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共 11828 条记录，以下是151-160 订阅

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A Hybrid Slime Mould Meta Heuristic Algorithm and Machine learning Technique for Intrusion Detection System 3

A Hybrid Slime Mould Meta Heuristic Algorithm and Machine Le...

引用

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

作者： Umarani, V. Swetha, K. Computer Science and Engineering Saveetha Engineering College Thandalam TamilNadu Chennai India

ISBN: (纸本)9798350361186

Network anomaly prediction is a crucial aspect of network security, as it helps identify unusual or potentially malicious activities within a computer network. There are several approaches and techniques used for network anomaly prediction, and Machine learning (ML) is often employed due to its ability to detect patterns and anomalies in large datasets. In this research, network anomaly detection is carried out by three stages. In first stage, data set from IEEE data port is collected and LRC, RFC models are trained for prediction. In second stage, dimension reduction algorithm PCA is used for preprocessing and ML models are trained for prediction. In third stage, the hybrid PCA +SMA is proposed along with ML models for prediction. From this analysis, the hybrid PCA+SMA+ML provide better performance (accuracy 96) compared with previous three stages. Finally, the hybrid PCA+SMA+RFC is selected for final deployment. © 2024 IEEE.

关键词： Machine learning

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Emerging of Machine learning and Deep learning Technology: Addressing in intelligent Wireless Network optimization 3

Emerging of Machine Learning and Deep Learning Technology: A...

引用

3rd IEEE International conference on intelligent Technologies, CONIT 2023

作者： Hailemariam, Kaleab Singh, Gurpreet Madata, Mariam Khamis Franck Elyse Yao, Amemou Singh, Jaspreet Chandigarh University Department of Mathematics Mohali India Chandigarh University Department of CSE Mohali India

ISBN: (纸本)9798350338607

Wireless networks have grown into an essential component of modern life as a result of the proliferation of wireless technology over what has been a decade. The science underlying machine learning (ML) focuses on encouraging computers to act rather than additional programming. Throughout the prior ten years, machine learning has been helping us develop autonomous transportation systems, usable speech detection, effective web searches, and a much greater awareness of the genetic code of people. Deep learning (DL) is a breakthrough technology that makes automatically and self-sufficient managing networks possible. Incorporating DL creativity into wireless networks has the possibility of helping improve the efficiency of systems in instantaneously as well as replace the manually performed strategies currently required in engineers typically do the following-intensive network management obligations. This paper presents an essential understanding of during which exactly the superiority of ML based approach originates from compared to the traditional modeling based strategies by carefully reviewing recent attempts in employing DL for addressing wireless network optimization challenges. Along with discovering comparisons and difficulties, the fundamental research difficulties including a few prospective research areas for completely realizing the possibility of ML in wireless network optimization have also been highlighted. At last, learning-related comparisons between machine learning as well as deep learning are made. © 2023 IEEE.

关键词： Wireless networks

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Fake News Detection System: An Ensemble Approach 3

Fake News Detection System: An Ensemble Approach

引用

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

作者： Kavitha, A.R. Lita Pansy, D. Vishnu Balaji, R. Harsh, J. Suki Sivam, S. Chennai Institute of Technology India

ISBN: (纸本)9798331527549

Fake news has become a critical challenge in digital communication, threatening information integrity and public discourse, especially in this internet era where misunderstanding can lead to dispute of opinions, conflicts and wars. This research presents a comprehensive machine learning framework for fake news detection using ensemble architecture involving Machine learning and Deep learning. By integrating natural language processing, content-based feature extraction, and lexical analysis, a robust detection model was developed. Our methodology leveraged a diverse dataset of 72,134 news articles, applying ensemble learning algorithms to classify potential misinformation. The proposed approach achieved a remarkable 0.97 accuracy. Our findings demonstrate the potential of ensemble techniques in combating digital misinformation, offering a scalable solution for identifying and mitigating fake news across digital platforms. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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A Review on Mixed Reality in Education, Portable Solutions for Overcoming Accessibility Challenges 3

A Review on Mixed Reality in Education, Portable Solutions f...

