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2nd International Conference on Advances in engineering Science and technology, AEST 2022

作者： Flayeh, Azhar Kassem Hamodi, Yaser Issam Zaki, Nashwan Dheyaa Ministry of Higher Education & Scientific Research Department of Computer Engineering & Information Technology Baghdad Iraq Ministry of Higher Education & Scientific Research Computer Engineering College of Electrical and Electronic Techniques Baghdad Iraq University of Information Technology and Communications College of Engineering Media Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350334906

In the modern era of information explosion, where there is enormous information including thousands and millions of texts loaded on the Internet of over 1.2 million terabytes, there are no accurate statistics on the size of texts within this huge amount of data. Given the multiplicity of websites on the Internet and the spread of social networking services like Twitter, for example, it is stated that about 6000 tweets are posted per second in the year 2019. This work aims to analyze these texts through resorting to artificial intelligence to help solve this problem. In this article, a review is presented on the latest concepts in the field of word processing, information extraction, and implementing an interface for text analysis using the C++ programming language and Visual Studio 2015 edition. A comparison is made between two articles in terms of the number of verbs, prepositions, article strength, number of letters, with the possibility of sorting the text into verbs, nouns, object, and adjectives, as will be displayed in the program interface. © 2022 IEEE.

关键词： Sentiment analysis

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Vision-Based Navigation for a Small-Sized Drone: A Simple AI Strategy at IMAV’22

Vision-Based Navigation for a Small-Sized Drone: A Simple AI...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Adil Farooq Syeda Zillay Nain Zukhraf Sadman Shafi Mohsin Kamal Narumol Chumuang Department of Electrical and Computer Engineering University of Cyprus Cyprus Department of Electrical Engineering Penn State University United States School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Department of Digital Media Technology Muban Chombueng Rajabhat University Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodology, and technical development of a lightweight collision avoidance module for small drones, employed during our participation in the IMAV 2022 Nanocopter AI Challenge held at TU Delft, The Nether-lands. Tailored for nano-sized drones, this competition presented challenges related to agile maneuvers, obstacle avoidance, and navigation in a dynamic indoor arena. We provide detailed insights into our obstacle avoidance algorithm, employing an AI computer vision approach and showcasing its robust performance in dynamic environments. The accuracy of the proposed strategy was verified through simulation testing, highlighting the effectiveness of our approach in a simulated Webots environment across diverse parameter settings. The shared insights aim to contribute significantly to the ongoing evolution of autonomous drone navigation. Moreover, the simulation of our proposed strategy can be seen on the YouTube video link: https://***/HVZPAfyf57M.

关键词： Technological innovation Video on demand Navigation Heuristic algorithms Web sites Collision avoidance Artificial intelligence

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Dual-Stage Interleaved On-board Charger for Electric Vehicle using Snow Geese Algorithm 4

Dual-Stage Interleaved On-board Charger for Electric Vehicle...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chandrashekhar, A. Mallikarjuna, B. Thiyagesan, M. Umasankar, L. Ezhil, G. Devi, Rukmani Icfai Foundation for Higher Education Faculty of Science and Technology Department of Mechanical Engineering Telangana Hyderabad501203 India Rns Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru560098 India R.M.K Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai601206 India S. A. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600077 India Bannari Amman Institute of Technology Department of English Tamil Nadu Sathyamangalam638401 India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The growing demand for electric vehicles (EVs) has highlighted the need for efficient and reliable on-board chargers (OBCs) to reduce charging time and enhance battery life. Nevertheless, there are some issues in classic OBC designs: harmonic distortion may become too large, electromagnetic interference increases, and thermal management is not proper;all of which directly affects other elements of the system. As an optimization approach to enhance the performance of the dual-stage interleaved on-board charger (OBC) for electric vehicles (EVs), this work introduces the Snow Geese Algorithm (SGA). The objective of the proposed method is to enhance charging efficiency and minimize cost. The proposed SGA is used to optimize the dual-stage interleaved converter, focusing on reliable performance and effective operation within EV charging systems. The suggested approach is then put into practice on the MATLAB platform and contrasted with a number of current techniques, including Artificial Bee Colony (ABC), Simulated Annealing (SA), Multi-objective Genetic Algorithm (MOGA), and Particle Swarm Optimization (PSO). The proposed SGO method for dual-stage interleaved EV chargers achieves a cost of 1,050.80 and an efficiency of 97.2 %, demonstrating its effectiveness in reducing expenses while maximizing optimization performance in charging systems. © 2025 IEEE.

