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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Tseng, Shih-Huan Lin, De-Yu Huang, Shih-Shinh National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350389210

This paper aims to develop a deep reinforcement learning-based face tracking system for Zenbo Jr. As robots become increasingly integrated into daily life, human-robot interaction (HRI) has emerged as a critical research area, especially in adhering to social norms. This project combines deep learning with visual servo systems to enhance the accuracy and response speed of robotic face tracking. The primary objectives of the study include: adjusting robot movements based on face position, designing a virtual environment using OpenAI Gym that matches Zenbo Jr.'s perspective for model training, and testing and validating the model on Zenbo Jr. The research methods involve designing the virtual environment, establishing reward functions, and constructing and training the DQN model. Experimental results compare the training outcomes of Q-Learning and Deep Q-Learning, confirming that Q-Learning leads to an excessively large Q-table in scenarios with numerous state-action combinations, making effective training impossible, whereas Deep Q-Learning achieves stable convergence and good results. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Comparative Analysis of Card Sorting and IDM Approaches in e-Tourism Website Design 26

A Comparative Analysis of Card Sorting and IDM Approaches in...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rahman, Raiyan Islam, Muhammad Nazrul Ibn Aziz, Faisal Hossain, Md Zahid Afroze, Sumaiya Suha, Sayma Alam United International University Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka1216 Bangladesh Bangladesh Army International University of Science and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

While Interactive Dialogue Model (IDM) and card sorting are popular methods for designing websites, research is continuously being done on which method is best suited for the design of multichannel or web-based applications, based on the complexity and feature set of the application to be developed. In this research, we explore which design method performs better for websites with fairly complex navigational structures with multiple levels of page traversal. To explore, the IDM and Card Sorting methods were meticulously followed starting from early designs to fully implemented prototypes e-Tourism websites for a group of hotels or resorts. Then, both websites were evaluated following a between-subject experimental approach to assess and compare their usability. The study showed that although the navigational design with IDM may be a bit more complex and may require more clicks for completing some tasks, it far outperformed the card sorting design for our case in all significant parameters. Overall, IDM gives a better outcome in the usability and satisfaction for all users, thus pointing to the fact that IDM would be the better way to design complex structured websites like an e-Tourism website. © 2023 IEEE.

关键词： Card Sorting e-Tourism HCI Interactive Dialogue Model (IDM) Usability Testing Web Design Methods

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Utilizing Deep Learning and Image Processing for Detection of Pests 2

Utilizing Deep Learning and Image Processing for Detection o...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Santhosh, B.J. Adithya, H.R. Akash Kumar, G.S. Hemanth Gowda, R.M. Jaswanth, H.V. BGS Institute of Technology Adichunchanagiri University India 20CSE001 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE004 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE024 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE032 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri university India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Pests are one of the biggest hazards to crops since they significantly reduce the amount of food produced. Furthermore, by enabling farmers to take the necessary preventive measures, early and accurate pest identification can help minimize damage and improve crop quality. Inspection takes a lot of time and labour because many pest species seem to be identical to one another. This work presents a novel deep-learning technique called Faster-Pest Net, which aims to overcome the limitations of physical pest inspection. Using the Mobile Net as its foundation network, adescriptively enhanced Faster-RCNN technique is created, calibrated on pest samples to identify crop pests across many categories, and dubbed Faster- PestNet. ©2024 IEEE.

关键词： Adversarial machine learning

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Deployment of ML Techniques for Optimization of Energy in Residential & Commercial Structures: A Review 5

Deployment of ML Techniques for Optimization of Energy in Re...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mahajan, Prerna Dahiya, Priya Upadhyay, Rimpi Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383522

Every aspect of daily life in buildings (whether commercial or residential) relies on a constant supply of energy. Energy is used whenever a building's lights are turned on, the temperature is adjusted (using space heaters or air conditioners), or a device is used (such as a computer or printer in an office building or a microwave, water heater, or washing machine in a home or hotel). The HVAC (Heating, Ventilating, and Air Conditioning) system of a commercial building is responsible for a significant percentage of the structure's overall energy use. In order to keep all these devices running properly in present and in the coming years, it is crucial that fuel is enhanced or used as effectively as possible. This study provides a comprehensive review of the literature on the use of Machine Learning techniques to the problem of energy optimization in buildings. © 2024 IEEE.

