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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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Faster Greedy Optimization of Resistance-based Graph Robustness 14

Faster Greedy Optimization of Resistance-based Graph Robustn...

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Predari, Maria Kooij, Robert Meyerhenke, Henning Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9781665456616

The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G. We consider the optimization problem of adding k new edges to G such that the resulting graph has minimal total effective resistance (i. e., is most robust). The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion;yet, this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in established generic greedy heuristics. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph-and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process large graphs for which the application of the state-of-the-art greedy approach was infeasible before. As far as we know, we are the first to be able to process graphs with 100K+ nodes in the order of minutes. © 2022 IEEE.

关键词： Optimization

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Efficient Joint Key Authentication Model in E-Healthcare

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computers, Materials & Continua 2022年第5期71卷 2739-2753页

作者： Muhammad Sajjad Tauqeer Safdar Malik Shahzada Khurram Akber Abid Gardezi Fawaz Alassery Habib Hamam Omar Cheikhrouhou Muhammad Shafiq Department of Computer Science Air University Multan CampusMultan60000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur 63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada CES Laboratory National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and *** devices and mobile applications are now increasingly used and integrated through the medical Internet of Things(mIoT).mIoT is an important part of the digital transformation of healthcare,because it can introduce new business models and allow efficiency improvements,cost control and improve patient *** the mIoT system,when migrating from traditional medical services to electronic medical services,patient protection and privacy are the priorities of each ***,it is recommended to use different user authentication and authorization methods to improve security and *** this paper,our prosed model involves a shared identity verification process with different situations in the e-health *** aim to reduce the strict and formal specification of the joint key authentication *** use the AVISPA tool to verify through the wellknown HLPSL specification language to develop user authentication and smart card use cases in a user-friendly *** model has economic and strategic advantages for healthcare organizations and healthcare *** medical staff can increase their knowledge and ability to analyze medical data more *** model can continuously track health indicators to automatically manage treatments and monitor health data in real ***,it can help customers prevent chronic diseases with the enhanced cognitive functions *** necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.

关键词： E-health systems joint key authentication mutual authentication

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Traffic Congestion Control with Emergency Awareness and Optimized Communication Infrastructure using Reinforcement Learning and Non-Dominated Sorting Genetic Algorithm

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IEEE Access 2025年

作者： Al-Heety, Othman S. Zakaria, Zahriladha Abu-Khadrah, Ahmed Ismail, Mahamod Shakir, Mohammed Mudhafar Alani, Sameer Alsariera, Hussein Faculty of Electronic and Computer Engineering Durian Tunggal Malaysia Al-Balqa Applied University College of Engineering Technology Department of Electrical Engineering Amman Jordan Universiti Kebangsaan Malaysia UKM Bangi Department of Electrical Electronic and System Engineering Selangor Malaysia Faculty of Engineering and Electronic Engineering Batu Pahat Malaysia University of Anbar Computer Center Anbar5543 Iraq

Urban centers are grappling with increasing traffic congestion, which hampers mobility and reduces the effectiveness of emergency responses. Current traffic management systems struggle to dynamically prioritize emergency vehicles during peak congestion, highlighting the need for an advanced solution that improves both everyday traffic flow and emergency response efficiency. Traditional traffic light systems typically follow either fixed schedules ("Fixed"approach) or make real-time adjustments without achieving long-term efficiency ("Greedy"approach), often failing to effectively balance regular traffic dynamics with emergency needs. We propose the "Avg Log "method, an adaptive Q-learning strategy that enhances conventional traffic management systems by incorporating an emergency-aware module. This module uses real-time data to prioritize emergency vehicles and adjust traffic signals accordingly. Additionally, our approach employs a multi-objective optimization algorithm to strengthen communication infrastructure, optimizing latency, throughput, and reliability. Through simulations in a multi-intersection urban setting, we compared the performance of our method against the standard "Greedy"and "Fixed"approaches. Key metrics assessed included average waiting times, system throughput, and the efficiency of emergency vehicle passage. The "Avg Log"method outperformed traditional methods, showing superior long-term adaptability and learning. It demonstrated lower average waiting times and effectively managed emergency situations, significantly reducing delays for emergency vehicles compared to both control methods. This method provides an efficient balance between overall traffic flow and emergency prioritization, suggesting a scalable solution for complex urban environments. © 2025 IEEE.

