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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chitra, P. Sridar, K. Govindan, Sudha Britto Raj, S. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. St Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Veerammal Engineering College Department of Computer Science and Engineering Dindigul India Sona College of Technology Department of Computer Science and Engineering Salem India RRASE College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798331515720

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Transfer Learning and Domain Adaptation in Hyperspectral Image Processing: an Overview

Transfer Learning and Domain Adaptation in Hyperspectral Ima...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Komal Pokale Shiv Nath Chaudhri Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology DMIHER (DU) Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Fine-tuning a pre-trained model is a type of transfer learning: a pre-trained model that works on a large dataset is transferred onto a particular task or a smaller dataset, such as a hyperspectral image. The efficacy of such a technique is most visible in hyperspectral imaging since the data is usually too high-dimensional with a very scarce number of available annotated datasets. By using the pre-trained model, fine-tuning enables spatial and spectral features to be captured with lower computational overheads than fully training models. Domain adaptation approaches, including feature alignment and adversarial training, are also adapted to overcome limitations caused by the domain shift phenomenon due to the variability of the sensor, environmental changes, and temporal changes. Hyperspectral imaging is applied in diverse fields, including agriculture, environmental monitoring, mineral exploration, defense, medical diagnostics, and food quality assessment. Such applications are unique in the sense that HSI can integrate spatial and spectral information to achieve detailed material analysis. Advanced learning techniques will enhance the effectiveness of HSI models in real-world scenarios.

关键词： Training Deep learning Adaptation models Computational modeling Transfer learning data models Agriculture Medical diagnosis Environmental monitoring Hyperspectral imaging

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The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

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China Communications 2024年第3期21卷 283-295页

作者： Zheng Quan Yan Wenliang Wu Rong Tan Xiaobin Yang Jian Yuan Liu Xu Zhenghuan Laboratory for Future Networks Department of AutomationUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230088China Suzhou Nuclear Power Research Institute Suzhou 215004China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NEL-PSRPC) China Academy of Electronics and Information Technology of CETCBeijing 100041China

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’***,the Information-Centric Networking(ICN)came into *** a technical point of view,ICN is a promising future network *** and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of *** current research on ICN pricing mechanism is focused on paid ***,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to *** this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN *** solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each *** extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

关键词： advertisers cache free content Information-Centric Networking pricing strategy

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids 1

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Neelamegam, G. Ashforn, Hermina J.M. Raja, Mohamed S. Priyadharshini, J. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350377002

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change. © 2024 IEEE.

关键词： Microgrids

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Enhanced Bitcoin Price Prediction using RNN-GRU Algorithm with Optimized Parameters: Overcoming LSTM Ambiguities for Improved Accuracy and Efficiency 3

Enhanced Bitcoin Price Prediction using RNN-GRU Algorithm wi...

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

作者： Kathiravan, M. Meenakshi, M. Kaviya, M. Sreesubha, S. Sathyadurga, V. Neela Gandhi, V. Chennai India Department of CSE Department of Computational Intelligence SRM Institute of Science and Technology Kattangulathur Chennai India Department of Artificial Intelligence And Data Science Panimalar Engineering College Chennai India Department of AIML Rajalakshmi Engineering College Thandalam Chennai India Department of CSE Hindustan Institute of Technology and Science padur Chennai India Department of CSE Panimalar Engineering college Chennai India

ISBN: (纸本)9798331527549

Bitcoin's decentralised character and introduction in January 2009 can be mostly attributed to Satoshi Nakamoto, the unidentified designer of the digital money. Unlike regular money, Bitcoin has no value in and of itself and is not supported by any government or financial institution. The "mining" process consumes a lot of resources thus it cannot manage trades without a network of computers, often known as "nodes" or "miners." The Bitcoin blockchain, a catalogue of all the past transactions, is maintained by these nodes. Like equities, bitcoin is getting more and more well-known even if it is somewhat erratic as an investment. Its value varies greatly;hence it is difficult to predict when prices will adjust. Since Bitcoin is so erratic, automated approaches are growing more crucial for future projections regarding it. Often utilised for prediction, long short-term memory (LSTM) networks have constraints. LSTM focusses on short-term input and has a complex and occasionally ambiguous design, thus some claim it does not operate well when prior data is not significant to current forecasts. This has caused some to wonder if one can forecast Bitcoin's price using this approach. Made as a substitute for standard RNNs, the Gated Recurrent Unit (GRU) helps to prevent the fading gradient issue. Bitcoin price projections made by the GRU design are far more accurate than those derived from more antiquated methods. © 2025 IEEE.

