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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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Memory Matching is Not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation 27th

Memory Matching is Not Enough: Jointly Improving Memory Matc...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zheng, Jintu Liang, Yun Zhang, Yuqing Su, Wanchao University of Chinese Academy of Sciences Beijing China South China Agricultural University Guangdong Guangzhou China Beijing University of Technology Beijing China Department of Human Centered Computing Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9783031783111

Memory-based video object segmentation methods model multiple objects over long temporal-spatial spans by establishing memory bank, which achieve the remarkable performance. However, they struggle to overcome the false matching and are prone to lose critical information, resulting in confusion among different objects. In this paper, we propose an effective approach which jointly improving the matching and decoding stages to alleviate the false matching issue. For the memory matching stage, we present a cost aware mechanism that suppresses the slight errors for short-term memory and a shunted cross-scale matching for long-term memory which establish a wide filed matching spaces for various object scales. For the readout decoding stage, we implement a compensatory mechanism aims at recovering the essential information where missing at the matching stage. Our approach achieves the outstanding performance in several popular benchmarks (i.e., DAVIS 2016 &2017 Val (92.4% &88.1%), and DAVIS 2017 Test (83.9%)), and achieves 84.8% &84.6% on YouTubeVOS 2018 &2019 Val. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decoding

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development

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IEEE Access 2025年 13卷 31785-31808页

作者： Almogahed, Abdullah Mahdin, Hairulnizam Hyeon Gu, Yeong Al-Masni, Mohammed A. Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Obaid Barraood, Samera Rehman Gilal, Abdul Ameen Bakather, Adnan Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Parit Raja Johor86400 Malaysia Sejong University College of AI Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of King Fahd University of Petroleum and Minerals Interdisciplinary Research Center in Finance and Digital Economy Business School Dhahran31261 Saudi Arabia Universiti Utara Malaysia School of Computing Sintok06010 Malaysia Hadhramout University College of Computers and Information Technology Department of Computer Science Hadhramaut Yemen Florida International University Knight Foundation School of Computing and Information Sciences MiamiFL33199 United States

Adopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge because the benefits of refactoring are vague and very difficult for the developers to quantify, as different refactoring strategies have different effects on quality attributes. No research has developed a multi-classification refactoring framework using artificial neural networks (ANN), specifically hopfield neural networks (HNN), to classify refactoring strategies and improve external software quality and sustainability. Therefore, this study proposes a multi-classification refactoring framework using HNN that classifies refactoring strategies by their impact on external quality attributes. Five stages have been conducted to perform this study, including selecting case studies, identifying the external quality attributes, identifying the most commonly used refactoring strategies in practice, conducting the experiments, and conducting the classification process using HNN. The proposed framework categorizes the refactoring strategies into three categories (positive, negative, and ineffective). By providing clear classifications and descriptions of each strategy, the proposed framework helps developers make informed decisions about how to improve the design and structure of their code. It helps developers mitigate risks associated with code changes by providing guidance on which strategies are likely to yield positive results for specific quality attributes. The proposed multi-classification refactoring framework enhances software sustainability by enhancing critical quality attributes. It supports maintainability, adaptability, and long-term viability, helping to ensure that the software systems remain relevant, efficient, and valuable over time. © 2025 IEEE.

关键词： Hopfield neural networks

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Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing

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PeerJ Computer Science 2025年 11卷 e2698-e2698页

作者： Alzahrani, Ahmed Asghar, Muhammad Zubair Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Faculty of Computing Gomal University KP Dera Ismail Khan Pakistan

Vitamin D deficiency (VDD) has emerged as a serious global health concern that can lead to far-reaching consequences, including skeletal issues and long-term illness. Classical diagnostic approaches, although effective, often include invasive techniques and lacks to leverage the massive amount of healthcare data. There is an increasing demand for noninvasive prediction approaches for determining the severity of VDD. This work proposes a novel approach to detect VDD levels by combining deep learning techniques with evolutionary computing (EC). Specifically, we employ a hybrid deep learning model that includes convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to predict VDD data effectively. To improve the models effectiveness and guarantee the optimal choice of the features and hyper-parameters, we incorporate evolutionary computing methods, particularly genetic algorithms (GA). The proposed method has been proven effective through a comprehensive assessment on a benchmark dataset, with 97% accuracy, 96% precision, 97% recall, and 96% F1-score. Our approach yielded improved performance, when compared to earlier methods. This research not only push forward predictive healthcare models but also shows the potential of merging deep learning with evolutionary computing to address intricate health-care issues. Copyright 2025 Alzahrani and Asghar Distributed under Creative Commons CC-BY 4.0

关键词： Electronic health record

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Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Nhan Vo, Van Quoc Long, Nguyen Dang, Viet-Hung Dac Ho, Tu Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Duy Tan University Faculty of Information Technology Da Nang550000 Viet Nam Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Information Security and Communication Technology Trondheim7034 Norway National Economics University DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology Hanoi12116 Viet Nam University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Esch-sur-Alzette4365 Luxembourg Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok10900 Thailand Khon Kaen University Applied Network Technology College of Computing Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

