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共 1874 条记录，以下是321-330 订阅

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Real-Time Detection of AI-Generated Deepfake Audio: A Novel Approach

Real-Time Detection of AI-Generated Deepfake Audio: A Novel ...

引用

international conference on ICT in Business Industry & Government (ICTBIG)

作者： Prathamesh Chiddarwar Computer Science Engineering (Specialization in Cyber Security and Forensics) MIT World Peace University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

the rise of AI-generated deepfake audio presents a significant challenge in audio forensics, as traditional detection methods are inadequate against sophisticated manipulations. this study proposes a novel approach combining deep learning algorithms with signal processing techniques to detect deepfake audio in real-time. By leveraging voice pattern recognition and anomaly detection, the method achieves high accuracy and low latency. Experimental results show a significant improvement in detection performance, with the integrated system outperforming traditional methods. the proposed solution demonstrates potential for enhancing security and digital forensics by enabling the swift identification of deepfake audio.

关键词： Deep learning Deepfakes Accuracy Government Signal processing algorithms Signal processing Real-time systems pattern recognition Security Low latency communication

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Study on Adaptive Cruise Control systems of Passenger Vehicles and Solutions Based on V2X Technologies

Study on Adaptive Cruise Control Systems of Passenger Vehicl...

引用

Computer Engineering and intelligent Control (ICCEIC) international conference on

作者： Tiancheng Luo College of Metropolitan Transportation Beijing University of Technology Beijing China

Adaptive Cruise Control (ACC) systems play a crucial role in enhancing automotive safety. However, ACC systems suffer from limitations such as inadequate single-vehicle perception. Current research predominantly focuses on target recognition algorithms, overlooking the technological pathways for enhancing ACC system performance through vehicle-to-vehicle and vehicle-to-infrastructure cooperative technologies. this paper provides a comprehensive review of ACC technology’s development and current state, encompassing ACC products from automotive manufacturers, relevant standards, regulations, and associated research theories while summarizing the existing issues in contemporary ACC technology. Furthermore, this paper explores ACC technology solutions based on these cooperative and networked approaches by analyzing the current status and advantages of vehicle-to-vehicle cooperation and vehicle-to-infrastructure communication technology (V2X). the study reveals that by integrating vehicle-to-vehicle cooperation and vehicle-to-infrastructure communication technologies, it is possible to cost-effectively improve the reliability and safety of ACC systems in scenarios such as cornering, driving on wet or slippery surfaces, and encountering stationary obstacles. this research provides theoretical support for the development of ACC systems, further enhancing road traffic safety and operational efficiency.

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An intelligent Framework for Crime Prediction Using Behavioural Tracking and Motion Analysis 4

An Intelligent Framework for Crime Prediction Using Behaviou...

引用

4th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2022

作者： Shenoy, Rajat Yadav, Deepak Lakhotiya, Harshita Sisodia, Jignesh Sardar Patel Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9781665400732

Closed Circuit Television systems are now being deployed in most public spaces to make the city more secure. Manual observation of these clips for the prevention of crime would take up a lot of manpower. this paper proposes an intelligent Framework using the power of Artificial Intelligence to ensure the safety of the surroundings. the system will use different Computer Vision techniques for video analysis. It will monitor CCTV footage for any criminal offenders, violent objects, and suspicious behavior which could lead to crime. SSD Mobilenet Model, an architecture for concealed object detection, is trained for labeling weapons in the frame. the images captured are processed using Face Detection algorithms to identify human faces. Facial recognition API using libraries in python is implemented to recognize the offenders from criminal records. A ResNet-GRU Model was trained for human behavior analysis which detects suspicious actions. An alert is generated when there are signs of crime and concerned authorities are notified. the proposed framework aims to make societies secure by correctly identifying criminals and crime-related objects. © 2022 IEEE.

