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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: Xu, Chuannuo | Cheng, Xuezhen | Wang, Yi
Article Type: Research Article
Abstract: Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and …classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis. Show more
Keywords: Least squares support vector machine, rolling fault, salp swarm algorithm, variational mode decomposition, whale algorithm
DOI: 10.3233/JIFS-236532
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4669-4680, 2024
Authors: Jothi, J. Sathiya | Chinnadurai, M.
Article Type: Research Article
Abstract: The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. …The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer. Show more
Keywords: Lung cancer, convolution neural network, computer aided diagnosis, chimp optimization
DOI: 10.3233/JIFS-237339
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4681-4696, 2024
Authors: Gnanasundari, P. | Sheela Sobana Rani, K.
Article Type: Research Article
Abstract: Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with …WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. Show more
Keywords: Node Validation, energy efficiency, cybersecurity, blockchain, WSN
DOI: 10.3233/JIFS-230020
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4697-4711, 2024
Authors: Chen, Zhe | Ye, Lin | Zhang, Hongli | Zhang, Yunting
Article Type: Research Article
Abstract: The purpose of the Chinese similar case matching task is to compare the similarity of two case texts with a given anchor text and find out which text is more similar to the anchor text. In the area of law, it plays an important role and has been of interest to many researchers. Previous approaches have compared legal texts only at the text semantic level, without incorporating article information of law. In addition, the position correlation of words in case texts is often important, but it has not been considered in previous approaches. This paper proposes a method which extracts …features from the semantic similarity level and from the level of related articles of law, respectively, to enable similarity comparisons of legal case texts. When similarity comparisons are made at the semantic similarity level, a novel capsule network method is proposed based on siamese structure that introduces the position correlation and the routing mechanism within the capsule network is improved so that deep text features between case pairs can be learned. When similarity comparisons are made at the level of related articles of law, related articles of law are selected and coded and interacted with the case text features to generate legal features. Experiment is conducted with a real-world legal text dataset, and the proposed model outperformed all baseline models, demonstrating effectiveness of the proposed model. Further, to confirm the generality of the improved capsule network proposed in the paper on long text datasets, this paper also carried out experiments on two long text datasets, demonstrating effectiveness of the improved capsule network proposed in the model. Show more
Keywords: Chinese similar case matching, integrating articles of law, capsule network, dynamic routing with position correlation
DOI: 10.3233/JIFS-232185
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4713-4731, 2024
Authors: Ramasamy, Karthikeyan | Sundaramurthy, Arivoli | Vaithiyalingam, Chitra
Article Type: Research Article
Abstract: The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing …the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable. Show more
Keywords: Engineering optimization, Micro-grid, BIANN, stability assessment, mathematical model
DOI: 10.3233/JIFS-233112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4733-4744, 2024
Authors: Chen, Minghao | Wang, Shuai | Zhang, Jiazhong
Article Type: Research Article
Abstract: The influence maximization problem is one of the hot research topics in the field of complex networks in recent years. The so-called influence maximization problem is how to select the seed set that propagates the largest amount of information on a given network. In practical applications, networks are often exposed to complicated environments, and both link-specific and node-specific attacks can have a significant impact on the network’s propagation performance. Several pilot studies have revealed the crux of the robust influence maximization problem, but the current work available is not comprehensive. On the one hand, existing studies only consider the case …that the network structure is stable or under link-specific attacks, and few researches have concentrated on the case when the network structure is under node-specific attacks. On the other hand, the current algorithm fails to combine the information of the search process well to solve the robust influence maximization problem. Aiming at these deficiencies, in this paper, a metric for evaluating the robust influence performance of seeds under node-specific attacks is developed. Guided by this, a genetic algorithm (GA) maintaining the principle of diversity concern (DC) to solve the Robust Influence Maximization (RIM) problem is designed, called DC-GA-RIM. DC-GA-RIM contains several problem-orientated operators and fully considers diverse information in the optimization process, which significantly improves the search ability of the algorithm. The effectiveness of DC-GA-RIM in solving the RIM problem is demonstrated on a variety of networks. The superiority of this algorithm over other approaches is shown. Show more
Keywords: Complex networks, influence maximization problem, robustness, optimization, genetic algorithm
DOI: 10.3233/JIFS-233222
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4745-4759, 2024
Authors: Wang, Xiaoli | Wang, Chongguo | Shi, Gang
Article Type: Research Article
Abstract: As a complex uncertain differential equation, how to solve the multi-dimensional uncertain differential equation is a complicated and difficult problem. This paper will be devoted to the α-path of some special multi-dimensional uncertain differential equations, namely, multi-factor uncertain differential equations, nested uncertain differential equations and multi-factor nested uncertain differential equations. The α-path method is used to study the numerical solution problems of the above three special multi-dimensional uncertain differential equations. At the same time, the inverse uncertainty distributions and expected values of these three special multi-dimensional uncertain differential equations are also obtained. At last, the numerical algorithm examples are given …to verify it. Show more
Keywords: Uncertainty theory, multi-dimensional uncertain differential equation, α-path, numerical algorithm
DOI: 10.3233/JIFS-234432
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4761-4776, 2024
Authors: Wu, Huimin
Article Type: Research Article
Abstract: Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding …empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance. Show more
Keywords: Automated extractive summarization, generative adversarial network, bidirectional encoder representations from transformers, dilated convolution layer
DOI: 10.3233/JIFS-234709
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4777-4790, 2024
Authors: Adnan, R. Syed Aamir | Kumaravel, R.
