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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinqiang Wang Wenhuan Lu Ke Zheng Junhai Xu College of Intelligence and Computing Tianjin University Tianjin China School of Software and Communication Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, semi-supervised 3D medical image segmentation has gained significant attention. However, current methods often struggle with multi-scale voxel differences and overlook the importance of loss weight balancing. To address these issues, we propose an adaptive multi-scale local correction method (AMLC). Our key contributions are: (1) a multi-scale local correction module to accurately capture differences between 3D voxel blocks at various scales; (2) an adaptive weighting adjustment module that dynamically balances losses, improving robustness and training. Experiments show that AMLC significantly outperforms existing methods on the public dataset, demonstrating its effectiveness.

关键词： Training Image segmentation Adaptation models Solid modeling Three-dimensional displays Computational modeling Signal processing Robustness Speech processing Biomedical imaging

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AI Integrated Healthcare System for Disease Predictions and Virtual Consultations

AI Integrated Healthcare System for Disease Predictions and ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Wilfred Blessing N.R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

AI-based Disease Prediction and Online Consultation System aims to redesign the way people experience healthcare. The deep growth of machine learning opened doors for new opportunities, such as this system that carefully inputs and validates patients' symptoms, and subsequently predicts possible diseases. In addition, it fosters remote consultation in a user-friendly manner over the internet and erases the barriers of distance and mobility. It is expected that this combination of data-driven forecasting and logic will enhance the treatment accuracy in terms of increased availability of healthcare services. AI-based Disease Prediction and Online Consultation System is a practical showcase on how to transform equity in healthcare. The increasing change in technology can give us possibilities for the utmost usage of this system. In this regard, the complex predictive analytics associated with the system may in effect change how HEIs (health education institutions) practice healthcare management. Patients will be offered a practice which supports the idea of delivering the right information on time and stress the universal right of access to healthcare. Its transformational power has far more prospects than it seems at first. At the core it sets the landscape and, indeed, the stage for the most equal, efficient and inclusive healthcare ecosystem that could ever exist.

关键词： Renewable energy sources Accuracy Medical services Machine learning Transforms Predictive models Medical diagnostic imaging Stress Streams Diseases

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ABL-SMOTE: A Novel Resampling Method by Handling noisy and borderline challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Ghorashi, Sara Abdelwahab Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University P.O. Box 84428 Collage of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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The Role of Mobile and Social Media Services in Enhancing Freedom of Expression: Opportunities, Challenges, and Prospects for Local Platform Development in Uganda's Digital Ecosystem

arXiv

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arXiv 2025年

作者： Alex, Bazigu Nafuna, Jacinta Mirembe, Drake Department of Networks College of Computing and Information Sciences Makerere University Kampala Uganda Faculty of Science and Technology Victoria University Kampala Uganda

Utilizing mobile and social media platforms is a transformative approach to enhancing freedom of expression and fostering digital engagement. However, Uganda’s digital ecosystem faces challenges such as restrictive legislation, financial barriers, and the absence of localized platforms tailored to cultural contexts. This study employed a mixed-methods approach to explore how these platforms influence public discourse, activism, and civic participation while highlighting opportunities for local innovation. The research further identified the critical need for regulatory reforms, investments in digital literacy, and collaborative efforts to develop sustainable and culturally relevant platforms, ensuring a more inclusive and empowered digital society. © 2025, CC BY.

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RDD4D: 4D Attention-Guided Road Damage Detection And Classification

arXiv

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arXiv 2025年

作者： Alkalbani, Asma Saqib, Muhammad Alrawahi, Ahmed Salim Anwar, Abbas Adak, Chandarnath Anwar, Saeed The Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri 516 Oman NCMI CSIRO The University of Technology Sydney Australia FutureDataMinds Indian Institute of Technology Patna India The School of Computing Australian National University Australia

Road damage detection and assessment are crucial components of infrastructure maintenance. However, current methods often struggle with detecting multiple types of road damage in a single image, particularly at varying scales. This is due to the lack of road datasets with various damage types having varying scales. To overcome this deficiency, first, we present a novel dataset called Diverse Road Damage Dataset (DRDD) for road damage detection that captures the diverse road damage types in individual images, addressing a crucial gap in existing datasets. Then, we provide our model, RDD4D, that exploits Attention4D blocks, enabling better feature refinement across multiple scales. The Attention4D module processes feature maps through an attention mechanism combining positional encoding and "Talking Head" components to capture local and global contextual information. In our comprehensive experimental analysis comparing various state-of-the-art models on our proposed, our enhanced model demonstrated superior performance in detecting large-sized road cracks with an Average Precision (AP) of 0.458 and maintained competitive performance with an overall AP of 0.445. Moreover, we also provide results on the CrackTinyNet dataset;our model achieved around a 0.21 increase in performance. The code, model weights, dataset, and our results are available on https://***/msaqib17/Road_Damage_Detection. © 2025, CC BY.

关键词： Damage detection

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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Computer Modeling in Engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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SUDCOPA: WSN-based IoT Energy Optimization and Load Balancing through a Context-Sensitive Minimum Clustering Radius 24

SUDCOPA: WSN-based IoT Energy Optimization and Load Balancin...

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Proceedings of the 2024 13th International Conference on Software and Information Engineering

作者： Foudil Mir Ahcene Bounceur Farid Meziane Medical Computing Laboratory (LIMED) Faculty of Exact Sciences University of Bejaia Bejaia Algeria College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates College of Science and Engineering Data Science Research Centre University of Derby Derby United Kingdom

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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

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computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon South Korea Technology Management Stony Brook University New York USA Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

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Predictive Modelling of Protest Event Signatures: Analyzing Temporal Dynamics and Digital Activism Discourse Across Global Movements

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Journal of Social computing 2025年第1期6卷 43-50页

作者： Amila Chethana Nanayakkara B. T. G. Samantha Kumara R. M. Kapila Tharanga Rathnayaka Center for Computer Studies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

This research introduced a predictive modeling framework to analyze the temporal dynamics of social media discourse during three global movements: the Mahsa Amini Protests (2022), South African Unrest (2021), and the Black Lives Matter Movement (2020). By utilizing Twitter data, the study developed a Protest Social Media Archetype to capture the evolution of tweet activity during key protest periods, identifying common patterns and notable variations in public engagement. Techniques such as LOESS regression and correlation analysis were used to model fluctuations in online activity and assess the impact of social media on public mobilization during socio-political unrest. The findings revealed distinct temporal signatures for each movement, showing similarities in initial engagement and differences in sustained activity across various contexts. These insights underscore the role of digital platforms in protest organization and global solidarity, offering a framework for future studies on digital activism and protest dynamics.

关键词： Analytical models Social computing Fluctuations Social networking (online) Blogs Refining Organizations Predictive models Real-time systems Mathematics

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