关键词： Object detection

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An intelligent Access Control recognition Framework Based on Asian Face Data Augmentation and Edge Computing Optimization in Private University Campuses

An Intelligent Access Control Recognition Framework Based on...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Yuankun Zhu Kun Liu College of Information Engineering Guangxi University of Foreign Languages Nanning China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

To address the practical needs of campus security management in private universities, this paper proposes an intelligent access control system based on facial recognition. To enhance the accuracy of the model in Asian facial recognition tasks, we designed a data augmentation strategy tailored to Asian facial features, thereby increasing the diversity and richness of the training data. By integrating edge computing technology, we optimized the facial recognition model through methods including model pruning, quantization, and knowledge distillation, achieving efficient deployment on edge devices. Experiments conducted on a subset of Asian faces from the VGGFace2 dataset demonstrate that the proposed method outperforms traditional approaches such as LSTM and CNN in terms of accuracy, inference time, model size, and energy consumption. this success enables real-time and efficient facial recognition on edge devices. the research findings provide effective technical support for smart campus security management in private universities.

关键词： Access control Accuracy Face recognition Computational modeling Security management Data augmentation Real-time systems Faces Long short term memory Edge computing

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OpenCV-enabled Gesture Control for Smart Devices: A User-Centric Approach

OpenCV-enabled Gesture Control for Smart Devices: A User-Cen...

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intelligent Technologies (CONIT), international conference on

作者： Priyanka Devi Shivani Chadha Saumyamani Bhardwaz Department of Computer Science and Engineering Chandigarh University Mohali India University Institute of Computing Chandigarh University Mohali India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this research delves into the realm of human-computer-interaction, presenting an innovative user-centric approach togesture control for smart devices. Integrating the powerful OpenCV (Open-Source Computer Vision Library), our system demonstrates enhanced accuracy, responsiveness, and user satisfaction in real-world scenarios. the study includes a comprehensive evaluation of the system’s performance, addressing identified limitations, proposing improvement strategies, and exploring potential applications in emerging technologies. By analyzing a diverse dataset and employing a Convolutional Neural Network (CNN), our OpenCV-enabled gesture control system showcases its adaptability to varied gestures and environmental conditions. Future directions include addressing limitations, expanding application scopes, and integrating with emerging technologies, positioning this research at the forefront of advancements in gesture recognition technology.

关键词： Performance evaluation Computer vision Accuracy Gesture recognition Control systems Libraries Convolutional neural networks

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Digital Twinning of Lap-based Marathon Infrastructure 22

Digital Twinning of Lap-based Marathon Infrastructure

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22nd Annual international conference of the Rapid-Product-Development-Association-of-South-Africa (RAPDASA) / Robotics and Mechatronics conference (RobMech) / conference of pattern-recognition-Association-of-South-Africa (PRASA)

作者： Broekman, Andre Steyn, Wynand JvdM Univ Pretoria Dept Civil Engn Pretoria South Africa

ISBN: (纸本)9781665408035

State-of-the-art sensor technologies, such as Real-Time Kinematic Global Navigation Satellite systems (RTK GNSS) and high-density Light Detection and Ranging (LiDAR), provide unparalleled accuracy for creating digital twins of infrastructure against the backdrop of the 4th Industrial Revolution (4IR). A new generation of low-power sensor platforms and distributed, Long-Range Wide Area Network (LoRaWAN) communications provide continuous, online monitoring of the influence of the built environment on the surrounding environment. the built environment remains inextricably linked to sports engineering applications, notably marathons. the development and deployment of these technologies stand to benefit high-performance sporting events. this paper demonstrates quantitative micro- and macroclimatic measurements (temperature, relative humidity, altitude, CO2 concentrations, air quality, organic volatiles, and geolocation) associated with the built environment over the course of a 42.2 km lap-based marathon hosted at the University of Pretoria (UP) in South Africa, which were measured and wirelessly relayed in real-time. Digitization of the marathon route using RTK GNSS and LiDAR sensing capabilities was done, allowing for detailed geometry measurements and environmental analysis before and after such events. this intersection of engineering and sports disciplines and technology illustrates the potential realization of smart cities and recreation in an evolutionary society.

