检索结果-内蒙古大学图书馆

4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

来源：评论

学校读者我要写书评

暂无评论

IoT based Health Monitoring System with Disease Prediction using ML

IoT based Health Monitoring System with Disease Prediction u...

引用

2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Gupta, Monika Katta, Sonika Gayathri D, Smitha Upadhyay, Vivek Nigam, Swati Nataraj, Navaneeth New Horizon College of Engineering Department of Electronics & Communication Engineering Bangalore India Suresh Gyan Vihar University Department of Computer Applications Jaipur India BNMIT Department of Electronics & Communication Engineering Bangalore India Poornima University Department of Electrical and Electronics Engineering Jaipur India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Dr. H N National College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331512088

A technical solution that enables medical professionals to remotely diagnose, treat, and monitor patients in various locations is a portable telemedicine system. For patients who are unable to reach standard healthcare facilities because of mobility issues, distance, or other factors, it is a practical and accessible method of providing medical care. In addition to specialized medical software, communication tools, and sensors, the system usually consists of a portable device, such as a tablet or smartphone. © 2025 IEEE.

关键词： Electrotherapeutics

来源：评论

学校读者我要写书评

暂无评论

Image-based Plant Diseases Detection using Deep Learning

引用

Materials Today: Proceedings 2023年 80卷 3500-3506页

作者： Panchal, Adesh V. Patel, Subhash Chandra Bagyalakshmi, K. Kumar, Pankaj Khan, Ihtiram Raza Soni, Mukesh Department of Computer Science & Engineering GEC Patan India School of Computer Science and Engineering VIT Bhopal University Sehore MP Bhopal India Electrical and Electronics Engineering SRI Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science & Engineering Institute of Engineering & Technology Uttar Pradesh Greater Noida India Jamia Hamdard Computer Science Department Delhi India Jagran Lakecity University Bhopal India

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. © 2021

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Safe and Reliable Live-Streaming Traffic Monitoring System using OpenCV and Yolov8

Safe and Reliable Live-Streaming Traffic Monitoring System u...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Ashmit Sen, Kuheli Jain, Neeraj Sharma, Ravi Pragya Shivahare, Basu Dev Galgotias University Scse Department of Computer Science & Engineering Greater Noida India Galgotias University Department of Electrical Electronics & Communication Engineering Greater Noida India

ISBN: (纸本)9798331508685

Traffic monitoring is critical for avoiding fatal accidents on the roads as well as essential for ensuring that traffic rules are followed. An effective traffic monitoring system is made possible by cutting-edge computer vision technology, raising the standard of urban traffic safety and lowering the possibility of traffic accidents. The field of urban safety management will benefit greatly from this creative idea. This paper aims to create an automated traffic vehicle monitoring system using an object detection algorithm known as You Only Look Once Version 8 (YOLOv8). The model's reliability has been enhanced by the use of the YOLOv8 framework for successful vehicle object detection, speed estimation within streaming videos, as well as for license plate recognition. YOLOv8 model is used to identify vehicle objects in every single frame. The model accurately detects vehicles, while the license plate recognition module extracts the text from diverse plate formats. The study's conclusions are important because they could lead to the development of a real- time, highly accurate, and reasonably priced traffic monitoring system. Road safety might get significantly improved by this research. © 2025 IEEE.

关键词： Video streaming

来源：评论

学校读者我要写书评

暂无评论

GuavaCareNet: Empowering Guava Farms with Federated Learning for Real-Time Disease Diagnosis 2

GuavaCareNet: Empowering Guava Farms with Federated Learning...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Kirubha, D. Krishnan, R. Santhana Raj, J. Relin Francis Soundiraraj, N. Murali, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331505745

