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检索条件"任意字段=International Conference on Advanced Research on Computer Science and Information Engineering"

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Geographic information System of Recording and Analyzing Geographic Data on Traffic Control Facilities 3

Geographic Information System of Recording and Analyzing Geo...

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3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Poletaikin, Alexei N. Monastyrskaya, Tatiana I. Skvortsova, Elena B. Englezi, Irina P. Kuban State University Department of Information Technologies Krasnodar Russia Siberian State University of Telecommunications and Computer Science Department of Sociology Novosibirsk Russia Novosibirsk State Technical University Department of Foreign Languages for Technical Faculties Novosibirsk Russia Donetsk Transport Academy Donetsk Russia

ISBN: (纸本)9798331516321

The research is related to the development of interactive applications for processing geodata about the state of the road network in cities. This area is rapidly developing and is promising for research, analysis and development of information systems. Due to a number of shortcomings of the existing geographic information systems the goal was set to develop a new advanced geographic information system considering the problems described above. To achieve this goal, it was necessary to develop new mathematical models for analyzing geodata on the state of technical means of traffic management. Also, the solutions about maintenance of these means had to be developed. An analysis of the subject area was carried out, on the basis of which the task of creating a geographic information system was set. Using modern tools, a geographic information system prototype was developed and its user testing was carried out. research has shown the best results when developing an optimal solution using the method of genetic algorithms. © 2024 IEEE.

关键词： Highway administration

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

引用

6th IEEE Novel Intelligent and Leading Emerging sciences conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning-Powered Camouflaged Object Recognition Model 2

Deep Learning-Powered Camouflaged Object Recognition Model

引用

2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military Technology Object Recognition Situational Awareness YOLOv8

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An Improved Parallel Adder 4

An Improved Parallel Adder

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4th international conference on computer science, Electronic information engineering and Intelligent Control Technology, CEI 2024

作者： Wang, Yanding Ouyang, Shan School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350378542

This paper presents an improved parallel adder. Based on the achievements of parallel adders developed by the team of Ternary Optical computer(TOC), this adder introduces variable distance shifting technology, which eliminates the intermediate result storage operations in memory, thereby reducing the memory access time during the entire addition process and enhancing the operational speed of the adder. To validate the feasibility and advancement of this research, we established a corresponding experimental platform on Field-Programmable Gate Array(FPGA) and compared the performances of the two adders. Experimental results indicate that the improved parallel adder significantly outperforms the original parallel adder, demonstrating that the variable distance shifting technology effectively enhances the computational efficiency of the parallel adder. © 2024 IEEE.

关键词： Shift registers

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Sybil Attacks detection for dynamic environment in Federated Learning 4

Sybil Attacks detection for dynamic environment in Federated...

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4th international conference on Telecommunications, Optics, and computer science, TOCS 2023

作者： Lin, Lihuang Zhu, Xi Wang, Junbo The School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen518107 China The School of System Science and Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Federated learning can utilize its distributed structure to protect data privacy security of clients and improve efficiency of machine learning. However, its distributed framework also make itself be susceptible to sybil attacks. While previous research has already proposed defense methods to address this issue, they often fail to guarantee effective performance in a dynamic federated learning system, where some clients dynamically join in and out. To tackle this problem, our paper introduces a novel defense method specifically designed to mitigate sybil attacks in dynamic federated learning scenario. Our proposed method consists of three mechanisms: similarity mechanism, validation mechanism, and reputation mechanism. These mechanisms can address the problem of missing information and effectively resist sybil attacks in dynamic federated learning. We evaluate the performance of our method on the MNIST and KDDCup datasets and demonstrate its advanced ability in defending against sybil attacks in dynamic federated learning compared to existing methods. © 2024 SPIE.

关键词： Dynamics

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Theoretical study of a dual-wavelength laser amplifier based on intracavity pumping technology 4

Theoretical study of a dual-wavelength laser amplifier based...