引用

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

作者： Nedusezhiyan, Roopa Alfred, Marie Celia Rajes Kannan, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Mixed reality revolutionized education by creating comprehensive and dynamic educational settings that foster student participation and understanding. Through a comparative analysis of Mixed Reality applications in fields such as STEM, medical training, and vocational education, this work identifies critical limitations including high costs, internet dependency, and the lack of localized content, particularly in rural and underserved areas. This work presents a novel strategy using Portable Mixed Reality learning Pods (MRLPs) to get around these obstacles. These compact and energy efficient units integrate affordable hardware, renewable energy systems, AI-driven personalized learning, and culturally adaptive content. By bridging the gaps in accessibility and infrastructure, Portable Mixed Reality learning Pods offer a scalable and sustainable pathway to democratize education and ensure equitable learning opportunities for all. © 2025 IEEE.

关键词： Federated learning

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Research on optimization of Unmanned Vehicle Scheduling in Phosphate Mine Tunnels 24

Research on Optimization of Unmanned Vehicle Scheduling in P...

引用

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

作者： Du, Xuan Du, Xuancheng School of Mechanical and Power Engineering China Three Gorges University Hubei Yichang China

ISBN: (纸本)9798400707308

In order to improve the transportation efficiency of unmanned underground phosphate ore transport vehicles, ensure safety, and control costs, this paper constructs a vehicle scheduling optimization model based on an improved genetic algorithm. First, the multi-working face is grouped and sequence-optimized according to the actual process flow. A binary real number encoding is used to describe the grouping, sequence, and vehicle configuration on the chromosome, thereby improving transportation efficiency. Secondly, combined with the UWB positioning system to obtain real-time vehicle location, a meeting strategy is designed to improve the safety and efficiency of vehicle encounters in the tunnels, with the meeting time accurately calculated in the fitness function. Finally, experimental verification shows that the optimized scheme significantly reduces transportation costs and improves efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Mining workflow

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HATO-Deep LSTM: Hybrid Adaptive Trust Aware optimization Enabled Deep learning Classifier For Energy Efficient Routing And Attack Mitigation In IoT-WSN 3

HATO-Deep LSTM: Hybrid Adaptive Trust Aware Optimization Ena...

引用

3rd International conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Srivastava, Abhishek Paulus, Rajeev Shuats Department of Electronics and Communication Engineering Prayagraj India

ISBN: (纸本)9798331528201

The Internet of Things (IoT) is an emerging technology that gained significant attention in both academia as well as industry. It is a set of heterogeneous devices that are interconnected together for exchanging information between devices without any human intervention. Wireless Sensor Network (WSN) plays a major core part in IoT that enables to generation of seamless data and influences network lifespan. Each device in the WSN is referred to as a sensor node that can detect and process the sensor data between sensor nodes. Routing plays a major role in saving energy, but energy conservation is a major issue in resource-constrained networks. Hence, this research designs a Hybrid Adaptive Trust aware optimization based on Deep Long Short-Term Memory (HATO-Deep LSTM) for selecting the optimal node as cluster head (CH) and optimally performing the routing strategy. The trust factors determine the trustworthiness of all the adjoining nodes through collaboration among nodes and this factor enables the optimization to obtain the global best solution feasibly. The deep learning classifier employed in this research is highly effective in detecting malicious nodes by avoiding false nodes entering the data communication path. The proposed model effectively detects the misbehaving nodes and thereby the network throughput is increased. The performance attained by the proposed model in terms of number of alive nodes, end-to-end delay, throughput, and normalized energy is reported as 76, 0.81ms, 0.29bps, and 0.38J respectively with 100 number of nodes. © 2024 IEEE.

关键词： Sensor nodes

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Construction of Digital Media AI Intelligence Based on Deep learning and 5G Network Technology 3

Construction of Digital Media AI Intelligence Based on Deep ...

