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Enhancing V2X Communication with Edge computing: (A Path to Low-Latency and Intelligent Transportation)

Enhancing V2X Communication with Edge Computing: (A Path to ...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Kavinesh S Karunambikai Department of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The highly established transport infrastructure, effective, precise, and timely vehicle-to-vehicle communication is a necessary process. Edge computing is transforming V2X communication by virtue of near-source real-time processing of data and thus accelerating decision-making and the system in turn. In this paper, an improved framework of Reinforcement Learning and Random Forest algorithms for improving data filtering, bandwidth utilization, and system scalability is presented. Compared to the classical cloud-based systems, the emerging edge computing paradigm allows for faster response times in mission-critical applications such as collision avoidance, intelligent traffic signaling, and real-time navigation. The system makes use of Reinforcement Learning for real-time decision-making tasks and Random Forest algorithms for optimal data clustering with ***. The approach also minimizes unnecessary data transfer to the cloud, thereby minimizing bandwidth usage and maximizing performance. Experimental results corroborate that the target model attains 20% reduced latency, 15% increased bandwidth efficiency, and 10% improved prediction accuracy over conventional V2X systems. In addition, network convergence with the 5G network infrastructure facilitates improved communication speed and quality and facilitates seamless end-to-end connectivity among and to connected and autonomous vehicles. While the system has positive impacts, current global issues such as hardware limitation and energy consumption are still present. Future research will further extend the hardware support, accelerate data processing mechanisms, and improve scalability to large-scale V2X environments. In conclusion, this paper presents a realistic and effective solution to improving V2X communication and making transportation systems safer, smarter, and more efficient.

关键词： Scalability Decision making Transportation Reinforcement learning Bandwidth Real-time systems Hardware Vehicle-to-everything Random forests Edge computing

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Emotional Contribution to Parkinson's Disease Diagnosis Using Machine Learning for EEG Dataset Generation

Emotional Contribution to Parkinson's Disease Diagnosis Usin...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： S. Arockiya Selvi T. Kamalakannan Department of Computer science VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Emotion is the root cause of nearly all harmful illnesses in humans. Emotion affects blood pressure, heart rate, kidney function, and a few disorders of the nervous system. Parkinson's disease, which causes impairment of movement, is one of the brain disorders. This issue is caused by a reduction in dopamine release in the cortex of the brain. Dopamine production deviations have been related to essential hypertension. Dopamine has a crucial role in anxiety modulation in multiple regions of the brain. Patients with Parkinson's disease frequently indicate that acute stress worsens their motor symptoms, especially gait freezing, dyskinesia, and tremors and the emotions play a substantial role in Parkinson's disease. This study focuses on the Classification Learner algorithms and the Electro Encephalogram (EEG) dataset, both of which covered the diagnostic range of PD development. The classifiers were evaluated using key performance indicators such as error rate, accuracy, sensitivity, specificity, and precision. The findings illustrate the convergence of emotional analysis, machine learning (ML), and neuroscience in improving diagnostic capabilities by demonstrating how a combination of EEG datasets with KNN algorithms offers a reliable and accurate approach for early Parkinson's disease identification.

关键词： Machine learning algorithms Accuracy Sensitivity Parkinson's disease Error analysis Machine learning Nearest neighbor methods Electroencephalography Classification algorithms Reliability

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Parametric Investigation of Surface Morphology in Through Glass Vias(TGVs) Fabricated via Femtosecond Laser Processing

Parametric Investigation of Surface Morphology in Through Gl...

引用

international conference on Electronics Packaging (ICEP)

作者： Jaebeom Lee Taesik Kim Seung Hwan Lee Jiyong Park Advanced Packaging Integration Center Korea Institute of Industrial Technology (KITECH) Incheon Republic of Korea Department of Mechanical Engineering Hanyang University Seoul Republic of Korea Department of Materials Science and Engineering Hanyang University Seoul Republic of Korea Department of Convergence Manufacturing System Engineering Korea National University of Science and Technology (UST) Daejeon Republic of Korea

ISBN: (数字)9784991191190

ISBN: (纸本)9798331514747

2.5D packaging integrates chips with diverse functionalities using an interposer substrate, enabling high-density interconnection and shorter interconnection length, making it ideal for electronic devices, high performance computing (HPC), and internet of things (IoT). Glass has emerged as a promising material for interposers due to its low dielectric constant, high dimensional stability, and adjustable coefficient of thermal expansion (CTE). Femtosecond pulse laser is highly suitable for micromachining brittle materials like glass, as its ultrashort pulse duration minimizes thermal effects during through glass vias (TGVs) processing. However, laser-induced periodic surface structures (LIPSS) formed on sidewall of the via during processing can adversely affect reliability after plating. Therefore, in this study, we investigate the surface morphology that changes with the number of pulses of laser and propose a burst mode approach to suppress LIPSS formation and improve TGVs quality.

