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共 11817 条记录，以下是141-150 订阅

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Plant Diseases Detection Using Deep learning and Machine Vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

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

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

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3rd International conference on intelligent Systems and Sustainable Computing, ICISSC 2023

3rd International Conference on Intelligent Systems and Sust...

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3rd International conference on intelligent Systems and Sustainable Computing, ICISSC 2023

ISBN: (纸本)9789819783540

The proceedings contain 52 papers. The special focus in this conference is on intelligent Systems and Sustainable Computing. The topics include: Supervised Machine learning Method for Bone Cancer Detection and Classification;heart Disease Identification Using Ensemble learning and Deep learning;Eigenvalue-Based MRAC for All-Quadrant Vector-Controlled Induction Motor Drives Using Fictitious Resistance;PCOS Screening Using Machine learning and Ensemble learning Techniques in a Smart Way;MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm;decentralizing Rides: A Blockchain Approach to Carpooling;a Privacy Preserving Decision Tree Classification Algorithm for Prediction of Health Care Monitoring System;drug Review Classification by Using Sentiment Analysis;dynamic Resource Management of Cloud Resources Using Machine learning Classification Approaches;deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine learning Framework;integrating Blockchain for Supply Chain Management of Agricultural and Food Products;a Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking Systems Using Deep learning Techniques;a Multi-objective Smart Grid optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency;generative Adversarial Networks for Semi-Supervised learning in Clinical Decision Support Systems;Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems;optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach;scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine learning Model;AI Enabled Swarm Application for Fire-Fighting Bots Over Web-Bluetooth APIs;disaster Management: Sentimental Analysis Using Tweets Based on Experience and Rehabilitation;movie Sense—Hybrid Movie Recommendation System with Sentimental An

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intelligent Assembly optimization Algorithm for Aircraft Panels Based on Machine learning 3

Intelligent Assembly Optimization Algorithm for Aircraft Pan...

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3rd IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhang, Wei Zhang, Yueqin Xuan, Yan Xu, Zilong Nanjing Vocational College of Information Technology Jiangsu Nanjing China

ISBN: (纸本)9798350317022

With the rapid development of computer technology, more and more people are starting to use computers in their daily lives and work. During aircraft flight, traditional aviation products require a lot of manpower and material resources to meet design requirements due to factors such as complex aircraft panel shapes, high precision requirements, and relatively dense structures. In order to save time and improve efficiency, this article utilizes research based on machine learning algorithms to optimize the layout of aircraft internal structures, including simplifying models, modifying geometric parameters, etc., in order to obtain more accurate analysis results. The test results show that the average accuracy of the system is around 0.95 plus or minus 0.02. By using the performance testing indicators and methods mentioned above, the performance of intelligent assembly optimization algorithms can be comprehensively evaluated for better application in practical problem solving. © 2023 IEEE.

关键词： Aircraft Panels intelligent Assembly Machine learning optimization Algorithms

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Research on intelligent Signal Detection in the Internet of Things Based on Deep learning Algorithms 24

Research on Intelligent Signal Detection in the Internet of ...

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

作者： Liu, Xingcen School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400718120

With the rapid development of Internet of Things technology, intelligent signal detection has become a highly challenging key issue. Traditional manual feature extraction methods are no longer able to cope with complex and ever-changing practical environments. In response to this issue, this study proposes an intelligent signal detection model for the Internet of Things based on deep learning. This model adopts a convolutional neural network structure, which can automatically learn local pattern features in signals, thereby improving its adaptability to complex environments. Research a large-scale wireless signal dataset constructed based on real IoT scenarios, and adopt reasonable data preprocessing and augmentation strategies. Through scientific model training and multi-dimensional evaluation index settings, a comprehensive evaluation of the proposed model was conducted on the test set. The experimental results show that the model has achieved excellent performance over traditional methods in key indicators such as accuracy, recall, and F1 score, especially in complex signal recognition scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning algorithms

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The Fusion of Deep Reinforcement learning and Edge Computing for Real-time Monitoring and Control optimization in IoT Environments 3

The Fusion of Deep Reinforcement Learning and Edge Computing...