关键词： Simulated annealing

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Self-Adaptive Electronics using Artificial Neural Networks 9

Self-Adaptive Electronics using Artificial Neural Networks

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Anita, S. Joon, Rakesh Kumar Devi, S. Rukmani Khandan, K. Lakshmi Howard, Eric Rajendiran, M. R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Ganga Institute of Technology And Management Department of Electronics And Communication Engineering Haryana Kablana124104 India Saveetha College of Liberal Arts And Sciences Simats Deemed To Be University Department of Computer Science Tamil Nadu Chennai600124 India Madanapalle Institute of Technology & Science Department of Electrical And Electronics Engineering Andhra Pradesh Madanapalle517325 India Macquarie University Department of Physics And Astronomy Australia Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Electronic gadgets and systems are growing rapidly, requiring new adaptability methods. This research uses Artificial Neural Networks to self-adapt electronic systems. ANNs, inspired by the brain, can optimize electronic circuits and devices in real ***-adaptive electronics uses ANNs for system control and decision-making. ANNs learn to adapt to changing operational circumstances, environmental factors, and user preferences through supervised and reinforcement learning. Feedback systems let neural networks improve energy usage, system parameters, and performance without operator interaction.A sensor array, artificial neural network-based control unit, and actuators make up the self-adaptive electronics system. The system smoothly integrates sensors and computing. Self-adaptive electronics could be used in loT, wearable, and autonomous systems, the research says. Electronic systems operate in unpredictable and dynamic contexts, thus the architecture is ***, self-adaptive electronics outperform static systems in performance, energy efficiency, and adaptability. Artificial neural networks can enable smarter, more responsive, and autonomous gadgets, according to the findings. Finally, self-adapting artificial neural networks in electronics could lead to intelligent systems. This research advances adaptive electronics, enabling new applications in the ever-changing electronics and technology environment. © 2024 IEEE.

关键词： Neural networks

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A robust kernel-based fuzzy clustering algorithm for time series forecasting

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Nguyen, Quoc-Dung Phamtoan, Dinh Phan, Nguyet-Minh Vu, Tuong Quyen Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Faculty of Information Technology Sai Gon University 273 An Duong Vuong Ward 3 District 5 Ho Chi Minh City Viet Nam School of Media Design College of Technology and Design University of Economics Ho Chi Minh City Viet Nam

This paper proposes a new forecasting model for time series based on the Kernel-based fuzzy clustering algorithm. In the proposed fuzzy clustering model, the algorithm performs according to two phases. The first phase determines the optimal number of clusters based on the clustering problem. This phase uses the similarity coefficient index (SCI) as the evaluative measure for the distance of elements. In the second phase, the algorithm establishes the fuzzy relationship among elements using the Kernel-based fuzzy c-mean algorithm, and subsequently performs de-fuzzification and forecasts the time series according to a proposed mathematical rule. The proposed model is evaluated on the various benchmark datasets such as the Enrollment dataset, the Outpatient dataset, the M3-competition dataset, and the Covid-19 datasets of infections and fatalities. The forecasting performance of this model is also compared to that of other models using two statistical assessment criteria: the mean absolute error (MAE) and the symmetric mean absolute percentage error (sMAPE). The models are verified on a range of forecasting horizons and the errors are averaged over multiple tests to have a better accurate performance evaluation. The experiment results show that the fuzzy clustering model outperforms the other models on all the experimented datasets. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Clustering Forecasting Kernel-based fuzzy Time series

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Wireless Embedded Index Coded Transmissions with Multiple Antennas 14

Wireless Embedded Index Coded Transmissions with Multiple An...