Article Type: Research Article
Abstract: Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R 2 ) and Root Mean-Squared Error (RMSE) have been used to evaluate …the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction. Show more
Keywords: Weather forecasting, fuzzy logic, interval type –2 Fuzzy Inference System, Environmental parameters
DOI: 10.3233/JIFS-235828
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4791-4802, 2024
Authors: Paul, Ann Rija | Grace Mary Kanaga, E.
Article Type: Research Article
Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, …speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc. Show more
Keywords: Vehicle tracking, surveillance, collision detection, trajectory and angle of intersection, deep convolutional neural network
DOI: 10.3233/JIFS-235911
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4803-4816, 2024
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
Authors: Qin, Yong | Xu, Zeshui | Wang, Xinxin | Škare, Marinko
Article Type: Research Article
Abstract: Fuzzy decision-making is increasingly becoming a pivotal approach to solving complex and intricate issues in tourism and hospitality management. The primary objective of this study is to unveil the developmental status, key themes and research trends within fuzzy decision-making in tourism and hospitality management (FDMTH) using Bibliometrix, CorText Manager and VOSviewer tools. As such, we conduct a comprehensive bibliometric and content-wise analysis of selected 341 publications concerning FDMTH. For one thing, we use valuable bibliometric indicators to conduct a general feature peek and performance analysis of the audited corpus. The research findings reveal a sustained scholarly interest in FDMTH. As …a critical player, Pappas, Nikolaos leads the volume of publications. The Journal of Intelligent & Fuzzy Systems stands out as the preferred outlet for FDMTH research. For another, the contingency matrix and bump graph modules are employed to detect the knowledge flow and intellectual connections in FDMTH. The results of network mapping tentatively identify geographic and thematic biases in FDMTH. More importantly, bibliographic coupling analysis reveals four specific themes, namely multi-criteria decision-making and evaluation, factors identification, fuzzy programming and forecasting, and fuzzy intelligence. Our pioneer work will contribute to the present understanding of the complexity and interdisciplinarity of FDMTH. Show more
Keywords: Fuzzy decision-making, tourism and hospitality, CorText manager, intellectual connections, thematic bias
DOI: 10.3233/JIFS-236618
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4955-4980, 2024
Authors: Yang, Xiangfei | Zhang, Faping | Wei, Jianfeng
Article Type: Research Article
Abstract: To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and …can effectively identify faults in recoil system. Show more
Keywords: Fuzzy method, RBF neural network, knowledge and data fusion, the recoil system, fault diagnosis
DOI: 10.3233/JIFS-230683
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4981-4994, 2024
Authors: Senthil Kumar, V. | Aruna, R. | Varalatchoumy, M. | Manikannan, P. | Santhana Krishnan, T. | Usha Rani, B. | Kumar, Ashok | Rajaram, A.
Article Type: Research Article
Abstract: As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we …showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience. Show more
DOI: 10.3233/JIFS-235578
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4995-5011, 2024
Authors: Sivakumaran, V. | Sankar, K.
Article Type: Research Article
Abstract: Consider a simple graph G = 〈V , E 〉 with n vertices. Define a function f from vertex set of G to set of integers 1, 2, …, n subject to the condition that there exists an integer k > 0 such that the sum of adjacent labels of each vertex in G is equal to k . In this paper, we prove that the graph K n - ⌊ n 2 ⌋ e has DML for all n ≥ 1, decomposition of distance magic graph K 2n - {ne …} into n ( n - 1 ) 2 edge distinct copies of C 4 for all n ≥ 2 and DML of join of complete graphs gives unique mathematical model and it will be a useful model in big data analysis. Show more
Keywords: Distance magic labeling, magic constant, decomposition of distance magic graph, join of complete graphs, big data analysis
DOI: 10.3233/JIFS-224511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5013-5020, 2024
Authors: Wang, Xiaoying | Chen, Xiaohai | Zhang, Zhongwen | He, Haisheng
Article Type: Research Article
Abstract: Intelligent Transportation Systems (ITS) have experienced significant growth over the past decade thanks to advances in control, communication, and information technology applied to vehicles, roads, and traffic control systems. Vehicle type classification plays a vital role in implementing ITS because of its ability to collect useful traffic information, enable future development of transport infrastructures, and increase human comfort. As a branch of machine learning, deep learning represents a frontier for artificial intelligence, which seeks to be closer to its primary goal. Deep learning is a powerful tool for classifying vehicle types because it can capture complex traffic data characteristics and …learn from large amounts of data. This means that it can be used to accurately classify traffic data and generate valuable insights that can be used to improve traffic management. Researchers have successfully adopted these algorithms as a solution to propose optimal vehicle-type classification strategies. This paper highlights the role of deep learning algorithms in solving the vehicle type classification problem, reviewing the state-of-the-art approaches in this field. Show more
Keywords: Transportation systems, ITS, vehicle type, deep learning, optimization
DOI: 10.3233/JIFS-233302
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5021-5032, 2024
Authors: Subbaian, Santhi | Balasubramanian, Anand | Marimuthu, Murugan | Chandrasekaran, Suresh | Muthusaravanan, Gokila
Article Type: Research Article
Abstract: Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced …features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model’s ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model’s predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. Show more
Keywords: Coconut leaf disease, YOLO v4, precision agriculture, pest, faster RCNN, YOLO-SPP
DOI: 10.3233/JIFS-233831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5033-5045, 2024
Authors: Zhang, Mingming | Wu, Qingling
Article Type: Research Article