关键词： 4IR Digital Twin SNOET marathon RTK GNSS LiDAR smart infrastructure LoRaWAN

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Research on Human Action recognition Based on Edge Intelligence

Research on Human Action Recognition Based on Edge Intellige...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE international conference on

作者： Di Wang Zhonglin Yang Gaotian Liu Fourth Technology Department China North Vehicle Research Institute Beijing China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

intelligent recognition algorithms deployed on edge devices offer strong real-time processing capabilities and high security for online video image analysis. However, real-time video image recognition remains challenging, particularly when deploying computationally intensive action recognition models on resource-constrained edge devices. this paper introduces a dual-network human action recognition method that leverages the strengths of both 2D and 3D convolutional neural networks (CNNs). Initially, a 2D CNN detects hu-man targets, followed by the generation of time-sequence data for these tar-gets using a matching method. Human action recognition is then achieved through a 3D CNN. this method addresses the challenges of extracting and modeling temporal action characteristics, enabling accurate human action localization and classification. Five distinct action recognition datasets were constructed to train the 3D CNN, which were subsequently deployed on an edge intelligent platform. Empirical testing of the action recognition system demonstrated a recognition speed of 10 frames per second, with accuracy exceeding 80% for certain action categories.

关键词： three-dimensional displays Accuracy Target recognition Image edge detection Streaming media Real-time systems Convolutional neural networks Human activity recognition Security Testing

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OdinEye: An AI Based Visual Assistive Device for the Blind and Partially Sighted

OdinEye: An AI Based Visual Assistive Device for the Blind a...

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Ubiquitous Computing and intelligent Information systems (ICUIS), international conference on

作者： AaditYa Patil Yash Bendale Prathamesh Bhangare Siddhasen Patil Department of Computer Engineering G. H. Raisoni College of Engineering and Management Pune India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Vision impairment is a significant global challenge, with over 2.2 billion people affected by near or distance vision impairment, and 36 million experiencing complete blindness. Many of these cases remain unaddressed, despite technological advancements. Existing solutions often fall short of providing comprehensive assistance due to limitations in adaptability and precision. To address this gap, this work proposes OdinEye, an AI-based visual assistive system designed to support individuals with blindness or severe visual impairment. By leveraging cutting-edge generative AI, OdinEye aims to offer a more versatile and personalized solution. the system integrates advanced components, including the Gemini API to enhance real-time object detection, navigation assistance, and environment interpretation, empowering users with greater independence and safety.

关键词： Performance evaluation Visualization Text recognition Visual impairment Blindness Cameras Ubiquitous computing Real-time systems Text to speech Sensors

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Comprehensive Real-Time Intrusion Detection System Using IoT, Computer Vision (OpenCV), and Machine Learning (YOLO) Algorithms

Comprehensive Real-Time Intrusion Detection System Using IoT...

引用

Ubiquitous Computing and intelligent Information systems (ICUIS), international conference on

作者： Shailaja Uke Pranay Junghare Shrishti Kenjale Srushti Korade Aniket Kothwade Dept of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

the objective of this project is to develop an advanced quality human intrusion detection system, integrating IoT hardware with advanced software technologies. this will be done by relying on real-time video footage and using image processing techniques in the identification process of human presence and other potential threats, such as weapons. A face recognition module will be attached, which will let the entry of only authorized people and immediately detect unauthorized intrusions. the system includes live alerts for efficient monitoring and protection of secure places. this system, with high algorithms used and strong hardware, is scalable and reliable, proving to be fully comprehensive in relation to the potential threats against the security offered.

关键词： YOLO Accuracy Machine learning algorithms Weapons Intrusion detection Streaming media Real-time systems Hardware Software Monitoring

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Deep Learning Based Intrusion Detection for IoT Networks

Deep Learning Based Intrusion Detection for IoT Networks

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Ubiquitous Computing and intelligent Information systems (ICUIS), international conference on

作者： P. Ananthi K. Nirmaladevi Mothika G Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology &Science MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. this study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. the project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. the novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. the TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. the ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance.

关键词： Deep learning Accuracy Intrusion detection Telecommunication traffic Prediction algorithms Ubiquitous computing Convolutional neural networks Internet of things Transient analysis Optimization

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