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共 11817 条记录，以下是81-90 订阅

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Design of intelligent Intrusion Detection System using Machine learning 3

Design of Intelligent Intrusion Detection System using Machi...

引用

3rd IEEE International conference on intelligent Techniques in Control, optimization and Signal Processing, INCOS 2024

作者： Meenakshi Labrado, Ofelia A. Anqui, Abrahem P. Enaldo, Meriam A. Flores, Maria Christina A. Sanchez, Domenic T. Apeejay Stya University Haryana Sohna India College of Technology Cebu Technological University-NEC Philippines

ISBN: (纸本)9798350361186

An intrusion is any deliberate attempt to get through a network's defenses and make it less available, less private, or all three. Intrusions include anything from threats to break the rules for acceptable use to threats to break basic security rules. When an attacker gains access to a system through a trusted person or the internet of systems in order to gain more privileges and then use them in some way, this is called a breach. 'Intrusion detection' is the process of checking to see if someone has tried to break security, privacy, or the availability of resources. This article presents development of intelligent intrusion detection system using machine learning. ACO is used for feature optimization and Support Vector machine, K-NN, and Naïve Bayes algorithm is used for classification. © 2024 IEEE.

关键词： Intrusion detection

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Effects of virtual clinic-based Baduanjin exercise on cognitive optimization in elderly patients 24

Effects of virtual clinic-based Baduanjin exercise on cognit...

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3rd International conference on Public Health and Data Science, ICPHDS 2024

作者： Gong, Fengxiang He, Yuting Mei, Yuchen College of Nursing Shaoyang University Hunan Shaoyang China

ISBN: (纸本)9798400711671

Bike Fitting is a professional service aimed at helping riders personalize and adjust their bicycles to achieve synchronization between the vehicle and the rider, in order to achieve the most comfortable, efficient, powerful, and safe riding posture. In this paper, an intelligent Bike Fitting system is designed based on OpenPose algorithm, aiming to help riders improve the riding effect and comfort through posture recognition and analysis. The system utilizes a camera to capture the rider's posture information, and provides real-time analysis and feedback through computer vision and machine learning techniques. The experimental results show that the system can accurately identify the riding posture and provide personalized adjustment suggestions, which is highly practical and feasible. © 2024 Copyright held by the owner/author(s).

关键词： Bicycles

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Deep learning-Based Skin type Classification using Fine-Tuned ResNet50 Architecture 3

Deep Learning-Based Skin type Classification using Fine-Tune...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chauhan, Shanvi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

Personalized skincare depends critically on the classification of skin types since different skin types - normal, oily, and dry - need different treatment plans to preserve beauty and health. Recommendation of suitable skincare products, prevention of side effects, and optimization of therapies for common dermatological diseases depend on precisely identifying skin types, therefore promoting excellent health and well-being. In this work, we classified skin types from a dataset of 3,152 photos using a deep learning method leveraging ResNet50 architecture. There were 2,522 photos for training, 314 for validation, and 316 for testing out of the dataset. ResNet50, a well-known convolutional neural network (CNN), was tuned for this particular purpose by changing its final fully connected layer to output three classifications matching to normal, oily, and dry skin types. Following training, the model attained an accuracy of 85% on the test set, therefore encouraging infrastructure and creativity in dermatological research. This study emphasizes the relevance of deep learning in dermatological domains and opens the path for automated, customized skincare recommendations, so supporting responsible consumption and manufacturing of skincare products and so helping to lower inequalities by guaranteeing skincare solutions for different skin types. © 2025 IEEE.

关键词： Convolutional neural networks

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Dynamic Planning and intelligent Decision-Making Model of Power Grid Project Reserve Integrating Multi-Source Data and Deep learning 3

Dynamic Planning and Intelligent Decision-Making Model of Po...

