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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: Liu, Fuchen | Zhou, Sijia | Zhang, Dezhou | Wang, Xiaocui
Article Type: Research Article
Abstract: Deep learning has demonstrated remarkable advantages in the field of human pose estimation. However, traditional methods often rely on widening and deepening networks to enhance the performance of human pose estimation, consequently increasing the parameter count and complexity of the networks. To address this issue, this paper introduces Ghost Attentional Down network, a lightweight human pose estimation network based on HRNet. This network leverages the fusion of features from high-resolution and low-resolution branches to boost performance. Additionally, GADNet utilizes GaBlock and GdBlock, which incorporate lightweight convolutions and attention mechanisms, for feature extraction, thereby reducing the parameter count and computational complexity …of the network. The fusion of relationships between different channels ensures the optimal utilization of informative feature channels and resolves the issue of feature redundancy. Experimental results conducted on the COCO dataset, with consistent image resolution and environmental settings, demonstrate that employing GADNet leads to a reduction of 60.7% in parameter count and 61.2% in computational complexity compared to the HRNet network model, while achieving comparable accuracy levels. Moreover, when compared to commonly used human pose estimation networks such as Cascaded Pyramid Network (CPN), Stacked Hourglass Network, and HRNet, GADNet achieves high-precision detection of human keypoints even with fewer parameters and lower computational complexity, our network has higher accuracy compared to MobileNet and ShuffleNet. Show more
Keywords: Human pose estimation, high-resolution network, attention mechanism, feature redundancy
DOI: 10.3233/JIFS-233501
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Vusirikkayala, Gowthami | Madhu Viswanatham, V.
Article Type: Research Article
Abstract: Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for …community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102 ], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks. Show more
Keywords: Community detection (CD), complex networks (CN), graph neural network (GNN), deep learning (DL), communities, clusters
DOI: 10.3233/JIFS-235913
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Abu-Sharkh, Osama M.F. | Surkhi, Ibrahim | Zabin, Hamzah | Alhasan, Maher
Article Type: Research Article
Abstract: As the entire world is becoming increasingly a global village, the need for reliable, smooth, and easy-to-use applications that facilitate the communication process between people speaking different languages worldwide becomes essential, especially in the tourism industry. While numerous online and mobile applications attempt to bridge the linguistic gap using text-to-text, text-to-voice, or voice-to-text-to-voice translators, they often fall short due to constraints such as the need for a single shared device, manual setup of speaker’s gender and preferred language, and an inability to communicate from a distance. These applications struggle to mimic the practical nature of real-time multilingual conversations where immediate …and clear communication is paramount. This paper introduces an intelligent peer-to-peer polyglot voice-to-voice mobile application to facilitate the communication of people speaking different languages worldwide transparently mimicking a live conversation whether the involved parties are close to each other or at a nearby distance. People can interact with others transparently using their preferred language, irrespective of others’ languages, while the application automatically recognizes the language, gender of the speaker, and spoken words with very high accuracy. Five languages were implemented in the developed application as a proof-of-concept, and it is designed to smoothly and simply adapt more in future updates. Show more
Keywords: Multilingual, intelligent, text-to-voice, translation, voice-to-text
DOI: 10.3233/JIFS-219388
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Tariq, Sana | Amin, Asjad
Article Type: Research Article
Abstract: The emergence of machine learning in the recent decade has excelled in determining new potential features and nonlinear relationships existing between the data derived from the DNA sequences of genetic diseases. Machine learning also enhances the process of handling data with maximum predicted variables compared to observations during the data mining process of prediction. In this context, our study presents a deep learning model for predicting Transcription Factor Binding Sites (TFBS) in DNA sequences, with a focus on features within genetic data associated with diseases. Transcription Factors (TFs) play a crucial role in modulating gene expression by binding to TFBS. …The accurate prediction of TFBS is essential for understanding genome function and evolution. Thus, we develop an efficient deep learning model that considers TFBS prediction as a nucleotide-level binary classification task. In our proposed model, first we create an input matrix using the original DNA sequences. Next, we encode these DNA sequences using one-hot encoding, representing them as a sequence of numerical values. We then employ three convolutional layers, allowing our model to capture intricate patterns and motif features over a larger spatial range. To capture important features within the DNA sequence and to focus on them, we incorporate an attention layer. Finally, a dense layer, consisting of two fully connected layers and a dropout layer, calculates the probability of TF binding site occurrence based on the features learned by the proposed model. Our experimental results, using in-vivo datasets obtained from Chip-seq, demonstrate the superior performance of our proposed deep learning model in TFBS prediction compared to other existing state-of-the-art methods. The improvement in accuracy is due to additional layers of CNN and then an attention layer in the model. Thus, this result in a better performance of our approach in predicting the transcription factor binding sites and enhancing our understanding of gene regulation and genome function. Show more
