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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

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Nuclear science and Techniques 2024年第5期35卷 173-185页

作者： He-Lin Gong Han Li Dunhui Xiao Sibo Cheng Paris Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China School of Mathematical Sciences Tongji UniversityShanghai 200092China Department of Computing Data Science InstituteImperial College LondonLondon SW72AZUK

The aging of operational reactors leads to increased mechanical vibrations in the reactor *** vibration of the incore sensors near their nominal locations is a new problem for neutronic field *** field-reconstruction methods fail to handle spatially moving *** this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this *** from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the *** convolutional neural networks were used to learn maps from observations to the global *** proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

关键词： Voronoi tessellation Field reconstruction Nuclear reactors Reactor physics On-line monitoring

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CLIPCEIL: Domain Generalization through CLIP via Channel rEfinement and Image-text aLignment 38

CLIPCEIL: Domain Generalization through CLIP via Channel rEf...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Xi Yoo, Shinjae Lin, Yuewei Artificial Intelligence Department Computing and Data Science United States Directorate Brookhaven National Laboratory UptonNY11973 United States

Domain generalization (DG) is a fundamental yet challenging topic in machine learning. Recently, the remarkable zero-shot capabilities of the large pre-trained vision-language model (e.g., CLIP) have made it popular for various downstream tasks. However, the effectiveness of this capacity often degrades when there are shifts in data distribution during testing compared to the training data. In this paper, we propose a novel method, known as CLIPCEIL, a model that utilizes Channel rEfinement and Image-text aLignment to facilitate the CLIP to the inaccessible out-of-distribution test datasets that exhibit domain shifts. Specifically, we refine the feature channels in the visual domain to ensure they contain domain-invariant and class-relevant features by using a lightweight adapter. This is achieved by minimizing the inter-domain variance while maximizing the inter-class variance. In the meantime, we ensure the image-text alignment by aligning text embeddings of the class descriptions and their corresponding image embedding while further removing the domain-specific features. Moreover, our model integrates multi-scale CLIP features by utilizing a self-attention fusion module, technically implemented through one Transformer layer. Extensive experiments on five widely used benchmark datasets demonstrate that CLIPCEIL outperforms the existing state-of-the-art methods. The source code is available at https://***/yuxi120407/CLIPCEIL. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards LLM-driven Automated Verification of Best Practices in Digital Public Infrastructures

Towards LLM-driven Automated Verification of Best Practices ...

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2024 SICSA Workshop on Reasoning, Evaluation and Application of Large Language Models, SICSA-REALLM 2024

作者： Markovic, Milan Sripada, Somayajulu Chakrabarti, Sujit Kumar Diddigi, Raghuram Bharadwaj Department of Computing Science University of Aberdeen United Kingdom Department of Computer Science International Institute of Information Technology Bangalore India Department of Data Science and Artificial Intelligence International Institute of Information Technology Bangalore India

In this position paper, we discuss three distinct approaches for assessing risks associated with Digital Public Infrastructures (DPI) and how Large Language Models could provide automated knowledge extraction to support such analyses at scale. We further outline future research directions inspired by the domain of collective intelligence and formal verification methods. © 2024 Copyright for this paper by its authors.

关键词： Digital Public Infrastructures Formal Verification Infrastructure Analysis Large Language Model

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Harnessing Deep Neural Networks for Micro-Blog Sentiment Analysis: A Novel Approach

Harnessing Deep Neural Networks for Micro-Blog Sentiment Ana...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Syed Ismail, A. Nikkath Bushra, S. Department of Data Science and Business System India Srm Institute of Science and Technology Department of Computing Technologies SRM Nagar Kattankulathur603203 India

ISBN: (纸本)9798350313987

Analyzing sentiment in micro-blogs, such as tweets or short social media posts, is a challenging yet important task in natural language processing (NLP). Deep learning techniques have shown great promise in this domain. Here's a novel approach for sentimental analysis using a deep neural network for micro-blogs. With the exponential growth of user-generated content on online platforms, YouTube has emerged as a dominant space for both creators and viewers to interact. One crucial aspect of this interaction is the comment section, where viewers express their thoughts and sentiments. This research presents a robust sentiment analysis model tailored specifically for YouTube video comments, aiming to extract valuable insights from this rich source of user-generated content. Ultimately, our novel deep neural network approach for micro-blog sentiment analysis demonstrates promising results, offering valuable insights into public sentiment across various social media platforms Using a deep neural network, this study presents a sentiment analysis model for YouTube video comments. This research contributes to the advancement of sentiment analysis methodologies, addressing the unique challenges posed by the concise and informal nature of micro-blogs. Our method, a mathematical model, and individuals were utilized to categorize the comments on various blogs. It was assumed that the classification of humans was entirely accurate. The results showed that, with classification accuracy ranging from 90% to 97%, our model is more precise than the numerical model. © 2023 IEEE.