Abstract: High-performance concrete (HPC) is a specialized type of concrete designed to meet stringent performance and uniformity standards that are difficult to achieve with conventional materials and standard mixing, placing, and curing methods. The testing process to determine the mechanical properties of HPC specimens is complex and time-consuming, and making improvements can be difficult after the test result does not meet the required properties. Anticipating concrete characteristics is a pivotal facet in the realm of High-Performance Concrete (HPC) manufacturing. Machine learning (ML)-driven models emerge as a promising avenue to tackle this formidable task within this context. This research endeavors to employ …a synergy of ML hybrid and ensemble frameworks for the prognostication of the mechanical attributes within HPC, encompassing compressive strength (CS), slump (SL), and flexural strength (FS). The formulation of these hybrid and ensemble constructs was executed through the integration of Support Vector Regression (SVR) with three distinct meta-heuristic algorithms: Prairie Dog Optimization (PDO), Pelican Optimization Algorithm (POA), and Mountain Gazelle Optimizer (MGO). Some criteria evaluators were used in the training, validation, and testing phases to assess the robustness of the established models, and the best model was proposed for practical applications through comparative analysis of the results. As a result, the hybrid and ensemble models were the potential methods to predict concrete properties accurately and efficiently, thereby reducing the need for expensive and time-consuming testing procedures. In general, the ensemble model, i.e., SVPPM, had a more suitable performance with high values of R2 equal to 0.989 (MPa), 0.984 (mm), and 0.992 (MPa) and RMSE = 3.82 (MPa), 9.5 (mm), and 0.30 (MPa) for CS, SL, FS compared to other models, respectively. Show more
Keywords: High-Performance dynamic properties, support vector regression, prairie dog optimization, pelican optimization algorithm, mountain gazelle optimizer
DOI: 10.3233/JIFS-234125
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5047-5072, 2024
Authors: Wang, Haomiao | Li, Yibin | Jiang, Mingshun | Zhang, Faye
Article Type: Research Article
Abstract: Domain adaptation (DA) technology has the ability to solve fault diagnosis (FD) problems under variable operating conditions. However, DA technology faces two issues: (1) in general, vibration signals inevitably contain noise, which makes it difficult to extract discriminant features.(2) there are unknown fault types in target domain. These issues will lead to poor diagnostic performance. To solve above issues, a new cross-domain open-set transfer FD method called feature improvement adversarial network (FIAN) is proposed in this article. Specifically, to alleviate noise interference, a feature improvement module (FIM) is proposed and embedded into the backbone convolutional neural network to form new …feature extractor. FIM uses soft threshold function to enhance important information and suppresses redundant information. Furthermore,open-set DA by back-propagation (OSBP) is introduced into FIAN. OSBP can predict the probability that a target domain sample belongs to an unknown category, so that it can effectively identify unknown and known category samples. Experimental results demonstrated its effectiveness and superiority in two bearing datasets. Show more
Keywords: Fault diagnosis, rolling bearing, open-set domain adaptation, feature improvement module, adversarial network
DOI: 10.3233/JIFS-236593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5073-5085, 2024
Authors: Chandana Mani, R.K. | Kamalakannan, J.
Article Type: Research Article
Abstract: Breast cancer (BC) is the most common cancer amongst women that threatens the health of women, initial diagnosis of BC becomes essential. Though there were several means to diagnose BC, the standard way is pathological analysis. Precise diagnosis of BC necessitates experienced histopathologists and needs more effort and time for completing this task. Recently, machine learning (ML) was successfully implemented in text classification, image recognition, and object recognition. With the emergence of computer aided diagnoses (CAD) technology, ML was effectively implemented for BC diagnosis. Histopathological image classification depends on deep learning (DL), particularly convolution neural network (CNN), which frequently needs …a large amount of labelled training models, whereas the labelled data was hard to obtain. This study develops an Aquila Optimizer(AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification on Histopathological Images (AOHRD-BC2HI). The proposed AOHRD-BC2HI technique inspects the histopathological images for the diagnosis of breast cancer. To accomplish this, the presented AOHRD-BC2HI technique uses hybridization of Resnet with Densenet (HRD) model for feature extraction. Moreover, the HRD method can be enforced for feature extracting procedure in which the DenseNet (feature value memory by concatenation) and ResNet (refinement of feature value by addition) were interpreted. For BC detection and classification, the DSAE model is utilized. The AO algorithm is exploited to improve the detection performance of DSAE model. The experimental validation of the presented AOHRD-BC2HI approach is tested using benchmark dataset and the results are investigated under distinct measures.Also the proposed model achieved the accuracy of 96%. The comparative result reports the improved performance of the presented AOHRD-BC2HI technique over other recent methods. Show more
Keywords: Breast cancer, histopathological images, aquila optimizer, computer aided diagnosis, deep learning
DOI: 10.3233/JIFS-236636
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5087-5102, 2024
Authors: Jyoti, | Singh, Jaspreeti | Gosain, Anjana
Article Type: Research Article
Abstract: Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM’s performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with …six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE & MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall’s W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria. Show more
Keywords: Incomplete data, missing value imputation, fuzzy clustering, LI, LIKFCM
DOI: 10.3233/JIFS-236869
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5103-5123, 2024
Authors: Zhang, Xiang | Huang, Jianhua
Article Type: Research Article
Abstract: The occurrence of safety incidents for existing glass curtain walls (EGCWs) pronounced menace to the security of both lives and property. Undertaking safety assessment for EGCWs carries essential practical significance. However, current fuzzy evaluation methods overlook the uncertainty of indicator weights and the intricacies of rank attribution. In response, this paper proposes a novel approach to the safety assessment of EGCWs. This research establishes a framework of evaluation indicators for EGCWs and divides the safety ranks of each indicator into four tiers: Safe, Mild risk, Moderate risk, and High risk. Quantitative and qualitative indicators are quantified via the variable fuzzy …cloud algorithm and cloud model. The information cloud combination weighting method is introduced to determine the weight clouds of indicators. Finally, a two-dimensional assessment result is derived using an improved fuzzy comprehensive evaluation method and fuzzy entropy. The exemplified outcomes demonstrate that this approach captures the safety status of evaluation subjects based on risk ranks, and fuzzy entropy addresses two issues: inconsistent level attribution and the comparison of identical risk ranks. The appraisal method further unveils the safety details of EGCWs, with findings that align consistently with the actual situation. Show more
