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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: Annamalai, Surya | Jayakumar, Vimala
Article Type: Research Article
Abstract: The Hypersoft set (HSS) theory was created by extending the soft set (SS) theory. The q-Rung linear diophantine fuzzy set (q-RLDFS) is a major development in fuzzy set theory (FS). By fusing q-RLDFS with HSS, the concept of q-rung linear diophantine fuzzy hypersoft set (q-RLDFHSS) is presented in this study. This study also discusses the concepts of lattice ordered q-RLDFHSS (LOq-RLDFHSS) and LOq-RLDFHS Matrix (LOq-RLDFHSM) as well as some standard operations of LOq-RLDFHSM. A medical diagnosis methodology based on LOq-RLDFHSM is proposed to evaluate multi-sub-attributed medical diagnosis difficulties incredibly well along with a diagnosis problem based on patients with comorbidities. …Further, between the proposed and current theories, comparison analysis and discussion have been given in this study. Show more
Keywords: q-Rung linear diophantine fuzzy set (q-RLDFS), hypersoft set(HSS), lattice, medical diagnosis
DOI: 10.3233/JIFS-219414
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Amrutha Raj, V. | Malu, G.
Article Type: Research Article
Abstract: Deep learning has gained popularity across several industries, including object recognition and classification. In the case of Convolutional Neural Networks (CNN), the first layers extract the most noticeable elements, such as shape and margin. As the model progresses, it learns to extract more complex features such as texture and color; conversely, skeleton features encompass significant locations (joints) that do not naturally align with the grid-like architecture intended for these networks. This study emphasizes the importance of structural features in enhancing the performance of deep learning models. It introduces the Gesture Analysis Module Network (GAMNet), which computes abstract structural values within …the architecture for feature extraction, prioritization, and classification. These values go through a rigorous evaluation process along with the cutting-edge deep learning model, CNN, and result in intermediate representations, leading to better performance in gesture analysis. An automated dance gesture identification system can address the challenges of recognizing hand movements in unpredictable lighting, varied backgrounds, noise, and changing camera angles. Despite these challenges, GAMNet performed remarkably well, surpassing renowned models like VGGNet, ResNet, EfficientNet, and CNN, achieving a classification accuracy of 96.80%, even in challenging image circumstances. This paper highlights how GAMNet can revolutionize the world of classical Indian dance, opening up new opportunities for research and development in this field. Show more
Keywords: Data augmentation, deep architecture, gesture recognition, structural features, skeleton, convolutional neural network
DOI: 10.3233/JIFS-219395
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Asthana, Amit | Dwivedi, Sanjay K.
Article Type: Research Article
Abstract: Understanding machine translation (MT) quality is becoming more and more important as MT usage continues to rise in the translation industry. The acceptance of MT output based on their performance and, ultimately, how acceptable the translators actually are, have received relatively less attention so far. MT plays a vital role in CLIR systems and their retrieval efficiency is directly proportional to the translation accuracy of the queries. The varied meanings of words, sentences carrying multiple interpretations, and differing grammatical structures across languages contribute to the complexity of the MT task. The lack of structural constraints and the presence of ambiguity …further compound the complications especially in case of web queries. The objective of this work is to assess the accuracy of free online translators in translating Hindi web queries. The accuracy of the translators has been evaluated on various metrics, i.e., BLEU, NIST, METEOR, hLepor, CHRF and GLEU. Our findings indicate that the translation accuracy for longer queries is higher than the shorter ones. Overall Google translator’s performance has been found the best while Systran performs the worst with 42.06% performance difference between the two. The present work intends to help researchers in further evaluating and analyzing the MT systems specially in context of web query translation, ultimately leading to improved translation quality and retrieval accuracy in CLIR. Show more
Keywords: Machine translation, evaluation metrics, Hindi web query
DOI: 10.3233/JIFS-235532
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Rong, Mansong | Wei, Yuan | Xiao, Zhijun | Peng, Hongchong | Schröder, Kai-Uwe
Article Type: Research Article
Abstract: In order to improve the identification accuracy of bearing fault diagnosis, overcome the training difficulties and poor generalization ability of fault diagnosis model under the condition of small samples, this work constructs the LSTM-GAN model by combining long short-term memory network (LSTM) with generative adductive neural network (GAN). Firstly, LSTM is used to build a generator to generate adversarial neural network model, and the feature extraction capability of LSTM is adopted to improve the quality of generated samples. Then, the convolutional neural network (CNN) is improved to enhance its classification ability, and the improved CNN is used to classify faults. …Finally, CNN and convolutional autoencoder (CAE) are used to diagnose bearing faults under different working conditions to enhance the diagnostic effect of the model under different working conditions. The results show that LSTM-GAN can capture the feature information in the original data well, and the generated samples can improve the diagnosis accuracy of bearing fault diagnosis under the condition of small samples. The diagnostic model still has high accuracy under different working conditions, which provides support for the research and application of bearing fault diagnosis. Show more
Keywords: Fault diagnosis, data enhancement, variable working conditions, deep learning
DOI: 10.3233/JIFS-240105
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhang, Hongling | Zhang, Hongzhi
