检索结果-内蒙古大学图书馆

5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Leo, L. Megalan Yogalakshmi, S. Simla, A. Jerrin Prabhu, R. Thandiah Yokesh, V. School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Panimalar Institute of Technology Department of Computer Science and Engineering Jeppiaar Institute of Technology Department of Electronics and Communication Engineering

ISBN: (纸本)9781665412728

In recent years many people age above 25 are suffering from Neural Foraminal Stenosis (NFS) disease. Neural Formanial Stenosis is a problem of narrowing the spaces between the bones of the spine. It is curable at the earliest stage. A simple exercise can give relief to this problem. But Identification of this problem at the initial stage is a difficult task. It requires more screening techniques and knowledge about the problem. The motivation for this work is to identify the disease at the earliest stage. In clinically, it is identified by analyzing the CT and MRI images. Accurate boundary delineation is important in the clinical diagnosis and also for the proper treatment. Surveys say that boundary detection is subjective to the experience of the clinician. These variations make an impact on the detection of Boundary delineation. Image segmentation algorithms can be used to assist clinicians to detect the boundaries. The classifier can also be used to classify the level of the NFS. This paper utilizes the hierarchical clustering to detect the boundary of the affected region and it is found that, it gives a better assistance to the clinicians. © 2021 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Real-Time Monitoring for Industrial Automation

IoT-Based Real-Time Monitoring for Industrial Automation

引用

Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Rahul Bhagwan Ingle Vipin Sharma D. Kalaiyarasi Shyamprakash. D R. Pandian P. Ramakrishnan Department of Mechanical Engineering Mauli College of Engineering & Technology Shegaon Maharashtra India Department of Mechanical Engineering Medi-Caps University Indore Madhya Pradesh India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering (Autonomous) Karur Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Artificial robotization experienced a paradigm shift because Internet of Things (IoT) technology enables users to track and immediate control artificial processes. This research seeks to study Internet of Things (IoT)powered real-time monitoring systems used in artificial robotization because these systems affect operational efficiency alongside predictive maintenance and decisionrelated functionalities. Uncertified systems become capable of nonstop measurement of vital parameters including temperature and pressure as well as machine performance when applying IoT detectors and sensors together with robust data analytics and computational resources. Real-time data access through analysis helps teams execute rapid decisions thus they can shorten downtimes and boost operational efficiency. An analysis of suitable Internet of Things (IoT) infrastructure structures and communication protocols for simulated environments along with depicted case studies of effective deployments constitutes this paper. The problems related to data security alongside interoperability challenges and scalability issues are pointed out so researchers achieve deeper insight into experimental results. The research establishes that artificial robotization systems developed through Internet of Things technology have the capability to advance intelligence and responsiveness so new advancements in the field are possible.

关键词： Temperature sensors Temperature measurement Data analysis Protocols Service robots Scalability Real-time systems Internet of Things Monitoring Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

An optimal strategy to countermeasure the impersonation attack in wireless mesh network

引用

International Journal of Information Technology (Singapore) 2021年第3期13卷 1033-1038页

作者： Rajendran, Regan Martin leo manickam, J. Department of Computer Science and Engineering University College of Engineering Villupuram Villupuram India Department of Electronics and Communication Engineering St. Joseph’s College of Engineering Chennai India

A group of mobile nodes that dynamically create a short-term network called an wireless mesh network. In which the nodes are proficient in communicating with other node without the help of any central administrator or a network infrastructure. Since the wireless mesh network shows the characteristics like open medium, dynamic topology and distributed cooperation, the network is so weak against security attacks. The security attacks in routing may upset the successful communication and cause partly or wholly blocked network. Thus, the security has become a vital apprehension to achieve a successful and proper communication between mobile nodes in wireless mesh network. Impersonation is a security attack in which the attacker tries to modify the topology of the network or separate any certified node from the network by using the identity of a certified node in the network. In the proposed system, the Unique Secret Code (USC) is used to avoid impersonation and the corresponding node is kept separated from the network. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Malicious node Public key authentication Security Unique Secret Code Wireless mesh network

来源：评论

学校读者我要写书评

暂无评论

Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Priya K. Priyadharshini R. Santhana Krishnan J. Relin Francis Raj Imran Javeed Settu A. Srinivasan Department of Computer Science and Engineering Ramco Institute of Technology India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

关键词： Adaptive systems Smart cities Roads Traffic control Predictive models Air quality Real-time systems Fuels Time factors Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

U-Net based Pancreas Segmentation from Computed Tomography Images

U-Net based Pancreas Segmentation from Computed Tomography I...

