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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Nguyen, Tran Khanh Dong, Nguyen Trinh Hai, Vu Tuan Luan, Pham Hoai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Nara Institute of Science and Technology Takayama-cho Nara Ikoma Japan

ISBN: (纸本)9789819755035

Image compression is an important problem in the image processing field and has been applied widely in various applications, from storage optimization and print media to image exchange. Almost all compressed image formats, such as Joint Photographic Experts Group (JPEG) and Portable Network Graphics (PNG), have pros and cons, but there is always a balance between file size and image quality;the larger the file size, the greater the loss on the image. However, the hybrid quantum-classical approach provides us with a new way to compress images more efficiently. In this research, we propose a scheme that turns the image into a unitary operator’s parameter, then we make a new image format via the quantum state preparation algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： hypergraph ansatz image compressing quantum computing

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HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques

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IEEE Access 2025年 13卷 94416-94436页

作者： Wishal Arshad Tehreem Masood H. M. Shahzad Hassan A. Ahmed Syed Hamza Ahmed Hafiz Muhammad Tayyab Khushi Faculty of Software Engineering and Information Technology University of Central Punjab Lahore Pakistan Faculty of Computer Science and Information Technology Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Information Systems Monte Ahuja College of Business Cleveland State University Cleveland OH USA

Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches ( $50\times 50$ pixels) from Paul Mooney’s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting under sampled IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology.

关键词： Breast cancer Cancer Accuracy Tumors Feature extraction Computational modeling Imaging Histopathology Deep learning Breast

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Investigating the Impact of Different Data Representation with Several Classification Models on Magnetic Resonance Imaging (MRI) 1

Investigating the Impact of Different Data Representation wi...

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2021

作者： Elfallah, Sokena Omar Elfallah, Ehab A. Abdalla, Mohamed A. E. Aljabour, Bilal Faculty of Information Technology Software Engineering Department Libya University of Benghazi Faculty of Information Technology Software Engineering Department Libya

ISBN: (纸本)9781665418560

Alzheimer is one of the dementia diseases. It often infects elderly people who are beyond 65 years old. Alzheimer diagnosis (AD) relies on the analysis of the magnetic resonance imaging (MRI) and the scale of the clinical dementia rating (CDR). The automatic AD has widely been carried out adopting different methods. The investigation of the digital MRI samples with CDR scale has been the cornerstone of the automatic studies of AD. Those studies have resulted in various accuracy rates when they applied diverse techniques. This paper presents and compares several techniques of image normalization and classification models for improving the accuracy rates of AD. Four normalizations techniques, which is Z-score, Min-Max, Decimal Scaling and Standard deviation are examined. Whilst, the classifiers are Naïve Bayes, Logistic regression, decision tree (DT), k-nearest neighbors (kNN), Artificial Neural Network (ANN), and support vector machine (SVM). The analyzed dataset involves MRI from 150 individuals and its CDR scales. The experiments of this paper has been implemented using Orange software. Although the overall accuracy rates are quite good, the best findings are 91.2% from logistic regression when it analyzed the normalized dataset using Z-score. Comparing with other studies, the results of this study have shown significant improvement based on utilizing the normalization techniques. © 2021 IEEE.

关键词： Magnetic resonance imaging

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A Hybrid Deep Learning-based Person Re-identification Across Multi-Cameras 10

A Hybrid Deep Learning-based Person Re-identification Across...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Aonon, Manaschai Charoenkwan, Phasit Singkhamfu, Phudinun Kaewkamol, Porntida Chiang Mai University Data Science Consortium Faculty of Engineering Chiang Mai Thailand Chiang Mai University Modern Management and Information Technology College of Arts Media and Technology Chiang Mai Thailand Chiang Mai University Software Engineering College of Arts Media and Technology Chiang Mai Thailand

ISBN: (纸本)9798331543273

Surveillance systems play an essential role in enhancing security and ensuring safety within society. Public spaces and critical areas are commonly monitored through multi-camera networks for tracking individuals in real-time to enable quick responses to potential security threats. However, variations in lighting, camera angles, and occlusions pose significant challenges to the process of recognizing individuals across multiple camera views. This research proposes a Person Re-Identification (Re-ID) with deep learning and feature extraction method to improve the accuracy of individual matching and identification despite these challenges. This research encompasses multiple neural network topologies, such as ResNet50, ResNet101, and OSNet architectures with traditional computer vision methodologies such as Scale-Invariant Feature Transform (SIFT) to examine the body part weighting coefficients and feature selection mechanisms. The results demonstrated that integrating dimensionality reduction via principal component analysis (PCA) coupled with semantic segmentation techniques yields statistically significant improvements in re-identification accuracy. The ResNet101 architecture emerges as the superior methodology, achieving performance with a Rank-1 accuracy of 0.88 and mean Average Precision (mAP) of 0.94 on the test dataset. © 2025 IEEE.