Article Type: Research Article
Abstract: The qualities of the materials employed to manufacture concrete are significantly impacted by high temperatures, which results in a noticeable decrease in the material’s strength characteristics. Concrete must be worked very hard and allowed to reach the required compressive strength (f c ). Nevertheless, a preliminary estimation of the desired outcome may be made with an outstanding degree of reliability by using supervised machine learning algorithms. The study combined the Dingo optimization algorithm (DOA), Coot bird optimization (COA), and Artificial rabbit optimization (ARO) with Random Forests (RF) evaluation to determine the f c of concrete at high …temperatures. The abbreviations used for the combined methods are RFD, RFC, and RFA, respectively. Remarkably, removing the temperature (T ) parameter from the input set leads to a remarkable 1100% improvement in the effectiveness index (PI) and normalized root mean squared error (NRMSE), while causing a significant fall in the coefficient of determination (R 2 ). The findings suggest that all RFD, RFC, and RFA have substantial promise in properly forecasting the f c of concrete at high temperatures. More precisely, the RFD algorithm demonstrated exceptional precision with R 2 values of 0.9885 and 0.9873 throughout the training and testing stages, respectively. Through a comparison of the error percentages for RFD, RFC, and RFA in error-based measurements, it becomes evident that RFD exhibits an error rate that is about 50% smaller compared to that of RFC and RFA. This prediction is crucial for various industries and applications where concrete structures are subjected to elevated temperatures, such as in fire resistance assessments for buildings, tunnels, bridges, and other infrastructure. By accurately forecasting the compressive strength of concrete under these conditions, engineers and designers can make informed decisions regarding the material’s suitability and performance in high-temperature environments, leading to enhanced safety, durability, and cost-effectiveness of structures. Show more
Keywords: Concrete, elevated temperature, strength, random forests, Dingo optimization algorithm, sensitivity analysis
DOI: 10.3233/JIFS-240513
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: John, Manu | Mathew, Terry Jacob | Bindu, V.R.
Article Type: Research Article
Abstract: Content-Based Image Retrieval (CBIR) is a technique that involves retrieving similar images from a large database by analysing the content features of the query image. The heavy usage of digital platforms and devices has in a way promoted CBIR and its allied technologies in computer vision and artificial intelligence. The process entails comparing the representative features of the query image with those of the images in the dataset to rank them for retrieval. Past research was centered around handcrafted feature descriptors based on traditional visual features. But with the advent of deep learning the traditional manual method of feature engineering …gave way to automatic feature extraction. In this study, a cascaded network is utilised for CBIR. In the first stage, the model employs multi-modal features from variational autoencoders and super-pixelated image characteristics to narrow down the search space. In the subsequent stage, an end-to-end deep learning network known as a Convolutional Siamese Neural Network (CSNN) is used. The concept of pseudo-labeling is incorporated to categorise images according to their affinity and similarity with the query image. Using this pseudo-supervised learning approach, this network evaluates the similarity between a query image and available image samples. The Siamese network assigns a similarity score to each target image, and those that surpass a predefined threshold are ranked and retrieved. The suggested CBIR system undergoes testing on a widely recognized public dataset: the Oxford dataset and its performance is measured against cutting-edge image retrieval methods. The findings reveal substantial enhancements in retrieval performance in terms of several standard benchmarks such as average precision, average error rate, average false positive rate etc., providing strong support for utilising images from interconnected devices. Show more
Keywords: CBIR, siamese neural networks, deep learning, computer vision, clustering
DOI: 10.3233/JIFS-219396
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Kather Mohideen, Ashma Banu | Jayakumar, Vimala | Pethaperumal, Mahalakshmi | Kannan, Jeevitha
Article Type: Research Article
Abstract: As the globe enters a new era, web applications will become indispensable to managing business. Businesses can easily grow, become simpler, and accomplish their objective much faster by employing web applications. Creating a web application in cloud computing allows for the more affordable leveraging of cloud-based services. This makes it easier to avoid setting up and maintaining several servers. To get around cloud computing’s built-in restrictions such as scalability, security, and bandwidth limitations, the future smart world of cloud computing will be coupled with LiFi connectivity. Beyond creating the web application, it is important to promote this web application among …the network of users as quickly and effectively as possible. This manuscript proposes a strategy to address these challenges. There are two primary components to this MCDM technique. The first step is to model the problem as a graph and weigh the edges by employing the Hamacher aggregation operator. The second step involves using a fresh iteration of Kruskal’s technique in conjunction with this approach to discover a Minimum Spanning Tree as a resolution. This manuscript adds to the literature by solving real-world Minimum Spanning Tree problems by combining existing algorithms with MCDM techniques. This technique is demonstrated for marketing a web application(created via cloud service) in a future smart world using LiFi technology. Show more
Keywords: Cloud computing, LiFi technology, Kruskal’s technique, minimum spanning tree, Hamacher aggregation operator
DOI: 10.3233/JIFS-219423
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Jenefa, A. | Taurshia, Antony | Edward Naveen, V. | Kuriakose, Bessy M. | Thiyagu, T.M.