This research presents a Federated Learning-based approach for the detection of guava leaf diseases, aimed at enhancing agricultural practices while ensuring data privacy. Guava farms across various regions provide images of guava leaves, both healthy and diseased, to train a deep learning model for disease classification. The EfficientNet-B3 model, optimized with Neural Architecture Search (NAS) and Transfer Learning, is used for accurate disease detection, including conditions like Anthracnose and Powdery Mildew. To ensure privacy, Federated Learning (FL) is employed, where the model is trained locally on edge devices such as smartphones and Jetson Nano devices situated at guava farms. These devices process the data locally and send model updates, rather than raw data, to a central server. This method enables the aggregation of knowledge from multiple farms without sharing sensitive information, thereby protecting farmers' data privacy. The use of Federated Learning allows the model to adapt to specific conditions at each farm, such as different diseases, leaf appearances, and lighting variations, improving its robustness and accuracy. Additionally, AWS IoT integration allows real-time monitoring and alerts, further empowering farmers with timely disease detection. The decentralized nature of this approach not only enhances disease detection in guava crops but also fosters data privacy, ensuring farmers' information remains secure. © 2025 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Machine Learning–Based Pre-Stroke Detection System

Machine Learning–Based Pre-Stroke Detection System

引用

2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Maheshwaran, M. Devi, Kiruthiga V. Gunapriya, D. Pushpalatha, N. Sam Karthik, S. Selvi, A. Department of Mechatronics Engineering Nehru Institute of Engineering and Technology Thirumalayampalayam Tamilnadu Coimbatore641105 India Department of Electrical and Electronics Engineering K.S.R College of Engineering Tamilnadu India Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore641 202 India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur639113 India

ISBN: (纸本)9798350376913

Stroke remains a significant health concern worldwide, often leading to severe disabilities and mortality. Early detection and intervention are crucial for preventing strokes and mitigating their impacts. In this study, the proposed machine learning-based pre-stroke detection system aimed at identifying individuals at risk of experiencing a stroke before the onset of symptoms. The proposed system utilizes a diverse dataset containing demographic information, medical history, lifestyle factors, and biomarkers relevant to stroke risk. Various machine learning algorithms, including Decision Tree Algorithm, Random Forest Algorithm, Naïve Bayes Algorithm, Multilayer Perceptron Algorithm, and J Rip Algorithm, were trained, and their evaluation was conducted separately using the dataset. The proposed system uses a cloud-based stroke warning system that utilizes machine learning technology to predict impending strokes. The peculiar scrutiny of many machine learning methods led to the development of an effective machine learning strategy for the precise detection of strokes. A microcontroller-based system was used to establish and exhibit an actual patient monitoring system that can sense a variety of real-time indicators, including core temperature, blood pressure, blood flow, heart rhythm, and oxygen level, to let caregivers and doctors keep an eye on stroke patients around the clock. Decisions can be made quickly and easily with the aid of various decision-making algorithms, and anyone can access the database according to their needs. © 2024 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

引用

International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 438-453页

作者： Deepak S.Uplaonkar Virupakshappa Nagabhushan Patil Research Scholar in Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia Computer Science and Engineering Department Sharnbasva UniversityKalaburagiIndia Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia

Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the ***/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better ***,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set ***-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation *** proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and *** experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing *** implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based ***,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based ***/value-The image preprocessing is carried out using CLAHE *** preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM *** this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

关键词： Adaptively regularized kernel-based fuzzy C means Contrast-limited adaptive histogram equalization Level set algorithm Liver tumor segmentation Local ternary pattern

来源：评论

学校读者我要写书评

暂无评论

Advances in 3D fusion of multimodal medical images: 3D reconstruction of bone, muscle, and ligament structures under load from radiographs and magnetic resonance imaging

引用

Research on Biomedical engineering 2025年第1期41卷 1-35页

作者： da Silva, Daniel S. Astolfi, Rodrigo S. Jagatheesaperumal, Senthil K. Selvaraj, Priyadharsini Russel, Newlin Shebiah de Albuquerque, Victor Hugo C. Post-Graduation Program of Medical Surgical Sciences Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Teleinformatics Engineering Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Electronics and Communication Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India Department of Computer Science and Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India

