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Recent Trends in computer Science and Technology (ICRTCST), International Conference on

作者： Bhawani Sankar Panigrahi Thiyagarajan T M. Tamilselvi S. B G Tilak Babu Pavithra G Bazani Shaik Department of IT Vardhaman College of Engineering Hyderabad Telangana Department of Computer Applications Nehru Institute of Information Technology and Management Coimbatore Tamilnadu Computer Science And Engineering Roever Engineering College Perambalur Tamilnadu Department of ECE Aditya Engineering College Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka Department of Mechanical Engineering Ramachandra College of Engineering Eluru Andhra Pradesh India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The use of deep learning techniques for the purpose of improving fault detection in industrial machinery. It is of the utmost importance to have defect detection mechanisms that are both reliable and effective, since the complexity of industrial processes continues to increase. In this paper, the implementation of deep learning algorithms is investigated. These algorithms make use of neural networks to understand complex patterns and anomalies that are present in data coming from machinery. There are many different models that are being researched to see whether or not they are effective in detecting defects at early stages, limiting downtime, and eliminating costly interruptions. These models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For the purpose of this , the performance of various methodologies is evaluated over a wide range of industrial situations, taking into consideration issues such as the variability of sensor data and noise. The findings demonstrate the promise of deep learning as a significant tool for enhancing defect detection skills, thereby paving the way for industrial equipment systems that are more reliable and resilient.

关键词： Deep learning Recurrent neural networks Limiting Fault detection Noise Market research Reliability

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Distributed Source Localization Under Anchor Position Uncertainty

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Chinese Journal of electronics 2025年第1期23卷 93-96页

作者： Hongqing Liu Yuntao Wu Department of Electronic and Computer Engineering National University of Singapore Singapore Key Laboratory of Intelligent Robot in Hubei Province Wuhan Institute of Technology Wuhan China

In this work, a distributed source positioning approach is developed based on Alternating direction method of multipliers (ADMM). First, a centralized positioning method is developed under case of the anchor uncertainty. And then, the method is realized in a distributed way using ADMM. Simulation results show that the centralized one is robust to the anchor errors and distributed one has similar performance as the centralized one.

关键词： Location awareness Uncertainty Codes Simulation Robot sensing systems Convex functions Intelligent robots

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ML assisted enhanced recommendation system for reliable volunteering environment

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Procedia computer Science 2024年 235卷 22-31页

作者： Prabakaran N Meenakshi K J. Kumarnath Rajasekaran P School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 India Department of Mathematics Presidency University Bengaluru 560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul 624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu 603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created.

关键词： Decision Tree ID3 C4.5 CART Volunteers NGO

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Automated Brain Abnormality Segmentation using Modified JAYA algorithm based Fuzzy C-Means Clustering

Automated Brain Abnormality Segmentation using Modified JAYA...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： N. Jeenath Shafana Priya Lakshmi. S Vijayalakshmi U. Ramani Kottaimalai Ramaraj M Thilagaraj Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering K Ramakrishnan College of Engineering Tiruchirappalli India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The main risk factors in humans, irrespective of gender or location, are anomalies of the brain, such as tumors and strokes. In healthcare facilities, the degree of disability and underlying cause of brain lesions are typically evaluated by means of regular methods created by trained clinicians. During this procedure, signals and images of the brain are collected in a controlled setting. Selecting an appropriate separation method is essential to getting the desired results. The novel JAYA algorithm, which relies on meta-heuristics, aids in the reclustering of the FCM segmentation results in our suggested techniques, yielding more productive delineation results with clear borders with 86% of Dice Score. However, the exploitation and exploration techniques for searching become more flexible and help identify the optimal solution faster by adjusting the desired function with respect to the accommodating cluster location and population size. Regarding the processing speed, optimal exploitation, and exploratory abilities, the suggested strategy outperforms the other available metaheuristic optimization techniques.

关键词： Image segmentation Visualization Accuracy Magnetic resonance imaging Metaheuristics Software algorithms Clustering algorithms Software systems Resonance Tumors

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Efficient Fault Classification in Rotating Machinery Using the K-Nearest Neighbor Algorithm

Efficient Fault Classification in Rotating Machinery Using t...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Sujit Kumar Alka Kumari Shalu Priya Nisha Bharti Durgesh Nandani Manish Kumar Electrical Engineering Department Government Engineering College Banka Bihar India Electronics and Communication Engineering Department Government Engineering College Banka Bihar India Computer Science Engineering Department Government Engineering College Banka Bihar India Electrical Engineering Department Indian Institute of Technology Patna Bihar India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

Rotating machines are widely used in industries. Rolling bearings, which are essential components of these ma-chines, are prone to damage. Therefore, diagnosing faults in rolling bearings quickly and accurately is crucial for maintenance purposes. Machine learning has become a very effective tool for diagnosing faults in rolling bearings. The KNN Algorithm is one such technique that effectively categorizes faults. This study proposes an intelligent fault classification system for bearing faults based on the KNN Algorithm. The proposed model accurately identifies different types of fault conditions in rolling bearings. Our proposed model achieves superior results and has been compared with existing methods.

