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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: Javed, Hira | Sufyan Beg, M.M. | Akhtar, Nadeem | Alroobaea, Roobaea
Article Type: Research Article
Abstract: Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence …to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer. Show more
Keywords: Multimodal, captioning, summarization, etc
DOI: 10.3233/JIFS-219394
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Kostiuk, Yevhen | Tonja, Atnafu Lambebo | Sidorov, Grigori | Kolesnikova, Olga
Article Type: Research Article
Abstract: In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. We used a cross-validation approach and an average of the metrics per fold for evaluation. We achieved a 0.236 sentenceBLEU score on four folds. This study aims to contribute to developing more effective methods for reducing the …spread of hate speech in online communities. Show more
Keywords: Hate speech, translation, Spanish
DOI: 10.3233/JIFS-219348
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: George, Neetha | Ramachandran, Sivakumar | Jiji, C.V.
Article Type: Research Article
Abstract: Macula is the part of retina responsible for sharp and clear vision. Macular edema is caused by the accumulation of intraretinal fluid (IRF) in the macula, which is further distinguished by the compromised integrity of the blood-retinal barrier, particularly evident in the retinal vasculature. This results in swelling, that may lead to vision impairment and is the dominant sign of several ocular diseases, including age-related macular degeneration, diabetic retinopathy, etc. Quantitative analysis of the fluid regions in macular edema helps in ascertaining the severity as well as the response to treatment of the diseases. Optical coherence tomography (OCT) is a …major tool used by ophthalmologists for visualizing edema. The prevalent practice for diagnosing and treating macular edema involves measuring Central Retinal Thickness (CRT). Segmenting the IRF in OCT images offers the potential for a more accurate and better quantification of macular edema. This paper proposes a novel method combining convolutional neural network (CNN) and active contour model for segmenting the IRF to ascertain the severity of macular edema. The IRF region is initially segmented using an encoder-decoder architecture. Contour evolution is then performed on this segmented image to demarcate the IRF boundaries. The advantage of the method is that it does not require precisely labeled images for training the CNN. A comparison of the experimental results with models employing CNN alone and with other state-of-the art methods demonstrates the superior performance and consistency of the proposed method. Show more
Keywords: edema segmentation, convolutional neural network, active contour model
DOI: 10.3233/JIFS-219401
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Wu, Donghui | Wang, Jinfeng | Zhao, Wanwan | Geng, Xin | Liu, Guozhi | Qiu, Sen
Article Type: Research Article
Abstract: Gesture recognition based on wearable sensors has received extensive attention in recent years. This paper proposes a gesture recognition model (CGR_ATT) based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) fused attention mechanism to improve accuracy rate of wearable sensors. First, CNN serves as a feature extractor, learning features automatically from sensor data by performing multiple layers of convolution and pooling operations, capturing spatial features of gestures. Furthermore, a temporal modeling unit GRU is introduced to capture the temporal dynamics in gesture sequences. By controlling the information flow through gate mechanisms, it effectively handles the temporal relationships in …sensor data. Finally, an attention mechanism is introduced to assign different weights to the hidden state of the GRU. By calculating the attention weights for each time period, the model automatically selects key time periods related to gesture movements. The GR-dataset proposed in this paper involves 910 sets of training parameters. The model achieves an ultimate accuracy of 97.57% . In compare with CLA-net, CLT-net, CGR, GRU, LSTM and CNN, the experimental results demonstrate that the proposed method has superior accuracy. Show more
Keywords: Wearable gesture recognition system, CGR_ATT model, deep learning, wearable devices
DOI: 10.3233/JIFS-240427
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Visvanathan, P. | Durai Raj Vincent, P.M.