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3rd International conference on Energy and Power Engineering, Control Engineering (EPECE)

作者： Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang No Arizona Univ 1900 S Knoles Dr Flagstaff AZ 86011 USA Univ Elect Sci & Technol China Chengdu Peoples R China Trine Univ Phoenix AZ USA Yantai Univ Tokyo Japan Northwestern Univ Atlanta GA USA

ISBN: (纸本)9798350366105;9798350366099

In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control.

关键词： Deep reinforcement learning Edge computing Industrial Internet of Things Lightweight policy network Dynamic resource allocation

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Service Path optimization of Hotel Food Delivery Robot Based on Ant Colony Algorithm 24

Service Path Optimization of Hotel Food Delivery Robot Based...

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3rd International conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhao, Guogang Li, Chenhao Chen, Yajun Yin, Zelong Zhou, Jinyan Jiao, Yang Yu, Lianjun Changchun Univ Sch Comp Sci & Technol Changchun 130022 Peoples R China Changchun Sixth Middle Sch Changchun 130031 Jilin Peoples R China Changchun Acad Agr Sci Changchun 130118 Jilin Peoples R China

ISBN: (纸本)9798400709777

Artificial Intelligence is currently in full swing, sweeping through many areas of our lives and production. This popular trend continues to strengthen, and there are many industries that are exploring convergence with the field of artificial intelligence. The hotel industry is profoundly affected by it, in order to solve the problem of saving human resources and enhancing the image of the hotel to attract customers to stay, many hotels have realized intelligence, using robots to complete the task of customer ordering as well as carrying out the transfer of items, to a certain extent, to protect the privacy of customers. In the case of large hotel specifications and the period of soaring customer traffic during holidays, how to make robots carry out activities more efficiently is an urgent problem to be solved. The ant colony algorithm shows good optimization ability in solving the VRP problem, and in this paper, the ant colony algorithm is improved and applied to the path selection of robots' activities in the hotel, so as to improve the customer experience. Verified by simulation examples, the method can better solve the hotel robot activity route planning problem, which has certain positive significance for the intelligent development of the hotel industry.

关键词： CCS CONCEPTS Artifical intelligence Computing methodologies Planning and scheduling

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optimization Algorithm for Multi AP MESH Wireless Network Coverage in Underground Pipe Gallery Environment 3

Optimization Algorithm for Multi AP MESH Wireless Network Co...

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3rd International conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Li, Roufei Cai, Yingming Tian, Anqi Hong, Fu Zhang, Zhihao State Grid Weihai Power Supply Company Shandong Weihai264200 China State Grid Shandong Electric Power Company Information & Telecommunications Company Shandong Jinan250000 China

ISBN: (纸本)9798331530280

This paper proposes an optimization algorithm for the coverage range of multi AP MESH wireless networking to address the problems of small wireless communication coverage and poor quality in underground pipe gallery environments. Based on the specific structure and environmental characteristics of the underground pipe gallery, design a reasonable network topology structure, adopt multi AP MESH networking technology, and construct a comprehensive coverage network through precise layout of multiple wireless access points and intelligent interconnection. Innovatively introduce the Q-learning mechanism of the GEAR improvement algorithm, effectively avoid network congestion, achieve load balancing, and significantly expand the coverage range of wireless networking. The experimental results show that the optimization algorithm significantly improves the wireless coverage and communication quality of underground pipe galleries, providing solid technical support for operation and emergency response. © 2024 IEEE.

关键词： Mesh generation

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Machine learning Models for Android Malware Detection–A Comparative Study 3

Machine Learning Models for Android Malware Detection–A Com...

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

作者： Arun, N. Nisha Dayana, T.R. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

In the mobile ecosystem, Android apps are expanding quickly, but malicious software for Android is also proliferating in ever-increasing quantities. The issue of identifying malware on Android has been examined by several researchers, who have proposed ideas and approaches from various angles. According to current research, machine learning (ML) is a promising and successful method of detecting Android malware. This study is performed to compare the various ML methods for Android malware detection, including Random Forest (RF), Decision Tree,(DT) Random Tree(RT), Support Vector Machine (SVM), and XGBoost. The study assessed these models using a Androzoo dataset of Android apps, taking into account the important performance parameters as detection latency, accuracy, precision, and false alarm rate (FAR). © 2025 IEEE.

关键词： Adversarial machine learning

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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...

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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...

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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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