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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raghuram, Kadambari Altarawni, Ibrahim Vijayalakshmi, K. Kumar, Ajay Jadeja, Abhijeetsinh Chandra, Pankaj Jntuk Department of Computer Science and Engineering Andhra Pradesh Kakinada India Aqaba University of Technology Applied Computing and Artificial Intelligent Department Aqaba Jordan Ramapuram Campus Srmist India INurture Education Solution Pvt. Ltd It Academics Delhi India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India Guru Ghasi das Viswavidyalaya Dept of Information Technology Chattisgarh Bilaspur India

ISBN: (纸本)9798350399264

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links. © 2023 IEEE.

关键词： Autonomous vehicles

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Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

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computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar Reyad Mohammed Abd-Elnaby Abdel-Haleem Abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 ***,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 *** this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US *** to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware *** main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US *** image dataset(POCUS)was used to validate our proposed COVID-LWNet framework *** classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this *** results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,*** paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound

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Multi-agent Deep Reinforcement Learning-based Trajectory Design for UAV-aided Edge Computing System

Multi-agent Deep Reinforcement Learning-based Trajectory Des...

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IEEE Conference on Wireless Communications and Networking

作者： Gengyuan Lu Zheng Chang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

The unmanned aerial vehicle (UAV)-aided communication system is able to extend the coverage of the cellular network and provide flexible connectivity to the ground users. Recently, in order to achieve the ambitious goal of ubiquitous computing and intelligence, UAV-aided mobile edge computing (MEC) system also attracts significant research interests. In this work, we investigate the trajectory design for a multi-UAV-aided MEC system to maximize the defined quality of experience (QoE) and service fairness of multiple ground user equipments (UEs). Since the practical communication environment is complex and time-varying, it is challenging for the multiple UAVs to dynamically make autonomous decisions. Meanwhile, the centralized decision-making scheme may also have a certain degree of transmission delay and affect efficiency of the considered system. Therefore, bearing in mind these challenges, in order to solve the formulated problem, we propose a multi-agent deep reinforcement learning algorithm using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to design UAV’s trajectory via optimizing the flying angle and distance. The simulation results show that our proposed solution outperforms the traditional algorithms.

关键词： Deep learning Multi-access edge computing Simulation Heuristic algorithms Decision making Reinforcement learning Ubiquitous computing

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New hybrid decentralized evolutionary approach for DIMACS challenge graph coloring & wireless network instances

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International Journal of Cognitive Computing in Engineering 2023年第1期4卷 259-265页

作者： Balakrishnan, S. Suresh, Tamilarasi Marappan, Raja Venkatesan, R. Sabri, Abdelouahed Research Scholar Department of Computer Science and Engineering St. Peter's Institute of Higher Education and Research Tamilnadu India Department of Information Technology St. Peter's Institute of Higher Education and Research Tamilnadu India Vellore Institute of Technology Chennai600127 India SASTRA Deemed University Thanjavur India Faculty of Sciences Dhar El Mahraz University Sidi Mohamed Ben Abdellah Fez Morocco

The Graph Coloring Problem is an NP-hard combinatorial optimization problem, and it is being used in different real-world environments. The chromatic integer is determined using different probabilistic methods. This paper explores a new hybrid decentralized evolutionary approach that applies the fixed colors and reduces the edge conflicts iteratively using greedy, split and conquer strategies. This article explores a new hybrid decentralized stochastic methodology for solving graph coloring. The method is constructed by combining the following strategies: Greedy heuristics, split & conquer, and decentralized strategy with an advanced & enhanced global search evolutionary operator. These hybrid design strategies are exhibited on complex DIMACS challenge benchmark graphs and wireless network instances. The proposed approach minimizes the complexity and converges to the optimal solution within a minimal time. The minimum percentage of successful runs obtained for the DIMACS benchmarks lies in (82%, 85%) except for the difficult instance latin_square_***, the vertices count n = 900 and edges count m = 307350. For the latin_square_*** graph, the minimum color is reduced to 97 compared to other methods with less successful runs percentage. For the difficult instance flat1000_76_*** graph, the minimum color is reduced to 76 compared to other methods, resulting in a better successful run. The method obtains the minimum color as χ(G) for the difficult instances *** and *** graphs compared to other methods. The time taken to execute the developed technique is compared with the competing methods, and the proposed method outperforms very competitively in finding the minimum color for large graphs and also in finding the better solution with the high frequency of convergence (> 98%) in the channel allocation of wireless networks compared to the current methods. © 2023

关键词： Combinatorial optimization

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Babu, Vunnava Dinesh Ramkumar, M. Ayyadurai, M. Vivek, K. Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones. © 2024 IEEE.

关键词： Fruits

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Blockchain Based Secure Interoperable Framework for the Internet of Medical Things 16th

Blockchain Based Secure Interoperable Framework for the Inte...