Keywords: Safety evaluation, existing glass curtain wall, variable fuzzy cloud algorithm, cloud model, fuzzy entropy
DOI: 10.3233/JIFS-237414
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5125-5137, 2024
Authors: Fu, Zhiyu | Fu, Zhihui
Article Type: Research Article
Abstract: Textural translations from diverse foreign languages require reference corpora for providing error-free readability. This applies to any foreign language example English or any other language translation to Japanese. Therefore Japanese corpus repository verifies the consistency of the translated texts, words, and sentences for its readability. This article introduces a Semantic Translation Model (STM) using Fuzzy Control (FC) for supporting the aforementioned fact. The proposed model analyzes the translated word semantics based on its sentence occurrence and meaning. These two factors are analyzed using two-level fuzzy control; the first level identifies the word placement/occurrence-based readability and the second level identifies the …meaning retention. If any inconsistency is observed in the first level, the second is not carried out preventing readability errors. The fuzzy control process relies on near-to-same Japanese corpus inputs for improving readability. If the case fails then a new word replacement or displacement of the semantics is enforced. Therefore the fuzzy control levels are expanded based on different word occurrences, preventing time complexity. Show more
Keywords: Japanese translation, fuzzy control, readability, semantic analysis
DOI: 10.3233/JIFS-234575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5139-5153, 2024
Authors: Zhao, Weisen
Article Type: Research Article
Abstract: Electrochemical energy storage battery fault prediction and diagnosis can provide timely feedback and accurate judgment for the battery management system(BMS), so that this enables timely adoption of appropriate measures to rectify the faults, thereby ensuring the long-term operation and high efficiency of the energy storage battery system. Based on the idea of data driven, this paper applies the Long-Short Term Memory(LSTM) algorithm in the field of artificial intelligence to establish the fault prediction model of energy storage battery, which can realize the prediction of the voltage difference over-limit fault according to the operation data of the energy storage battery, and …introduce the parameter of the difference between maximum voltage and minimum voltage(DMM) at the cluster level to quantitatively determine whether the battery cluster has a fault. It provides powerful guidance and effective methods for the safe and stable operation of electrochemical energy storage power stations. Show more
Keywords: Fault prediction, data driven, LSTM, artificial intelligence
DOI: 10.3233/JIFS-235726
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5155-5164, 2024
Authors: Zheng, Guangyuan | Cheng, Chen | Dong, Xinling | Liu, Yi
Article Type: Research Article
Abstract: Acceleration and deceleration control, as one of the key technologies in high-speed CNC system, directly affects the machining efficiency, the stability of machining process and the error of machining follow. Therefore, it is necessary to study and explore new acceleration and deceleration control methods in high-speed CNC system to ensure the smooth feed and improve the machining accuracy. Therefore, the acceleration and deceleration control algorithm of NC system based on deep reinforcement learning and single chip microcomputer is studied. The theoretical basis of deep reinforcement learning is analyzed, and the acceleration of acceleration and deceleration is calculated based on the …linear acceleration and deceleration control. The whole integer operation of single chip microcomputer is used to estimate the value range of each step. The variation characteristics of trajectory motion are predicted so that acceleration and deceleration can be processed across program segments. The velocity of transfer point is calculated by the rate of change of feed velocity vector, and the speed of multi-program is smoothed by adjusting the allowable contour error. Based on the proximal strategy optimization algorithm in deep reinforcement learning, the acceleration and deceleration control model of CNC system is established to realize the acceleration and deceleration control. The experimental results show that the proposed algorithm has better control effect, shorter time and smaller interpolation error, which can ensure the NC system to feed smoothly at high speed. Show more
Keywords: Deep reinforcement learning, single chip microcomputer, near-end strategy optimization algorithm, CNC system, acceleration and deceleration control, Smooth processing
DOI: 10.3233/JIFS-238195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5165-5174, 2024
Authors: Yi, Yangyang | Yu, Long | Tian, Shengwei | Gao, Xuezhuang | Li, Jie | Zhao, Xingang
Article Type: Research Article
Abstract: In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing …BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model’s flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN’s superior accuracy compared to other methods, affirming its efficacy in 3D object detection. Show more
Keywords: 3D object detection, Point clouds, deep learning
DOI: 10.3233/JIFS-234024
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5175-5189, 2024
Authors: he, Jia-long | zhang, Xiao-Lin | wang, Yong-Ping | gu, Rui-Chun | liu, Li-xin | xu, En-Hui
Article Type: Research Article
Abstract: Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based …approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks. Show more
Keywords: Text classification, adversarial samples, adversarial training
DOI: 10.3233/JIFS-234034
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5191-5202, 2024
Authors: Fan, Kun | Zhang, Dingran | Lv, Yuanyuan | Zhou, Lang | Qu, Hua
Article Type: Research Article
Abstract: In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers’ cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) to solve …the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-II algorithm, and the experimental results also demonstrate the superior performance of the algorithm. Show more
Keywords: Hybrid job-shop scheduling, multiprocessor task, cooperative effect, multi-objective optimization, improved non-dominated sorting genetic algorithm-II
DOI: 10.3233/JIFS-235047
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5203-5217, 2024
Authors: Guo, Lixin
Article Type: Research Article
Abstract: We analyze the properties and characteristics of the information structure in incomplete lattice-valued information system (ILIS), we redefine the information structure and the dependence and information distance between the two information structures. In addition, in order to evaluate the uncertainty of ILIS, the concepts of granular measure and entropy measure are expounded, including information granulation, information quantity, rough entropy and information entropy. Finally, we carry out numerical experiments to verify the feasibility of the method, and carry out effective statistical analysis. These results are conducive to the establishment of granular computing framework in ILIS.
