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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Barhanpurkar, Kanishk Mandlik, Nikita Rajawat, Anand Singh Goyal, S.B. Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona Thomas J. Watson College of Engineering and Applied Science Binghamton University United States School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Ramnicu Valcea Romania

ISBN: (纸本)9798350321388

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies. © 2023 IEEE.

关键词： Economic and social effects

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Industrial Speed Control of im Based Model Predictive Controller Using Zeta Converter 1

Industrial Speed Control of im Based Model Predictive Contro...

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Pattanaik, Balachandra Barani Sundaram, B. Mishra, Manish Kumar Thirumoorthy, Dhanabal Rastogi, Umang Department of Electronics and Communications Bule Hora University Bule Hora Ethiopia Department of Computer Sciences Bule Hora University Bule Hora Ethiopia Department of Computer Sciences University of Gondar Ethiopia College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Science Engineering Meerut Institute of Engineering and Technology Uttar Pradesh Meerut India

For induction, machine-based configuration, the model predictive control scheme, power converter based renewable system is presented in this paper. The PV system is proposed to provide an input source for the model predictive control (MPC) based system. The control method predictive is used based on vector control. According to this, the drive system behaviour of the fault-tolerant capacity is enhanced. The ZETA converter is proposed to improve the system input source, which fed on the PV system. The proposed ZETA converter is used to increase the DC link voltage of the system. The motor's speed and torque can be controlled by the model predictive control method based on vector control. The results of the proposed system are obtained and verified using the MATLAB/Simulink. © Published under licence by IOP Publishing Ltd.

关键词： Model predictive control

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RFID-Based Student Identification Card Attendance Monitoring System 1

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Aderinto, Muqeetat Tolulope Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (数字)9789819919833

ISBN: (纸本)9789819919826

Passive (low-frequency) radio-frequency identification (RFID) technology is being used in an increasing number of contactless systems. This project presents contactless attendance system passive radio-frequency identification (RFID)-based approach. RFID is a technology that uses radio waves to send information from an RFID tag attached to an object to a reader for the purpose of monitoring and identifying the object. This technology is one of the most widely used in the world;almost every organization uses RFID-based tracking, identification, and other functions on some level. A contactless RFID-based attendance monitoring system is a less cost-effective and efficient automated solution. The proposed system focuses on an RFID-based attendance monitoring system that uses RFID technology in connection with a programmable logic circuit (such as an Arduino) to address the problems that traditional paper and contact-based attendance systems experience. Each user (student or lecturer) will be given an RFID card. When the user puts the RFID card near the RFID reader, the RFID reader scans the user's data stored on the card, recording the current time using the real-time clock (RTC) module and the RFID tag number (UID) on the SD card. The Arduino uses the SD card module to communicate with the SD card. The buzzer beeps when a tag is scanned, and the system employs a 16 × 2 LCD module for display purposes. This system will take attendance automatically, and the attendance records will be stored in the database. The SD card serves as backup and can help to generate accurate reports when required. Therefore, the system rectifies human errors, saves time, and gives clear attendance. The system has been implemented using C# programming language. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radio waves

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning

Predictive Design for Quality Assessment Employing Cloud Com...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Gaurav Jindal Vidhika Tiwari Riyaz Mahomad Anita Gehlot Mimoh Jindal Dibyhash Bordoloi Department of Master of Computer Applications G L Bajaj College of Technology and Management Gr. Noida Mechanical Engineering Department DPGITM Gurugram School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India National Informatics Centre Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed Rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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Dual Dielectric Metasurface for Simultaneous Sensing and Reconfigurable Reflections

arXiv

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arXiv 2025年

作者： Birari, Mahesh Nagarkoti, Deepak Singh Katsounaros, Anestis Mudireddy, Hruday Kumar Reddy Malik, Jagannath Alexandropoulos, George C. EpsilonR United Kingdom Texas Instruments United States Space Innovation Solutions Greece Indian Institute of Technology Patna India Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago United States

This paper presents a novel dual-functional hybrid Reconfigurable Intelligent Surface (RIS) for simultaneous sensing and reconfigurable reflections. We design a novel hybrid unit cell featuring dual elements, which share the same phase center, to support both intended functionalities, with the antenna being miniaturized via a high dielectric material approach. The hybrid unit cell has a size of one eighth of the wavelength forming the foundation of an innovative metasurface that incorporates a sub-wavelength reflecting array of split-ring unit cells integrated with a load-tuning matrix. In particular, two interleaved sensing arrays of half-wavelength spacing, orthogonal polarization, and quarter-wavelength offset are embedded within the proposed dual-functional RIS, each tasked to sense the channel parameters towards one of the end communication nodes wishing to profit from the surface’s reconfigurable reflections. Our full-wave simulations, indicatively centered around the frequency of 5.5 GHz, showcase the promising performance of both designed hybrid unit cells and reflective split-ring ones. © 2025, CC BY-NC-ND.

