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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bhaskar, Hari Naga Vijaya Mayil, V. Valli KL University Dept. of Computer Science and Applications India

ISBN: (纸本)9798331523923

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5-10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. This review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements. © 2025 IEEE.

关键词： Air Pollution Forecasting Environmental Impact Machine Learning Metropolitan Cities Public Health Urban Transportation

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Handling the Challenges of Microbiome data through Supervised Autoencoders for the Non-invasive Disease Diagnosis

Handling the Challenges of Microbiome Data through Supervise...

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2024 Discovery science Late Breaking Contributions, DS-LB 2024

作者： Buttaro, Veronica Ceci, Michelangelo Pio, Gianvito Dept. of Computer Science University of Bari Aldo Moro Bari Italy Data Science Lab CINI Consortium Rome Italy Dept. of Knowledge Technologies Jozef Stefan Institute Ljubljana Slovenia

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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data Complexity and Stability Induced Predictive Distributed Learning Approach 19th

Data Complexity and Stability Induced Predictive Distributed...

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19th International Middle Eastern Simulation and Modelling Conference, MESM 2024

作者： Igodan, E.C. Ukaoha, K.C. Al-Akaidi, M. Dept. of Computer Science University of Benin Benin City Nigeria Dept. of Computer Science Wigwe University Isiokpo Nigeria College of Science & Computing Wigwe University Isiokpo Nigeria

ISBN: (纸本)9789492859341

The performance of most classifiers degrades because the majority of feature selection algorithms do not scale well, primarily due to the inherent characteristics in high-dimensional microarray datasets, making them eventually inapplicable. This research study proposed a novel predictive distributed FS model by sample partitioning while maintaining class distribution using data complexity and stability measures. For implementation, brain tumor microarray datasets were adopted. Six feature selection algorithms and classifiers, along with their ensemble learning methodology, were implemented. The dataset was preprocessed and the SMOTE technique applied, so as to balance the class distribution and the feature/sample size ratio. The study showed significant improvement in the proposed distributed model with an accuracy of 99.67% and demonstrated a sharp reduction both in the runtime complexity and the number of selected features respectively. © 2025 EUROSIS-ETI.

关键词： Contrastive Learning

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ML-JET: A Benchmark dataset for Classifying Heavy Ion Collisions

ML-JET: A Benchmark Dataset for Classifying Heavy Ion Collis...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision Workshops, WACVW 2025

作者： Mehryar, Haydar Mao, Chengzhi Schwiebert, Loren Wayne State University Dept. of Computer Science United States Columbia University Dept. of Computer Science United States

ISBN: (纸本)9798331536626

Understanding relativistic heavy ion collisions is important for studying the evolution of the universe. Models of these collisions require hyperparameter tuning to accurately reproduce experimental results. Traditionally, Bayesian analysis, which is costly and non-scalable, has been used for this tuning. Deep learning is being explored as a potential alternative, although it is still in the early stages. In this work, we propose a novel benchmark dataset for anomaly detection in visual data generated from relativistic heavy ion collisions using the JETSCAPE framework. This dataset consists of 10.8 million jet event images, enabling the application of computer vision techniques to this domain. Our dataset converts complex physics simulations into event images, which are compatible with standard vision classifiers. Using the standard Convolutional Neural Networks (CNN), our initial results attain a 92% accuracy in energy loss module classification, while concurrently accelerating the tuning process by an order of magni-tude and saving millions of CPU/GPU hours over Bayesian analysis. This work demonstrates the potential of leveraging AI-based anomaly detection techniques to advance both physics and computer vision, offering a scalable solution for identifying anomalies in particle collision data. © 2025 IEEE.

关键词： Deep learning

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Cyberbullying Detection using Deep Learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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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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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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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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