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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: 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: Du, Baigang | Rong, Yuying | Guo, Jun
Article Type: Research Article
Abstract: Quality Function Deployment (QFD) is a powerful approach for improving product quality that can transform customer requirements (CRs) into engineering characteristics (ECs) during product manufacturing. The limitations of traditional QFD methods lead to imprecise quantification of CRs and difficulty in accurately mapping customer needs. To address these issues, this paper introduces an innovative QFD approach that integrates extended hesitant fuzzy linguistic term sets (EHFLTSs), CRITIC, and cumulative prospect theory. The method expresses the subjectivity and hesitancy of decision makers when evaluating the relationship between ECs and CRs using EHFLTSs, considering the conflicts among CRs. The CRITIC is used to comprehensively …evaluate the comparison strength and conflict between indicators, and the cumulative prospect theory is utilized to derive the prioritization of ECs. A case study is presented to demonstrate the effectiveness of the proposed approach. Show more
Keywords: Extended hesitant fuzzy linguistic term set, cumulative prospect theory, quality function deployment, CRITIC
DOI: 10.3233/JIFS-237217
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Martín-del-Campo-Rodríguez, C. | Batyrshin, Ildar | Sidorov, Grigori
Article Type: Research Article
Abstract: Word embeddings have been successfully used in diverse tasks of Natural Language Processing, including sentiment analysis and emotion classification, even though these embeddings do not contain any emotional or sentimental information. This article proposes a method to refine pre-trained embeddings with emotional and sentimental content. To this end, a Multi-output Neural Network is proposed to learn emotions and sentiments simultaneously. The resulting embeddings are tested in emotion classification and sentiment analysis tasks, showing an improvement compared with the pre-trained vectors and other proposes in the state-of-the-art for fine-grained emotion classification.
Keywords: Word embedding, multi-output neural network, VAD, polarity, emotion classification
DOI: 10.3233/JIFS-219354
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Mathi, Senthilkumar | Jothi, Uma | Saravanan, G. | Ramalingam, Venkadeshan | Sreejith, K.
Article Type: Research Article
Abstract: Mobile devices have risen due to internet growth in recent years. The next generation of internet protocol is evolving for mobile devices to generate their addresses and get continuous services across networks to support the enormous number of addresses in network-based mobility. The mobile device updates its current location to its home network and the correspondent users through a binding update scheme in the visited network. Numerous studies have investigated binding update schemes to verify the reachability of the mobile device at its home network. However, most schemes endure security threats due to the incompetence of authenticating user identity and …concealing the temporary location of mobile devices. To address these issues, this paper proposes a secure and efficient binding update scheme (One-CLU) by incorporating a one-key-based cryptographically generated address (CGA) to validate and conceal the address ownership of mobile devices with minimal computations. The security correctness of the proposed One-CLU scheme is verified using AVISPA – a model checker. Finally, the simulation and the numerical results showthat the proposed scheme significantly reduces communication payloads and costs for the binding update, binding refresh, and packet delivery. Show more
Keywords: Mobile communication, routing, privacy, cryptography, communication security
DOI: 10.3233/JIFS-219422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Al-Azani, Sadam | Almeshari, Ridha | El-Alfy, El-Sayed
Article Type: Research Article
Abstract: Speaker demographic recognition and segmentation analytics play a key role in offering personalized experiences across different automated industries and businesses. This paper aims at developing a multi-label demographic recognition system for Arabic speakers from audio and associated textual modalities. The system can detect age groups, genders, and dialects, but it can be easily extended to incorporate more demographic traits. The proposed method is based on deep learning for feature learning and recognition. Representations of audio modality are learned through 3D spectrogram and AlexNet CNN-based architecture. An AraBERT transformer is employed for learning representations of the textual modality. Additionally, a method …is provided for fusing audio and textual representations. The effectiveness of the proposed method is evaluated using the Saudi Audio Dataset for Arabic (SADA), which is a recently published database containing audio recordings of TV shows in different Arabic dialects. The experimental findings show that when using models with standalone modalities for multi-label demographic classification, textual modality using AraBERT performed better than the audio modality represented using 3D spectrogram along with AlexNet CNN-based architecture. Furthermore, when combining both modalities, audio and textual, significant improvement has been attained for all demographic traits. Show more
Keywords: Demographic, 3D spectrogram, AraBERT, multi-label classification, Arabic LLMs, multimodal deep learning
DOI: 10.3233/JIFS-219389
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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