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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Babangida-Sabo, Sumayya Avemaria Utulu, Samuel C. Ajibesin, Abel American University of Nigeria Department of Informaton Systems School of Informtaon Technology and Computing Yola Nigeria Amerucan University of Nigeria Department of Software Engineering School of Information Technology and Computing Yola Nigeria

ISBN: (纸本)9798350358155

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria's attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR. © 2024 IEEE.

关键词： Sustainable development goals

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Design a Hybrid Neural Network Tracking System Using Multiple Cameras 7

Design a Hybrid Neural Network Tracking System Using Multipl...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Ismail, Ahmad Vishnyakov, Sergey Zedan, Mohammad Moscow Power Engineering Institute Department of Computing Machines Systems and Networks Moscow Russia Moscow Power Engineering Institute Department of Diagnostic Information Technologies Moscow Russia

ISBN: (纸本)9798350350890

Neural networks have become a transformative technology, enabling significant advances over the past few decades. In particular, detection and tracking systems benefit from the ability of neural networks to learn and extract features, resulting in accurate object classification and localization. In this paper, we propose a hybrid neural network framework that integrates data from multiple cameras positioned along an object's path. This multi-camera configuration expands the system's field of view and improves prediction accuracy when tracking an object's position. We conduct extensive experiments to evaluate the performance of our proposed model, which demonstrates superior tracking accuracy and robustness across different environments compared to traditional single-camera systems. © 2024 IEEE.

关键词： Multiple cameras Multiple objects Neural Network Prediction trajectory Tracking system

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Survey of road anomalies detection methods

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International Journal of Intelligent systems Technologies and Applications 2023年第3期21卷 280-302页

作者： Saffarini, Rasha Khamayseh, Faisal Daraghmi, Yousef-Awwad Elyan, Derar Sabha, Muath Department of Natural Engineering and Technology Sciences Arab American University Jenin Palestine Department of Computer Systems Engineering Palestine Polytechnic University Hebron Palestine Department of Computer Systems Engineering Palestine Technical University Tulkarm Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine

Automatic road anomaly detection and recognition systems are essential due to their effect on the comfort and safety of drivers and passengers. Drivers should be aware of bad road conditions and the existence of anomalies in routes to avoid accidents, reduce the possibility of car malfunction, and take the most appropriate route to their destinations. This led to increased research interest in automatically detecting and recognising road anomalies. The related studies can be categorised into accelerometer-based techniques and vision-based techniques. In both techniques, deep learning and mathematical methods have been utilised. This paper reviews the latest research in the anomaly detection and classification field. Several types of road anomalies are discussed, such as potholes, cracks, and speed bumps. Additionally, road damage detection techniques are used for different types of road anomalies, challenges, and limitations of current research. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Computer vision

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Incorporating a High-speed Camera in the Lightning Current Measurement System for Wind Turbines 4

Incorporating a High-speed Camera in the Lightning Current M...

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Vukovic, Franjo Milardic, Viktor Filipovic-Grcic, Bozidar Stipetic, Nina Franc, Bojan Milos, Dominik University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9798350323597

A lightning current measurement system for wind turbines was developed and tested in the High Voltage Laboratory at the Faculty of Electrical engineering and computing, University of Zagreb, to determine actual distributions of lightning current parameters in a particular wind turbine area. The output of the measurement system is a waveform of lightning current. The measurement system consists of two current sensors with different frequency and amplitude ranges and a real-Time controller with two digitizers and a GPS module. The current sensors are Rogowski coils connected to integrators with coaxial cables. The prototype was installed on a wind turbine in a high winter lightning activity area. The current stage of development involves integrating the high-speed camera into the prototype measurement system to obtain the visual confirmation of the waveform measurements and help categorize lightning strikes into downward or upward strikes. The camera control application was developed in LabVIEW. The camera was set to trigger by the real-Time controller. Also, a redundant trigger occurs when there is a change in motion in the camera frame in case the prototype measurement system fails to register a lightning strike and therefore fails to send a trigger signal to the camera. The camera control application was tested in the High Voltage Laboratory by recording a flashover in a spark gap and a flashover on the insulator. © 2023 IEEE.

