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共 89784 条记录，以下是101-110 订阅

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Face Mask Detection in Real Time using Machine Learning algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, V. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： Image segmentation

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Adaptive Load Balancing for Distributed Multi-Controller SDNs through Switch Migration Strategy 17

Adaptive Load Balancing for Distributed Multi-Controller SDN...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Rao, Allu Sudharshan Khanal, Bipal Alam Ansari, Md. Sarfaraj National Institute of Technology Sikkim Department of Computer Science and Engineering Sikkim Ravangla India

ISBN: (纸本)9798331531195

Software-defined networking (SDN) is a transformative technology that systematically centralises and manages network resources. This paradigm shift allows for greater flexibility, agility, and efficiency in network management. In the ever-evolving landscape of distributed software-defined networks, the escalating demand for cloud services and data traffic underscores the critical need for a sophisticated load-balancing scheme. This paper addresses the pivotal challenge of optimising resource utilisation in SDN controllers to mitigate bottlenecks, enhance responsiveness, and ensure efficient network operations. This paper proposes a switch migration algorithm for efficient load balancing in distributed multi-controller topology. The proposed framework uses resource utilisation, load balancing rate, and re-association time for performance measurement. The proposed algorithm considers the switch to controller distance and remaining processing capacity to select an immigration controller. It also minimizes re-association time in the process of switch migration. The proposed algorithms are compared with benchmark algorithms like 'Closest Switch Migration' and 'Maximum Utilization Switch Migration' using various performance metrics. It has shown promising results and outperforms the benchmark algorithms. © 2025 IEEE.

关键词： Benchmarking

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Performance Prediction of Server Using Neural Network Algorithm Compared with Random Forest Algorithm Based on Option Posted by Players 2nd

Performance Prediction of Server Using Neural Network Algori...

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

作者： Janardhan, K.P. Selvakumar, A. Ramesh, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (纸本)9789819776023

The objective of this study is to improve server speed using neural network algorithms compared with random forest techniques to increase accuracy using machine learning. Option posts by players and upvoting-based prediction and suggestions are also used. Research focuses on two groups: group 1's random forest and neural network algorithms, and group 2's Template matching method. There are twenty samples in each category. Given that the computation has a significant value of 80%, the estimated sample size is 40 utilizing a G power of 80%. The accuracy from the neural network algorithms is 87.96, it seems superior to Random forest algorithms is 80.94% respectively, and has an important difference p = 0.001In the SPSS statistical analysis, (p © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy Gaming economic development Machine learning Novel neural network algorithms Prediction Random forest algorithms

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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

Single-Loop Variance-Reduced Stochastic Algorithm for Noncon...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Xia Zhu, Linglingzhi Zheng, Taoli So, Anthony Man-Cho Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of O(ϵ−4), surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations. © 2025 IEEE.

关键词： nonconvex-concave minimax optimization single-loop stochastic algorithm variance reduction

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Color Image Enhancement Using Rao algorithms 5

Color Image Enhancement Using Rao Algorithms

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5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Sarangi, Partha Pratim Bose, Sinjini Mathur, Ananya Bhaskar, Sneha Kiit Deemed to be University School of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9798331522100

Enhancing visual data, especially color images is essential in the healthcare sector, security sector, and multimedia applications. Unlike gray-scale images, color images provide richer details but pose unique challenges due to the interdependence of RGB channels. Traditional enhancement methods, such as Histogram Equalization (HE), often fall short for color images, resulting in color distortion and uneven improvements. This paper looks into the effectiveness of metaheuristic optimization algorithms in improving color images. In particular, it discusses metaphor-based algorithms, inspired by natural behaviors such as particle swarm optimization (PSO), with metaphor-less algorithms like Rao-1, Rao-2, and Rao-3 that are based solely on mathematical principles. With the help of these algorithms, several color images were enhanced and performance was measured using measures including SSIM, VIF, PSNR and entropy to measure visual clarity, detail retention, and computational efficiency. This study highlights the strengths and weaknesses of both metaphor-based and metaphor-less metaheuristic approaches. The research gives a methodical comparison that throws light on the suitability of these algorithms and emphasizes the significance of optimization methods with a scientific foundation for advancing picture enhancing tasks. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Identification and Classification of Medicinal Plants using Machine Learning algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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Leveraging Machine Learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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All-optical tunable integrator based on microring resonator assisted by GST superlattice 2

All-optical tunable integrator based on microring resonator ...

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2nd international conference on Optical Communication and Optical Information Processing, OCOIP 2024

作者： Zhong, Wentao Dong, Yida Shen, Mei Lei, Lei Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen518060 China China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Academy for Advanced Interdisciplinary Studies Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518000 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

This paper presents the design and simulation analysis of a tunable optical integrator based on ZnS-SiO2/Ge2Sb2Te5 superlattice (GST-SL) phase change material (PCM). The transfer function of the integrator is realized through a silicon microring resonator cladded with a three-layer GST-SL (GST-SL3-MRR). The integration of the PCM into the microring resonator enables a reconfigurable Q-factor, allowing for tunable integration orders between 0.5th and 1st. Through laser-induced phase transitions between amorphous and crystalline states, the transmission spectrum of the MRR can be dynamically adjusted. Simulation results show that the integration time window reaches 300 ps for the first-order integration, and the tunable integration results closely align with the ideal mathematical predictions. demonstrating the device's highly tunable performance. Further simulations of applications for quickly solving ordinary differential equations highlight the potential applications of our device in areas such as optical signal processing and image recognition. © 2025 SPIE.

关键词： Zinc Selenide

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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