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Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Meixiu Zhou Hanying Chen Shancheng Zhao Xia Wu Department of Electronic Engineering Jinan University Guangzhou China Computing Center Jinan University Guangzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The challenge of real-time dynamic gesture recognition is how to accurately locate the gestures in the video and take into account the real-time and accuracy in the recognition process. Aiming at the problem of dynamic gesture recognition in video, this paper proposes a dynamic gesture recognition method based on the multi-scale spatio-temporal residual graph convolution. The convolution module based on spatio-temporal residual graph is used to capture the motion information at different time and spatial scales, expand the receptive field of spatio-temporal dimension, and use the residual structure system to process hierarchically. The dynamic gesture recognition algorithm is designed by using the characteristics of time series, gesture category, gesture coordinates and gesture area to complete the dynamic gesture recognition. The gesture recognition performance of the model is tested under simple and complex background. Under the same level model, the performance of this model has certain advantages.

关键词： Accuracy Convolution Heuristic algorithms Dynamics time series analysis Gesture recognition Streaming media real-time systems Power electronics Convolutional neural networks

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Theoretical perspective to the juggling sequence rotation performance pattern from system memory locality reference viewpoint

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Journal of Supercomputing 2025年第7期81卷 1-26页

作者： Erho, Joseph Agaroghenefuoma Japheth, Bunakiye Richard Kenekayoro, Patrick Tarilayefa Consul, Juliana Iworikumo Computer Science Department Faculty of Science Niger Delta University Bayelsa State Amassoma Nigeria Computer Science Department Niger Delta University P.M.B. 071 Bayelsa State Wilberforce Island Amassoma Nigeria Mathematics Department Faculty of Science Niger Delta University Bayelsa State Amassoma Nigeria Mathematics Department Niger Delta University P.M.B. 071 Bayelsa State Wilberforce Island Amassoma Nigeria

Any software/algorithm meant to run on computer systems is desirable to be optimal and efficient, in terms of system resource usage—whether embedded systems that demand minimal memory usage, real-time systems that must meet deadlines or any other computer system without special restrictions. However, some software/algorithms executions fail to exhibit these properties—wholly or partially. Meanwhile, these attributes can be requirements in some systems. Juggling sequence rotation is one of such algorithms that fail to show efficiency. Its average performance is poor within offsets chosen from around the square root of the sequence size. Yet, it is optimal in element assignments and performs well with offsets selected from within the first few cycle gaps and some around the middle of the sequence size. This inconsistency has attracted some research attention to unveil the cause of the pattern in order to advance performance of the algorithm and related cache behaviour. The attempt has identified hidden properties of the algorithm, by definitions and formulating equations and laying experimental framework for improving performance of the algorithm. This research is poised to advance the ongoing study to include validating some of the property concepts, represented in definitions and equations, of the algorithm. Formal proof methods were adopted for the validation. The results are formulation of 2 lemmas and 5 theorems with proofs. The most important proof is seqrotcircum ("SEQuence ROTation CIRCUMference"), defined in previous study (Erho et al. in: Proceedings of the 2023 ieee 15th international conference on Computer Research and Development (ICCRD), ieee, China, pp 70–78, 2023). The seqrotcircum concept along with base cycle gap, core in the ongoing study, is key to redesigning the juggling rotation algorithm and to catch up with cache memory behaviour. The results of this study may be useful not only to many computing systems, including embedded and real-time syste

关键词： embedded systems

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Quantum Dot-Based real-time Disease Detection Using embedded systems: A Novel Approach for Portable Health Monitoring

Quantum Dot-Based Real-Time Disease Detection Using Embedded...

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international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Soumil Sarna Mukesh K Sarna School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore India Department of General Medicine Mahatma Gandhi Medical College and Hospital MGUMST Jaipur

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This paper presents an innovative approach to real-time disease diagnostics using a microcontroller integrated quantum dot sensor device. Quantum dots, renowned for their high sensitivity and precise fluorescence properties, are deployed to detect specific biomarkers in biological samples, offering rapid and accurate health diagnostics. This work lays the foundation for future advancements in embedded systems for biomedical applications, offering an efficient and low-cost solution to improve patient outcomes through timely intervention.