引用

3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Meifang Yang, Lina Yang, Liu Wang, Tao Jia, Junqiang Hu, Junjun State Grid Xinjiang Information and Communication Company Xinjiang Urumqi830000 China State Grid Electric Power Research Institute Nanjing Jiangsu Nanjing210000 China

ISBN: (纸本)9798331519032

In this paper, a dynamic planning and intelligent decision-making model of power grid project reserve based on multi-source data and deep learning is proposed to meet the new challenges of power grid project reserve and decision-making under the background of current energy transformation and smart grid construction. In the process of data fusion, hierarchical fusion strategy is adopted, including data preprocessing, feature extraction and feature level fusion. The deep learning module adopts a model combining convolutional neural networks (CNN) and long short-term memory (LSTM) to capture the spatial and temporal features of power grid data. CNN is used to extract local spatial features such as voltage fluctuations and current changes, while LSTM captures the temporal variations of these features. The fused eigenvectors are used as the input of the deep learning model, and the data base is provided for the dynamic programming algorithm by accurately predicting the power grid operation state and load demand. According to the prediction results, the dynamic programming algorithm comprehensively considers the project cost, income, implementation time and resource constraints, and optimizes the reserve strategy of power grid projects. Through the combination of dynamic planning and deep learning, the optimal allocation of resources and efficient management of projects are realized, aiming at minimizing operating costs, maximizing power grid stability and optimizing return on investment. The experimental results show that, compared with the traditional methods, the model has significantly improved the prediction accuracy and decision-making optimization effect, with the prediction accuracy reaching 91.8% and the decision-making optimization effect reaching 84.1%. This research not only improves the scientific and intelligent level of power grid project reserve, but also provides strong support for the sustainable development of smart grid. © 2024 IEEE.

关键词： deep learning dynamic planning intelligent decision-making multi-source data power grid

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Research on Multi-Aircraft Trajectory Planning Based on Reinforcement learning 24

Research on Multi-Aircraft Trajectory Planning Based on Rein...

引用

3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Zhang, Zhaorui Dai, Wenfei Han, Hongming Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China

ISBN: (纸本)9798400707308

This study explores the application of deep reinforcement learning algorithms in the field of air traffic, particularly focusing on intelligent trajectory planning for multiple aircraft. By using the K-means clustering algorithm to identify trajectory patterns in the Shanghai terminal area and employing a grid method for three-dimensional airspace modeling, the trajectory planning space for aircraft was significantly reduced. Finally, the DDQN algorithm was used to achieve trajectory planning for aircraft within the Shanghai terminal area. The research covers terminal area airspace modeling, trajectory pattern recognition, and aircraft trajectory planning. By configuring a simulation environment based on the Shanghai terminal area airspace model, the effectiveness of the DDQN algorithm in improving the efficiency and safety of autonomous aircraft scheduling was validated, demonstrating the great potential of deep reinforcement learning algorithms in solving multi-aircraft trajectory planning problems. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Maximizing Throughput and Minimizing Latency: Evolutionary Algorithms for Optimizing Wireless Cellular Networks 3

Maximizing Throughput and Minimizing Latency: Evolutionary A...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pechetti, Jyoshitha Yadav, Sneha Tarunya, G. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents a novel approach to multi-objective optimization to enhance overall network performance using Genetic Algorithms (GA), Pareto, and Particle Swarm optimization (PSO). By employing optimization algorithms, a balance is sought in terms of trade-off of other parameters while maximizing data throughput and minimizing latency. Simulation layers indicate the comparative study further solidifies the efficacy of the value of evolutionary algorithms in optimizing wireless networks to satisfy the growing needs of high speed and less latency services in today’s communication landscape. © 2025 IEEE.

关键词： optimization algorithms

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

3rd International Conference on Intelligent Data Communicati...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

ISBN: (纸本)9798331527549

The proceedings contain 354 papers. The topics discussed include: quantifying effectiveness of governmental agriculture policies using RNN-transformer based sentiment analysis;artificial intelligence to enhance the security of internet of things: a comprehensive review;dual-plate verification system for vehicle identification using IoT and embedded systems;eco-friendly next-gen air conditioning with IoT-based embedded monitoring and control for reduced energy consumption;investigation on attack detection in IoT networks: a study and analysis of the existing machine learning and deep learning techniques;monitoring and leak detection of hazardous gas using internet of things;and navigating SAP ERP implementation: identifying success drivers and pitfalls.

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Coordinated Scheduling Strategy for Multi-Agent System in Active Distribution Grid based on Deep Reinforcement learning Method 3

Coordinated Scheduling Strategy for Multi-Agent System in Ac...

