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共 559 条记录，以下是421-430 订阅

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human Activity Recognition: Fight Detection using Long term Recurrent Convolutional Network (LRCN)

Human Activity Recognition: Fight Detection using Long term ...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Ngrddy Jhnvi S.K. Bhrdwj Ajay Sharma Shamneesh Sharma Ankur Sodhi Saikat Gochhait School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Education Private Limited Bangalore Karnataka India Enterprise Businees and Career Services upGrad Education Private Limited Bangalore Karnataka India Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed University Pune India

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

human Activity Recognition for Fight Detection is an important research domain aimed at automatically identifying patterns indicative of physical altercations. Leveraging deep learning models like CNNs and RNNs, this approach extracts features from video frames to recognize fight-related behavioural patterns. the primary objective is to develop a machine learning model capable of autonomously detecting instances of fights in video footage, using techniques such as the Long-term Recurrent Convolutional Network (LRCN). Training involves a comprehensive dataset encompassing examples of both fights and non-fight activities, with model performance evaluated using standard metrics. Each frame undergoes individual analysis by the model to predict the presence of any indications of a fight. the model predicts the action with an accuracy of 98.03% and the movements given as input can be categorized as fights or no fights.

关键词： Training Measurement Industries Predictive models Feature extraction Pattern recognition Convolutional neural networks Object recognition human activity recognition Standards

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ICA based Noise Reduction in Mobile Phone Speech Communications

ICA based Noise Reduction in Mobile Phone Speech Communicati...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Lavanya Lahari Obillaneni Navya Sree Nallamothu Chandini Geda Mounisha Challamalla Nilu Singh Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram -522302 Guntur Andhra Pradesh India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Speech signals captured during video conferencing, mobile phones, teleconferencing etc., include noise in speech signals. Many algorithms exist for noise reduction, such as, time-varying adaptive filter and multiband adaptive gain control etc. Independent Component Analysis (ICA) is a statistical computational technique used for the cancellation of ambient noise in the selected part of a speech signal. Background noise reduction is a good application in human-machine interaction. the adverse impact on a mobile phone call is noisy location. Hence, to improve speech quality in mobile phones, noise decreasing techniques are proposed. this study has presented a literature on noise reduction techniques and algorithms and advantages of it.

关键词： Teleconferencing Working environment noise Noise Noise reduction Signal processing algorithms Mobile handsets Background noise

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Improved R3D for human Action Recognition Based on Transfer learning

Improved R3D for Human Action Recognition Based on Transfer ...

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Robotics and computer Vision (ICRCV), international conference on

作者： Xiaowei Han Qiuyang Guo Yunjing Lu Jiwu Liu Chaolong Fei Postdoctoral research workstation of Northeast Asia Service Outsourcing Research Center Post-Doctoral Flow Station of Applied Economics Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbi China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Guangdong Technology College Zhaoqing China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

human action recognition is an important research direction in the field of computer vision and machine learning, which is widely used in intelligent surveillance, human-computer interaction, sports analysis and other fields. Traditional action recognition methods usually rely on large-scale labeled datasets and complex feature extraction processes, which have problems such as high data acquisition cost and long model training time, as an effective solution, transfer learning can significantly improve the performance of the model and reduce the dependence on labeled data by transferring the features learned by the pre-trained model on large-scale datasets to the target task. In this paper, the R3D model is used as the backbone network, and the model is improved by strengthening the channel and temporal feature extraction capabilities. the model achieves good performance on Shopping Action_ v l by transfer learning based on HMDB51 and UCF101 datasets, and obtains 89.53% and 94.14% of the optimal Top-1 and Top-5 indicators after pre-training on UCF101 dataset. In summary, the C-TSAB ResNets model is proposed in this paper performs well in accuracy and robustness on human action recognition tasks through transfer learning, which verifies the actual effect of the innovation in the research work of this topic.

关键词： Training computer vision Technological innovation Target recognition Surveillance Transfer learning Neural networks Feature extraction Data models human activity recognition

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Real-Time Stress Monitoring using IoT and machine learning for Cognitive Wellness

Real-Time Stress Monitoring using IoT and Machine Learning f...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： thangam S Garikipati Karthik Tejaswi Muppala Vinitha Chowdary A J Jesy Janet Kumari Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Stress monitoring and management became some of the most vital requirements for maintaining overall well-being in high-stress environments. this work deals with the challenging issue of real-time stress detection using a fusion of the Internet of things and machine learning technology implemented on a Raspberry Pi. Using the proposed model, the following physiological signals are collected using a Pulse Sensor for heart rate, a DS18B20 sensor for temperature, a DHT22 sensor for humidity, and a Grove GSR sensor for galvanic skin response (GSR). Based on the collected data, a hybrid model of Support Vector machines and Random forests was trained, which would ultimately classify stress into different levels. A Snake game dynamics were controlled through a joystick. Using the predicted stress levels, the game difficulty is automatically adjusted, to provide real-time biofeedback for the user's cognitive wellness and stress management. this ensures that the user is calm and focused. the system effectively adapted to the gaming environment based on the stress levels of the player and it will serve as a promising tool to enhance cognitive wellness through game interaction.