引用

Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Tirumalasetti Devendhar Lakshmipriya. B Jayalakshmy. S School of Computer Science & Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Engineering Jawaharlal Nehru University New Delhi India Electronics & Communication Engineering IFET College of Engineering India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Delineation of pancreas from computed tomography (CT) scan images is burdensome owing to its anatomic variation in size, shape and its presence with respect to adjacent organs. This work explores the U-Net architecture to delineate pancreas from the CT volume of abdomen. U-Net was trained for variable number of epochs to identify the optimal learning environment for better segmentation performance. U-Net when trained for pancreas segmentation using CT dataset taken from The Cancer Imaging Archive (TCIA) has resulted in a segmentation performance of dice similarity coefficient of 0.8138, intersection over union of 0.7962 and boundary F1 score of 0.8036.

关键词： Image segmentation Shape Computed tomography Imaging Transforms Feature extraction Pancreas

来源：评论

学校读者我要写书评

暂无评论

Analysis of Spam Messages Using Various Machine Learning Classifier

Analysis of Spam Messages Using Various Machine Learning Cla...

引用

2022 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Nagaraj, P. Gopal, R. Sunethra, B. Sumathi, R. Muneeswaran, V. Vignesh, K. Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Bannari Amman Institute of Technology Sathyamangalam Department of Information Science and Engineering Erode India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350333848

Background:As people using social media increases the data generation also increases and the data generated may be safe or unsafe. If we see some applications like Twitter and mail. We get a lot of emails or twits that include all dangerous and useful things. Here to be safe from the threats and dangers we need a filter that separates useful messages from spam and helps us not to drown in a trap. And one of the approaches to do this is explained in this paper. In this paper, the algorithm followed is the Naïve Bayes classifier. This also provides the comparison between using Naïve Bayes, KNN, and Logistic Regression to solve the same problem that is spam filtering and term frequency-inverse document frequency (TFIDF). © 2022 IEEE.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning approach for Detecting Malicious URL using different algorithms and NLP techniques

A Machine Learning approach for Detecting Malicious URL usin...

引用

2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Waheed, M.A. Gadgay, Baswaraj Shubhangi, D.C. Vishwanath, P. Ain, Qurat Ul Science and Engineering Department of Computer VTU CPGS Karnataka Kalaburagi India VTU Regional Office Karnataka Kalaburagi India VTU Department of Computer Science and Engineering CPGS Karnataka Kalaburagi India H.K.E. Society's S.L.N College of Engineering Department of Electronics and Communication Engineering Karnataka Raichur India

ISBN: (纸本)9781665453424

A malicious URL is one that was made specifically to attack through spam or fraud. Due to the billions of dollars that are compromised, malicious URLs pose a severe threat to security software. Finding secure and phishing links is therefore crucial. Therefore, machine learning is quite helpful for resolving security-related challenges. In this study, we use about 5 lakh URLs that were retrieved from the Kaggle dataset. We are utilizing three NLP approaches, including the count vectorizer, hash vectorizer, and TF-IDF vectorizer. Six machine learning classifiers, including the decision tree, random forest, K-NN, NB, SVM, and logistic regression, were used in conjunction with all these techniques. The highest accuracy results of 98.2 percent are produced by random forest. To determine whether or not the URL supplied is malicious, we built a web app using Flask. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Enhancing Device Authentication in IoT Security Application using Artificial Intelligence: A Survey

Enhancing Device Authentication in IoT Security Application ...