关键词： embedded systems Accuracy Sensitivity Microcontrollers Quantum dots Point of care Biomarkers Fluorescence real-time systems Human immunodeficiency virus

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 ieee.

关键词： Convolutional neural networks

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TakuNet: An Energy-Efficient CNN for real-time Inference on embedded UAV systems in Emergency Response Scenarios

TakuNet: An Energy-Efficient CNN for Real-Time Inference on ...

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ieee Winter applications and Computer Vision Workshops (WACVW)

作者： Daniel Rossi Guido Borghi Roberto Vezzani University of Modena and Reggio Emilia Italy

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this work, we introduce TakuNet, a novel light-weight architecture which employs techniques such as depth-wise convolutions and an early downsampling stem to reduce computational complexity while maintaining high accuracy. It leverages dense connections for fast convergence during training and uses 16-bit floating-point precision for optimization on embedded hardware accelerators. Experimental evaluation on two public datasets shows that TakuNet achieves near-state-of-the-art accuracy in classifying aerial images of emergency situations, despite its minimal parameter count. real-world tests on embedded devices, namely Jetson Orin Nano and Raspberry Pi, confirm TakuNet's efficiency, achieving more than 650 fps on the 15W Jetson board, making it suitable for real-time AI processing on resource-constrained platforms and advancing the applicability of drones in emergency scenarios. The code and implementation details are publicly released 1 1 https://***/DanielRossi1/TakuNet.

关键词： Performance evaluation Accuracy Computational modeling Neural networks Emergency services real-time systems Energy efficiency Convolutional neural networks Hardware acceleration Drones

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MQTT Enhanced Medical Task Off-loading in Internet of Vehicles

MQTT Enhanced Medical Task Off-loading in Internet of Vehicl...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Mrudula P M Ashwini L S Adwitiya Mukhopadhyay Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The paper proposes building an edge computing model for heart rate monitoring using ESP32 modules and the MQTT protocol optimized for the Internet of Vehicles. The system continuously monitors the patient’s heart rate using a pulse sensor. Reduce data transmission and enable faster response times by processing raw sensor data at the edge of the ESP32 device. The system establishes heart rate thresholds to detect potentially dangerous conditions (tachycardia or bradycardia) and sends alerts through the MQTT broker. Processed heartbeat data and critical alerts are sent to a central MQTT broker for monitoring and possible intervention by subscriber applications. This process leverages edge computing to provide a portable, efficient, and potentially life-saving real-time heart rate monitoring system.

关键词： Technological innovation Protocols Tachycardia Scalability Medical services real-time systems time factors Monitoring Edge computing Internet of Vehicles

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AI applications in Cybersecurity for Proactive Threat Management 1

AI Applications in Cybersecurity for Proactive Threat Manage...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mishra, Manas Kumar Grover, Varun Jain, Rahul Changalva, Ramsundernag Dhami, Aatishkumar Goel, Om G.L. Bajaj Insitute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India Carlson School of Management University of Minnesota MinneapolisMN55455 United States Texas A&M University College StationTX77840 United States Liverpool John Moores University Liverpool United Kingdom California State University Los AngelesCA90032 United States ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331518578