Keywords: Granular computing, incomplete lattice-valued information system, information distance, information granule, information structure
DOI: 10.3233/JIFS-235777
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5219-5237, 2024
Authors: Li, Chunhua | Zhu, Ying | Zhan, Xiaoqin | Huang, Huawei
Article Type: Research Article
Abstract: Type B semigroups are described as the generalized inverse semigroups in the range of abundant semigroup. Motivated by studying fuzzy congruences in inverse semigroups, and as a continuation of N. Kuroki’s work in inverse semigroups and our work in abundant semigroups in terms of fuzzy subsets, this paper considers fuzzy admissible congruences on some classes of type B semigroups. Our main purpose is to show when a fuzzy admissible congruence on a type B semigroup with E -properties is E -properties preserving. In particular, we get some sufficient and necessary conditions for some classes of type B semigroups to be …primitive, E -unitary and E -reflexive, respectively. As an application, we extend our results to the cases of inverse semigroups. Show more
Keywords: Fuzzy admissible congruences, type B semigroups, E-unitary, primitive, E-reflexive
DOI: 10.3233/JIFS-230383
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5239-5247, 2024
Authors: You, Xingye | Mao, Jian | Liu, Jingming | Huang, Kai
Article Type: Research Article
Abstract: Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure …and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data. Show more
Keywords: Information security, electromagnetic information leakage, feature extraction, low signal-to-noise ratio, denoising
DOI: 10.3233/JIFS-232371
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5249-5261, 2024
Authors: Pu, Shihua | Liu, Zuohua
Article Type: Research Article
Abstract: Under the highly valued environment of intelligent breeding, rapid and accurate detection of pigs in the breeding process can scientifically monitor the health of pigs and improve the welfare level of pigs. At present, the methods of live pig detection cannot complete the detection task in real time and accurately, so a pig detection model named TR-YOLO is proposed. Using cameras to collect data at the pig breeding site in Rongchang District, Chongqing City, LabelImg software is used to mark the position of pigs in the image, and data augmentation methods are used to expand the data samples, thus constructing …a pig dataset. The lightweight YOLOv5n is selected as the baseline detection model. In order to complete the pig detection task more accurately, a C3DW module constructed by depth wise separable convolution with large convolution kernels is used to replace the C3 module in YOLOv5n, which enhances the receptive field of the whole detection model; a C3TR module constructed by Transformer structure is used to extract more refined global feature information. Contrast with the baseline model YOLOv5n, the new detection model does not increase additional computational load, and improves the accuracy of detection by 1.6 percentage points. Compared with other lightweight detection models, the new detection model has corresponding advantages in terms of parameter quantity, computational load, detection accuracy and so on. It can detect pigs in feeding more accurately while satisfying the real-time performance of target detection, providing an effective method for live monitoring and analysis of pigs at the production site. Show more
Keywords: Pig, deep learning, target detection, detection network, transformer
DOI: 10.3233/JIFS-236674
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5263-5273, 2024
Authors: Li, Hai | Gao, Mingjian | Lin, Zhizhan | Peng, Jian | Xie, Liangzhen | Ma, Junjie
Article Type: Research Article
Abstract: Background: Atrial fibrillation (AF), one of the most prevalent heart rhythm disorders, may lead to thromboembolism, heart failure, and sudden death. However, the mechanism of AF has not yet been fully explained. Objective: This study aims to identify novel gene signatures and to investigate the potential therapeutic targets of AF with an integrated bioinformatic approach. Methods: The gene expression and methylation datasets of AF were obtained through the Gene Expression Omnibus (GEO) database. Subsequently, a set of differentially expressed genes and differential methylation sites were identified. Gene functional annotation analysis was conducted to explore the potential …function of differentially-methylated/expressed genes. Then, we constructed a PPI network and TF–miRNA–mRNA network. Finally, weighted gene co-expression network analysis (WGCNA) was presented to study critical modules of AF. Results: Seven hypomethylated-high expression genes and nine hypermethylated-low expression genes were acquired from AF patients. Functional enrichment results indicated that the differentially-methylated/expressed genes were mainly concentrated in decidualization, maternal placenta development, regulation of nitric-oxide synthase activity, and osteoclast differentiation. Based on the results of the PPI, we defined 4 key genes namely FHL2, STC2, ALPK3, and RAP1GAP2 as the core genes, playing essential roles in the TF-miRNA-mRNA network. In the end, we constructed two co-expression modules that highly correlated with AF-related phenotype. Conclusion: In our study, we found critical genes for AF that might help understand the molecular changes in AF. Show more
Keywords: Atrial fibrillation, differentially expressed genes, DNA methylation, hub genes, miRNA, co-expression analysis
DOI: 10.3233/JIFS-234306
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5275-5285, 2024
Authors: Xhafaj, Evgjeni | Qendraj, Daniela Halidini | Salillari, Denisa
Article Type: Research Article