Article Type: Research Article
Abstract: A Stroke is a sudden loss of blood circulation in certain parts of the brain that results in a loss of neurological function. To save a patient from stroke, an immediate diagnosis and treatment plan must be implemented. Artificial intelligence-based machine learning algorithms play a major role in the prediction. To predict a person likely to have a stroke, stroke healthcare data records must be accessed, which is very sensitive. Data shared for machine learning training pose security risks and have concerns about privacy. To overcome this issue, Genetic Algorithm and Federated Learning (GA-FL) –based hybridization approach is proposed to …predict the risk of stroke in a person. Federated Learning was developed by Google, which can provide security to the data during the training process because every client participating in this training process needs to exchange only the training parameters without sharing the data. In addition to the security features, a genetic algorithm was used to optimize the parameters required to train a model using the perceptron neural network model. The experimental results show that our proposed research model (GA-FL) provides security and predicts the risk of stroke more accurately than any other existing algorithm. Show more
Keywords: Federated learning, genetic algorithm, stroke risk, perceptron neural network
DOI: 10.3233/JIFS-236354
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Hu, Junhua | Zhou, Yingling | Li, Huiyu | Liang, Pei
Article Type: Research Article
Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for …policy-making. Show more
Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network
DOI: 10.3233/JIFS-236766
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Hossain, AKM B. | Salam, Md. Sah Bin Hj. | Alam, Muhammad S. | Hossain, AKM Bellal
Article Type: Research Article
Abstract: Semantic segmentation is crucial for the treatment and prevention of brain cancers. Several neural network–based strategies were rapidly presented by research groups to enhance brain tumor thread segmentation. The tumor’s uneven form necessitates the usage of neural networks for its detection. Therefore, improved patient outcomes may be achieved with precise segmentation of brain tumor. Brain tumors can range widely in size, form, and position, making diagnosis difficult. Thus, this work offers a Multi-level U-Net (MU-Net) approach for analyzing the brain tumor data augmentation for improved segmentation. Therefore, a significant amount of data augmentation is employed to successfully train the recommended …system, removing the problem of a lack of data when using MR images for the diagnosis of multi-grade brain cancers. Here, we presented the “Multi-Level Pyramidal Pooling (MLPP)” component, where a new pyramidal pool will be employed to capture contextual data for augmentation. The “High-Grade Glioma” (HGG) datasets from the Kaggle and BraTs2021 were used to assess the proposed MU-Net. Overall Tumor (OT), Enhancing Core (EC), and Tumor Core (TC) were the three main designations to be segmented. The dice score was used to contrast the results empirically. The suggested MU-Net fared better than most existing methods. Researchers in the fields of bioinformatics and medicine might greatly benefit from the high-performance MU-Net. Show more
Keywords: Brain tumor, Data Augmentation (DA), Multi-level U-Net (MU-Net), Multi-Level Pyramidal Pooling (MLPP), Adaptive Curvelet Transform (ACT), wavelet threshold
DOI: 10.3233/JIFS-232782
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wu, Jie | Hou, Mengshu
Article Type: Research Article
Abstract: Table-based fact verification (TFV) is a binary classification task that requires understanding and reasoning about both table and text. This task poses many challenges, such as table parsing, text comprehension, and numerical reasoning. However, existing methods tend to depend solely on pre-trained models for tables, treating all types of reasoning equally and disregarding the importance of identifying logic types in inference process. In this regard, we propose MoETFV, an efficient and explanatory approach to solving TFV, which is based on a Mixture-of-Experts (MoE) framework. This approach can detect the underlying logic types of statements and leverage multiple independent experts to …emulate diverse logical reasoning. It consists of one shared expert for general semantic understanding and several specific experts with distinct responsibilities for different logical inferences. Moreover, the practical applications of the MoE method in TFV are thoroughly investigated. This model doesn’t necessitate any table pre-trained models, and aligns closely with human cognitive processes in addressing such issues. Experimental results demonstrate the innovation and feasibility of the proposed approach. Show more
Keywords: Tabular data, fact verification, mixture-of-experts, logical reasoning, natural language processing
DOI: 10.3233/JIFS-238142
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Chen, Longkai | Huang, Jingjing
Article Type: Research Article
Abstract: Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of …clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. Show more
Keywords: Support vector machine, traffic accident pattern recognition, cluster analysis, machine learning
DOI: 10.3233/JIFS-241018
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Liu, Fei
Article Type: Research Article
Abstract: In China, aesthetic education at the college level is essential for students’ quality because it improves their understanding of art, helps them progress in their professional career development, and helps them comprehend more fully the attractiveness of creative creations. As a result, it needs to prioritize aesthetic education at the institution and endeavor to nurture students’ feelings progressively and improve their aesthetic abilities at different levels. Artificial intelligence (AI) is used in this project to create a novel, interdisciplinary teaching technique that will maximize students’ artistic and intellectual potential and help them make more, better art. In this research, the …Osprey Optimization method improves the interdisciplinary teaching technique for aesthetic education based on a light Exclusive gradient-boosting mechanism (OOM-LEGBM). The exploration-exploitation dynamics of the OOM are incorporated into LEGBM, providing the students with a tangible and relatable technique to understand complex-solving processes. This research develops an enhanced quality framework for college aesthetic education based on the multi-model data fusion system about the implication and necessity of aesthetic education. The influence of college aesthetic education on students’ creative capacity and artistic literacy was investigated to inform instructional activities better to develop students’ aesthetic skills. The experimental findings suggest that the proposed approach achieved an improved accuracy of 99.90%, higher precision of 99.88%, and greater recall of 99.91%. Moreover, it obtained a minimum Root Mean Square Error (RMSE) of 0.26% and a lower Mean Absolute Error (MAE) of 0.34%, showing that the suggested model greatly improved preference learning accuracy while keeping overall accuracy at an identical level. Innovation capacity building in college aesthetic education can help students become more self-aware, improve their study habits, visually literate, and more comprehensive. Show more
Keywords: Interdisciplinary teaching, aesthetic education, curriculum, multimodal data fusion, artificial intelligence, and big data
DOI: 10.3233/JIFS-240723
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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