关键词： Convolution neural network Deep learning model Micro-blogs NLP Sentimental analysis

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Unveiling the Digital Shadows: A Comprehensive Exploration of Network Forensics Methodologies 2

Unveiling the Digital Shadows: A Comprehensive Exploration o...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Nandini, Dhanalakota Sai Jyothika Lanitha, B. Azath, H. Ganeshan, R. Ahmed, Sajjad VIT Bhopal University Kothrikalan School of Computing Science and Engineering Sehore India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India VIT Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331534950

The Internet has experienced significant growth in attacks that affect network security features such as confidentiality, integrity, and availability. Network security and network forensics work together to ensure system integrity, availability, and confidentiality. Network security measures prevent unau-theorized access, attacks, and breaches, while network forensics investigates and analyses the aftermath of security incidents. Network forensics refers to the process of collecting, analyzing, and preserving digital evidence from network activity in order to investigate and respond to security incidents, breaches, or other malicious activities. It involves reconstructing the events to determine attack methods, compromised data, and potential perpetrators. It lies at the intersection of digital forensics, incident response and network security. This paper is going to analyse the methodologies which are used in network forensics and application of new technologies in each network forensics methodology through ten figures. This paper critically examines network forensics methodologies, focusing on the evolution of these methods and the integration of emerging technologies. It assesses new strategies and discusses inherent issues and limitations of existing techniques, emphasizing the need for cutting-edge techniques to enhance the effectiveness of network forensic processes. © 2024 IEEE.

关键词： Network intrusion

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Gesture Showdown: Rock Paper Scissors with AI Vision

Gesture Showdown: Rock Paper Scissors with AI Vision

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hemal Babu, H. Yeshwanth, C.D. Shravan, N. Bose, K. Regin Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Institute of Technology Department of AI&DS and Cloud Computing Chennai India

ISBN: (纸本)9798331512965

This paper presents a gesture-based RockPaper-Scissors game that leverages computer vision and hand gesture recognition to enable intuitive human-computer interaction. Utilizing MediaPipe's pre-trained hand tracking module, the system detects and identifies 21 hand landmarks in real-time to classify gestures as rock, paper or scissors based on angular relationships between key points. OpenCV is used for webcam integration and video frame preprocessing, ensuring seamless interaction. The game compares the recognized gesture with a computer-generated move, dynamically displaying the results and resetting after each round for continuous play. The system identifies gestures through precise angle calculations, differentiating between closed fists, open palms and extended fingers. Sample outputs demonstrate real-time gameplay scenarios, highlighting gesture recognition accuracy and user engagement. Future enhancements include refining recognition accuracy, adding multiplayer functionality, introducing customizable gestures, and deploying the game on web and mobile platforms for broader accessibility. This project showcases the potential of integrating AI and computer vision in creating engaging and user-friendly applications. © 2025 IEEE.

关键词： Gesture recognition

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Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

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SN Computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values

VIVA: A Benchmark for Vision-Grounded Decision-Making with H...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hu, Zhe Ren, Yixiao Li, Jing Yin, Yu Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong Department of Computer and Data Sciences Case Western Reserve University United States

ISBN: (纸本)9798891761643

Large vision language models (VLMs) have demonstrated significant potential for integration into daily life, making it crucial for them to incorporate human values when making decisions in real-world situations. This paper introduces VIVA, a benchmark for VIsion-grounded decision-making driven by human VAlues. While most large VLMs focus on physical-level skills, our work is the first to examine their multimodal capabilities in leveraging human values to make decisions under a vision-depicted situation. VIVA contains 1,240 images depicting diverse real-world situations and the manually annotated decisions grounded in them. Given an image there, the model should select the most appropriate action to address the situation and provide the relevant human values and reason underlying the decision. Extensive experiments based on VIVA show the limitation of VLMs in using human values to make multimodal decisions. Further analyses indicate the potential benefits of exploiting action consequences and predicted human values. Our code and dataset are available at https://***/Derekkk/VIVA_EMNLP24. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection 4

Enhanced Route Optimization: Incorporating Road Safety Facto...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai India Gitam School of Technology Gandhi Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences. © 2024 IEEE.

关键词： Travel time

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