Purpose: Foot and ankle pathologies are among the most prevalent conditions within the human locomotor system. Imaging examinations are crucial for diagnosing, treating, and achieving satisfactory functional outcomes for these conditions. However, conventional imaging exams often fail to capture all the necessary details for precise analysis, necessitating the use of multiple imaging modalities to evaluate bones, ligaments, cartilage, and other structures. Objective: This research aims to explore computational technologies and algorithms for 3D reconstruction and fusion of foot and ankle images, alongside the analysis, segmentation, classification, and evaluation techniques applied to these images. Methods: The study investigates different computational approaches for 3D reconstruction and multimodal image fusion, integrating X-ray and MRI data. Advanced segmentation and classification techniques are applied to enhance the precision of structural assessment. Additionally, we assess the availability and quality of databases for training and validating these models. Results: Our findings highlight a significant gap in research concerning these specific applications, as well as a shortage of suitable databases for 3D reconstruction and fusion of images from X-rays and MRI. Conclusion: The study emphasizes the need for dedicated datasets and further research to develop effective computational solutions for multimodal foot and ankle imaging. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Impact of fraud on revenue loss, detection features, and energy fraud identification 1st

Impact of fraud on revenue loss, detection features, and ene...

引用

1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Indarjit, Elisha Balyan, Vipin Adonis, Marco Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town Bellville South Africa

ISBN: (纸本)9781032874036

The increase of fraudsters and fraud attacks on the communication network plays a major role in the loss of revenue, network abuse, and degradation of services. Communication and cloud networks belong to separate companies and consist of handover points from one network to another. The scope is to bring the communication and cloud networks closer and understand network traffic profiles using Software-Defined Networking (SDN) concept. The SDN controller will serve to route the extracted tapped data to a central server instead of making use of the live network. To apply policies to the traffic and identify which user traffic is a fraud case, and further to send a block signal to the network charging element on the communication network. The flow process will be automated, and to protect the network from fraud attacks, using the SDN controller in a seamless approach and maintaining the performance of the network. The article defines the impact of fraud and fraud case detection. At which Smart Grid leverages intelligent communication technologies to modernize the electric infrastructure and raises new vectors for cyber risks and energy frauds. While services on the network require a secure platform that serves to block fraudster. The main purpose is to achieve a higher energy/service fraud detection rate than similar work in the field, to provide a solution that integrates within the traditional network, and further blocks the fraud within the network. © 2025 the Author(s).

关键词： Cloud network Communication network Detection Fraud Software-Defined Network

来源：评论

学校读者我要写书评

暂无评论

Cyber-Physical Attack Detection in Additive Manufacturing: An LSTM-Based Autoencoder Approach Using Reconstruction Error Analysis from Side-Channel Monitoring

Cyber-Physical Attack Detection in Additive Manufacturing: A...

引用

2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Chithras, T. Brijesh, K.J. Paul, M. Mano Raja Karthick, S. Bhuvanesh, A. Keerthanadevi, R. Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Gregorian Institute of Technology Department of Automobile Engineering Kottayam India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

In order to identify cyber-physical threats in additive manufacturing systems, this study suggests a sophisticated technique that uses data from side-channel monitoring. Strong attack detection capabilities are guaranteed by the method's integration of many important approaches for pre-processing, analyzing, and classifying time-series data. In order to make the continuous time-series data more understandable and efficient to analyze, a pre-processing method based on defined Window Sliding (FWS) is used to divide the data into manageable windows of defined size. After that, we use Discrete Wavelet Transform (DWT) to get all the features out of each window, collecting important data from all the different frequency bands. Using Particle Swarm Optimization (PSO) to sift through the DWT coefficients and extract the most useful features improves classification performance by making sure that only the most informative characteristics are used. In order to train a deep learning (DL) approach that can identify anomalies via reconstruction errors, the optimal feature set is used. This technique is an LSTM-A autoencoder. We show that the suggested approach can differentiate between normal and attack cases with a 99% accuracy rate by applying it to the problem of side-channel attack detection in additive manufacturing. To protect cyber-physical systems from advanced cyberattacks, this method offers a scalable and adaptable solution while simultaneously increasing detection accuracy. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：