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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Big Data Clustering Algorithms: Improving Efficiency and Scalability for Massive Data Mining and Pattern Recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Muthu, Shanmugam Kalyan, G Ramesh Samuthira Pandi, V. Shobana, D. Lakshmi Priya, J. Jaishika, S. Shipt Inc 420 20th St N # 100 BirminghamAL35203 United States Sns College of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798331508685

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study. © 2025 IEEE.

关键词： Data accuracy

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RADAR: Robust DRL-Based Resource Allocation Against Adversarial Attacks in Intelligent O-RAN

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Ergu, Yared Abera Nguyen, Van-Linh Department of Computer Science and Information Engineering Chiayi Taiwan Advanced Institute of Manufacturing with High-tech Innovations CCU Taiwan

The advent of open radio access networks (O-RAN) has introduced intelligent, flexible, and multi-vendor network ecosystems. While O-RAN's open interfaces and artificial intelligence (AI)-driven solutions offer improved performance, energy efficiency, and resource minimization for green networking, they also expose the system to new security vulnerabilities, particularly adversarial attacks. This paper presents a robust defense approach, termed RADAR, designed to secure deep reinforcement learning (DRL)-powered resource allocation mechanisms in O-RAN. RADAR is a multi-faceted defense framework that integrates adversarial input sanitization, proactive adversarial training, and adapted defensive distillation to counter policy infiltration attacks, gradient-based deceptive loss maximization, and signal perturbation injections into the O-CU via the O-DU in O-RAN. This study evaluates the effectiveness of RADAR not only against a novel attack variant - policy infiltration attack (PIA), which manipulates environmental parameters to disrupt allocation decisions, but also against well-known adversarial techniques such as the fast gradient sign method (FGSM) and projected gradient descent (PGD). Experimental results demonstrate that RADAR achieves significant recovery in user data rates across three network slices: 73.33% for eMBB, 64.71% for mMTC and 52.94% for uRLLC, outperforming the existing standalone approach. The findings highlight RADAR's effectiveness in mitigating adversarial attack techniques, underscoring its potential to secure AI-driven core functions in intelligent O-RAN. © 2025 IEEE.

关键词： Energy efficiency

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Convolutional Neural Network for Facial Emotion Detection

Convolutional Neural Network for Facial Emotion Detection

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Tina Babu Rekha R Nair A G Kavya Tenzin Sungrab Norbu Sunil Reddy J Harsha Vardhan Reddy Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Facial Emotion Recognition is considered one of the important domains in computer vision, which allows machines to interpret human emotions and, consequently, find applications in adaptive gaming, healthcare, and so forth. In this work, a Convolutional Neural Network (CNN)-based approach to emotion classification using FER-2013 dataset is proposed. This dataset contains 35,887 grayscale images with seven categories of emotions such as angry, disgust, fear, happy, sad, surprise, and also neutral. The architecture integrates convolutional layers, max-pooling, dropout regularization, and fully connected layers for robust feature extraction and classification. Data augmentation techniques in terms of rotation, zooming, and flipping are applied to reduce overfitting and enhance generalization. Training uses the Adam optimizer with a decaying learning rate, in addition to categorical cross-entropy loss, in a total number of 30 epochs. Experiments are performed to get a 54.7% accuracy on the validation set; strong performance is achieved for emotions “happy” and “surprise,” whereas “neutral” and “fear” cannot be well distinguished. The experiment stresses that one ought to balance datasets, exploit multimodal data and advanced model optimization techniques for improved accuracy and making models useful in the real world. Findings set the benchmark for upgrades in FER into better robust and scalable systems to cater to varying demands for applications.