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16th International Conference on information technology and Applications, ICITA 2022

作者： Rafique, Wajid Shah, Babar Hakak, Saqib Khan, Maqbool Anwar, Sajid Department of Computer Science and Operations Research University of Montreal Quebec Canada Center of Excellence in IT Institute of Management Sciences Peshawar Pakistan Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick Fredericton Canada Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan Software Competence Center Hagenberg Vienna Austria College of Information Technology Zayed University Academic United Arab Emirates

ISBN: (纸本)9789811993305

Internet of Medical Things (IoMT) has revolutionized the way medical infrastructure has been managed in the past. Multiple platforms in IoMT have disparate communication standards, data format requirements, and access policies, which produce immense overhead during data transfer among these platforms. In order to provide seamless healthcare services using IoMT, interoperability concerns of heterogeneous devices need to be addressed. Smart contracts using blockchain provide a secure communication for distributed objects to interact in a secure way. We propose a Blockchain-based Secure Interoperable Framework (BSIIoMT) using smart contracts for secure communication in IoMT. We present components, workflow, and design considerations of the BSIIoMT framework to show the feasibility of using edge-enabled blockchain for secure interoperability in IoMT. The BSIIoMT framework is an ongoing project where we present the framework and its components in this research where further results and evaluation will be presented in future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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An Analysis of COVID-19 Related IPRs : Should they be Promoted, Waived or Pooled?

An Analysis of COVID-19 Related IPRs : Should they be Promot...

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2022 Portland International Conference on Management of Engineering and technology, PICMET 2022

作者： Mitsumori, Yaeko Kato, Hiroshi Kato, Akiko Kamijo, Koichi Osaka University Chega Osaka Suita Japan Nihon University The Faculty of Law Tokyo Japan International Professional University of Technology in Tokyo Department of Information and Computer Science Tokyo Japan

ISBN: (数字)9781890843410

ISBN: (纸本)9781890843410

In past decades, there have been abundant discussions regarding management of medical Intellectual Patent Rights (IPRs) since India's Indira Priyadarshini Gandhi, then Prime Minister, eliminated medical product patent protection and enacted India's Patents Act, 1970. There were many changes to IPRs related to AIDS drugs during the 1980's and 1990's. The Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement also hosted numerous discussions over the Doha Declaration, an issuance of compulsory licensure, and product patent waivers for least developed countries (LDCs). COVID-19 emerged in January 2000, making salient this controversial issue. Should medical patents be protected, waived, or pooled? Both USA President Joe Biden and Chinese President Xi Jinpin stated that they would support waivers for COVID-19 related IPRs in May 2021. The following week, pharmaceutical industry representatives released statements arguing for the protection of pharmaceutical IPRs. Meanwhile, developing countries, the World Health Organization (WHO), and other stakeholders contend that medical IPRs should be limited. This paper will first describe the current global status of the COVID-19 pandemic and then present COVID-19 related medical patent data using a commercial database offered by Clarivate Analytics. Based on this data, we will then closely analyze stakeholder positions on COVID-19 IPRs. In this study we selected three widely used schemes of pharmaceutical IPR and drug/vaccine distribution - COVAX, Medicines Patent Pool (MPP) and voluntary licensing. After close examination, we found voluntary licensing is the most applicable solution for COVID-19 pandemic at the time being. © 2022 PICMET.

关键词： COVID-19

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Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System 1st

Adaptive RBF-Neural-Network Control with Force Observer for...

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1st International Conference on Cognitive Computation and Systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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Efficient Fault-Tolerant Search by Fast Indexing of Subnetworks 39

Efficient Fault-Tolerant Search by Fast Indexing of Subnetwo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bilò, Davide Choudhary, Keerti Cohen, Sarel Friedrich, Tobias Schirneck, Martin Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy Department of Computer Science and Engineering Indian Institute of Technology Delhi India School of Computer Science The Academic College of Tel Aviv-Yaffo Israel Hasso Plattner Institute University of Potsdam Germany Faculty of Computer Science University of Vienna Austria

ISBN: (纸本)157735897X

We design sensitivity oracles for error-prone networks. For a network problem Π, the data structure preprocesses a network G = (V, E) and sensitivity parameter f such that, for any set F ⊆ V ∪ E of up to f link or node failures, it can report the solution of Π in G−F. We study three network problems Π. • L-HOP SHORTEST PATH: Given s, t ∈ V, is there a shortest s-t-path in G−F with at most L links? • k-PATH: Does G−F contain a simple path with k links? • k-CLIQUE: Does G−F contain a clique of k nodes? Our main technical contribution is a new construction of (L, f)-replacement path coverings ((L, f)-RPC) in the parameter realm where f = o(log L). An (L, f)-RPC is a family G of subnetworks of G which, for every F ⊆E with |F|e(f2Lf) to Oe(f25 Lo(1)). It also improves over the size and query time of the (L, f)-RPC by Karthik and Parter (2021) by nearly a factor of L. From this construction, we derive oracles for L-HOP SHORTEST PATH, k-PATH, and k-CLIQUE. Notably, our solution for k-PATH improves the query time of the one by Bilò et al. (2022a) for f = o(log k). Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Analysis of EEG Signals Using Empirical Mode Decomposition and Relative Band Energy 10

Analysis of EEG Signals Using Empirical Mode Decomposition a...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Matei, Radu Faculty of Electronics Telecommunications and Information Technology "Gheorghe Asachi" Technical University of Iaşi Romania Institute of Computer Science Romanian Academy Iaşi Branch Iaşi Romania

ISBN: (纸本)9781665485579

This paper approaches an analysis of various epileptiform EEG signals using Empirical Mode Decomposition in the time domain and relative band energy distribution, in the frequency domain. Applying EMD, various EEG waveforms are decomposed into a set of 8 intrinsic mode functions, then to each of them FFT is applied, and finally their relative band energy distribution is obtained. A statistical analysis of the instantaneous frequency values of each IMF is also performed. Thus, we study the possibility to describe a given EEG pattern by relative band energy distribution of its components given by EMD analysis. © 2022 IEEE.

关键词： Empirical mode decomposition

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