引用

communication, Control and Intelligent Systems (CCIS)

作者： Maame Serwah Dumfeh Brogya Enrique Ayuso Aasheesh Shukla Amit Kumar Goyal Mangal Sain Dept. of Computer Engineering Dongseo University Busan Republic of Korea Department of Electronics and Communication GLA University Mathura India Department of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Technological advancement over the last decade has seen significant technological development, particularly in IoT, which is the most used and commercialized. This is due to its convenience, as it gives users remote control of their appliances via portable devices like smartphones and tablets, without needing to be physically present or close to those devices. As IoT technologies continue to grow and new innovative solutions for everyday applications are being developed there are more interconnected devices and large volumes of data being generated every second. However, the rapid growth of IoT has heightened security concerns, especially around device authentication. This paper reviews IoT authentication complexities and explores Machine Learning solutions under AI, comparing various algorithms and, providing a foundation for researchers and developers in the field.

关键词： Surveys Technological innovation Reviews Scalability Authentication Security Artificial intelligence Smart phones Remote control Next generation networking

来源：评论

学校读者我要写书评

暂无评论

Personalised Travel Ratings using AI and Enhancing User Experience Through Multi-Dimensional Analytics

Personalised Travel Ratings using AI and Enhancing User Expe...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Poornima S A. Krishnaveni J. Relin Francis Raj R. Divya G. Vinoth Rajkumar I. Sakthidevi Department of Computer Science Sri Ramakrishna College of Arts and Science for Women Coimbatore India Department of Mechanical Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Information Technology Adhiyamaan college of Engineering Hosur India Department of Electronics and Communication Engineering J. P. College of Engineering Tenkasi India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The rapid advancements in Artificial Intelligence (AI) have enabled the development of intelligent systems for personalized travel recommendations. This study proposes a novel framework, Generative Adversarial Networks with Real-Time Analytics for Travel Experience (GAN-RATE), designed to enhance user satisfaction by integrating synthetic data generation and real-time feedback mechanisms. The GAN component addresses the challenge of sparsely rated travel attributes by generating high-quality synthetic data, while the Real- Time Analytics module evaluates user interactions and feedback to refine the recommendation process dynamically. The proposed framework considers multiple dimensions, including comfort, cleanliness, timeliness, entertainment amenities, and customer feedback, ensuring a holistic approach to travel rating prediction. Simulation analysis is conducted to compare the performance of GAN-RATE with existing algorithms. Metrics such as accuracy, mean absolute error (MAE), precision, recall, and computational efficiency are employed to evaluate system performance. Results indicate that GAN-RATE outperforms baseline models in prediction accuracy and adaptability to sparse datasets while demonstrating superior real-time response capabilities. The findings highlight the potential of GAN-RATE in improving user experience through personalized, data-driven recommendations, setting a new benchmark for AI-based travel recommendation systems.

关键词： Training Adaptation models Accuracy Heuristic algorithms Generative adversarial networks Prediction algorithms Real-time systems User experience Recommender systems Synthetic data

来源：评论

学校读者我要写书评

暂无评论

On Connectivity of Flying Ad Hoc Networks in the Presence of Ground Terminal

引用

China communications 2021年第9期18卷 1-10页

作者： Chao Liu Zhongshan Zhang School of Computer and Communication Engineering University of Science and Technology(USTB)BeijingBeijing 100083China School of Information and Electronics Beijing Institute of Technology(BIT)Beijing 100081China

The Unmanned Aerial Vehicle(UAV)technologies are envisioned to play an important role in the era of Air-Space-Ground integrated *** this paper,we investigate the connectivity of a Flying Ad hoc Network(FANET)in the presence of a groundbased *** particular,the connected probability of the UAV-to-UAV (U2U) link as well as that of the UAV-to-Ground (U2G) link in a three dimensional (3D) space are ***,to mitigate the aggregate interference from UAV individuals,a priority based power control scheme is implemented for enhancing the connectivity of both U2U and U2G *** results illustrate the effectiveness of the proposed analysis.

关键词： flying ad hoc network UAV swarm connectivity power control connected probability

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：