关键词： Commercial buildings Energy optimization HVAC system Machine Learning

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Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16 2

Diagnosis of bacterial leaf blight, leaf smut, and brown spo...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Mannepalli, Praveen Kumar Pathre, Ayonija Chhabra, Gunjan Ujjainkar, Priyanka Anup Wanjari, Shrutika Department of Computer Science and Engineering G.H. Raisoni Institute of Engineering and Technology Nagpur India Department of Computer Science and Engineering G.H. Raisoni University Amravati India Department of Computer Science Graphic Era Hill University Dehradun India Department of Artificial Intelligence G.H. Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G.H. Raisoni Institute of Engineering and Technology Nagpur India

In the context of agricultural sustainability and food security, timely and precise identification of plant diseases holds paramount significance. This research proposes an innovative approach to diagnose bacterial leaf blight (BLB), rice blast (RB), and brown spot (BS) in rice leaves utilizing the VGG16 convolutional neural network (CNN). By leveraging deep learning capabilities, the proposed model analyzes high-resolution images of rice leaves to classify and distinguish between the different diseases. The VGG16 architecture has shown remarkable performance in image recognition tasks, and this study aims to harness its potential for precision agriculture. Rice (Oryza sativa) is a staple crop that plays a crucial role in global food security. Timely and precise diagnosis of these diseases is vital for effective disease management and crop protection. Our methodology involves the collection and curation of a diverse dataset of rice leaf images. The VGG16 model, a proven deep convolutional neural network (CNN) architecture, is employed for extraction of features and classification. The results of our study demonstrate the efficacy of the VGG16 model in distinguishing between different rice leaf diseases with remarkable precision. This high level of accuracy holds great promise for the field of agriculture, offering a non-invasive and efficient means of disease detection. Early disease identification can facilitate timely intervention, reducing crop losses and mitigating the economic impact on farmers. The classification accuracy of the proposed method is 97.77% using the publicly available dataset. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Personalized Method for Voting in Wireless Mobile Adhoc Networks with Intrusion Detection using NS2 Simulation Model 5

Personalized Method for Voting in Wireless Mobile Adhoc Netw...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Muthukumar, S. Senthil Pandi, S. Menaga, D. Sudhahar, T.N. St.Joseph's Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331504403

Mobile Ad hoc Networks (MANETs) are wireless networks that are self-configuring, infrastructure-less, and dynamic. The nodes in these networks have limited access to available resources. MANETs use intrusion detection systems, also known as IDS, to monitor activities in order to identify any potential intrusions into the network, which would otherwise be vulnerable. In this study, we offer efficient techniques for maximizing the length of time that intrusion detection systems need to be active in a mobile ad hoc network and monitoring how much time they need to be active for. The dissemination of a probabilistic model that makes use of neighborhood node IDS collaboration in order to reduce the amount of individual active time. In order to monitor network activities, an IDS often needs to be running nonstop on every node. In terms of electricity and processing resources, this could end up being a costly overhead for a mobile device that runs on batteries. We therefore want to shorten the IDSs' active time in this work without sacrificing their effectiveness. We simulate the interactions between IDSs as a multiplayer cooperative game with partially cooperative and partially opposing aims in order to validate our suggested methodology. We investigate this game theoretically and provide evidence from simulations. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning for Fraud Detection in Bitcoin Transactions: An AI Approach

Deep Learning for Fraud Detection in Bitcoin Transactions: A...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shravan, M. Ajitha, E. Arnone, Gioia Renu, Nekkanti Tiwari, Mohit Chouhan, Kuldeep Sr University School of Business Telangana Warangal India St. Joseph's Institute of Technology Computer Science and Engineering India Vrije Universiteit Brussel Department of Private and Economic Law Bruxelles Belgium Department of Computer Science and Engineering Duvvada India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