关键词： High speed cameras

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Aggregated Model of a Flexible Multi-Energy Distribution System for Coordinated TSO-DSO Fast Frequency Services Provision

Aggregated Model of a Flexible Multi-Energy Distribution Sys...

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2023 International Conference on Smart Energy systems and Technologies, SEST 2023

作者： Barac, Bojana Krpan, Matej Capuder, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9798350397901

The massive penetration of distributed energy resources (DER) encourages the provision of frequency regulation services at the distribution network level following large frequency disturbances occurring at the transmission system side. The availability of reduced-order or aggregated models will facilitate the coordination between transmission and distribution system operators. As there are no models that can capture the dynamics of diverse distributed technologies and their control systems by a unified single device model, this paper presents the single equivalent device based on the virtual synchronous machine (VSM) technology that can subsume the frequency response of different DER and loads. The validation of the proposed model is performed on the adjusted IEEE 9-bus system for different multi-energy systems. Simulated scenarios have demonstrated the accuracy of proposed aggregation with a maximum relative error of 0.1% as well as computational improvement of almost 100%. © 2023 IEEE.

关键词： Electric power distribution

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Line Parameter and Switching State Identification Method for Radial and Meshed Distribution Networks

Line Parameter and Switching State Identification Method for...

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2023 International Conference on Smart Energy systems and Technologies, SEST 2023

作者： Matijasevic, Terezija Antic, Tomislav Capuder, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9798350397901

Current major changes in distribution systems require better monitoring from energy utilities to prevent possible issues in the form of deterioration of power quality and stability, voltage oscillations, and many others. However, distribution networks are still characterized by their limited observability, where information such as switching state and line parameters are not known. This constraint further complicates analyses concerning the observations of distribution networks. Therefore, this paper proposes a methodology that, as an answer to these problems, offers machine learning-based algorithms that aim to determine the switching state and line parameters of distribution networks using only collected historical Advanced Metering Infrastructure (AMI) measurements. This model was tested on the radial IEEE 33-bus benchmark system and both meshed and radial real-world distribution networks. The results demonstrate that the proposed method can provide an accurate estimation of line parameters and network topology relying only on voltage magnitudes and active and reactive power measurements. © 2023 IEEE.

关键词： Power quality

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Auto Identifying Key Information in Video Lectures to Generate a Navigation Structure

Auto Identifying Key Information in Video Lectures to Genera...

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2024 International Research Conference on Smart computing and systems engineering, SCSE 2024

作者： Dhanushika, Thisaru Weerasinghe, Thushani A. University of Colombo School of Computing Postgraduate Division Colombo07 Sri Lanka University of Colombo School of Computing Department of Information Systems Engineering Colombo07 Sri Lanka

ISBN: (纸本)9798350375688

Online learning platforms are crucial today to ensure uninterrupted teaching and learning. Lecturers upload recorded lectures on these platforms, aiding students having connectivity issues and supporting distance education. However, these lecture recordings are often too lengthy to efficiently navigate to the most attentional content, which includes essential information provided in the lecture. This study proposes a new method to navigate lengthy videos by automatically identifying the most attentional content in video lectures. It would be a better alternative to the existing findings, such as text search in the audio transcript or manual video pausing. Three sub-questions drove the research: what are lecturers' most common techniques to emphasise the most attentional content, how can these techniques be identified in slides, and how can a navigational structure be created to access the most attentional content in video lectures? The research employed a constructive research approach. It started with a questionnaire to uncover lecturers' common techniques, such as text highlighting, underlining, and writing on the slides to denote the most attentional content. The study included a comparative analysis of existing image processing techniques such as Contour Detection, Canny Edge Detection, and Probabilistic Hough Line Transformation, and a machine learning object detection model named EfficientDet DO 512x512 to identify the most attentional content in video frames. The findings revealed that the object detection model was more effective in identifying the most attentional content. Finally, the research employed a strategy involving video frame capturing, slide transition detection, tagging video frames with timestamps, and relevant transcribed words to generate the navigation structure. The findings demonstrated that the navigation structure could effectively locate slides containing the most attentional content with high precision values. Hence, this research can

关键词： Navigation

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