关键词： Training Emotion recognition Data privacy Accuracy Computational modeling Medical services Data augmentation Data models Convolutional neural networks Optimization

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Hybrid Approach: Combining Hill Climbing and Genetic Algorithms for Traveling Salesman Problem

Hybrid Approach: Combining Hill Climbing and Genetic Algorit...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Tina Babu Rekha R Nair S Sumanth Ishith T P Chitra K S Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The Traveling Salesman Problem (TSP) is a classic NP-hard optimization challenge, requiring an efficient approach to identify near-optimal solutions. This paper presents a hybrid algorithm combining Genetic Algorithm (GA) and Hill Climbing (HC) to improve solution accuracy and convergence speed. GA provides global search capabilities through selection, crossover, and mutation, while HC enhances local optimization by refining solutions iteratively. The hybrid approach leverages GA's exploration strength and HC's exploitative power, reducing premature convergence and improving search efficiency. Experimental results indicate that the hybrid GA-HC algorithm outperforms standalone GA and HC in terms of tour length reduction and computational efficiency. Benchmarked on TSP instances, the proposed method demonstrates a 15% improvement in solution quality compared to traditional approaches. Additionally, the hybrid method achieves faster convergence, requiring 30% fewer iterations than standalone GA. The results highlight the hybrid algorithm's potential for real-world applications such as logistics, network design, and supply chain management. This study contributes to the growing research on hybrid metaheuristics, showcasing the benefits of integrating global and local search strategies to solve complex combinatorial problems.

关键词： Traveling salesman problems Benchmark testing Search problems Hybrid power systems Computational efficiency Optimization Tuning Genetic algorithms Convergence Logistics

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Propositional Logic for Automated Reasoning in Legal Expert Systems

Propositional Logic for Automated Reasoning in Legal Expert ...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Tina Babu Pranitha Karanam Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

As legal systems become increasingly complex and the demand for automated decision-making grows, there is a pressing need for tools that can assist legal professionals in applying legal rules efficiently. Legal expert systems, which aim to automate the reasoning process of applying laws to specific situations, depend on effective mechanisms for legal inference. Propositional logic, a fundamental branch of logic that works with statements that can either be true or false, has been recognized for its potential in underpinning such systems due to its clarity and structure. This paper explores the application of propositional logic in automating legal reasoning within legal expert systems, particularly focusing on how legal rules can be formalized as logical propositions. These systems can then use logical inference to derive legal conclusions from a given set of facts. However, propositional logic has limitations in modeling the complexities of legal language, which often requires interpretation of contextual meaning and handling intricate conditions. The paper also explores how integrating propositional logic with advanced artificial intelligence techniques like natural language processing (NLP) and machine learning (ML) can improve the system's capacity to address more sophisticated legal reasoning challenges. It highlights current obstacles, such as adapting to continuously changing legal frameworks, managing contradictions, and ensuring scalability. Finally, the paper discusses potential future research, particularly the development of hybrid systems that merge propositional logic with other reasoning approaches to create more adaptable, accurate, and effective legal expert systems.

关键词： Technological innovation Accuracy Law Scalability Machine learning Transformers Cognition Natural language processing Logic Expert systems

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Flexible Spectrum Sensing in NOMA System for LEO Satellite-Terrestrial Uplink Communications

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IEEE Internet of Things Journal 2025年第11期12卷 18439-18442页

作者： Zhu, Jie Xu, Tianheng Wang, Chao Ying, Kai Zhou, Ting Zhang, Haijun Hu, Honglin Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai201210 China Tongji University Department of Information and Communication Engineering Shanghai200092 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai University School of Microelectronics Shanghai200444 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

With the sixth-generation mobile communication technology (6G) experiencing a shift of spatial expansion, the integration of terrestrial architecture and satellites has become one core feature of 6G. While the scarcity of spectrum resources has become a significant challenge hindering the advancement of integrated satellite-terrestrial networks, it is crucial to push the development of effective strategies to optimize spectrum utilization. This letter introduces a flexible spectrum sensing technique in the Non-Orthogonal Multiple Access (NOMA) uplink system for Low Earth Orbit (LEO) satellite-terrestrial communications, striking an optimal balance between under-sensing and over-sensing. Confronted with diverse complicated scenarios of multi-satellite coverage, we conceive the operational principles and derive the sensing thresholds, exploiting spectrum holes and mitigating the fluctuation of false alarms. Notably, numerical outcomes showcase that the proposed technique outperforms the benchmarks, achieving a maximum throughput gain of 61.5% in dynamic communication environments. © 2014 IEEE.

关键词： Satellite communication systems

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