Abstract: The adoption of online banking is a big challenge as well as an emergent paradigm which is evolving quickly. The study develops a new exploration model that is used to determine significant constructs of the UTAUT theory that influence the adoption of online-banking. A study was conducted through an online survey of online banking users. Three methods were used; firstly, PLS-SEM was used to determine which of the constructs have a significant impact on behavioral intention to use e-banking, secondly, an incorporated neural network model was used to classify the relative impact of significant variables obtained from PLS-SEM, and finally …a hybrid procedure is incorporated from PLS-SEM to initialize the Fuzzy TOPSIS. The findings of the research paper constitute the ranked results and a comparison between them. The results of PLS-SEM and ANN analysis showed the same ranking for the constructs, while the decision making method Fuzzy TOPSIS introduced some changes in the ranking. This study presents valuable insights for the banking system to bring effective projects that increase the possibilities of using online banking. Show more
Keywords: PLS-SEM, ANN, online banking, fuzzy TOPSIS, UTAUT
DOI: 10.3233/JIFS-235388
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5287-5297, 2024
Authors: Zheng, Xuehui | Wang, Jun | Gao, You
Article Type: Research Article
Abstract: Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by …GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND). Show more
Keywords: Wireless sensor network, sparse learning, TSK fuzzy system, GS-TSK
DOI: 10.3233/JIFS-224252
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5299-5311, 2024
Authors: Rani, R.M. | Dwarakanath, B. | Kathiravan, M. | Murugesan, S. | Bharathiraja, N. | Vinoth Kumar, M.
Article Type: Research Article
Abstract: Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) …for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI’2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes. Show more
Keywords: Liver segmentation, classification, deep learning, and mask RCNN
DOI: 10.3233/JIFS-232195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5313-5328, 2024
Authors: Mohananthini, N. | Rajeshkumar, K. | Ananth, C.
Article Type: Research Article
Abstract: Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents …a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment. Show more
Keywords: Heart disease detection, healthcare, blockchain, security, fuzzy logic, feature selection
DOI: 10.3233/JIFS-232902
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5329-5342, 2024
Authors: Adar, Tuba | Delice, Elif Kılıç | Delice, Orhan
Article Type: Research Article
Abstract: Accurate and rapid diagnosis is a significant factor in reducing incidence rate; especially when the number of people inflicted with a disease is considerably high. In the healthcare sector, the decision-making process might be a complex and error-prone one due to excessive workload, negligence, time restrictions, incorrect or incomplete evaluation of medical reports and analyses, and lack of experience as well as insufficient knowledge and skills. Clinical decision support systems (CDSSs) are those developed to improve effectiveness of decisions by supporting physicians’ decision-making process regarding their patients. In this study, a new artificial intelligence-based CDSS and a user-friendly interface for …this system were developed to ensure rapid and accurate detection of pandemic diseases. The proposed CDSS, which is called panCdss, uses hybrid models consisting of the Convolutional Neural Network (CNN) model and Machine Learning (ML) methods in order to detect covid-19 from lung computed tomography (CT) images. Transfer Learning (TL) models were used to detect monkeypox from skin lesion images and covid-19 from chest X-Ray images. The results obtained from these models were evaluated according to accuracy, precision, recall and F1-score performance metrics. Of these models, the ones with the highest classification performance were used in the panCdss. The highest classification values obtained for each dataset were as follows: % 91.71 accuracy, % 92.07 precision, % 90.29 recall and % 91.71 F1-score for covid-19 CT dataset by using CNN+RF hybrid model; % 99.56 accuracy, % 100 precision, % 99.12 recall and % 99.55 F1-score for covid-19 X-ray dataset by using VGG16 model; and % 90.38 accuracy, % 93.32 precision, % 88.11 recall and % 90.64 F1-score for monkeypox dataset by using MobileNetV2. It is believed that panCdss can be successfully employed for rapid and accurate classification of pandemic diseases and can help reduce physicians’ workload. Furthermore, the study showed that the proposed CDSS is an adaptable, flexible and dynamic system that can be practiced not only for the detection of pandemic diseases but also for other diseases. To the authors’ knowledge, this proposed CDSS is the first CDSS developed for pandemic disease detection. Show more
Keywords: Clinical decision support system, artificial intelligence, deep learning, user interface, pandemic diseases
DOI: 10.3233/JIFS-232477
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5343-5358, 2024
Authors: Qin, Xiwen | Ji, Xing | Zhang, Siqi | Xu, Dingxin
Article Type: Research Article
Abstract: The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy …data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model. Show more
Keywords: Credit scoring, improved PSO, Fuzzy C-means, undersampling, ensemble model
DOI: 10.3233/JIFS-233334
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5359-5376, 2024
Authors: Wu, Yixun | Wang, Taiyu | Gu, Runze | Liu, Chao | Xu, Boqiang
Article Type: Research Article