关键词： Surface morphology Surface emitting lasers Morphology Process control Glass Laser modes Ultrafast electronics Reliability Surface treatment Thermal stability

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Optimizing Machine Learning for Malicious Blockchain Node Detection Using Modified Metaheuristic

Optimizing Machine Learning for Malicious Blockchain Node De...

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AIML-Applications for Engineering & technology (ICAET), international conference on

作者： Nebojsa Bacanin Marko Vićentijevic Luka Jovanovic Angelina Njegus Branislav Radomirovic Miodrag Zivkovic Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance.

关键词： Proof of stake Analytical models Accuracy Prevention and mitigation Metaheuristics Machine learning Linear programming Security Particle swarm optimization convergence

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Framework for Edu-Metaverse: Integrating Immersive Technologies for Collaborative and Experimental Learning

Framework for Edu-Metaverse: Integrating Immersive Technolog...

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international conference on Electronics, Materials Engineering and Nano-technology (IEMENTech)

作者： Amartya Mukherjee Rohan Bhattacharjee Sudipa Dutta Ayan Kumar Panja Souvik Chatterjee Institute of Engineering & Management University of Engineering & Management Kolkata West Bengal IEMA Research & Development Pvt. Ltd. Kolkata West Bengal

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The convergence of education and technology has paved the way for transformative innovations, with the Metaverse and immersive experiences offering unprecedented opportunities for interactive learning. However, a significant gap persists in the development of educational applications-namely, the absence of a robust framework for seamlessly integrating these technologies into educational ecosystems. To address this, we propose an innovative approach that incorporates artificial intelligence through a 3D chatbot, designed to act as an interactive guide for students. This chatbot not only answers questions but also delivers supplementary learning resources, fostering a more engaging learning experience. Additionally, our framework integrates live streaming and chat functionalities, enabling realtime communication between students and educators, regardless of geographic location. By bridging these technological gaps, our solution aims to enhance traditional educational environments, significantly improving engagement and accessibility for learners worldwide. This initiative represents a critical step toward a future where education is both highly interactive and profoundly impactful. This is an exciting step towards creating a future where learning is not just informative, but genuinely engaging and impactful.

关键词： Technological innovation Visualization Three-dimensional displays Metaverse Education Chatbots User experience Spatial computing Synchronization Immersive learning

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Investigating Quantum-Inspired Evolutionary Algorithm with Restarts for Solving IEEE CEC 2022 Benchmark Problems 5th

Investigating Quantum-Inspired Evolutionary Algorithm with R...

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5th international conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2023

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9789819600465

This paper investigates the effectiveness of two restart methods, local and global restarts, in enhancing the performance of the quantum-inspired evolutionary algorithm (QiEA) to solve optimization problems. The study compares the result of these modifications by testing their performance against the widely recognized and challenging IEEE CEC 2022 benchmark functions. The objective is to analyze the impact of the restart methods on the convergence and optimization outcomes of the QiEA. Through experimental evaluation, it was sought to determine whether the inclusion of restart methods in the QiEA framework would lead to improved convergence toward the optimal solution. Also, a novel Greedy Local Restart algorithm is proposed. The results demonstrate that the QiEA variants consistently outperform the basic QiEA, achieving superior optimization results in terms of convergence and accuracy. Restart approaches are thus observed as an indispensable asset in handling complex optimization problems, greatly enhancing the effectiveness and applicability of evolutionary algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary algorithms

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Advancing Sustainable Site Reliability in Cloud Operations: An Implementation Research Study of Architecture Best Practices 2nd

Advancing Sustainable Site Reliability in Cloud Operations: ...

引用

2nd international conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Gutta, Leela Manush Kanchepu, Nageswararao DevOps/SRE Engineer Saint Charles United States Solution Architect in Cloud Applications Tata Consultancy Services Information Technology HoustonTX United States

ISBN: (纸本)9783031717284

This research delves into the convergence of sustainable development practices with the critical domain of site reliability in cloud operations. The study conducts an in-depth exploration and implementation research of architecture best practices aimed at advancing the reliability and efficiency of cloud-based systems. Focusing on the integration of sustainable principles, the abstract outlines the methodology employed to enhance site reliability while minimizing environmental impact. By synthesizing architecture best practices and sustainability considerations, this research contributes valuable insights to the evolving landscape of cloud technology, emphasizing the dual imperative of operational excellence and responsible, sustainable development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud computing architecture

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Design and implementation of cooperative multi-agent based on MAPPO 5

Design and implementation of cooperative multi-agent based o...

