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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: Zhao, Liang | Wang, Jiawei | Liu, Shipeng | Yang, Xiaoyan
Article Type: Research Article
Abstract: Tunnels water leakage detection in complex environments is difficult to detect the edge information due to the structural similarity between the region of water seepage and wet stains. In order to address the issue, this study proposes a model comprising a multilevel transformer encoder and an adaptive multitask decoder. The multilevel transformer encoder is a layered transformer to extract the multilevel characteristics of water leakage information, and the adaptive multitask decoder comprises the adaptive network branches. The adaptive network branches generate the ground truths of wet stains and water seepage through the threshold value and transmit them to the network …for training. The converged network, the U-net, fuses coarse images from the adaptive multitask decoder, and the fusion images are the final segmentation results of water leakage in tunnels. The experimental results indicate that the proposed model achieves 95.1% Dice and 90.4% MIOU, respectively. This proposed model demonstrates a superior level of precision and generalization when compared to other related models. Show more
Keywords: Water leakage, multilevel transformer encoder, adaptive multitask decoder, adaptive network branches, converged network
DOI: 10.3233/JIFS-224315
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Kadry, Heba | Samak, Ahmed H. | Ghorashi, Sara | Alhammad, Sarah M. | Abukwaik, Abdulwahab | Taloba, Ahmed I. | Zanaty, Elnomery A.
Article Type: Research Article
Abstract: Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training …samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate. Show more
Keywords: Coronavirus, quantum machine learning, quanvolutional neural network, Q-deformed entropy
DOI: 10.3233/JIFS-233633
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Pradeep, M. | Sivaji, U. | Nithya, B. | Kadiravan, G. | Preethi, D. | Painam, Ranjith Kumar
Article Type: Research Article
Abstract: The mapping function must identify the reference model and detect coordinate arrangement by observing a repository with deep learning. Progression model with coordinate arrangement composition should have various positional displacements from one location to another. A prerogative classification model is an evolution of factor accomplishment in a repository method. Coordinate arrangement with calculation method must formulate a model locality twirl in classification method of a reference in dominance factor of perpetuity position observation by procession of reference localities. In a procession model observation by location, tendency method should be rotated from locality position into another coordinate method, with a PDD …factor measuring DPA of cadent RFT with an origin of 92.6, a cadent DS intermediate factor of 95.2, culmination factor of cadent RFT of 94.1. The docile exploratory arrangement of heuristic parameters is used in existing system to perceive phenomena such as sprout, enrollment discernment, demeanour, gravest perforation measure, Model of a heretic in apprehension method by premonition incongruity. Annotation should identify classification process using a proposed model to obtain massive measure of imputation function, In PDD measure of DPA in Cadent DS, with inception of 96.1, intercession of Cadent RFT in 92.6, with crowning of Cadent RFT in 96.4, 93.2 Show more
Keywords: MRCAI, Goin Twirl, maginot, idiosyncrasy outline, coffer atavism, flocculent utter eminence kedge
DOI: 10.3233/JIFS-234739
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Ahamed, Ayoobkhan Mohamed Uvaze | Joel Devadass Daniel, D.J. | Seenivasan, D. | Rukumani Khandhan, C. | Radhakrishnan, S. | Daya Sagar, K.V. | Bhardwaj, Vivek | Nishant, Neerav
Article Type: Research Article
Abstract: Time-sensitive programs that are linked to smart services, such as smart healthcare as well as smart cities, are supported in large part by the fog computing domain. Due to the increased speed limitation of the cloud, Cloud Computing (CC) is a competent platform for fog in data processing, but it is unable to meet the demands of time-sensitive programs. The procedure of resource provisioning, as well as allocation in either a fog-cloud structure, takes into account dynamic changes in user requirements, and resources with limited access in fog devices are more difficult to manage. Due to the continual changes in …user requirement factors, the deadline represents the biggest obstacle in the fog computing structure. Hence the objective is to minimize the total cost involved in scheduling by maximizing resource utilization. For dynamic scheduling in the fog-cloud computing model, the efficiency of hybridization of the Grey Wolf Optimizer (GWO) and Lion Algorithm (LA) is developed in this study. In terms of energy costs, processing costs, and communication costs, the created GWOMLA-based Deep Belief Network (DBN) performed better and outruns the other traditional models. Show more
Keywords: Fog-cloud computing environment, deep learning, deep belief network (DBN), lion algorithm (LA), grey wolf optimizer (GWO).
DOI: 10.3233/JIFS-234030
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023
Authors: Kalaipriya, O. | Dhandapani, S.