关键词： Efficiency

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COVID 19 Navigator Taxi Application for Urban Mobility during Pandemic Period 2

COVID 19 Navigator Taxi Application for Urban Mobility durin...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Peiris, Rojith Kanchana, Bimsara Perera, Damitha Wickramarathne, Jagath Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

This research paper focuses on increasing the awareness between taxi users and can help to protect themselves in COVID-19 and take precautions. Since 2019 the global pandemic of Covid-19 is spreading at an astonishing rate and causes a negative impact for economic, social, and cultural factors. Government agencies are warning people to reduce the transportation and to maintain social distance. To stop the spread of COVID-19 required to identify persons who are susceptible to infection and need to trace the COVID-19 positive first contacts. Most people are reduced to using public transportations and taxi services due to unidentifiable health conditions in earlier users. The proposed approach can be used to track the taxi drivers and their passengers previous COVID-19 status as well as navigate the safest route by showing the COVID-19 contamination areas. By using this approach users can be aware of earlier users of the taxi service and COVID-19 status of the taxi driver or passenger before taking the trip as well as if they get touch with any COVID-19 patients, can take immediate precautions. This application helps to increase the usage of taxis by making users trust and confidence against COVID-19 infection. By testing with real users our system was able to trace down 45 passengers and 16 drivers within 3 months. © 2022 IEEE.

关键词： COVID-19

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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A 37 GHz Millimeter-Wave Antenna Array for 5G Communication Terminals

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computers, Materials & Continua 2023年第4期75卷 1317-1330页

作者： Jalal Khan Sadiq Ullah Usman Ali Ladislau Matekovits Farooq Ahmad Tahir Muhammad Inam Abbasi Telecommunication Engineering Department University of Engineering and TechnologyMardan23200Pakistan Department of Electronics and Telecommunications Politecnico di TorinoTurin10129Italy Istituto di Elettronica e di Ingegneria Dell’Informazione e Delle Telecomunicazioni National Research Council of ItalyTurin10129Italy Department of Measurements and Optical Electronics Politehnica University TimisoaraTimisoara300006Romania Research Institute for Microwave and Millimeter-Wave Studies(RIMMS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Department of Electronics and Computer Engineering Technology(JTKEK) Faculty of Electrical and Electronic Engineering Technology(FTKEE)Universiti Teknikal Malaysia Melaka(UTeM)Hang Tuah JayaDurian TunggalMelaka76100Malaysia

This work presents,design and specific absorption rate(SAR)analysis of a 37GHz antenna,for 5th Generation(5G)*** proposed antenna comprises of 4-elements of rectangular patch and an even *** radiating element is composed of copper material supported by Rogers RT5880 substrate of thickness,0.254 mm,dielectric constant(εr),2.2,and loss tangent,*** 4-elements array antenna is compact in size with a dimension of 8mm×20mm in length and *** radiating patch is excited with a 50 ohms connector i.e.,*** antenna resonates in the frequency band of 37 GHz,that covers the 5G *** antenna behavior is studied both in free space and in the proximity of the human *** models of the human body,i.e.,belly,hand,and head(contain skin,fat,muscles,and bone)are considered for on-body *** resonant frequency,the antenna gives a boresight gain of 11.6 *** antenna radiates efficiently with a radiated efficiency of more than 90%.Also,it is observed that the antenna detunes to the lowest in the proximity of the human body,but still a good impedance matching is achieved considering the−10 dB ***,SAR is also being *** safe limit of 2 W/kg for any 10 g of biological tissue,specified by the European International Electro Technical Commission(IEC)has been *** calculated values of SAR for human body models,i.e.,belly,hand and head are 1.82,1.81 and 1.09 W/kg,*** SAR values are less than the international recommendations for the three ***,the simulated and measured results of the antenna are in close agreement,which makes it,a potential candidate for the fifth-generation smart phones and other handheld devices.

关键词： 5G antenna array SAR millimeter-wave smart phones

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A new system for automatically generating a training dataset for Mars crater detection 4

A new system for automatically generating a training dataset...