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Wang, Haolong Fei, Rong Chen, Ruizhu School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

In the past few years, with the continuous development of deep reinforcement learning, the application of deep reinforcement has been set off within the realms of self-driving vehicle technology, game games, and natural language processing, and the proximal policy optimization algorithm has emerged as a focal point in the sphere of deep reinforcement learning research. In this paper, the Proximal Policy Optimization (PPO) algorithm and the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm are studied, and a cooperative multi-agent system is designed and implemented based on the MAPPO algorithm in the field of StarCraft game, which is compared with the popular Q-Value Mixing (QMIX) reinforcement learning algorithm in recent years, and proposes a direction for subsequent system improvement. In the proximal policy optimization algorithm, the traditional policy gradient algorithm, natural policy gradient algorithm, trust domain policy optimization algorithm, proximal policy optimization algorithm, and independent proximal policy optimization algorithm are studied. In the multi-agent proximal policy optimization algorithm, the centralized training decentralized execution architecture, athlete critic method, value standardization, experience playback, and GAE (GAE) adopted by the MAPPO algorithm are studied. Designing and implementing a cooperative multi-agent system, introducing the StarCraft environmental campaign. Regarding the specifics of the training process, the policy gradient algorithm is used to update the policy network, the time series difference algorithm is improved, and the generalized advantage estimation is used to update the value network. Compared with the QMIX learning algorithm, the model trained by the MAPPO algorithm has the advantages of faster convergence and better stability. © 2025 SPIE.

关键词： deep reinforcement learning generalized advantage estimation Proximal policy optimization temporal difference

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Minimizing Energy Mismatch in Electric Vehicle Batteries With the Osprey Optimization Algorithm: A Novel Approach to SOC and Range Enhancement

Minimizing Energy Mismatch in Electric Vehicle Batteries Wit...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Chodagam Srinivas Ibrahim Zafar C Sravani K Lalitha N Sunil Kumar Reddy K Chandu Babu Department of EEE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Efficient battery energy management is critical for enhancing the performance and sustainability of electric vehicles (EVs), especially in scenarios involving varying driving conditions and frequent charging cycles. This study presents a novel approach utilizing the Osprey Optimization Algorithm (OOA) to optimize essential battery parameters, including capacity, efficiency, and state-of-charge (SOC) limits. The primary objective is to minimize the energy mismatch between the required and available battery energy during operation, a challenge that significantly affects EV range and battery longevity. A comprehensive battery model is developed to simulate real-world conditions, accounting for factors such as self-discharge, SOC constraints, and dynamic energy demands. The OOA effectively improves SOC management by maintaining the SOC within optimal ranges, reducing energy deficits, and enhancing energy delivery efficiency. Compared to traditional methods, the OOA demonstrates superior convergence speed and accuracy, leading to more stable battery performance under diverse operating scenarios. These improvements contribute not only to prolonged battery life but also to increased reliability of EVs, making the proposed methodology a valuable tool for advancing nextgeneration EV technologies and sustainable energy solutions.

关键词： Analytical models Machine learning algorithms Sensitivity analysis Computational modeling Predictive models Prediction algorithms Electric vehicles Batteries State of charge Energy management

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Comparative Study of Different Models on MRI-Based Brain Tumor Detection

Comparative Study of Different Models on MRI-Based Brain Tum...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Aditya Raj Dron Rawat Deval Verma Shreeya Pandey Divyansh Rana Gaurav Verma SCSET Bennett University Greater Noida SCSET Bennett University Greater Noida India Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Brain tumors are one of the deadliest medical disorders that can be treated if identified early. Manually interpreting MRI scans is mostly challenging, time-consuming, and error prone. Deep learning techniques have emerged to automatically detect and classify brain tumors with high accuracy, mitigating existing limitations. This work mainly focuses on EfficientN etBO, an advanced transfer learning model, simultaneously examining various deep learning techniques for the identification of brain tumors. The model was trained on a publicly available MRI dataset comprising images of glioma, meningioma, and pituitary tumors, as well as healthy brain tissues. This work demonstrates that EfficientNetB0 outperforms traditional CNN architectures in accuracy and efficiency. Using techniques like data augmentation, cross-validation with 10 folds, and optimizations of learning rates, we achieved an impressive 97.5% validation accuracy to address issues like overfitting and improving convergence. The results show that EfficientNetB0 is a more scalable solution to identify brain tumors in comparison to other traditional and makes it better for real-world applications.

关键词： Deep learning Accuracy Magnetic resonance imaging Transfer learning Brain tumors Brain modeling Data augmentation Data models Optimization Overfitting

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