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检索条件"任意字段=2nd IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2023"

共 820 条记录，以下是1-10 订阅

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Next-Generation IoT Intrusion Detection: Leveraging Hybrid Deep Learning Algorithms for Enhanced Security 2

Next-Generation IoT Intrusion Detection: Leveraging Hybrid D...

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2nd international conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Urvashi Agarwal, Parul Raghuvanshi, Kamlesh Kumar Ahmed, Jawed Department of Computer Science and Engineering Jamia Hamdard University Delhi India Department of Computer Science University of Delhi Delhi India

ISBN: (纸本)9798331530389

Wireless sensor node and DDoS (Distributed Denial of Service) is a major attack way that can exhaust the energy of a sensor, disrupt the communication traffic of an IoT network. We must be on the lookout for these attacks. With the quantity of sensor nodes, traditional methods face difficulty in detecting network traffic analysis requiring big data analysis. This paper is dedicated to Network Traffic analysis using Big Data environment and detecting attacks using Deep Learning. Implemented with PySpark in Google Colab, using Keras and Scikit-Learn built-in libraries. To be more specific, training and testing are conducted on the 'CICIoT2023'/'TON IoT' datasets, respectively. When we use a correlation feature reducer, it virtually considers the most significant features for inclusion and reduces all of our features. The first contains a hybrid deep leaning model combining one-dimensional EfficientDet and Gradient Boosting machine (GBM), comparing it with ten other algorithms. It gen- erated 99.95% binary classification accuracy for the CICIoT2023 dataset and 99.75% for TON IoT data. © 2025 ieee.

关键词： Network intrusion

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Advancing Cucumber Disease Detection in Agriculture Through machine vision and Drone Technology 2nd

Advancing Cucumber Disease Detection in Agriculture Through...

引用

2nd international conference on Cyber intelligence and Information Retrieval, CIIR 2023

作者： Rahman, Syada Tasfia Vasker, Nishat Ahammed, Amir Khabbab Hasan, Mahamudul Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9789819776023

This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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Real-Time Yoga Pose Recognition and Correction Feedback System Using Deep Learning and computer vision 2

Real-Time Yoga Pose Recognition and Correction Feedback Syst...

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2nd international conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Chauhan, Vishal Aggarwal, Mukul Singhal, Yash Shukla, Swapnil Department of IT KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9798331530389

Yoga pose detection is a rapidly evolving field that integrates advanced technology with wellness, addressing the need for accurate and adaptable systems for diverse user groups. This paper proposes a novel framework that combines RGB images, depth maps, and skeletal joint data to enhance multi-modal feature extraction and improve spatial-temporal analysis for tasks such as action recognition and human activity modeling. The framework overcomes challenges such as pose variations and occlusions through techniques like data augmentation, model optimization, and robust performance evaluation. Experimental results demonstrate the system's high accuracy of 91.7%, successfully detecting six distinct yoga pose sequences in real time and providing self-correction feedback to users, thereby improving performance. The practical applications of this approach are significant, with potential use cases in yoga studios, fitness centers, and home environments, promoting scalable, precise, and globally accessible yoga practices. © 2025 ieee.

关键词： Accuracy AR/VR integration Deep learning F1-score machine learning Precision Yoga pose recognition

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InterACT: Enhancing Human-AI Interaction

InterACT: Enhancing Human-AI Interaction

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katta, Jithendra Kodumuru, Nikhil Reddy Katta, Swathi School of Engineering and Applied Sciences The George Washington University WashingtonDC United States Vignana Bharathi Institute of Technology Computer Science & Engineering dept. Hyderabad India

ISBN: (纸本)9798331505745

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools. © 2025 ieee.

关键词： Object detection

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Review on Artificial intelligence Techniques for Recognizing Arabic Sign Language 2

Review on Artificial Intelligence Techniques for Recognizing...

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2nd international conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Al-Jarboi, Taghreed Saleh Yafooz, Wael M.S. College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (纸本)9798331506995

Sign language is important to enable communication for people with hearing impairments. Over the past few years, artificial intelligence technologies have opened the door to designing models, which can help these individuals become more integrated and engaged into societies. these technologies can also increase their contribution as members to society. This paper provides a review of some of the AI methods used for sign language recognition, especially Arabic Sign Language (ArSL). It focuses on traditional machine learning, deep learning (including convolutional neural networks), transfer learning, and vision transformation models. The paper highlights the emergence of hybrid models that combine different artificial intelligence technologies using all architectural strength. This review emphasizes the growing potential of artificial intelligence technologies to revolutionize Arabic Sign Language recognition and suggests trends for future studies to improve the integration of the hearing impaired into society. This paper can be beneficial to the research of ASL. © 2025 ieee.

关键词： Transfer learning

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Next-Gen Waste Management: AI as a Catalyst for Sustainability

Next-Gen Waste Management: AI as a Catalyst for Sustainabili...