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Blind Stereoscopic Omnidirectional Image Quality Assessment via a Binocular Viewport Hypergraph Convolutional Network

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wan, Zhaolin Yan, Xiao Li, Zhiyang Fan, Xiaopeng Zuo, Wangmeng Zhao, Debin Harbin Institute of Technology Faculty of Computing Harbin China Dalian Maritime University Department of Information Science and Technology Dalian China

Omnidirectional images, offering immersive 360° views, have gained significant attention, but assessing their perceptual quality, especially for stereoscopic content, remains a complex challenge. A major limitation lies in the fact that head-mounted devices restrict the viewer's experience to a single viewport at a time, necessitating a comprehensive understanding of how multiple viewport images interact and aggregate during the viewing process. Moreover, the depth dimension inherent in stereoscopic content further complicates the 360° visual experience, a factor often oversimplified by existing methods, limiting their ability to accurately differentiate perceptual quality across viewports. To address these challenges, we propose a novel blind quality assessment model for stereoscopic omnidirectional images. Our approach integrates binocular vision principles within a viewport hypergraph convolutional network framework. First, guided by the unique viewing patterns of stereoscopic omnidirectional images, our model selects panoramic viewports that align with human visual preferences. Next, we devise an image feature extraction network that simulates the binocular fusion and rivalry mechanisms within the human visual system, leveraging a twin encoder-decoder network and tensor decomposition to capture key features. Finally, to assess overall image quality, we introduce a hypergraph structure module that captures complex positional and content-based interactions among sampled viewports through the Graph Influence Network. Extensive experiments on the NBU-SOID, SOLID, and LIVE 3D VR databases demonstrate the superior accuracy and robustness of our model compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Binoculars

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Data Generation in Healthcare Using Constraint Satisfaction Parameters (CSP) 2

Data Generation in Healthcare Using Constraint Satisfaction ...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Al-Barhamtoshy, Hassanin M. Himdi, Tarik King Abdulaziz University Information Technology Dept. Faculty of Computing Jeddah Saudi Arabia Jeddah International College Computer Science Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

In this research, we examine the synthetic creation, alteration, and enhancement of data through Generative Artificial Intelligence (GAI). This inaugural study focused on generating healthy data via GAI for analysis and detection of chronic diseases (Depression, Stress, and Anxiety). Following the generation process using this deep learning approach, we analyze and visualize synthetic data through various metrics. The outcomes of this methodology are expected to yield novel insights into data generation concerning pressure-related diseases (depression, stress, and anxiety). Integrating high-quality synthetic data is anticipated to improve the production of financial transaction data. Finally, the proposed model is evaluated, and the results of the initial GAI model are generated. In this paper, we introduce a specialized generative Transformer language model that has been pre-trained on an extensive collection of medical constraint satisfaction parameters. We assess generation across medical natural language processing tasks and show that it surpasses existing models in most evaluations. Additionally, we offer a comprehensive analysis of the model's limitations by detailing numerous errors and challenges it faces. The mean scores for the produced descriptive data and shape pair trends in continuous columns were around 0.99 and 0.92, respectively. Conversely, the average scores for the categorical text generated in the initial region were notably high. This highlights the GAI model utilized in implementing each method. Our evaluation revealed that a majority of studies employ synthetic data generators to (i) reduce the costs and duration linked to clinical trials for rare diseases, (ii) enhance the predictive performance of AI models in personalized medicine, and (iii) provide researchers with access to high-quality, representative multimodal datasets while protecting sensitive patient information, among other advantages. Synthetic data generators based on deep learn

关键词： Theranostics

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Reinforcement Learning for Intrusion Detection: Recent Advances and Datasets 2

Reinforcement Learning for Intrusion Detection: Recent Advan...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Fathi, Dalal Attiah, Afraa Hakeem, Abeer Cherif, Asma Kind Abdulaziz University Faculty of Computing and Information Technology It Department Jeddah Saudi Arabia Kind Abdulaziz University Center of Excellence in Smart Environment Research Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

With the development of technologies and increasing security threats, intrusion detection systems have become more critical in detecting and protecting operations from attacks. Deep learning has significantly contributed to advancements in intrusion detection, especially through reinforcement learning systems. This survey reviews the concepts of intrusion detection and Reinforcement Learning (RL) systems in intrusion detection (ID), with a focus on recent advancements using techniques such as Multi-Agent Reinforcement Learning (MARL), Adversarial Reinforcement Learning (AE-RL), and Inverse Reinforcement Learning (IRL). We also emphasize the crucial role of feature engineering in conjunction with RL techniques. By examining these cutting-edge approaches and their integration with advanced feature engineering methods, we aim to provide a comprehensive overview of the current state of the art in reinforcement learning-based intrusion detection systems and their potential to enhance cybersecurity measures. In addition to exploring the applications of reinforcement learning and feature engineering in intrusion detection, we highlight and analyze the most well-known databases used in this field, offering insights into the data resources that drive the development and evaluation of these advanced security systems. © 2025 IEEE.

关键词： Deep reinforcement learning

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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