关键词： Long short-term memory

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Arabic Fake News Detection Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 3647-3665页

作者： Khaled M.Fouad Sahar F.Sabbeh Walaa Medhat Faculty of Computers&Artificial Intelligence Benha UniversityEgypt University of Jeddah College of Computer Science and EngineeringJeddah21493Saudi Arabia Information Technology and Computer Science Nile UniversityEgypt

Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online ***,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment *** identification of fake news and rumors and their dissemination on social media has become a critical *** have adverse effects on users,businesses,enterprises,and even political regimes and *** of the art has tackled the English language for news and used feature-based *** paper proposes a model architecture to detect fake news in the Arabic language by using only textual *** learning and deep learning algorithms were *** deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+*** datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife *** results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.

关键词： Fake news detection deep learning machine learning natural language processing

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Prediction of Rice Leaf Diseases at an Early Stage Using Deep Neural Networks 1

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8th International Conference on Computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Srilatha, Y. Doppala, Bhanu Prakash Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Data Analytics Generation Australia SydneyNSW Australia

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Rice is the major crop in India, and India has been the biggest exporter and the second-largest producer in the entire world, so it is heavily reliant on rice for its economy and food supply. There has been an increase in rice production from 53.6 million tons in fiscal to 120 million tons over the last 40 years. Healthy and proper rice plant growth is required to maintain food production and supply in accordance with people’s needs and demands. The major diseases affecting these rice plants are leaf blast, hispa, and brown spot. The advancement of deep learning paved the way for these diseases to be detected using computer vision. Many researches have been conducted in the detection of rice leaf diseases, where the rice leaves have been deeply affected at a later stage. In this chapter, we propose an effective deep learning model for an early-stage disease detection of rice leaves. The proposed model was developed by an ensemble of the pretrained DenseNet-201 with the Naïve Inception module. The model was trained and tested over a Rice Diseases Image dataset;it attained an overall disease detection accuracy of 87.69% and surpasseed the existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Food supply

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The Innovation and Practice of a Remote Physical Experiment Method 24

The Innovation and Practice of a Remote Physical Experiment ...

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2024 International Symposium on artificial intelligence for Education, ISAIE 2024

作者： Liu, Hanchen Li, Yiqing Xin, Haoyu Wang, Fangfang Du, Yuheng Wu, Jianhai Arizona College of Technology Hebei University of Technology Tianjin China College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China School of Electrical Engineering Hebei University of Technology Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China College of Science Hebei University of Technology Tianjin China

ISBN: (纸本)9798400707100

In view of the problems existing in the current mainstream virtual simulation experiments in the restoration of the sense of experiment experience and the degree of reality, by modifying the physical experiment device and combining it with the Internet of Things technology and automatic control, students can complete the physical experiment by remotely controlling the real experiment instrument, and provide the operation experience similar to the offline experiment. The experimental platform includes the remote experimental device of Hall element magnetic field measurement and Michelson interferometer, and the innovative modules such as experiment reservation, teacher terminal, student data summary and new experimental device upload are designed. After the campus test operation, the experimental device demonstrates high image fluency, and there is little difference between the experimental data and that obtained from offline experiments, which allows students to successfully complete the corresponding experiment content. This results in a good teaching effect and provides a reference for the future construction of remote physics experiment platforms. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Michelson interferometers

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning 16

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Shrestha, Sujan Taniguchi, Yoji Tanaka, Tetsuo Graduate school of Engineering Kanagawa Institute of Tehnology Course of Information and computer sciences Atsugi Japan Daiichi Institute of Technology Artificial Intelligence and Data Science Department of Information Tokyo Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan

ISBN: (纸本)9798350377903

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence;however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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NEGOTIATOR: A Comprehensive Framework for Human-Agent Negotiation Integrating Preferences, Interaction, and Emotion 33

NEGOTIATOR: A Comprehensive Framework for Human-Agent Negoti...

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33rd International Joint Conference on artificial intelligence, IJCAI 2024

作者： Keskin, Mehmet Onur Buzcu, Berk Koçyiğit, Berkecan Çakan, Umut Doğru, Anıl Aydoğan, Reyhan Department of Computer Science Özyeğin University Istanbul Turkey HES-SO Valais-Wallis Sierre Switzerland Department of Artificial Intelligence and Data Engineering Özyeğin University Istanbul Turkey Interactive Intelligence Group Delft University of Technology Delft Netherlands

ISBN: (纸本)9781956792041

The paper introduces a comprehensive human-agent negotiation framework designed to facilitate the development and evaluation of research studies on human-agent negotiation without building each component from scratch. Leveraging the interoperability and reusability of its components, this framework offers various functionalities, including speech-to-text conversion, emotion recognition, a repository of negotiation strategies, and an interaction manager capable of managing gestures designed for Nao, Pepper, and QT, and coordinating message exchanges in a turn-taking fashion. This framework aims to lower the entry barrier for researchers in human-agent negotiation by providing a versatile platform that supports a wide range of research directions, including affective computing, natural language processing, decision-making, and non-verbal communication. © 2024 International Joint Conferences on artificial intelligence. All rights reserved.

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