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sheikh, Nishad Kumar, Praveen Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of computer science and medical engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331505745

Traditional waste management techniques frequently be afflicted by inefficiencies, high operational fees, and environmental drawbacks. However, latest improvements in Artificial intelligence, such as computer vision and Internet of Things, are remodeling waste management right into an automated and optimized procedure. This research study explores how AI-pushed technology enhance waste series, sorting, and recycling methods. machine learning algorithms enhance waste category accuracy, computer vision permits unique fabric separation, and IoT-powered smart containers optimize collection schedules, decreasing needless trips and gasoline consumption. Additionally, AI-pushed direction optimization algorithms Significantly reduce emissions by means of designing efficient series pathways. These advancements Contribute. Not only to improve operational efficiency but also to the broader dreams of decreasing environmental impact and promoting a Circular economy. Despite its transformative potential, AI integration in waste control affords monetary, ethical, and technological challenges that require cautious attention. This examine provides a comprehensive evaluation of AI packages, their blessings, boundaries, and destiny capacity in revolutionizing waste management. With AI solutions, the enterprise can transition towards a more adaptive, efficient, and sustainable waste management ecosystem, addressing both urban and environmental demanding situations in the process. © 2025 ieee.

关键词： Waste treatment

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A Comprehensive Review on Deep Learning Based Fall Detection in Elderly People

A Comprehensive Review on Deep Learning Based Fall Detection...

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Suryanarayana, S. Venkata Lakshmi, H.N. Chiranjeevi Swamy, Posimsetti T Mahesh, D. Bhanu Hyderabad India Hyderabad India Swarnandhra College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Andhra Pradesh Narsapuram India KMIT Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505745

Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based systems, face limitations in accuracy, comfort, and practicality. Recent advancements in deep learning have significantly improved fall detection capabilities by leveraging powerful computational models for data analysis and feature extraction. With an emphasis on important methodologies, architectures, and datasets utilized in the area, this paper offers a thorough literature analysis of deep learning-based fall detection strategies. It investigates several deep learning techniques applied to sensor-based and video-based fall detection systems, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. The review also emphasizes the difficulties encountered in practical application, including data privacy and computing cost, and the need for robust datasets. Finally, future research directions are proposed to address current limitations and improve fall detection systems' dependability and effectiveness for elderly care. © 2025 ieee.

关键词： Convolutional neural networks

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Analytical Insight into Cutting-Edge Image Captioning for Advanced ChatGPT Functionality 2nd

Analytical Insight into Cutting-Edge Image Captioning for Ad...

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2nd international conference on Business Data Analytics, ICBDA 2023

作者： Manisha Kumar, Avadhesh Yadav, Dileep Kumar School of Computer Science Engineering Galgotias University Greater Noida India SCSET Bennett University Greater Noida India

ISBN: (纸本)9783031807770

At present, Artificial intelligence (AI) and machine learning (ML) algorithm contribute to transforming the technology environment. Specifically, the development of chatbots has been on a rapid-fire overhead in recent years. The Image captioning with ChatGPT is most growing field for text generating for the future. In recent years, the innovation of virtual assistants has boomed with ChatGPT becoming an increasingly popular language model of artificial intelligence. This work evaluates the principles, goals, and problems of ChatGPT in the present study. This study explores the technology's background, growth, and development, along with its applications. In addition, we will look at the benefits of connecting information by ChatGPT in image processing and content writing that the image exposes. However, we believe that ChatGPT will contribute to improving human-AI interaction and bridging scientific gaps by focusing on various research and development opportunities, such as energy conservation, privacy, and security, expanding its scope to other technologies like robotics and computer vision, deep learning, and ML. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ChatGPT model computer vision NLP

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machine Translation Techniques using AI : A Review 2

Machine Translation Techniques using AI : A Review

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2nd ieee international conference on computer vision and machine intelligence, cvmi 2023

作者： Singh, Charanjit Bansal, Rakesh Kumar Bansal, Savina Giani Zail Singh Campus College of Engg. & Tech. Mrsptu Punjab Bathinda151001 India Department of Computer Science & Engineering Haryana Ambala133207 India

ISBN: (纸本)9798350305142

Automation of natural language translation is undergoing a significant evolution, with various approaches emerging over the years. In the context of a vast and diverse country like India, where numerous regional languages are spoken, and people frequently move for various reasons such as- trade, migration, education, tourism, and more- the need for a robust translation mechanism becomes paramount to overcome language barriers. This article delves into the research efforts of various scholars in the field of machine translation and explores the future prospects in this domain. Additionally, it provides insights into the diverse machine Learning and Artificial intelligence methodologies employed in machine Translation. © 2023 ieee.

关键词： machine translation

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Proceedings of the 2023 ieee international conference on computer vision and machine intelligence, cvmi 2023

Proceedings of the 2023 IEEE International Conference on Com...

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2nd ieee international conference on computer vision and machine intelligence, cvmi 2023

ISBN: (纸本)9798350305142

The proceedings contain 67 papers. The topics discussed include: hybrid convolutional neural networks to create an attack detection framework for a wireless sensor network based health care application;a survey on W-band systems for suspicious object detection using artificial intelligence;estimation of student’s interest reaction levels in class video;enhancing the video editing capabilities of text-to-video generators using DDPM inversion;machine translation techniques using AI : a review;elevating fingerprint matching with a unified dHash, SIFT, and LSH model;AI-based model for detection and classification of Alzheimer disease;rice impact illness acknowledgment employing a profound convolutional neural arrange;and image captioning with reinforcement learning.

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