Article Type: Research Article
Abstract: Lung cancer is one of the leading causes of mortality from cancer. Lung cancer is a kind of malignant lung tumor characterized by uncontrolled cell proliferation in lung tissues. Even though CT scans are the most often used imaging technology in medicine, clinicians find it challenging to interpret and diagnose cancer from CT scan pictures. As a result, computer-aided diagnostics can assist clinicians in precisely identifying malignant cells. Many computer-aided approaches were explored and applied, including image processing and machine learning. A comparison of the various classification methodologies will assist in enhancing the accuracy of lung cancer detection systems that …employ robust segmentation and classification algorithms presented in this research. This research proposed to enhance existing segmentation and classification-basedmethodsof human lung cancer detection with optimization in techniques. The workflow includes initial preprocessing of medical images, for segmentation a novel hybrid methodology is developed by combining enhanced k-means clustering and random forest and classification with an Artificial neural network enhanced with PSO parameter and feature optimization. Show more
Keywords: Machine learning, K-means, ANN, random forest, PSO, image processing technique
DOI: 10.3233/JIFS-233845
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Luo, Binghui | Liu, Xin | Qin, Long | Jiao, Xiaolong | Li, Wengui
Article Type: Research Article
Abstract: The short text matching models can be roughly divided into representation-based and interaction-based approaches. However, current representation-based text matching models often lack the ability to handle sentence pairs and typically only perform feature interactions at the network’s top layer, which can lead to a loss of semantic focus. The interactive text matching model has significant shortcomings in extracting differential information between sentences and may ignore global information. To address these issues, this article proposes a model structure that combines a dual-tower architecture with an interactive component, which compensates for their respective weaknesses in extracting sentence semantic information. Simultaneously, a method …for integrating semantic information is proposed, enabling the model to capture both the interactive information between sentence pairs and the differential information between sentences, thereby addressing the issues with the aforementioned approaches. In the process of network training, a combination of cross-entropy and cosine similarity is used to calculate the model loss. The model is optimized to a stable state. Experiments on the commonly used datasets of QQP and MRPC validate the effectiveness of the proposed model, and its performance is stably improved. Show more
Keywords: Short text matching, representational structure, interactive structure, BERT, multi-angle information
DOI: 10.3233/JIFS-230268
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Diao, Xiu-Li | Zhang, Hao-Ran | Zeng, Qing-Tian | Song, Zheng-Guo | Zhao, Hua
Article Type: Research Article
Abstract: At present, the Chinese text field is facing challenges from low resource issues such as data scarcity and annotation difficulties. Moreover, in the domain of cigarette tasting, cigarette tasting texts tend to be colloquial, making it difficult to obtain valuable and high-quality tasting texts. Therefore, in this paper, we construct a cigarette tasting dataset (CT2023) and propose a novel Chinese text classification method based on ERNIE and Comparative Learning for Low-Resource scenarios (ECLLR). Firstly, to address the issues of limited vocabulary diversity and sparse features in cigarette tasting text, we utilize Term Frequency-Inverse Document Frequency (TF-IDF) to extract key terms, …supplementing the discriminative features of the original text. Secondly, ERNIE is employed to obtain sentence-level vector embedding of the text. Finally, contrastive learning model is used to further refine the text after fusing the keyword features, thereby enhancing the performance of the proposed text classification model. Experiments on the CT2023 dataset demonstrate an accuracy rate of 96.33% for the proposed method, surpassing the baseline model by at least 11 percentage points, and showing good text classification performance. It is thus clear that the proposed approach can effectively provide recommendations and decision support for cigarette production processes in tobacco companies. Show more
Keywords: Low-resource, Cigarette Tasting, Contrastive Learning, Text classification
DOI: 10.3233/JIFS-237816
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ledesma Roque, Diana Anahí | Kolesnikova, Olga | Menchaca Méndez, Ricardo
Article Type: Research Article
Abstract: This study addresses the issue of semantic similarity in sentences using the BERT model through various aggregation techniques, such as max-pooling, mean-pooling, and an LSTM network applied to the output of the BERT model. Subsequently, the linguistic interpretability of the BERT-Base transformer model is analyzed through the unsupervised learning approach, specifically through dimensionality reduction using autoencoders and clustering algorithms, utilizing the representation of the classification token CLS. The results highlight that the CLS classification token achieves better abstractions than the proposed methods. In terms of interpretability, it is observed that sequence length is relevant in the early layers, with …a gradual decrease across the layers. Additionally, attention to semantic similarity is concentrated in the intermediate and upper layers, especially in layers 6, 8, 9, and 10. All these findings were obtained by addressing the semantic similarity task using the STS-Benchmark dataset. Show more
Keywords: Linguistic interpretability, aggregation methods, unsupervised learning, attention mechanisms, token CLS
DOI: 10.3233/JIFS-219359
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Cardoso-Moreno, Marco A. | Luján-García, Juan Eduardo | Yáñez-Márquez, Cornelio
Article Type: Research Article
Abstract: In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of …84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. Show more
Keywords: Emotion classification, signal preprocessing, convolutional neural network, ECG, GSR, EMD
DOI: 10.3233/JIFS-219334
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Yigezu, Mesay Gemeda | Kolesnikova, Olga | Gelbukh, Alexander | Sidorov, Grigori
Article Type: Research Article
Abstract: The rise of social media and micro-blogging platforms has led to concerns about hate speech, its potential to incite violence, psychological trauma, extremist beliefs, and self-harm. We have proposed a novel model, Odio-BERT for detecting hate speech using a pretrained BERT language model. This specialized model is specifically designed for detecting hate speech in the Spanish language, and when compared to existing models, it consistently outperforms them. The study provides valuable insights into addressing hate speech in the Spanish language and explores the impact of domain tasks.
Keywords: BERT, hate speech, domain task, fine tune, Odio-BERT, Spanish
DOI: 10.3233/JIFS-219349
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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