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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Roy, Prince Kumar, Rajneesh Morshed, Md Neaz Fairleigh Dickinson University Dept. of Applied Computer Science United States Dept. of Computer Science & Engineering India

ISBN: (纸本)9798331539948

The process of a supply chain involves a series of activities that distribute, assemble, and manage products, transferring them from the supplier to the end consumer. It operates as a complex, interconnected network regulated by supply and demand. Security in supply chain management is a crucial aspect of its overall security measures, focusing primarily on maintaining essential end-to-end protection. Without this, several attacks can be used to manipulate data and valuable information from the entire chain of networks. This paper explains how encrypted intermediates can secure the entire SCM by validating each intermediate using public-private key encryption. We also demonstrate and assess how the proposed AAF-SCM model can aid in tracing security breaches or unauthorized data tampering. We also validate this model against nefarious attacks like Fabrication, Assertion, Deletion, and Modification. Finally, we propose opportunities to develop innovative systems that enhance security through intermediary steps. © 2024 IEEE.

关键词： Supply chain management

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On the Rival Nature of data: Tech and Policy Implications 25

On the Rival Nature of Data: Tech and Policy Implications

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4th ACM Symposium on computer science and Law, CS and LAW 2025

作者： Gordon-Tapiero, Ayelet Ligett, Katrina Nissim, Kobbi Hebrew University Dept. of Computer Science Jerusalem Israel Georgetown University Dept. of Computer Science WashingtonDC United States

ISBN: (纸本)9798400714214

data is often thought of and treated as a non-rival good, which would imply that one person’s use of data does not inherently diminish its availability for others. Building on research in privacy and statistics, we argue that there exist many important settings in which data should be treated as a rival good. Our argument takes into account modern uses of data for statistics, machine learning, and a variety of other purposes, in conjunction with requirements of privacy protection and statistical validity. Excessive sharing or reuse of data about individuals can lead to leakage of sensitive personal information, potentially causing harm to those whose information is included in the data. Overuse of data in statistics or machine learning can lead to overfitting, i.e., models that perform well on training data but poorly on fresh unseen data. Recognizing the rival nature of data offers an opportunity to rethink the way data are managed and used. In an age where the training of AI models generates a massive appetite for data, this perspective has the potential to inform the creation of new regulation and technical infrastructure that will be able to safely and responsibly manage data, track their various uses, and ensure that privacy and statistical usefulness are respected and preserved. We observe that current EU regulation and existing approaches to science seeking to increase opportunities for data-sharing and reuse of data misconstrue the complex nature of data, inadvertently creating risks of privacy harms and overfitting, hence squandering the societal benefits that can be derived from data. Recognizing the rival nature of data has implications for policy and practice. Regulation should address the limitations and risks associated with data reuse and facilitate technological measures to track, analyze, and manage data usage with the goal of ensuring that privacy and statistical validity are maintained. © 2025 Copyright held by the owner/author(s).

关键词： Differential privacy

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Integrated Framework for Equitable Healthcare AI: Bias Mitigation, Community Participation, and Regulatory Governance 14

Integrated Framework for Equitable Healthcare AI: Bias Mitig...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Dept. of Computer Science and Engineering Chandigarh University Punjab Mohali India Dept. of Computer Science and Engineering MRIIRS Faridabad India

ISBN: (纸本)9798331531935

Artificial intelligence (AI) has revolutionized healthcare by enhancing diagnostic precision, personalizing treatments, and optimizing clinical workflows. However, algorithmic biases arising from imbalanced training data disproportionately affect marginalized populations, perpetuating health disparities. For instance, a widely-used predictive model underprioritized Black patients by equating healthcare costs with illness severity, restricting access to essential care. To address these challenges, this paper introduces an integrated framework combining adversarial debiasing, reinforcement learning (RL)-driven threshold optimization, synthetic data augmentation, and community engagement strategies. Empirical evaluations using the MIMIC-III dataset demonstrate near-perfect Area Under the ROC Curve (AUC = 0.994) alongside significant reductions in demographic parity (DP = 0.000) and error rate differences across subgroups. While the RL-optimized threshold ensures no missed diagnoses, it increases false positives, highlighting the trade-offs between fairness and precision. This work underscores the importance of synthesizing technical, regulatory, and community-driven approaches to promote equitable AI deployments. By embedding participatory design principles and aligning with emerging governance frameworks like the European Union’s AI Act, the proposed methodology offers a scalable pathway toward transparent, accountable, and high-impact healthcare AI solutions. © 2025 IEEE.