This research looks into how deep learning methods can be used to find fake Bitcoin transfers. It's mostly about the unique problems and chances that come with trading in cryptocurrencies and blockchain. Deep learning models are used to look at transaction trends and find oddities that could be signs of fraud. This is because Bitcoin's open structure makes it harder to use traditional methods of fraud detection. We offer a structure that connects CNNs (convolutional neural networks) and RNNs (recurrent neural networks) so that strange patterns can be found in big sets of blockchain transactions. An AI-driven method aims to improve detection accuracy while lowering false positives by taking on scammers' complicated and always-changing strategies. Experienced researchers have shown that the model is good at finding strange things, which means it can be used to safely stop theft in Bitcoin networks in real time. It looks like deep learning could make digital money safer and give us data that can be used in bigger blockchain-based money systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Diversity-Optimized Group Extraction in Social Networks

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IEEE Transactions on Computational Social Systems 2024年第1期11卷 756-769页

作者： Hsu, Bay-Yuan Chen, Yi-Ling Ho, Ya-Chi Chang, Po-Yuan Chang, Chih-Chieh Shia, Ben-Chang Shen, Chih-Ya National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu30013 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Hsinchu31040 Taiwan Fu Jen Catholic University Department of Business Administration New Taipei City24205 Taiwan National Taiwan University of Science and Technology Management Undergraduate Program Taipei10607 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximation algorithm to the SDGE problem studied in this article. We further propose three effective pruning strategies that are able to boost the efficiency of GSDM but do not deteriorate its performance. We conduct extensive experiments on multiple large-scale real datasets to evaluate the performance of GSDM. The experimental results show that our proposed GSDM outperforms the other baseline approaches significantly, in terms of solution quality and efficiency. Moreover, the experimental results also confirm that our proposed pruning strategies indeed boost the efficiency of the algorithm. © 2014 IEEE.

关键词： Costs

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A Real-Time Road Anamoly Detection Using Smart Phone Sensors 7

A Real-Time Road Anamoly Detection Using Smart Phone Sensors

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, Prabhat Sharma, Shilpi Amity School of Engineering and Technology Department of Computer Science Uttar Pradesh Noida India

ISBN: (纸本)9798350350067

The quality of the conventional transport system has been significantly impacted recently by the swift advancement of emerging technologies and the rapid growth of the economy. ITS which seeks to enhance the transportation network, is gaining popularity. Additionally, one of the main goals of ITS is to enhance traffic safety, and potholes seriously compromise the safety of vehicles. Therefore, by establishing a real-time pothole-detecting system and disseminating pothole information, drivers' safety may be enhanced. For assessing road conditions, we propose to analyze various supervised machine learning approaches employing mobile sensors and GPS data obtained from cellphones. Our research focuses on classifying three primary class labels: deep transverse cracks, potholes, and smooth roads. We test our hypothesis that employing information from the three sensors' axes yields more accurate results than using data from just one. We also study how well deep neural networks do in classifying different types of traffic situations. Our findings demonstrate that smartphone sensor axes outperform models using only one axis. This demonstrates that vibration signals for road abnormalities contain information throughout all three axes. We also note that substantially more accurate data classification is obtained when using neural networks. The techniques discussed here can be applied more broadly to monitor road conditions that could endanger people's safety. Commuters in addition to giving pertinent authorities maintenance information. © 2024 IEEE.

关键词： Smartphones

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YOLOv8 Based Detection of Fall Armyworm in Field Crops 5

YOLOv8 Based Detection of Fall Armyworm in Field Crops

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Mhala, Prit Gupta, Amogh Sharma, Sanjeev Indian Institute of Information Technology Pune Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350376753

In India, the role of agriculture is pivotal contributing to the growth of the Indian economy and employment. It is essential to focus on the security of food which ensures individuals' health. Pest plays a major role in damaging and harming crop growth leading to effective control measures for pests. One such pest, Spodoptera frugiperda(Fall armyworm), poses significant damage to various crops. Deep Learning provides promising solutions by using Object detection techniques to detect the fall armyworm pest at early stages to avoid damage. This work intends to use YOLO as an object detection method. This paper achieved remarkable results using the YOLOv8 models. A mean average precision (mAP50) value of 78.60%, 80.67%, and 89.50% was achieved using YOLOv81, YOLOv8m, and YOLOv8n, respectively. The best results were achieved by YOLOv8n giving 89.50% mean average precision. This work, thus aims for precise detection of fall armyworms to enhance crop safety. © 2024 IEEE.

关键词： Invertebrates

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