Article Type: Research Article
Abstract: In the realm of digital imaging, enhancing low-resolution images to high-definition quality is a pivotal challenge, particularly crucial for applications in medical imaging, security, and remote sensing. Traditional methods, primarily relying on basic interpolation techniques, often result in images that lack detail and fidelity. GANSharp introduces an innovative GAN-based framework that substantially improves the generator network, incorporating adversarial and perceptual loss functions for enhanced image reconstruction. The core issue addressed is the loss of critical information during down-sampling processes. To counteract this, we proposed a GAN-based method leveraging deep learning algorithms, trained using sets of both low- and high-resolution images. …Our approach, which focuses on expanding the generator network’s size and depth and integrating adversarial and perceptual loss, was thoroughly evaluated on various benchmark datasets. The experimental results showed remarkable outcomes. On the Set5 dataset, our method achieved a PSNR of 34.18 dB and a SSIM of 0.956. Comparatively, on the Set14 dataset, it yielded a PSNR of 31.16 dB and an SSIM of 0.920, and on the B100 dataset, it achieved a PSNR of 30.51 dB and an SSIM of 0.912. These results were superior or comparable to those of existing advanced algorithms, demonstrating the proposed method’s potential in generating high-quality, high-resolution images. Our research underscores the potency of GANs in image super-resolution, making it a promising tool for applications spanning medical diagnostics, security systems, and remote sensing. Future exploration could extend to the utilization of alternative loss functions and novel training techniques, aiming to further refine the efficacy of GAN-based image restoration algorithms. Show more
Keywords: Adversarial network training, enhanced image generation, image refinement, advanced neural architecture, improved resolution, quality assessment metrics, structural similarity evaluation
DOI: 10.3233/JIFS-238597
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Wang, Tianxing | Huang, Bing
Article Type: Research Article
Abstract: This paper makes a significant contribution to the field of conflict analysis by introducing a novel Interval-Valued Intuitionistic Fuzzy Three-Way Conflict Analysis (IVIFTWCA) method, which is anchored in cumulative prospect theory. The method’s key innovation lies in its use of interval-valued intuitionistic fuzzy numbers to represent an agent’s stance, addressing the psychological dimensions and risk tendencies of decision-makers that have been largely overlooked in previous studies. The IVIFTWCA method categorizes conflict situations into affirmative, impartial, and adverse coalitions, leveraging the evaluation of the closeness function and predefined thresholds. It incorporates a reference point, value functions and cumulative weight functions to …assess risk preferences, leading to the formulation of precise decision rules and thresholds. The method’s efficacy and applicability are demonstrated through detailed examples and comparative analysis, and its exceptional performance is confirmed through a series of experiments, offering a robust framework for real-world decision-making in conflict situations. Show more
Keywords: Three-way decision, conflict analysis, interval-valued intuitionistic fuzzy sets, cumulative prospect theory
DOI: 10.3233/JIFS-238873
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Pethaperumal, Mahalakshmi | Jayakumar, Vimala | Kannan, Jeevitha | Shanmugam, Nithya Sri
Article Type: Research Article
Abstract: The global challenges associated with urbanization and the escalating waste production have been magnified in recent times, particularly in the context of the COVID-19 pandemic. In response to these challenges, municipal authorities, especially in developing nations, are confronted with the imperative task of discerning the most suitable healthcare waste (HCW) disposal methods. These methods are crucial for the effective management of medical waste, both during and after the COVID-19 era. This study introduces a novel similarity measure designed for lattice ordered q-rung orthopair multi-fuzzy soft sets (Lq * q-ROMn FSSs) and exploring some of their essential characteristics. Currently, …no established methods are available for gauging the similarity of Lq * q-ROMn FSSs sets. Therefore, this paper takes a pioneering step by presenting similarity measures tailored for Lq * q-ROMn FSSs sets. Moreover, we propose an evaluation methodology that leverages the lattice ordered q-rung orthopair multi-fuzzy soft information to determine the optimal health care waste (HCW) disposal approach. This approach seeks to enhance decision-making within the realm of waste management, facilitating more informed and effective choices in handling healthcare waste. Show more
Keywords: Multi-fuzzy soft set, Lq* q-rung orthopair multi-fuzzy soft set, Lq* q-ROMnFS matrix, Lq* q-ROMnFS similarity measures, healthcare waste disposal technique
DOI: 10.3233/JIFS-219412
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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