Keywords: Transcription factor binding sites, one-hot encoding, convolutional layer, attention layer
DOI: 10.3233/JIFS-238159
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Vu, Hoai Nam | Nguyen, Quang Dung | Nguyen, Thuy Linh | Tran-Anh, Dat
Article Type: Research Article
Abstract: In the real world, the appearance of similar rice varieties depends on various factors such as resolution, angle, lighting conditions, and perspective. Additionally, complex environmental factors and characteristics of each rice type, such as enhanced light intensity, cross-polarization, and shading, rice background color, and image similarity, play a role. This indicates that the data augmentation process may enhance the accuracy of crop identification, particularly in the context of self-supervised machine learning. The aim of this research is to develop a precise rice segmentation method based on the improved Mask R-CNN (Region-based Convolutional Neural Network) with multitask data augmentation. The Mask …R-CNN model is enhanced by incorporating multitask input to improve feature extraction for rice. Experimental results demonstrate that the improved Mask R-CNN model can accurately segment various rice types under different conditions, such as different background colors and varying sizes of rice grains. The achieved precision, recall, F1 score, and segmentation mean Average Precision (mAP) are 95.5%, 96.3%, 95.9%, and 0.924, respectively. The average runtime on the test set is 0.35 seconds per image. Our method outperforms two comparative approaches, showcasing its ability to accurately segment rice in the market deployment phase with near real-time performance. This study establishes the foundation for the accurate detection of valuable agricultural products. Show more
Keywords: Multi-augmentation, deep learning, Mask RCNN, rice recognition, fusion metric
DOI: 10.3233/JIFS-241133
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Wang, Lin | Ye, Hongling | Wang, Pengfei | Xu, Chi | Qian, Aiwen
Article Type: Research Article
Abstract: To enhance the control performance of semi-active suspension systems, this research proposes a particle swarm optimization algorithm (PSO) with adaptive nonlinear correction of inertia weights, which is then integrated with a proportional integral differential (PID) algorithm. To this end, this research establishes quarter semi-active and passive suspension models of automobiles by utilizing the Matlab/Simulink simulation platform. In this foundation, this research further compares the advantages and disadvantages regarding performance indexes of semi-active suspension controlled by the adaptive inertia weighted particle swarm optimization (APSO) algorithm and the PID algorithm, as well as the PID-controlled semi-active suspension and passive suspension through simulation. …Simulation results indicate that performance indicator values for different suspension types increase with higher pavement grades. Compared with passive suspension, the semi-active suspension controlled by APSO and PID algorithms presents significantly improved performance indexes, with reductions of at least 31.61% in root mean square (RMS) concerning body vertical acceleration, 1.78% in suspension dynamic deflection, and 22.13% in tire dynamic loads. Moreover, analysis of suspension system frequency response characteristics demonstrates a significant decrease in droop acceleration transmission rate for the semi-active suspension with APSO and PID algorithms across the whole frequency range compared with that of the PID-controlled suspension and passive suspension. On the same note, despite the higher values of suspension dynamic deflection and tire dynamic load transfer rate in certain frequency bands, they are generally within acceptable suspension limits. Simply put, the findings confirm the feasibility of applying the APSO algorithm in PID-controlled semi-active suspension systems, which effectively improves both vehicle ride comfort and handling stability. Show more
Keywords: Semi-active suspension, PID control, improved particle swarm optimization algorithm
DOI: 10.3233/JIFS-234812
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wang, Xiao | Wang, Dan | Zhou, Jincheng
Article Type: Research Article
Abstract: The correspondence between the decision space and the objective space is often many-to-one in multi-objective optimization problems. Therefore, a class of problems with such mapping relationships is defined as a MMOPs. For these problems, how to ensure the final solution converges to each Pareto solution set and guarantees the diversity of the algorithm is an urgent problem. The method of the paper with opposition-based strategy, a multimodal multi-objective optimization algorithm, is proposed. The algorithm proposed is called MMODE_OP, which is framed by a differential evolutionary algorithm, and opposition-based learning is applied to the initialization phase and generation-hopping phase to filter …out the more promising individuals in the population for iteration to enhance the global search capability and the diversity of population. In addition, different Gaussian perturbation strategies are adopted with iteration to achieve the search of the neighborhood, which can further not only improve the quality of the Pareto solution set but also enable the convergence of the Pareto solution set quickly. This method improves the algorithm’s local and global search ability, and enables multiple the Pareto solution set and improving the convergence. In the meantime, adaptive scaling factors and crossover factors are designed in this paper to enhance the improved search capability. Finally, the experiment results of MMODE_OP and other excellent algorithms on 13 test problems corroborate the proposed methods have superior performance. Show more
Keywords: Multimodal, multi-objective, differential evolutionary algorithm, opposition-based learning
DOI: 10.3233/JIFS-233826
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Berretti, Stefano
Article Type: Editorial
DOI: 10.3233/JIFS-219381
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: David Raj, G. | Mukherjee, Saswathi | Jasmine, R.L.
