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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Kim, Ju-Chan Van Nguyen, Pham Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

This paper presents NDAS (Noise-Decomposed Abnormal Segmentation), an innovative framework for robust medical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS’s superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability. © 2025 IEEE.

关键词： Image segmentation

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Towards Enhancing Linked Data Retrieval in Conversational UIs Using Large Language Models 25th

Towards Enhancing Linked Data Retrieval in Conversational U...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Mussa, Omar Rana, Omer Goossens, Benoît Orozco-terWengel, Pablo Perera, Charith School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Biosciences Cardiff University Cardiff United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9789819605729

Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Attack Detection and Optimal Deployment for Underwater Constrained Wireless Sensor Networks via Hybrid Trust Evidence

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1791-1803页

作者： Jiang, Bin Zhou, Ronghao Luo, Fei Cui, Xuerong Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Northeastern University Computing Programs ArlingtonVA22209 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Underwater wireless sensor networks have been widely used in the acquisition and processing of oceanic information. The marine environment is complex and changeable, and the existence of obstacles is the main manifestation of the complex underwater environment, which affect the communication between underwater nodes. In addition, wireless sensor networks with obstacles are often more vulnerable to various attacks, making it more fragile. In order to address the aforementioned issues, we firstly propose a underwater wireless sensor deployment strategy with obstacle avoidance as the target (GEHO). After that, we use Tabtransformer algorithm to build trust model and detect attacks according to trust data set, which can enhance the robustness of the entire wireless sensor network. In the final stage, we collect the patterns of malicious attacks on nodes according to the detection results, which is convenient for us to make timely responses and reduce the losses of underwater wireless sensor networks due to malicious attacks. The simulation results show that the trust model can effectively detect malicious nodes and attack types in the network, and has higher detection accuracy than the existing trust model. © 2013 IEEE.

关键词： Hybrid sensors

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Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model With BERT Embedding and Data Augmentation

Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model W...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Saadat M. Alhashmi Saleema Hashim Ahmed Mohamed Khedr College of Computing and Informatics University of Sharjah UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. Also called major depressive disorder or clinical depression, it affects how you feel, think, and behave and can lead to a variety of emotional and physical problems. Automatic detection, as in a clinical interview can be derived from various modalities which includes video, audio, and text. Among them, detection from text data has emerged as a crucial task in mental health monitoring. In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM) layers to enhance depression detection accuracy. Using BERT embedding for robust feature extraction, the proposed model processes social media or clinical text data to identify signs of depression. Additionally, we incorporate a novel data augmentation technique using synonym replacement to address data imbalance and improve generalization. Evaluations are conducted using key performance metrics, including accuracy, precision, recall, and F1 score. With an accuracy of 86.2 %, the results demonstrate that the combined CNN-BiGRU-BiLSTM architecture, alongside BERT embedding and augmented data, significantly improves classification performance compared to traditional models. This approach shows promise in contributing to more effective automatic depression detection systems.

关键词： Deep learning Accuracy Social networking (online) Bidirectional long short term memory Bidirectional control Depression Data augmentation Encoding Data models Convolutional neural networks

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Decentralized Federated Learning in Metacomputing Based on Directed Acyclic Graph With Optimized Tip Selector

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IEEE Internet of Things Journal 2025年第10期12卷 13723-13733页

作者： Jiang, Bin Zhao, Bo Luo, Fei Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Computing Programs Northeastern University Arlington ArlingtonVA22209 United States University of Maryland at Baltimore County Department of Information Systems BaltimoreMD21250 United States

Metacomputing optimizes distributed computing resources to enhance federated learning (FL) systems by enabling efficient resource allocation, improved scheduling, and greater scalability, thereby addressing challenges in large-scale and dynamic environments. This article proposes an innovative framework integrating directed acyclic graph (DAG) technology with FL within a metacomputing environment. The key contributions include a three-layer decentralized FL model integrating DAG and metacomputing to enhance resilience and scalability, two advanced tip selection models LazyEval tip selector and precision tip selector to optimize node selection and improve data flow, and a benchmark improvement protocol (BIP) for efficient node publishing and role adaptation. The BIP ensures that only high-performing models are published by comparing new models against established benchmarks, which enhances node collaboration and optimizes resource allocation. LazyEval Tip Selector minimizes redundant computations by leveraging a global cache and employing a lazy evaluation strategy, thereby improving computational efficiency. On the other hand, Precision Tip Selector uses a precise scoring mechanism to ensure accurate tip selection, thereby enhancing the robustness and reliability of the entire system. Collectively, these innovations enhance model training efficiency, support real-time updates, and improve the scalability of FL systems, making them well-suited for managing complex, dynamic environments. © 2014 IEEE.