引用

3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Zhao, Yan Lanzhou Petrochemical University of Vocational Technology Lanzhou730060 China

ISBN: (纸本)9798350384598

Digital media art utilizes digital technologies such as artificial intelligence, big data, and 5G networks to analyze user habits and design and manufacture personalized and humanized products that meet user needs and preferences. This article combined deep learning and 5G network technology to apply it to the intelligent construction of digital media AI (Artificial Intelligence) to address the efficiency issues of traditional digital media. A deep learning model architecture including neural network architecture and parameter settings was established. The computing resources provided by 5G networks were utilized to train and optimize the built models, improving their accuracy and generalization performance. This article took video data as an example, improved deep learning and 5G network technology, and compared and analyzed them with traditional algorithms. The accuracy of traditional methods for video content recognition was 0.70, and the accuracy of video content classification was 0.65;the accuracy of video content recognition for deep learning+5G (optimization algorithm 2) was 0.92, and the accuracy of video content classification was 0.87. The video data analysis test fully demonstrated the advantages of deep learning and 5G network technology in the construction of digital media AI intelligence. © 2024 IEEE.

关键词： Data accuracy

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Fault Classification of Wind Turbine: A Comparison of Hyperparameter optimization Methods 3rd

Fault Classification of Wind Turbine: A Comparison of Hyperp...

引用

3rd International conference on optimization, learning Algorithms and Applications (OL2A)

作者： Pinna, Danielle Toso, Rodrigo Semaan, Gustavo de Sa, Fernando Pereira, Ana I. Ferreira, Angela Soares, Jorge Brandao, Diego Ctr Fed Educ Tecnol Celso Suckow Fonseca Rio De Janeiro Brazil Microsoft Redmond WA USA Univ Fed Fluminense Rio De Janeiro Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

The last few years have been marked by the insertion of renewable technologies in the global energy matrix, such as wind and solar energy, which are considered clean energies with low environmental impact. Wind turbines, responsible for the energy conversion process, are complex equipment that are expensive and susceptible to numerous failures. Monitoring turbine components can help detect failures before they occur, reducing equipment maintenance costs. This work compares the training time of different techniques for tuning hyperparameters in supervised machine-learning models for fault detection in wind turbines. Results show the importance of data optimization during model training.

关键词： Wind turbine Machine learning Fault classification

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A Tunnel Structural Health Monitoring and Damage Identification Algorithm Based on Deep Reinforcement learning 3

A Tunnel Structural Health Monitoring and Damage Identificat...

引用

3rd International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Wei, Yike Yang, Bin China Merchants Chongqing Testing Center for Highway Engineering CO. LTD Chongqing400067 China

ISBN: (纸本)9798350376173

With the continuous promotion of infrastructure construction such as water conservancy, transportation, and municipal engineering in China, the number of tunnels put into operation is increasing, making the healthy service and longterm operation of tunnel structures an urgent and important task. During tunnel operation, structural diseases such as cracks, voids, voids, and water seepage occur frequently. These diseases not only weaken the strength of the tunnel structure and shorten its service life, but also pose a serious threat to the safety of people's lives and property. This paper proposes a tunnel structure health monitoring and damage identification algorithm based on deep reinforcement learning (DRL) to address this issue. This algorithm fully utilizes the advantages of deep learning (DL) in feature extraction and pattern recognition, as well as the ability of reinforcement learning (RL) in decision optimization and adaptive learning. By constructing a model for the interaction between intelligent agents and the environment, it achieves real-time monitoring of the health status of tunnel structures and damage recognition. The experimental results show that the algorithm can accurately identify the damage situation of tunnel structures, effectively monitor the health status of tunnels, and provide strong technical support for the safe operation of tunnels. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on Flight intelligent Sorting Algorithm in AI-based Air Traffic Control Systems 24

Research on Flight Intelligent Sorting Algorithm in AI-based...

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3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Flight delay is a significant challenge faced by air traffic control systems, severely affecting passenger travel experience and airline operational efficiency. This paper addresses this issue by proposing an intelligent flight sorting method based on machine learning and deep learning. By preprocessing and feature engineering of flight historical data, airport data, and route data, a set of high-quality feature sets is constructed. On this basis, models such as random forests and LSTM are employed to predict flight delays and intelligently sort flights according to the prediction results. Experimental results demonstrate that this method significantly enhances the accuracy and efficiency of flight sorting, reducing the impact of flight delays on passenger travel. This study provides a novel and effective approach to addressing flight delay problems, offering broad application prospects and practical value. © 2024 Copyright held by the owner/author(s).

关键词： Delay control systems

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