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17th Asian Conference on computer Vision, ACCV 2024

作者： Mok, Dahyun Bum, Junghyun Tai, Le Duc Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819609000

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model’s ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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i-NVMe: Isolated NVMe over TCP for a Containerized Environment 42

i-NVMe: Isolated NVMe over TCP for a Containerized Environme...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Lee, Seongho Yeom, Ikjun Kim, Younghoon College of Computing and Informatics Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798350334142

Non-Volatile Memory Express (NVMe) over TCP is an efficient technology for accessing remote Solid State Drives (SSDs);however, it may cause a serious interference issue when used in a containerized environment. In this study, we propose a CPU isolation scheme for NVMe over TCP in such an environment. The proposed scheme measures the CPU usage of the NVMe over TCP worker, charges it to containers in proportion to their NVMe traffic, and schedules containers to ensure isolated sharing of the CPU. However, because the worker runs with a higher priority than normal containers, it may not be possible to achieve CPU isolation with container scheduling alone. To solve this problem, we also control the CPU usage of the worker by throttling NVMe over TCP traffic. The proposed scheme is implemented on a real testbed for evaluation. We perform extensive experiments with various workloads and demonstrate that the scheme can provide CPU isolation even in the presence of excessive NVMe traffic. © 2023 IEEE.

关键词： Containers

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval 19

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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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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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems Science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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IoT Task Offloading in Edge computing Using Non-Cooperative Game Theory for Healthcare Systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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Manhattan-based k-member clustering and enhanced rabbit optimization algorithm for k-anonymization in social network

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Multimedia Tools and Applications 2024年第30期83卷 74739-74756页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of computer science and engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of computing technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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