关键词： Federated learning

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Optimal control for unreliable manufacturing and remanufacturing system under environmental subsidy and overrun penalty mechanism

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IEEE Transactions on Engineering Management 2025年 72卷 1983-2002页

作者： Turki, Sadok Jemmali, Mahdi Hidri, Lotfi University of Lorraine Department of Logistic and Maintenance UFR MIM at Metz Metz17 France University of Sharjah Computer Science Department College of Computing and Informatics Sharjah United Arab Emirates King Saud University Industrial Engineering Department College of Engineering Riyadh11421 Saudi Arabia

Because of environmental concerns, remanufacturing has become an integral process for many production companies. Most published papers dealing with manufacturing and remanufacturing systems (MRSs) overlook some industrial features. These include machine unreliability, the distinction between newly manufactured and remanufactured products, and uncertainty about the returned used products (RUPs). As a result, these models do not reflect the reality of industrial systems. This study aims to bridge the gap in research on MRSs by elaborating an optimal and dynamic control of manufacturing, remanufacturing, and recovery of RUPs. The goal is to develop a model based on manufacturing remanufacturing emissions hedging point control policy (MREHP) to adjust the production of new and remanufactured products, and recovery of RUPs according to economic and environmental thresholds. To reduce carbon emissions, a new method is proposed based on the environmental subsidy and overrun penalty mechanism (ESOPM), and that is combined with MREHP to control in parallel the MRS and generated emissions. A robust optimization algorithm is developed to determine the optimal economic and environmental thresholds in MREHP and ESOPM. A comparative study is provided to evaluate our proposed ESOPM in comparison with traditional reduction policies: carbon cap and trade (CCT) and carbon tax regulation (CTR). The obtained results reveal that machines' reliability, rate of RUPs, emissions subsidy, and carbon emissions limit have notable impacts on economic and environmental thresholds. Additionally, results show that the proposed ESOPM performs higher profit than CTR and CCT while reducing emissions when the carbon price is below a certain value. © 1988-2012 IEEE.

关键词： Fabrication

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Meta-XPFL: An Explainable and Personalized Federated Meta-Learning Framework for Privacy-Aware IoMT

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IEEE Internet of Things Journal 2025年第10期12卷 13790-13805页

作者： Serhani, Mohamed Adel Tariq, Asadullah Qayyum, Tariq Taleb, Ikbal Din, Irfanud Trabelsi, Zouheir University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Zayed University College of Technological Innovation Abu Dhabi United Arab Emirates New Uzbekistan University Department of Computer Science Tashkent100007 Uzbekistan

In the Internet of Medical Things (IoMT), specifically in the field of medical image classification—particularly for skin cancer detection—traditional methods face challenges related to data privacy, heterogeneity, and the need for personalization across institutions. This research proposes a personalized federated learning (PFL) framework Meta-XPFL that addresses these challenges through a decentralized approach, allowing institutions to collaboratively train models without sharing raw data. The framework integrates meta-learning for adaptability, and self-supervised learning to leverage unlabeled data and secure multiparty computation (SMPC). Adversarial training improves model robustness, while attention mechanisms enhance the focus on relevant image features. The use of explainable AI techniques ensures interpretability, which is crucial in clinical settings. To validate the proposed framework, experiments were conducted on the HAM10000 dataset for skin cancer classification, demonstrating significant improvements in model accuracy, privacy preservation, and robustness against adversarial attacks compared to traditional methods. The results indicate that the framework not only enhances scalability and diagnostic accuracy but also offers a privacy-preserving solution that can be extended to various types of medical images, making it adaptable for broader applications in IoMT. © 2014 IEEE.

关键词： Federated learning

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A stacked ensemble approach for symptom-based monkeypox diagnosis

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Computers in Biology and Medicine 2025年 191卷 110140-110140页

作者： Nagro, Shimaa College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The recent monkeypox outbreak has raised global health concerns. Caused by a virus, it is characterized by symptoms such as skin lesions. Early detection is critical for treatment and controlling its spread. This study uses advanced machine learning and deep learning techniques, including Tab Transformer, Long Short-Term Memory, XGBoost, LightGBM, and a Stacking Classifier, to predict the presence of the virus based on patient symptoms. The performance of these models is evaluated using accuracy, precision, recall, and F1-score metrics. The experiments reveal that the Stacking Classifier significantly outperforms the other models, achieving an accuracy of 87.29 %, precision of 86.12 %, recall of 87.47 %, and an F1 score of 87.89 %. Additionally, applying Conditional Tabular GAN to generate synthetic data helps address data imbalance issues, further improving model robustness. These results highlight the proposed approach's potential for timely, accurate monkeypox detection, aiding in effective disease management and control. © 2025 The Author

关键词： Deep learning

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Multi-input CNN: a deep learning-based approach for predicting breast cancer prognosis using multi-modal data

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Discover Data 2025年第1期3卷 1-11页

作者： Uma Kandan, Shamita Alketbi, Mariam Mohamed Al Aghbari, Zaher College of Engineering University of Sharjah Sharjah UAE College of Computing and Informatics University of Sharjah Sharjah UAE

Breast cancer prognosis prediction is pivotal to improving the survival chances of breast cancer patients. Recently, deep learning models integrated with multi-modal data have been explored extensively to improve the reliability and accuracy of breast cancer prognosis prediction. However, multi-modal data poses challenges to feature extraction due to the varied distribution and dimensionality across the modalities. To address this, we developed a multi-input convolutional neural network model, which extracts features from each modality in the multi-modal dataset simultaneously using separate convolutional layers, then concatenates and passes them to shared dense layers. The proposed model achieved area under the receiver operating characteristic curve values of 0.893 and 0.865 in 5-fold cross-validation and unseen test data respectively (at threshold = 0.2). These outcomes surpassed those of single-input convolutional neural network models and a state-of-the-art method based on multi-modal data. The multi-input convolutional neural network model efficiently handles multi-modal data and is a promising tool for breast cancer prognosis prediction.

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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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13th International Conference on computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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