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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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Helping People and Changing the World with CS

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计算机教育 2024年第3期 17-19页

作者： Sukyoung Ryu School of Computing Korea Advanced Institute of Science and Technology291 Daehak-roYuseong-gu Daejeon 34141Korea

What should academic institutions be doing in order to benefit students,the community at large,and society in the new era?Particularly,as computing is impacting all aspects of human society,it also brings many challenges and opportunities for the academic departments of computer *** of these challenges include the increasing impact of software on society,issues involving privacy and data abuse,and ethical issues concerning *** addressing these challenges,the Korea advanced institute of Science and technology(KAIST)leverages its position as an educator in order to train the future computing workforce to be more aware of these *** achieve this goal,KAIST mainly adopts three strategies:Combining science&technology and humanities together;putting academy and industry together;training software experts while making them understand people and the world.

关键词： computing KAIST Privacy and data abuse Ethical issues

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Enhanced Camouflage Detection Using advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Influence of electrochemical solution activation on kinetics of pyrite flotation: Experimental and theoretical investigations

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Journal of Industrial and Engineering Chemistry 2025年 148卷 791-796页

作者： Prokhorov, Konstantin V. Chibisova, Mary A. Srivastava, Anurag Mining Institute Far Eastern Branch of the Russian Academy of Sciences 51 Turgenev Str. Khabarovsk680000 Russia Computing Center Far Eastern Branch of the Russian Academy of Sciences 65 Kim Yu Chen Str. Khabarovsk680000 Russia Advanced Materials Research Group CNT Lab ABV - Indian Institute of Information Technology and Management Gwalior Madhya Pradesh 474015 India

This study modeled the molecular structure of adsorbed water films on the pyrite (100) surface using density functional theory (DFT) to investigate the effects of hydronium ions on the surface bonding interactions. Adding hydronium ions to the water films weakened the Fe–O interaction, increasing the Fe–O bond distance from 2.145 to 3.245 Å and, consequently, reducing hydrogen bonding among water molecules within a 5.5-Å range of the FeS2 surface. Molecular dynamics simulations further revealed the proton transfer dynamics from the subsurface to upper water layers, aligning with experimental observations of the kinetics of pyrite particle attachment to air bubbles. Electrochemical activation of a sodium bicarbonate solution enhanced the pyrite particle loading on the bubbles by up to 32 % compared to a nonactivated solution. These results suggest that hydronium-mediated surface modifications intensify the flotation process, providing insights into optimizing mineral separation techniques. © 2025

关键词： Molecular dynamics

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Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

arXiv

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arXiv 2024年

作者： Jeong, Soyeong Baek, Jinheon Cho, Sukmin Hwang, Sung Ju Park, Jong C. School of Computing Graduate School of AI Korea Advanced Institute of Science and Technology Korea Republic of

Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA). However, even though there are various approaches dealing with queries of different complexities, they either handle simple queries with unnecessary computational overhead or fail to adequately address complex multi-step queries;yet, not all user requests fall into only one of the simple or complex categories. In this work, we propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs from the simplest to the most sophisticated ones based on the query complexity. Also, this selection process is operationalized with a classifier, which is a smaller LM trained to predict the complexity level of incoming queries with automatically collected labels, obtained from actual predicted outcomes of models and inherent inductive biases in datasets. This approach offers a balanced strategy, seamlessly adapting between the iterative and single-step retrieval-augmented LLMs, as well as the no-retrieval methods, in response to a range of query complexities. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems, compared to relevant baselines including the adaptive retrieval approaches. Code is available at: https://***/starsuzi/Adaptive-RAG. © 2024, CC0.

关键词： Iterative methods

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advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms 4

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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Innovative Solutions for Sustainable IoT in Smart City Infrastructure

Innovative Solutions for Sustainable IoT in Smart City Infra...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Arul P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants’ safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application.

关键词： Smart cities Reviews Video surveillance Cameras Robustness Real-time systems Safety Blockchains Recording Research and development

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A Cloud Based Information Retrieval Agent System Using Web Services 3

A Cloud Based Information Retrieval Agent System Using Web S...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Gupta, Namit Ojha, Ananta Ramya, V. Subha College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India University Karnataka Bangalore India Vels Institute of Science Technology Advanced Studies Department of Biomedical Engineering Channai India

ISBN: (纸本)9798350330724

Large, scalable computer resources are made available to consumers as a service via the cloud computing system, which runs over the Internet. Both the business and scientific communities have shown a lot of interest in this technology. Nevertheless, there are no mechanisms for information retrieval and knowledge discovery in the concept of cloud computing. In other words, clouds need autonomy and intelligence. Within cloud computing settings, Web Services are an essential part of Service Oriented computing (SOC). A single Web Service is unable to get targeted and specialized information from Web Services inside a cloud environment. Agents may be used to help in the successful composition of Web Services. The main goal of this project is to propose a system to extract information in cloud environment by employing web services along with concept of multi-agent systems. © 2023 IEEE.

关键词： Web services

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Towards Sustainable Waste Management: Exploring Machine Learning and Deep Learning Solutions for Biodegradable and Non-Biodegradable Waste Identification

Towards Sustainable Waste Management: Exploring Machine Lear...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： S. Sasi Rekha P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This study examines 20 research articles focused on AI-based waste management techniques, including machine learning, deep learning, and IoT sensors. Researchers aim to develop sustainable solutions for waste sorting and management. The articles highlight the significant role of deep learning and computer vision in waste classification, with a growing interest in recent years, particularly in 2022. These advanced technologies offer promising avenues for enhancing waste sorting accuracy, reducing manual labor reliance, and promoting recycling efforts. Moreover, the incorporation of IoT sensors plays a vital role in continuously monitoring waste levels and environmental parameters, thereby optimizing the entire waste collection and management workflow. These outcomes underscore the escalating adoption of sophisticated AI methodologies in waste management, suggesting a paradigm shift towards enhanced and environmentally conscious waste management practices. This research provides valuable insights for policymakers, waste management practitioners, and researchers seeking to address the challenges of waste management while promoting environmental sustainability.

关键词： Waste management Deep learning Pervasive computing Social networking (online) Green products Manuals Sensors

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Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators

Learning-based Initialization of Trajectory Optimization for...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Minsung Yoon Mincheul Kang Daehyung Park Sung-Eui Yoon School of Computing Korea Advanced Institute of Science and Technology South Korea

Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-trivial and requires a considerable time budget due to the extremely large space of the solution trajectories and the lack of prior knowledge about task constraints in configuration space. To alleviate the issue, we present a learning-based initial trajectory generation method that generates high-quality initial trajectories in a short time budget by adopting example-guided reinforcement learning. In addition, we suggest a null-space projected imitation reward to consider null-space constraints by efficiently learning kinematically feasible motion captured in expert demonstrations. Our statistical evaluation in simulation shows the improved optimality, efficiency, and applicability of TO when we plug in our method's output, compared with three other baselines. We also show the performance improvement and feasibility via real-world experiments with a seven-degree-of-freedom manipulator.

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