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Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN

Performance Evaluation of Multi-Contrast Dixon MRI and CT fo...

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IEEE International Conference on Big Data

作者： Andrew Heller Andrew Liu Lin-Ching Chang Gregg Cohen Elizabeth C. Jones Li-Yueh Hsu Department of Electrical Engineering and Computer Science Catholic University of America Washington DC USA Radiology and Imaging Sciences Clinical Center National Institutes of Health Bethesda MD USA Department of Informatics New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

We evaluate the performance of a deep learning framework for segmenting abdominal fat and muscle using multi-contrast Dixon magnetic resonance (MR) and computed tomography (CT) images. We aim to compare MR image segmentation by testing Dixon fat-only, water-only, and combination of both types of images and comparing the results with CT images. Nineteen subjects underwent abdominal CT and Dixon MR imaging on the same day. For each participant, three pairs of matched axial images from both CT and MR were selected at the intervertebral levels of L2-L3, L3-L4, and L4-L5 for analysis. References labels were generated through semi-automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and muscle areas. They were then used to train and evaluate a U-Net based Convolutional Neural Network (CNN) framework with a 3-fold cross-validation to compare the segmentation performance across CT, Dixon fat-only and water-only MR images. Combining the fat-only and water-only MR image inputs produced superior results in all labels. Our study demonstrates that CNN-based segmentation performance for abdominal fat and muscle improves with the inclusion of additional input channels, such as combining Dixon fat-only and water-only MR images. While CT results represent the gold standard in abdominal image segmentation, increasing the number of input image channels used in MR segmentation can approach, and even match, the results of CT.

关键词： Performance evaluation Deep learning Image segmentation Computed tomography Magnetic resonance imaging Magnetic resonance Muscles Fats Convolutional neural networks Testing

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Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots

Nonlinear Modes as a Tool for Comparing the Mathematical Str...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Pietro Pustina Davide Calzolari Alin Albu-Schäffer Alessandro De Luca Cosimo Della Santina Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Cognitive Robotics Delft University of Technology Delft Netherlands Institute of Robotics and Mechatronics German Aerospace Center Oberpfaffenhofen Germany Department of Informatics Technical University of Munich Garching Germany

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characteristics of the theoretically infinite-dimensional dynamics. However, there is no quantitative way to measure if the ROM of a soft robot has succeeded in this task. In other fields, like structural dynamics or flexible link robotics, linear normal modes are routinely used to this end. Yet, this theory is not applicable to soft robots due to their nonlinearities. In this work, we propose to use the recent nonlinear extension in modal theory -called eigenmanifolds- as a means to evaluate control-oriented models for soft robots and compare them. To achieve this, we propose three similarity metrics relying on the projection of the nonlinear modes of the system into a task space of interest. We use this approach to compare quantitatively, for the first time, ROMs of increasing order generated under the piecewise constant curvature (PCC) hypothesis with a high-dimensional finite element (FE)-like model of a soft arm. Results show that by increasing the order of the discretization, the eigenmanifolds of the PCC model converge to those of the FE model.

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Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

arXiv

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arXiv 2025年

作者： Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin Department of Informatics and Communication Engineering Xiamen University Fujian361102 China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun130012 China Navigation Institute Jimei University Fujian Xiamen361021 China Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

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Optik 2023年 272卷

作者： Upreti, Kamal Syed, Mohammad Haider Khan, Mohiuddin Ali Fatima, Huda Alam, Mohammad Shabbir Sharma, A.K. Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management Delhi India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer & Network Engineering Jazan University Jazan Saudi Arabia Department of Information Technology & Security Jazan University Jazan Saudi Arabia Department of Computer science College of Computer Science and Information Technology Jazan University Jizan Saudi Arabia Department of Creative Technologies & Product Design National Taipei University of Business Taipei City Taiwan

Financial technology" or "FinTech" refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %. © 2022

关键词： Semantics

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