electron microscopy, image segmentation, convolutional neural network, multiscale feature pyramid
DOI: 10.3233/JIFS-235138
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4895-4907, 2024
Authors: Shi, Liqing | Xiong, Taiping | Cui, Gengshen | Pan, Minghua | Zhu, Zhiguo | Cheng, Wei
Article Type: Research Article
Abstract: In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise …contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet . Show more
Keywords: Stereo matching, dual-stream pyramid, attention mechanism, binocular vision
DOI: 10.3233/JIFS-235415
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4909-4922, 2024
Authors: Zhang, Baishun | Su, Xue
Article Type: Research Article
Abstract: In practical applications of machine learning, only part of data is labeled because the cost of assessing class label is relatively high. Measure of uncertainty is abbreviated as MU. This paper explores MU for partially labeled real-valued data via a discernibility relation. First, a decision information system with partially labeled real-valued data (p-RVDIS) is separated into two decision information systems: one is the decision information system with labeled real-valued data (l-RVDIS) and the other is the decision information system with unlabeled real-valued data (u-RVDIS). Then, based on a discernibility relation, dependence function, conditional information entropy and conditional information amount, four …degrees of importance on an attribute subset in a p-RVDIS are defined. They are calculated by taking the weighted sum of l-RVDIS and u-RVDIS based on the missing rate, which can be considered as four MUs for a p-RVDIS. Combining l-RVDIS and u-RVDIS provides a more accurate assessment of the importance and classification ability of attribute subsets in a p-RVDIS. This is precisely the novelty of this paper. Finally, experimental analysis on several datasets verify the effectiveness of these MUs. These findings will contribute to the comprehension of the essence of the uncertainty in a p-RVDIS. Show more
Keywords: Partially labeled real-valued data, p-RVDIS, Discernibility relation, Uncertainty, Measure
DOI: 10.3233/JIFS-236958
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4923-4940, 2024
Authors: Sun, Ruixia | Xiao, Ping | Tang, Wei | Chen, Long | Chen, Dandan
Article Type: Research Article
Abstract: In order to further enhance energy conservation and emission reduction, the header lifting structure of a harvester is studied. First, a double electric pushrod structure is used to replace the oil cylinder and air cylinder lifting structure of a traditional header to reduce fuel consumption and harmful gas emission. Furthermore, a mathematical model and a simulation model of the electric pushrod are established. To enhance the control effect of the header lifting structure, an improved version of the traditional gray wolf optimization (GWO) algorithm is designed. The nonlinear convergence factor, Kent chaotic mapping and convergence surrounding and spiral updating operations …are introduced to increase the convergence speed and optimization accuracy of this algorithm. The improved GWO (IGWO) algorithm is applied to optimize the proportional-integral-derivative (PID) controller of the double pushrod coordinated control system. Then, a new IGWO-PID control algorithm is also designed. The cross-coupling control strategy of header’s double pushrods is then studied. Results of the simulation and bench test show that the IGWO-PID control algorithm and the cross-coupling control strategy can effectively enhance controlling effect of the harvester header. The left and right pushrods can achieve good synchronous and coordinated movements. Show more
Keywords: Harvester header, electric pushrod, IGWO-PID control algorithm, cross-coupled collocated control
DOI: 10.3233/JIFS-231193
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4941-4953, 2024
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