AI in cybersecurity has been gaining ground for years now, and it is used to help manage threats beyond monolithic cyber-attacks. AI today plays a critical role in proactive threat management. It is being used in attempting to understand and mitigate threats before they can escalate and cause harm, as it acts as the first line of understanding and defense against them. Tools/Techniques Used for AI-powered Security systems AI-powered security systems use advanced algorithms and machine learning techniques to monitor network activity continuously and detect anomalies and potential threats in real-time. These systems may also learn from past attacks and data breaches to evolve and refine their abilities to detect and prevent similar attacks in the future. Avoiding cyber threats from killing even half of any possible motive is possible. In cybersecurity, AI applications are capable of automating responses to detected threats, mitigating the repercussions of attacks and shortening the time needed to counteract security incidents. This is especially crucial in a time when the threat landscape changes every day, and attacks happen so regularly that traditional security controls and human intervention tend to fall short more often than not. This proactive approach enables organizations to identify potential threats before they escalate, providing a higher level of protection against a growing number of cyber threats that can compromise their data, assets, and reputation. Nevertheless, the way forward is not easy because the future of Cybercrime has arrived in the present which include Cyberattacks that violates AI tools to get an edge. © 2025 ieee.

关键词： Cyber attacks

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IPAS 2025 Abstract Page

IPAS 2025 Abstract Page

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international Image Processing, applications and systems conference (IPAS)

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real-time Analytics for Big Data in Smart City applications: Transforming Urban Environments with Data-Driven Insights

Real-Time Analytics for Big Data in Smart City Applications:...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Khanderi, Avinash Rajaram, Gnanajeyaraman Hussein, Ramy Riad Johri, Prashant Mohanraj, T. Balamurugan, M. Walmart Inc. BentonvilleAR United States Simats Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Excel Engineering College Department of Eee Komarapalayam India

ISBN: (纸本)9798331542375

This paper offers an overview of real-time analytics for smart cities proposing a framework for real-time data processing to improve the urban landscape. In the proposed system, large-scale data-streams can be processed using advanced machine learning algorithms, edge computing, and distributed data storage. Analyzing the outcomes of the evaluation procedure evidences reportable performance enhancements in the areas of smart city applications. Average traffic speed in transportation systems in the same period rose by 60%, concurrency and response to incidents likewise cut down by 40% and 53% respectively. The energy management systems responded with a 16% decrease in maximum energy load and the waste reduction was by 50%, while the costs savings was by 25% increase. Public safety applications alone realised a 60% decrease in the time taken to detect a crime and a 28.6% increase in efficiency on disaster resources with an overall spike in public satisfaction of 25.7%. Such findings improve the understanding of how this framework can increase urban efficiency, sustainability, and quality of life, which makes the solution usable in future smart city projects. © 2025 ieee.

关键词： Urban transportation

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Generative AI for Automated Security Operations in Cloud computing

Generative AI for Automated Security Operations in Cloud Com...

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AI in Cybersecurity (ICAIC), international conference on

作者： Advait Patel Pravin Pandey Hariharan Ragothaman Ramasankar Molleti Diwakar Reddy Peddinti Broadcom Tiffany Co Athenahealth Options Clearing Corporation Independent Researcher

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

New opportunities in cloud computing have brought many new risks that require effective protection of dynamic distributed environments. Introducing a new formative technology, generative AI, to cloud security has far-reaching benefits for automating threat detection, real-time incident addressing, and vulnerability management. This paper focuses on extending generative AI with cloud security tools like AWS GuardDuty and Google Cloud Security Command Center; the contemplation of accuracy enhancement and response efficiency highlights its aim. Concerning actual applications such as SOAR systems, the study demonstrates how media industry giants, such as Netflix and JPMorgan Chase, have used AI to minimize risk factors while increasing operational efficiency. The paper also discusses the significant increase in response time, enhanced detection accuracy, and the shift to proactive security strategies brought by generative AI. Drawing attention to AI systems’ opportunities, the study examines the subsequent issues connected with AI applications, including over-dependence on AI tools, adversarial risk to models, and the complex nature of decisionmaking in the context of AI systems. The present study also highlights the importance of generative AI in strengthening the defense of the cloud environment, but, at the same time, it recognizes the significance of preventive efforts and planned action plans to manage these technologies efficiently.

关键词： Technological innovation Accuracy Generative AI Cloud computing security Organizations real-time systems Threat assessment time factors Protection Monitoring

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