关键词： Patient treatment

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Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting 14

Evaluating the Effectiveness of a Facial Recognition-Based A...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nijgal, Dhamini R George, Stebin Subramanian, Parameswaran Christ University Dept. of Computer Science Bangalore India Christ University Dept. of Data Science Bangalore India

ISBN: (纸本)9798350335095

Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. © 2023 IEEE.

关键词： Face recognition

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Virtual Video Game as a Clinical Tool for Psychological Assessment and Analysis

Virtual Video Game as a Clinical Tool for Psychological Asse...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Gope, Aditya Kumar Pohane, Karthik Jakkanwar, Shivam Ramasamy, Gobi Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350309140

This paper proposes a very novel and dynamic approach to psychological assessment by utilising virtual video games as a psychometric tool. Traditional methods, such as interviews and surveys, often yield conscious responses, leading to potential inaccuracies. Leveraging the interactive and immersive nature of video games, our research aims to enhance the accuracy and engagement of data collection. We introduce a dynamic scale to quantify player responses, providing a structured framework for systematic assessment. Collected data can be analysed using statistical models or customised by experts to evaluate psychological indicators precisely. This research explores the benefits of virtual video games in improving the accuracy, engagement, and flexibility of psychological assessments, contributing to more effective solutions in clinical psychology. © 2024 IEEE.

关键词： Video analysis

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Deep Multimodal Classification of Musical Genres

Deep Multimodal Classification of Musical Genres

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Jandoubi, Bassem Akhloufi, Moulay A. Dept. of Computer Science MonctonNB Canada

ISBN: (纸本)9798331504847

Music genre classification is a challenging problem due to the subjective and complex nature of music perception. This project explores three complementary approaches to genre classification: audio-based classification using Mel-spectrograms and Convolutional Neural Networks (CNN), lyrics-based classification using RoBERTa for natural language processing, and a multimodal classification approach that integrates both modalities. The audio-based approach achieved remarkable performance improvements through augmentation techniques, while the lyrics-based model demonstrated effective text comprehension using pre-trained transformers. Finally, a multimodal approach that combines Mel-spectrograms and lyrics through concatenation achieved the highest accuracy, underscoring the advantages of integrating diverse data modalities. These results demonstrate the potential of multimodal deep learning for complex classification tasks, opening the way to improved music retrieval systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Robot Vision-Based Autonomous Navigation Method Using Sim2Real Domain Adaptation 22

Robot Vision-Based Autonomous Navigation Method Using Sim2Re...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Mengjiao Duan, Shihong Xu, Cheng Dept. Computer Science Beijing China

ISBN: (纸本)9798331509712

Vision-based autonomous navigation is pivotal in mobile robot technology, involving autonomous movement, path planning, obstacle avoidance, and target reaching in unknown environments. Reinforcement Learning (RL) offers a trial-and-error-driven learning method for autonomous navigation. However, training RL models directly on physical robots is time-intensive and poses potential risks. Simulators accelerate RL training efficiency and reduce costs, but they typically provide only approximate models of robot dynamics and their interactions with the environment. This leads to a simulation-to-reality gap (Sim2Real Gap), where strategies trained in simulation under-perform in real-world applications, sometimes resulting in task failures. To address the issue of reducing the Sim2Real Gap in visual sampling training, this paper establishes a bridging plugin between the simulator and ROS, enabling the subscription to interaction data between the ROS-based robot and its real-world environment. We propose a Sim2Real domain adaptation method based on CycleGAN, which generates effective visual observations for autonomous navigation learning. The experimental results demonstrate that this domain adaptation-based method, by utilizing minimal real-world data, significantly reduces the Sim2Real Gap compared to approaches relying solely on simulation data, achieving a performance improvement of 62.38%. © 2024 IEEE.