Abstract: In order to address the problem of decreased accuracy in vehicle object detection models when facing low-light conditions in nighttime environments, this paper proposes a method to enhance the accuracy and precision of object detection by using the image translation technology based on the Generative Adversarial Network (GAN) in the field of computer vision, specifically the CycleGAN, from the perspective of improving the training set of object detection models. This is achieved by transforming the existing well-established daytime vehicle dataset into a nighttime vehicle dataset. The proposed method adopts a comparative experimental approach to obtain translation models with different degrees …of fitting by changing the training set capacity, and selects the optimal model based on the evaluation of the effect. The translated dataset is then used to train the YOLO-v5-based object detection model, and the quality of the nighttime dataset is evaluated through the evaluation of annotation confidence and effectiveness. The research results indicate that utilizing the translated nighttime vehicle dataset for training the object detection model can increase the area under the PR curve and the peak F1 score by 10.4% and 9% respectively. This approach improves the annotation accuracy and precision of vehicle object detection models in nighttime environments without requiring additional labeling of vehicles in monitoring videos. Show more
Keywords: Vehicle object detection, CycleGAN, nighttime vehicle image dataset, deep learning, machine vision
DOI: 10.3233/JIFS-233899
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5377-5389, 2024
Authors: Li, Jie
Article Type: Research Article
Abstract: In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By …harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform’s functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system’s efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management. Show more
Keywords: Machine learning, wireless network technology, digital engineering audit, audit management strategy
DOI: 10.3233/JIFS-230759
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5391-5403, 2024
Authors: Zhang, Yehua | Zhang, Yan
Article Type: Research Article
Abstract: With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the …traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape “hometown” score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function. Show more
Keywords: Ethnic music, music therapy, repertoire selection, deep learning
DOI: 10.3233/JIFS-230893
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5405-5414, 2024
Authors: Sureka, V. | Kavya, G.
Article Type: Research Article
Abstract: Automobiles have undergone a transformation during the past two decades due to the merger of the electronics and automotive industries. The combination of autos and electronic sensors has resulted in a new generation of vehicles known as autonomous vehicles (AVs). These AVs have a few hundred thousand sensors, producing an enormous amount of raw data for computation. Data from the vehicular network can be offloaded to existing telecommunication infrastructure to address the problem of processing resources. In order to address vehicular network requirements, large-capacity servers deployed in major telecommunications networks are first used to offload resource-intensive tasks. Mobile Cloud Computing …(MCC) is a critical enabling technology for 5 G networks, which has a key feature of offloading to divide application tasks into local and cloud server execution components. This paper proposes a novel Three TierEdge cloud computing (T2 EC2 ) system which uses an Energy-aware Dynamic Task offloading and collaborative task execution algorithm (EA-DTOCTE) for multilayer vehicular cloud computing networks. The EA-DTOCTE algorithm is included in the decision-making engine in the proposed system, which selects whether to offload the task to the remote environment or implement it locally. EA-DTOCTE focuses on consumption of energy by tasks both locally and remotely since its goal is to efficiently and dynamically split the application into tasks and schedule them on local devices and cloud resources. The proposed T2 EC2 has been evaluated in terms of parameters such as energy consumption, completion time, and throughput. Experimental results indicate that the proposed T2 EC2 can save up to 28% of system energy consumption compared with other state-of-art techniques. Show more
Keywords: Autonomous vehicles, mobile cloud computing, application partitioning, offloading, scheduling, EA-DTOCTE, decision making engine, collaborative task execution
DOI: 10.3233/JIFS-220970
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5415-5427, 2024
Authors: Cao, Peng | Xiao, Jing
Article Type: Research Article
Abstract: The Belt and Road (B&R) plan is put out within the framework of global economics and strategic growth. This study examines the written material of popular tourist sites along B&R and the tourism assets from the viewpoint of B&R, based on the wireless network and AI technology, and using a big data platform and the Internet of Things (IoT) User Generated Content (UGC) network structure. To manage tourist pictures from customers’ views, online travel notes are first utilized as examples. Next, tourism texts’ keywords are extracted using Python big data and AI technology to understand consumers’ perceptions of scenic spot …preferences, tourism facilities and services, and social and cultural customs. The findings demonstrate that, when compared to the conventional tourism brand development strategy, the integrated development strategy based on the AI big data platform can not only increase the effectiveness of managing tourists’ perceptions of scenic locations but can also encourage the common development of national sports event components and intelligent tourism image management. Several sports tourist boutique picturesque locations have also been built along B&R following years of development of intelligent tourism and sports projects, which will strengthen the effect of multicultural exchanges. Show more
Keywords: The Belt and Road, traditionalsports, tourism brand, big data, artificial intelligence
DOI: 10.3233/JIFS-230547
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5429-5439, 2024
Authors: Mahalakshmi, G. | Uma, E.