关键词： Temperature sensors Temperature measurement Support vector machines Games Real-time systems Skin Vectors Biomedical monitoring Stress Monitoring

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A Comprehensive Study on Software Engineering and Emerging Technologies

A Comprehensive Study on Software Engineering and Emerging T...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Vivek Bhatnagar Tejinder Kaur Gulbir Singh Ritu Mishra Department of MMICTBM Maharishi Markaneshwar (Deemed to be University) Mullana Ambala Haryana India Department of Computer Science and Engineering Graphic Era Hill University Haldwani Campus Nainital Uttarakhand India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Recently, software engineering field is undergoing a significant transformation driven by the rapid advancement of emerging technologies. AI introduces intelligent automation in code generation, testing, and debugging by significantly enhancing the development efficiency and reducing human error. machine learning (ML) algorithms further enhance the predictive maintenance and performance optimization. Block chain technology enables the creation of decentralized applications (DApps) and smart contracts, ensuring enhanced security, transparency, and automation. Internet of things (IoT) brings a paradigm shift with an exponential increase in connected devices, necessitating robust software solutions for real-time data processing and analytics, while also highlighting the security challenges.

关键词： Technological innovation Software algorithms Smart contracts Decentralized applications Software Real-time systems Security Internet of things Software engineering Testing

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Empowering human-machine Communication in Code Generation with NLP

Empowering Human-Machine Communication in Code Generation wi...

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Recent Trends in computer Science and Technology (ICRTCST), international conference on

作者： Upendra Singh Aswal Pappala Mohan Rao B. R. Supreeth G. Manikandan R. Srinivasan Mohit Tiwari Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Science and System Engineering Andhra University College Of Engineering AU College Of Engineering Visakhapatnam AP Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Ullal Bangalore Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam India Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Telangana Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

the symbiotic interaction between people and computers in the realm of software development is experiencing a fundamental transition, spurred by improvements in Natural Language Processing (NLP). this study investigates how natural language processing (NLP) is changing the software development process by facilitating better human-machine collaboration throughout the coding phase. Data collection, NLP model creation, and assessment are all part of the rigorous process used in our work, which yields important results. the possibility for improving code quality and efficiency while also democratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to minimize bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds empirical insights but also imagines a future in which software development is more inclusive, efficient, and morally responsible. From improved natural language processing models to real-time feedback loops and multimodal techniques, the future of this transforming discipline promises to further influence the landscape of software development.

关键词： Symbiosis Codes human-machine systems Computational modeling Production Organizations Market research

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Design of an Intelligent Navigation System Integrating Reinforcement learning and computer Vision Algorithms

Design of an Intelligent Navigation System Integrating Reinf...

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5th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Wang, Lili Jilin Animation Institute Jilin Changchun130013 China

Traditional static guidance systems have problems such as poor interactivity and low path planning efficiency. this paper designs an intelligent guidance system to achieve efficient, accurate and personalized navigation services. this paper constructs an intelligent guidance system that integrates reinforcement learning and computer vision algorithms, and adopts a multi-level architecture: the perception layer collects environmental data, the data processing layer uses YOLO and semantic segmentation to extract features, the decision layer uses deep Q network (DQN) to plan and optimize the path, and the interaction layer provides intuitive navigation and user feedback mechanism. the system effectively solves the limitations of traditional guidance systems in complex environments and improves navigation efficiency and user experience. In terms of path planning efficiency, the average path planning time of the intelligent guidance system is shorter than that of the traditional system;in terms of path navigation accuracy, the average accuracy of the intelligent guidance system reaches 99.1%, which is much higher than the 95.2% of the traditional system. these data fully prove the effectiveness of the intelligent guidance system proposed in this paper in improving the quality of navigation services and user experience. © 2025 the Authors. Published by Elsevier B.V.

关键词： Reinforcement learning

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Overview of machine learning Methods for Genome-Wide Association Analysis 2021

Overview of Machine Learning Methods for Genome-Wide Associa...

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5th international conference on Biological Information and Biomedical Engineering, BIBE 2021

作者： Xie, Minzhu Liu, Fang Academy of Information Science and Engineering Hunan Normal University Hunan Changsha China

ISBN: (纸本)9781450389297

Genome-wide association studies (GWAS) is an effective way to reveal the pathogenic genes of complex diseases by analyzing the genotype information and related disease phenotype information on the SNP loci of the whole genome of a large number of living organisms. machine learning (ML) is a method that allows computers to simulate human cognitive processes to solve problems. the advantage of using machine learning methods to carry out genome-wide association analysis research is that it does not require false anchor points or gene-gene interaction models in advance Instead of exhaustive search, computer algorithms that simulate human cognitive processes can learn from a large amount of data to discover the ability of nonlinear high-dimensional gene-gene interactions. In recent years, a large number of machine learning methods have been used in the study of genome-wide association analysis. this article will briefly introduct these methods. © 2021 ACM.

关键词： Genes

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via machine learning Techniques

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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Emerging Technology (INCET), international conference for

作者： Amriti Gupta Kunal Nayyar Harshit Chopra Neha Gahlan Divyashikha Sethia Dept. of Software Engineering Delhi Technological University Dept. of Electronics & Communication Engineering Delhi Technological University Dept. of Computer Engineering Delhi Technological University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Emotions, intricate mental states shaped by neu-rophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. the Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. this paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. this research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. the exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested.

关键词： Emotion recognition Accuracy Sleep Cognitive processes machine learning Data collection Brain modeling

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Designing an AI driven intelligent Tutorial System

Designing an AI driven intelligent Tutorial System

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Recent Trends in computer Science and Technology (ICRTCST), international conference on

作者： Md Shamsher Alam Surjeet Kumar Zeeshan Khursheed Hemant Kumar Mahato Shadan Bashar Anjali Suman Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in NSHM Durgapur India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science & Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. this research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. the system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. the paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. the paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. the findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Ethics Data privacy machine learning algorithms Image recognition Decision making Tutorials Market research

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