关键词： Robot vision

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ChessMoveLLM: Large Language Models for Chess Next Move Prediction

ChessMoveLLM: Large Language Models for Chess Next Move Pred...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Diallo, Kassim B. Akhloufi, Moulay A. Dept. of Computer Science MonctonNB Canada

ISBN: (纸本)9798331504847

Creating a chess engine using language models is challenging due to the complexity of understanding the logic and dept. of moves. To solve this problem, we propose several approaches with different types of training of different LLMs that already have some knowledge of tasks and not necessarily on chess, and adjust them for faster learning. We propose three groups of methods and five approaches to enhance state-of-the-art results. Our approaches will involve different types of database manipulation, the training of different types of networks and the application of a new, widely used approach, the RAG (Retrieval-Augmented Generation). We demonstrated the potential of networks and approaches with promising results for the first 2 methods, and we demonstrated with RAG in combination with FAISS (Facebook AI Similarity Search) [1], algebraic notation and GPT3.5-turbo-instruct that it is possible to increase the ability of networks to predict good chess moves. The first approach demonstrated that successive move generation benefits generalization and reduces illegal moves;for the second approach, we proved that the use of noiseless algebraic notation gives the network the possibility of progressing significantly before giving illegal moves. The second group of approaches, demonstrated that using the FEN (Forsyth-Edwards Notation), a textual graphical representation, for the prediction of move sequences to force context understanding could give promising results with a low number of epochs. Even with limitations in computation time and epochs, we were able to find promising results with a part of our dataset that can be improved with more epochs and data. © 2025 IEEE.

关键词： Deep learning

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A Study on Optimization and Augmentation Techniques for Improving the Performance of CADx System for Cat Skin Disease

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 135-141页

作者： Won, Hyeong-Sik Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In modern society, the number of households raising pets is increasing. As pet ownership increases, the cost of treating companion cats is also rising, with a significant portion of these costs going toward the treatment of skin diseases. Skin diseases are among the common ailments in pets, and swift action is required if they occur in cats. However, early lesions lack distinctive characteristics, making accurate diagnosis difficult. Therefore, this study proposes a CADx(computer-Aided Diagnosis) system that classifies images of cat skin conditions into inflammatory lesions, non-inflammatory lesions, and normal images using a dataset of cat skin diseases. We selected the ConvNeXt model, based on CNN, which can learn regional information and features. To learn various patterns of skin lesions, we applied Mixup, an image augmentation technique. When Mixup was applied, the model accuracy was 0.8679, showing a 3.35% improvement compared to the original dataset. Additionally, Lookahead Optimizer was applied to ensure stable learning of Mixup. When both Mixup and Lookahead Optimizer were applied, the model accuracy was 0.8741, showing a high improvement of 3.97% compared to the original dataset. Therefore, the Lookahead Optimizer helps with the stable training of mixup, which can improve the performance of the model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Dermatology

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Development of a Deep Learning-Based System for Cat Skin Disease Classification and Grad-CAM Visualization

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 339-344页

作者： Won, Hyeong-Sik Cho, Hyun-Chong Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of

Skin diseases in companion cats can worsen if not treated promptly, and this can increase the financial burden on pet owners. To prevent this, early and accurate diagnosis is essential. This study introduces a deep learning-based computer-Aided Diagnosis (CADx) system designed to classify cat skin diseases into non-inflammatory and inflammatory lesions by comparing them with normal images. The system employs the EfficientNetV2 model and incorporates image augmentation techniques like AutoAugment and AugMix to enhance classification performance. The study results indicate that the developed model achieved an 84.68% accuracy for non-inflammatory lesions, reflecting a 10.06% improvement, and a 97.19% accuracy for inflammatory lesions, reflecting a 2.45% improvement. Furthermore, we applied Grad-CAM to visualize the regions of interest in the images, offering veterinarians critical insights into the location and characteristics of the lesions. This system has the potential to significantly improve the precision of diagnosing skin diseases in companion cats, thereby supporting better veterinary care. Copyright © The Korean Institute of Electrical Engineers.

关键词： Veterinary medicine

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