引用

4th international conference on advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Xu, Jian Jin, Long Jin, Yushi Wang, Zijian Dong, Yuan Jilin Key Laboratory of Solid Laser Technology and Application School of Science Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

We developed a novel theoretical model of a dual-wavelength laser amplifier based on intra-cavity pumping technology. In this model, we derived analytical expressions for the output power of the dual-wavelength laser amplifier. Simulations were conducted to explore the output characteristics of the dual-wavelength amplifier based on intra-cavity pumping technology. The effects of amplifier crystal length and beam radius on the laser's output performance were investigated, providing a solid foundation for further research. © 2025 SPIE.

关键词： Solid state lasers

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RAG-Enhanced Large Language Model for Intelligent Assistance from Web-Scraped Data 9

RAG-Enhanced Large Language Model for Intelligent Assistance...

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9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Kanataria, Nikunj Patel, Kunj Pareshbhai Patel, Hetul Niteshbhai Goel, Parth Patel, Krishna Garg, Dweepna Devang Patel Institute of Advance Technology and Research Computer Science & Engineering Department Changa Anand India Computer Science & Design Department Vallabh vidyanagar Anand India

ISBN: (纸本)9798350377972

The explosion of online information necessitates efficient and accurate methods for retrieving and analyzing relevant data. This research proposes a novel framework that leverages Retrieval-Augmented Generation (RAG) techniques to address this challenge. The framework utilizes web scraping to extract information from various sources and employs advanced language models like Llama 3, Mistral, and Gemini to generate concise and informative summaries. Furthermore, the framework incorporates embedding models such as nomic-embed-text and Gemini embedding model text embedding 001 to create semantic representations of the retrieved data and utilize FAISS for efficient indexing and retrieval. The RAGAS framework was used to evaluate the performance of different LLMs, with Llama 3.1 demonstrating the highest accuracy at 86.67%. This framework has significant implications for various applications, including personalized education, where it can be used to provide students with tailored learning materials and personalized tutoring experiences. © 2024 IEEE.

关键词： Semantics

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE international conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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Analysis, Trends, and Utilization of Security information and Event Management (SIEM) in Critical Infrastructures 10

Analysis, Trends, and Utilization of Security Information an...

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10th international conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Sashwin, K. Nithika, K.S. Priyadharshini, S. Maduvant, S.P. Saranya Sri Eshwar College of Engineering Department of Computer Science and Business Systems Computer Science and Business Systems Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Security information and Event Management (SIEM) systems have become essential assets in the realm of cybersecurity. They fulfill a central role in the prevention, detection, and response to cyber threats. Over time, SIEM solutions have matured into comprehensive platforms that offer extensive visibility, enabling organizations to pinpoint high-risk areas and take proactive measures to mitigate potential threats. This proactive approach not only bolsters security but also streamlines incident response, reducing both costs and response times. Recent developments in the field have witnessed the convergence of SIEM systems with big data analytics tools, opening doors to more sophisticated threat detection and response capabilities. This survey delves into the core features of widely adopted SIEM platforms and evaluates the external factors influencing the trajectory of the SIEM landscape over the medium and long term. Furthermore, the study presents a catalog of potential enhancements for the next generation of SIEMs, offering a thorough analysis of their advantages and practical applications within critical infrastructure settings. © 2024 IEEE.

关键词： Critical infrastructures

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Detecting Text Written by ChatBot Based on NLP and Machine Learning Technology 17

Detecting Text Written by ChatBot Based on NLP and Machine L...

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17th IEEE international conference on advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Vysotska, Victoria Lozynska, Olga Dudak, Oleh Chyrun, Lyubomyr Chyrun, Sofia Romanchuk, Roman Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine Ivan Franko National University of Lviv Applied Mathematics Department Lviv Ukraine Lviv Polytechnic National University Telecommunication Department Lviv Ukraine

ISBN: (纸本)9798331520564

The paper analyses methods for automatically detecting text generated by artificial intelligence. The subject of our research is the analysis and identification of the characteristics of artificially generated texts. It means that we study various aspects of texts that have been created with the help of artificial intelligence algorithms in detail. Our research focuses on identifying and analysing the specific features of these texts that distinguish them from those written by humans. For these purposes, a software system was developed. Our system is a new approach to training models on datasets to detect artificially generated content. It uses advanced algorithms and technologies to analyse text and determine its origin accurately. This program uses a comprehensive model training approach, including an extensive set of natural language processing, machine learning, and data analysis techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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