Article Type: Research Article
Abstract: To enhance the reliability of the document retrieval system, the most efficient techniques such as Query Expansion (QE) are utilized. It has offered more adequate queries for the user when assimilated over original or initial queries by adding up one or more expansion keywords. Moreover, these techniques are more effective to enhance the performance of document retrieval and return the unnecessary information. In recent times, searching the suitable documents in the huge datasets is tiresome work. Generally, the automatic QE is used to address the refining query. A typical technique for QE has included the extracted close expression and the …related documents clustering by utilizing the clusters. However, classical clustering poses some issues to QE. Hence, a novel optimized bi-clustering mechanism is proposed in this paper for patent retrieval by QE. The ultimate aim of this implemented model is to retrieve the patent information by expanding the request query. Initially, the patent-related data is collected from standard data sources in terms of abstract and text. It is then given to the text pre-processing stage. Consequently, the pre-processed text or word is converted into vector formation by using the Multi-cascade Transformer Network (MTN). Finally, the retrieval process is done by proposing the Optimal Bi-Clustering (OptBi-C) process, in which the parameters are optimally determined by a hybrid algorithm of Reptile Search Algorithm (RSA) and Lion Algorithm (LA) termed as Iteration-based Reptile Search and Lion Algorithm (IRSLA). Thus, the performance of the model is examined with certain metrics and compared with traditional techniques. The precision of the implemented patent retrieval system using the QE model is maximized by 8.82% of DHOA-OptBi-C, 7.35% of HHO-OptBi-C, 10.29% of RSA-OptBi-C, and 7.35% of LA-OptBi-C respectively when the number of retrieved data is 10. Moreover, the recall of the designed patent retrieval system using the QE model is enhanced by 21.83% of KNN, 24.13% of CNN, 19.54% of FUZZY, and 11.49% of Bi-clustering respectively when the number of retrieved data is 6. Thus, the findings demonstrate that the system improves the retrieval performance. Show more
Keywords: Patent retrieval system, query expansion, multi cascaded transformer network, iteration-based reptile search and lion algorithm, optimal bi-clustering
DOI: 10.3233/JIFS-241138
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Atef, Shimaa | El-Seidy, Essam | Abd El-Salam, Salsabeel M.
Article Type: Research Article
Abstract: Relatedness is necessary and causal in the development of social life. Interlayer relatedness is a measure of how one player’s decisions affect the decisions of other players in the game. The relatedness can be positive or negative. We had to determine how effective each strategy was under specific conditions, and how the correlation between players affected their payoffs. In this paper, we analytically study the strategies that enforce linear payoff relationships in the Iterated Prisoner’s Dilemma (IPD) game considering both a relatedness factor. As a result, we first reveal that the payoffs of two players and three players can be …represented by the form of determinants as shown by Press and Dyson even with the factor. Show more
Keywords: Equalizer, iterated prisoner’s dilemma (IPD), relatedness, two-player, three-player, zero-determinant strategies (ZD)
DOI: 10.3233/JIFS-239406
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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