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3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Liu, Xuan Li, Wanbin Song, Lin Yao, Zhanfeng Lu, Tianguang Zhang, Yuqi Bai, Xue Wang, Wenxin Zheng, Yanan Liu, Chunxiu State Grid Shandong Elect Power Co Dezhou Power S Dezhou Peoples R China Shandong Univ Sch Elect Engn Jinan Peoples R China

ISBN: (纸本)9798350375145;9798350375138

With the increasing complexity of the power system, the traditional centralized scheduling strategy has been unable to meet the demand for efficient management and stable operation of the system, and the multi-agent collaborative regulation technology has gradually become a key means of power system management. In this paper, a coordinated scheduling strategy for multi-agent system in power distribution network based on deep reinforcement learning method is proposed. First, each subject such as distributed power sources, energy storage devices and flexible loads is taken as an intelligent agent in an active distribution grid, and the real-time state information of different intelligent agents in the grid, including voltage, current, power, etc., is obtained. And then, based on the real-time state information, the expected cumulative rewards of taking load management actions under a given smart grid state are evaluated by a neural network representation of the Q-value function, in which a single intelligent agent obtains the optimal energy allocation strategy by maximizing the Q-value function. This paper also constructs a Maldivian network communication model, based on which information exchange between multiple intelligent agents is realized. Finally, a deep deterministic policy gradient algorithm is used to decide the energy allocation of the multi-agent system, and the effectiveness of the proposed method is verified.

关键词： Multi-agent optimization operation variable renewable energy power distribution network coordinated scheduling deep reinforcement learning

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Application of Machine learning Algorithms in Digital Twin Monitoring Systems: An Overview of Approaches, Methods, and Prospects 3

Application of Machine Learning Algorithms in Digital Twin M...

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3rd International conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Amirkhanova, Gulshat Amirkhanov, Bayurzham Tyulepberdinova, Gulnur Ishmurzin, Timur Artificial Intelligent and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798331529222

Digital Twins represent virtual replicas of real-world objects, processes, or systems, enabling continuous real-time monitoring, dynamic adaptation, and predictive analytics. Their integration has become central in industrial, urban, and transportation domains under the paradigms of Industry 4.0, the Industrial Internet of Things (IIoT), and smart cities. However, effectively harnessing the torrents of time-series data and complex system states integral to Digital Twins (DTs) requires advanced analytical tools. Machine learning (ML) methods—ranging from classical ensemble models to deep learning architectures and reinforcement learning—provide powerful mechanisms for anomaly detection, failure prediction, operational optimization, and scenario modeling. This review explores the essential ML algorithms, their application contexts, and the supportive infrastructures required for implementing ML-driven DT solutions. We also discuss interpretability techniques, address security and concept drift challenges, examine the role of MLOps in ensuring reliable deployment, and highlight future research directions, including physics-informed ML, multimodal data integration, and advanced transformers for time-series forecasting. ©2024 IEEE.

关键词： Predictive maintenance

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Convergence of Deep learning and Edge Computing using Model optimization 13

Convergence of Deep Learning and Edge Computing using Model ...

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13th Iranian/3rd International Machine Vision and Image Processing conference (MVIP)

作者： Babaei, Peyman Islamic Azad Univ Comp Dept West Tehran Branch Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Edge systems are undergoing a groundbreaking computing evolution to support artificial intelligence, deep learning, and complex computational algorithms. Using cloud servers to perform deep learning model inference poses challenges such as response delays, increased communication costs, and data privacy concerns. Therefore, significant efforts have been made to push the processing of deep learning models to edge systems, which has led to the creation of edge intelligence as the intersection of learning and edge computing. learning models, especially deep convolutional neural networks, have made significant achievements in machine vision, which provide high accuracy and predictability by spending computing power and memory. If these models are optimized and deployed on edge systems, there will be a revolution in the applications of edge systems in real time. In this paper, by using optimization techniques such as quantization, weight pruning, and weight clustering, the possibility of deploying a typical convolutional neural network model on edge systems that have limited computing resources and memory is investigated. The results show that by using a collaborative algorithm, despite the slight decrease in the accuracy of the model, it is possible to achieve a small-sized model that can even be deployed on microcontrollers.

关键词： Model optimization Quantization Weight pruning Weight clustering Edge computing Deep learning

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