关键词： Fault diagnosis Renewable energy sources Machine learning algorithms Rotating machines Power control Rolling bearings Nearest neighbor methods

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Performance of CoAP in the Limited Resource Environment: An Empirical Study

Performance of CoAP in the Limited Resource Environment: An ...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Sudhir K. Routray M. Pappa Mahesh K. Jha K P Sharmila Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

Several low bandwidth services have become very popular in the recent years. Wireless Sensor Networks (WSN) and the Internet of things (IoT) are two of them which need fewer resources than the typical modern communication systems. In order to handle the challenges associated with the limited resources, there are appropriate protocols designed for these low resource consuming systems. In case of IoT, where the resources are very limited these protocols play the crucial roles to provide the services without interruption. Constrained Application Protocol (CoAP) is one of them. It handles the resource limited scenarios very efficiently. In this study, features and characteristics of CoAP are analyzed. This protocol has the potential to maintain the long term sustainability of the low bandwidth IoT and WSN.

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Design of Broadband H-plane Horn Antenna with High-Order Mode Based on Substrate Integrated Waveguide (SIW) Technology

Design of Broadband H-plane Horn Antenna with High-Order Mod...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： LiBo Wang Shu Lin Tong Xu Xingqi Zhang Xinyue Zhang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electronic Engineering Maynooth University Maynooth Ireland

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This paper presents the development of a high-order mode, low-profile, broadband H-plane horn antenna utilizing substrate integrated waveguide (SIW) technology to increase its bandwidth. The design strategically modifies the antenna's hO $\text{rn}$ dimensions and incorporates high-order modes, thereby introducing resonant frequencies over a range of bands and significantly broadening its operational bandwidth. Furthermore, enhancements in the antenna's bandwidth and radiation properties are achieved through the extension of the waveguide aperture, modification of electromagnetic boundary conditions, and integration of impedance-matching structures. The antenna demonstrates a notable performance, with a reflection coefficient below -10 dB in the 30–52 GHz range, and a relative bandwidth of 53.6%. Additionally, it maintains a consistent gain of over 10 dBi across the operating frequency band with excellent directional radiation characteristics. The proposed SIW-based high-order mode H-plane horn antenna offers high bandwidth, low profile, and ease-of-manufacturing attributes, making it suitable for millimeter-wave applications in the Ka-band.

关键词： Horn antennas Millimeter wave technology Resonant frequency Bandwidth Electromagnetic waveguides Reflector antennas Broadband communication

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An Innovative Method to Detect Cyberbullying on Social Media

An Innovative Method to Detect Cyberbullying on Social Media

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electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Pandiaraja K. Muthumanickam M. Geetha P.C. Senthil Mahesh Vedhashree S K C. Selvarathi Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Kongunadu College of Engineering and Technology Tholurpatti Trichy Tamil Nadu India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking" perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm.

关键词： Uniform resource locators Support vector machines Logistic regression Accuracy Cyberbullying Feature extraction Boosting Bayes methods Classification algorithms Random forests

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Combating Fake News Utilizing Content-Based Word Embedding and Language Features

Combating Fake News Utilizing Content-Based Word Embedding a...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Mayank Kumar Jain Dinesh Gopalani Yogesh Kumar Meena Anupam Kumar Department of Computer Science and Engineering Amity University Rajasthan Jaipur Malaviya National Institute of Technology Jaipur Rajasthan India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Department of Electronics and Communication National Institute of Technology Patna Bihar India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The rapid growth of social media and online platforms has made disseminating false information a pressing concern. During the COVID-19 pandemic, there was an increase in the spread of false information, which caused confusion and panic and could be harmful to public health. As a result, researchers have focused their efforts on developing automated systems capable of detecting and preventing COVID-19 hoaxes. In the present research, 71 Language Features (LF) and content-based word embedding were used. The COVID-19 dataset’s properties were extracted and fed into machine learning classifiers for classification. We evaluated various models for the task and found that Adaboost outperformed all other models with 97.50% accuracy.

关键词： COVID-19 Support vector machines Vocabulary Technological innovation Accuracy Social networking (online) Feature extraction Vectors Fake news Optimization

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RMS: Real-time Motion Segmentation over the Internet of Vehicles

RMS: Real-time Motion Segmentation over the Internet of Vehi...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Zhan, Lei Hu, Kai Zou, Longhao Yuan, Zhenhui Muntean, Gabriel-Miro Institue of Future Networks Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Department of Broadband Communication Shenzhen China Northumbria University Department of Computer and Information Sciences Newcastle United Kingdom Dublin City University School of Electronic Engineering Dublin Ireland

ISBN: (纸本)9798350321524

In the context of autonomous driving, moving objects such as vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of cars. Unfortunately, due to the limited detection range of sensors, some distant and blocked objects cannot be detected, leading to slow responses when some unexpected situations occur during driving. To address this problem, a real-time motion segmentation multi-task model (RMS), running on an individual vehicle, is introduced to provide motion segmentation of moving objects within its field of view. RMS consists of a shared encoder, a multi-modal fusion module, and a dual decoder. An enhanced High Definition (HD) map constructed with the proposed RMS in line with the recommendations of the 3rd Generation Partnership Project (3GPP) Vehicle-to-everything (V2X) communication standard is produced. Extensive experiments demonstrate how RMS outperforms existing state-of-the-art motion segmentation methods in terms of multiple metrics, including mean Intersection over Union (mIoU). Additionally, Internet of Vehicles (IoV) simulation experiments show how the time required to update the map is better than the times achieved when using other methods. © 2023 IEEE.

关键词： Autonomous vehicles

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