关键词： Semantic Segmentation

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Efficient Byzantine-Robust Federated Learning Based on the Multi-Message Shuffle Protocol for Consumer Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Jin, Chunhua Li, Lulu Wang, Jiahao Li, Jinbiao Liu, Junyi Chen, Guanhua Zhang, Hao Weng, Jian Faculty of Computer Software Engineering with Huaiyin Institute of Technology Huai’an China Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai’an China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China Jinan University College of Information Science and Technology China

The accelerated advancement of consumer Internet of Things (CIoT) devices has resulted in their pervasive integration into the fabric of contemporary lifestyles. As these devices are capable of collecting vast quantities of data pertaining to users, they present a significant risk to the privacy and security of individuals. Federated learning is an advanced machine learning paradigm that prioritises the preservation of user data privacy and enhances security by eliminating the need for centralised data collection. As a result, it is a promising approach to address user data privacy and security concerns. However, federated learning still faces challenges such as privacy attacks, Byzantine attacks and high communication overheads. To address these challenges, we propose an efficient Byzantine-robust federated learning method based on multi-message shuffle protocol. To achieve privacy protection and Byzantine robustness, we introduce an innovative aggregation strategy inspired by the multi-message shuffle protocol for differential privacy. To improve communication efficiency, we develop an adaptive gradient compression scheme. Theoretical analysis indicates that our multi-message shuffle protocol is capable of satisfying the requisite differential privacy conditions. Compared with the state-of-the-art methods, our proposed scheme demonstrates superior convergence capabilities on the real-world dataset when approximately 40% of participants are Byzantine, achieving accuracies of 96.78%, which illustrates its strong robustness. Upon reduction of the transmitted model parameters to 1% of the original size, the proposed scheme exhibits a maximum accuracy degradation of 0.35%. which significantly reduces the communication overhead while achieving high accuracy. In addition, the scheme also achieves an accuracy of 88.73% on the Non-iid MotionSense dataset, which is superior to other schemes. © 2014 IEEE.

关键词： Federated learning

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Leveraging Cross-Modals for Cheapfakes Detection 23

Leveraging Cross-Modals for Cheapfakes Detection

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4th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval, ICDAR 2023

作者： Nguyen, Thanh-Son Dang, Vinh Tran, Minh-Triet Dang-Nguyen, Duc-Tien Software Engineering Lab and Faculty of Information Technology University of Science VNU-HCM Vietnam and Viet Nam National University Ho Chi Minh City Viet Nam Department of Information Science and Media Studies University of Bergen Bergen Norway

ISBN: (纸本)9798400701863

Detecting cheapfakes, particularly out-of-context images, is crucial for maintaining the integrity of information and preserving trust in multimedia content. This study proposes a new Cross-modals approach for cheapfakes detection that blends Natural Language Processing and Computer Vision techniques, including Image Captioning, Name Entity Recognition, and Natural Language Inference. Our approach enhances the robustness of the Cross-modals methods by not only considering both the meaning and context of the image titles but also using additional information such as Name Entity. According to the experiments, our method achieved an accuracy of , which can improve previous approaches. This paper highlights the potential of combining Natural Language Processing and Computer Vision techniques to tackle real-world problems, making it a significant advancement in cheapfakes detection. © 2023 ACM.

关键词： Image enhancement

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Mental Health Diagnostics Using Machine Learning: A Clustering and Predictive Modeling Approach 19

Mental Health Diagnostics Using Machine Learning: A Clusteri...

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

作者： Chauhan, Ritu Mishra, Aarushi Jena, Mehak Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur50250 Malaysia

ISBN: (纸本)9798331507817

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. Machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84% . Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively. © 2025 IEEE.

关键词： Electronic health record

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Utilizing Organizational Patterns to Build High-Performing Remote Teams 28

Utilizing Organizational Patterns to Build High-Performing R...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Toldy, Kristián Khail, Waheedullah Sulaiman Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer programming

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Financial Sustainability of Automotive software Compliance and Industry Quality Standards 8th

Financial Sustainability of Automotive Software Compliance ...

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8th International Congress on information and Communication technology, ICICT 2023

作者： Dakić, Pavle Todorović, Vladimir Vranić, Valentino Faculty of Informatics and Computing Singidunum University Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Business Studies and Law MB University Belgrade Serbia

ISBN: (纸本)9789819930906

Achieving a high level of quality in the AV industry involves understanding the complexity of the production cycle with the constant challenges of applying appropriate standards. The need for compliance based on relevant certificates implies the creation of an environment of trust between manufacturers, component suppliers, and end customers. Research should sublimate the most current standards from various fields related to data protection, security, system sustainability, environmental management, financial sustainability, and business transparency. The success of a complex parts supply system is impossible without a stable approach to IT security. The control of production processes sometimes involves using non-standardized techniques, and one of the applicable ones is work-in-progress (WIP), which describes the use of partially finished raw materials in the stages of obtaining final products. Key points and the contribution of the current study are in getting a better overview of the situation within this area related to standards and the financial problems that can arise due to a lack of understanding of standards compliance. The main limitations within the study itself refer to the consideration of the theoretical foundations that should be used during future research related to the practical part during implementation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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