Article Type: Research Article
Abstract: Intelligent Transportation Systems have become integral to daily life, with VANETs (vehicular ad-hoc networks) playing the pivotal role. VANETs, the subsets of MANETs, employ vehicles as nodes to establish intelligent transport systems. However, due to critical applications such as military use, these networks are susceptible to attacks. With features like high mobility, dynamic network topology, and coverage issues, security breaches are a concern. This necessitates a secure routing algorithm to mitigate attacks and ensure message delivery. In our study, we utilize the UNSW-NB15 intrusion detection dataset to develop training and testing models. Our proposed novel intrusion detection system employs a …feature selection algorithm that prioritizes significant arriving traffic attributes. This algorithm enhances abnormal activity detection while minimizing associated features. To achieve this, we modify the Conditional Random Field algorithm with fuzzy-based rules, resulting in a more efficient selection of influential and contributing features for detecting attacks such as DoS, Worms, Fuzzers, and Shellcode. Through appropriate feature selection using the modified Conditional Random Field and Support Vector Machine classification system in our experiments, we demonstrate a notable increase in security by reducing the false positive rate. Additionally, our approach excels in detecting accuracy of Fuzzers (98.86%), DoS (98.80%), Worms (34.45%), and Shellcode (89.308%), ultimately enhancing network performance. These findings underscore the effectiveness of our proposed method in enhancing intrusion detection and overall network efficiency. Show more
Keywords: Vehicular ad-hoc networks, intrusion detection, feature selection, classification
DOI: 10.3233/JIFS-234192
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5441-5453, 2024
Authors: Anandha Kumar, M. | Shanmuga Priya, M. | Arunprakash, R.
Article Type: Research Article
Abstract: In the past couple of years, neural networks have gained widespread use in network security analysis. This type of analysis is usually performed in a nonlinear and highly correlated manner. Due to the immense amount of data traffic, the current models are prone to false alarms and poor detection. Deep-learning models can help security researchers identify and extract data features that are related to an attack. They can also minimize the data’s dimensionality and detect intrusions. Unfortunately, the complexity of the network structure and hidden neurons of a deep-learning model can be set by error-prone procedures. In order to improve …the performance of deep learning models, a new algorithm is proposed. This method combines a gradient boost regression and particle swarm optimization. The proposes a method called the Spark-DBN-SVM-GBR algorithm. The simulations conducted proposed algorithm revealed that it has a better accuracy rate than other deep learning models and the experiments conducted on the PSO-GBR algorithm revealed that it performed better than the current optimization technique when detecting unauthorized attack activities. Show more
Keywords: Intrusion detection, Apache Spark, Support vector machine (SVM), particle swarm optimization and gradient boost regression
DOI: 10.3233/JIFS-221351
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5455-5463, 2024
Authors: Khatab, Hussein Ageel | Shareef, Salah Gazi
Article Type: Research Article
Abstract: The conjugate gradient (CG) techniques are a class of unconstrained optimization algorithms with strong local and global convergence qualities and minimal memory needs. While the quasi-Newton methods are reliable and efficient on a wide range of problems and these methods are converge faster than the conjugate gradient methods and require fewer function evaluations, however, they are request substantially more storage, and if the problem is ill-conditioned, they may require several iterations. There is another class, termed preconditioned conjugate gradient method, it is a technique that combines two methods conjugate gradient with quasi-Newton. In this work, we proposed a new two …limited memory preconditioned conjugate gradient methods (New1 and New2), to solve nonlinear unconstrained minimization problems, by using new modified symmetric rank one (NMSR1) and new modified Davidon, Fletcher, Powell (NMDFP), and also using projected vectors. We proved that these modifications fulfill some conditions. Also, the descent condition of the new technique has been proved. The numerical results showed the efficiency of the proposed new algorithms compared with some standard nonlinear, unconstrained problems. Show more
Keywords: Unconstrained optimization, projected quasi-newton methods, preconditioned conjugate gradient methods, limited memory preconditioned conjugate gradient methods
DOI: 10.3233/JIFS-233081
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5465-5478, 2024
Authors: Zhou, Sijiang | Mo, Kanglin | Yang, Xia | Ning, Zong
Article Type: Research Article
Abstract: OBJECTIVE: This research aims to pinpoint key biomarkers and immunological infiltration of idiopathic pulmonary fibrosis (IPF) through bioinformatics analysis. METHODS: From the GEO database, 12 gene expression profiles were obtained. The LIMMA tool in Bioconductor accustomed to identify the genes that are expressed differently (DEGs), and analyses of functional enrichment were performed. A protein-protein interaction network (PPI) was constructed using STRING and Cytoscape, and a modular analysis was performed. Analysis of the immunological infiltration of lung tissue between IPF and healthy groups was done using the CIBERSORTx method. RESULTS: 11,130 genes with differential expression (including 7,492 …up-regulated and 3,638 down-regulated) were found. The selected up-regulated DEGs were mainly involved in the progression of pulmonary fibrosis and the selected down-regulated DEGs maintain the relative stability of intracellular microenvironment, according to functional enrichment analysis. KEGG enrichment analysis revealed that up-regulated DEGs were primarily abundant in the PI3K-Akt signaling mechanism, whereas down-regulated DEGs were associated with cancer pathways. The most significant modules involving 8 hub genes were found after the PPI network was analyzed. IPF lung tissue had a greater percentage of B memory cells, plasma cells, T cells follicular helper, T cells regulatory, T cells gamma delta, macrophages M0 and resting mast cells. while a relatively low proportion of T cells CD4 memory resting, NK cells resting and neutrophils. CONCLUSION: This research demonstrates the differences of hub genes and immunological infiltration in IPF. Show more
Keywords: Idiopathic pulmonary fibrosis, biomarkers, immunological infiltration, lung tissue, bioinformatics analysis
DOI: 10.3233/JIFS-234957
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5479-5489, 2024
Authors: Al-Jamaan, Rawabe | Ykhlef, Mourad | Alothaim, Abdulrahman
Article Type: Correction
DOI: 10.3233/JIFS-219331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5491-5491, 2024
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