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4th International Conference on Advances in computer technology, Information Science and Communications, CTISC 2022

作者： Li, MingYang Liang, JiaRui Yan, PengCheng Tian, XiaoLin Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering China Macau University of Science and Technology Lunar and Planetary Science Laboratory Space Science Institute China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

This paper proposes an algorithm(system) to automatically mark and create a data set of Mars craters for deep learning based on the existing coordinate information. The main purpose of this automatic processing system is to solve the error problem and low efficiency caused by manual marking. To make such system, we need to know something about planetary science, such as the way to get a full view of a planet, the differences between DEM (Digital Elevation Model) image and TIR (Thermal Infrared image) and the way maps are *** this paper, the crater is used as the detection feature, I chose Mars DEM image as basic dataset to develop this system, and used YOLOv5 model to test the dataset, the results show that this system can quickly and successfully produce the crater data set that can be used for the training and detection of neural network object detection. Then Mars Thermal Infrared Image was used to expand my system. It also can create dataset that can be used for object detection model but haven't been proved by any object detection model yet. In this report, the details of how to achieve automatically generating training datasets and some essential problems will be introduced. © 2022 IEEE.

关键词： Object detection

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An Review of the Effective Energy Consumption Within the Green IT and Green Energy Strategies 14

An Review of the Effective Energy Consumption Within the Gre...

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14th International Scientific and Practical Conference on Environment. technology. Resources, ETR 2023

作者： Mamchur, Dmytro Peksa, Janis Department of Information Technology Turiba University Engineering and Electronics Department Kremenchuk Mykhailo Ostrohradskyi National University RigaLatvia Kremenchuk Ukraine Department of Information Technology Institute of Information Technology Riga Technical University Faculty of Computer Science and Information Technology Turiba University Riga Latvia

ISBN: (纸本)9781713874881

The paper provides analysis on the current state of the art in the field of green technologies, including green information technologies, and methods aimed to achieve effective energy consumption goals. Recent advances in the field summarized in prospective publications, were analysed. Modern Green IT and Green Energy strategies, aimed to reduce power consumption, environmental influence and improve sustainability, which assume the use of Cloud Technologies, renewable energy sources, proper recycling and reuse of devices and resources, were singled out. Finally, recommendations for IT and industrial companies aimed helping to achieve green strategies' goals, were formulated. © 2023 Rezekne Higher Education Institution. All rights reserved.

关键词： Green computing

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Deep Learning Model for Detecting Emotions Through Pipeline

Deep Learning Model for Detecting Emotions Through Pipeline

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International Conference on Advanced Technologies for Communications, ATC

作者： Afshan Fatima Masrath Parveen Vijender Kumar Solanki Ngoc Linh Nguyen Computer Science & Engineering CMR Institute of Technology Hyderabad Telangana India Computer Science &Engineering VJ Institute of Technology Hyderabad Telangana India Computer Science & Engineering Stanley College of Engineering and Technology for women Hyderabad Telangana India Faculty of Applied Science VNU-International School Hanoi Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Human emotions, a pervasive psychological phenomenon, significantly influence individuals' daily experiences and mental health. In today's society, emotional well-being has become a major focus of concern. Facial expressions convey a diverse array of emotions, including natural, joy, sorrow, revulsion, terror, rage, astonishment, and disdain. This research introduces an innovative real-time emotion detection system that analyzes facial images using a multi-kernel approach within a deep learning framework. A notable contribution is the implementation of a Support Vector Machine (SVM) classifier with multiple kernel functions, emphasizing the improved accuracy and decreased processing time achieved by the Polynomial Kernel. This model demonstrates substantial enhancements in precision and efficiency compared to existing single-kernel methods, making it ideal for real-time applications. The study explores the effectiveness of emotional indicators derived from social media platforms, such as chatbots, messages, photographs, and facial expressions. The paper proposes a deep learning-based workflow for emotion recognition through facial expressions, utilizing a multi-kernel approach. The model incorporates Linear, Radial Basis Function (RBF), and Polynomial kernels, with the Polynomial Kernel exhibiting superior performance, attaining the highest cross-validation accuracy of 77.97% on the FER-2013 dataset. This research provides a thorough assessment of the model's accuracy and computational efficiency, demonstrating its competitive edge over current emotion recognition techniques.

关键词： Support vector machines Deep learning Emotion recognition Accuracy Social networking (online) Computational modeling Face recognition Polynomials Real-time systems Kernel

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