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14th International Conference on Information and Communication technology Convergence, ICTC 2023

作者： Song, Heekang Son, Kyungrak Choi, Wan Korea Advanced Institute of Science and Technology School of Electrical Engineering Daejeon Korea Republic of Dept. of Inf. Commun. Engineering Yongin Korea Republic of Seoul National University Institute of New Media and Communications Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Embedded index coding, known as device-to-device index coding, is a promising transmission scheme that effectively reduces the number of transmissions required in device-to-device communications. Leveraging spatial multiplexing gain attained with multiple antennas in wireless environment, this paper explores the benefits of spatially multiplexed embedded index codes through the joint design of embedded index coding and multicast beamformers. Through our simulations, we are able to demonstrate significant improvements obtained by the proposed scheme in terms of spatially multiplexed embedded index coding. © 2023 IEEE.

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Optical Data Center Implementation within Data Center Networks Context Based on Mininet 2

Optical Data Center Implementation within Data Center Networ...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmahdi, Esraa Ali Ali, Adnan Hussein Ibrahim, Raed Khalid Al-Askery, Ali Alhamdane, Hayder Jasim Mahmood, Amal Ibrahim Electrical Engineering College Middle Technical University Student at Dept. of Computer Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Electrical Engineering College Middle Technical University Department of Control and Automation Technical Engineering Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Electrical Engineering College Middle Technical University Dept. of Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350373363

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time. © 2023 IEEE.

关键词： Optical switches

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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An Examination of Machine Learning Techniques for Automating and Optimizing VLSI Design

An Examination of Machine Learning Techniques for Automating...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Jeyarohini, R. Sathya, R. Sellapaandi, S.P. Kavitha, P. Raja Ramesh, D. Mukherjee, Aparajita Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul624622 India Department of Computer Applications Alpha Arts and Science College Tamil Nadu Chennai600 116 India Department of Electrical and Electronics Engineering R.M.K. Engineering College Tamil Nadu Kavaraipettai601206 India Andhra Pradesh Vizianagaram535005 India Department of Computer Science and Engineering IEM-UEM Kolkata700160 India

ISBN: (纸本)9798350376913

This research examines the use of machine learning (ML) techniques to automate and optimise Very Large Scale Integration (VLSI) design procedures. Very large scale integrated circuits (VLSI) require a number of laborious and complex processes to be designed, such as placement, routing, and logic synthesis. Historically, manual involvement and specialised knowledge have been needed to complete these jobs. The goal of this research is to use machine learning approaches to increase chip performance, improve design efficiency, and streamline the workflows involved in VLSI design. Numerous machine learning (ML) techniques, including evolutionary algorithms, reinforcement learning, and supervised learning, are being investigated to determine their efficacy in automating design tasks, optimising design parameters, and cutting down on design cycle durations. This research shows that incorporating machine learning techniques into VLSI design processes is not only possible but also advantageous through extensive tests and case studies. The results of this study are highly relevant to the semiconductor industry in this era of sophisticated microelectronics. They could open the door to more scalable and effective VLSI design processes. © 2024 IEEE.

关键词： Learning algorithms

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Multi Socket Network Transmission with Elliptic Curve Cryptography Encryption Algorithm 1

Multi Socket Network Transmission with Elliptic Curve Crypto...

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1st International Conference on technology, engineering, and Computing Applications, ICTECA 2023

作者： Kawet, Hetly Brinner Yusak Heikhmakhtiar, Aulia Rahardjo, Budi Mantoro, Teddy The Republic of Indonesia Defence University Sains and Defence Technology Faculty Bogor Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Sampoerna University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

Information systems play a crucial role in facilitating effective and efficient media sharing by enabling the exchange of information. However, the current technological landscape is plagued by numerous potential attacks that can compromise the security of exchanged information. Examples of such attacks include stealing user data through interception of user-server communication, spoofing, and sniffing. Robust security measures are essential to mitigate the risks of data leakage and ensure data confidentiality during network-based information exchange. This study proposes designing a communication system based on a multi-socket network, employing the elliptic curve cryptography (ECC) encryption algorithm. The system comprises a single server and multiple clients. This paper analyzes the security of ECC from the performance comparison of ECC and RSA when implemented in multi sochet server. This work uses cryptography library implementation using C programming language. The utilization of the ECC encryption algorithm in network transmission ensures a high level of security and data confidentiality during information exchange. Furthermore, the data transmission system implemented with the ECC algorithm exhibits a swift computation time, thereby expediting key processing and message transmission while maintaining a strong level of security. © 2023 IEEE.

关键词： Geometry

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