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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Kaswan, Suresh Goyal, Parul Khirasaria, Vivek Yugal, Lisha Amita Chandigarh University Department of Computer Science & Engineering Punjab Mohali India M. M. Engineering College Maharishi Markandeshwar Deemed to Be University Computer Science & Engineering Department Haryana Mullana India Sharda University Faculty of Engineering and Technology Uzbekistan Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350379945

This research study explores the impact of cloud computing on engineering education in India, focusing onhow it enhances educational outcomes. This study examines the potentialof strategic cloud implementation in higher education to accelerate development and competitiveness. The study analyzes the current state of technical education in India and identifies areas where cloud integration can drive positive changes. Key research questions are formulated to guide the exploration of creating intelligent cloud applications for effective technical education. © 2024 IEEE.

关键词： Cloud platforms

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CRISP: Cloud resilient infrastructure for self-healing platforms in dynamic adaptation

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International Journal of Information Technology (Singapore) 2024年 1-14页

作者： Rawas, Soha Samala, Agariadne Dwinggo Fortuna, Aprilla Faculty of Science Department of Mathematics and Computer science Beirut Arab University Soha Beirut Lebanon Faculty of Engineering Universitas Negeri Padang West Sumatera Indonesia

As cloud computing evolves, traditional resilience strategies must adapt to new challenges. This paper introduces CRISP, Cloud Resilient Infrastructure for Self-Healing Platforms, a pioneering framework designed to enhance cloud resilience through autonomous fault management. By leveraging advanced machine learning, predictive analytics, and dynamic adaptation, CRISP Clouds provides a robust solution for real-time fault detection, diagnosis, and automated recovery. Our findings highlight CRISP Clouds’ ability to significantly improve system agility, minimize downtime, and optimize resource usage. This work presents a critical advancement in resilience engineering, setting the stage for future innovations in cloud technology. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Autonomous systems Cloud Computing Dynamic adaptation Fault Management Machine learning Predictive analytics Resilience enhancement Self-healing

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers 15

Biological Influence of Low-Frequency Electromagnetic Field ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Cichorowski, Robert Kocikowska, Olga Bajtos, Marek Radil, Roman Janousek, Ladislav De Carrillo, Daria Gendosz Student, Sebastian Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Department of Engineering and Systems Biology Gliwice Poland Silesian University of Medicine Faculty of Medical Sciences Department of Physiology Katowice Poland Faculty of Electrical Engineering and Informational Technology Department of Electromagnetic and Biomedical Engineering Žilina Slovakia Silesian University of Medicine Faculty of Medical Sciences in Zabrze Department of Histology Zabrze Poland Silesian University of Technology Biotechnology Center Gliwice Poland

ISBN: (纸本)9798350372359

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week. © 2024 IEEE.

关键词： Electromagnetic fields

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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AI-Enhanced Virtual Twin Modelling for Strengthening IoT Software Security Protocols 1

AI-Enhanced Virtual Twin Modelling for Strengthening IoT Sof...

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1st International Conference on Software, systems and Information Technology, SSITCON 2024

作者： Kumara, Bharath Barmola, Pareshwar Prasad Arora, Renuka Parikh, Swapnil M Maranan, Ramya Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Parul Institute of Engineering and Technology Parul University Faculty of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices in real-time, the suggested method makes use of virtual twin technology that is combined with machine learning techniques. Automated response generation, continuous threat assessment, and anomaly detection are made possible by creating a digital clone of the actual IoT network using the virtual twin paradigm. By combining deep learning models like CNNs and LSTMs, it becomes easier to forecast possible security risks in network traffic data by seeing intricate spatial and temporal patterns. When tested in a virtual Internet of Things (IoT) setting, the suggested framework outperforms conventional rule-based approaches by a wide margin, reducing reaction time by 35% while achieving an accuracy of 9 6. 2 %in threat identification. © 2024 IEEE.

关键词： Risk assessment

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Application of Machine Learning for Detection and Prediction of Malaria: A Review 3

Application of Machine Learning for Detection and Prediction...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Apetorgbor, Mawuli Wankhede, Vidhi Dakhare, Kajal Verma, Prateek Raut, Yudhishthir Tripathi, Chanchla Department of Science and Technology Maharashtra Wardha India Faculty of Engineering and Technology Maharashtra Wardha India Pimpri Chichwad University Department of Computer Science and Engineering Maval Sate Pune India Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350381931

Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all considerably diminish the consequences of malaria. Machine learning is a promising method to tackle this problem with its capability to unveil and understand complex patterns from broad and multifaceted dataset. Accuracy of drug dosage and disease management in healthcare industry has proven phenomenal accuracy with use of machine learning algorithm. The current analysis explores the application of machine learning for prediction of malaria cases. This paper showcase the inclusion of numerous data source, comprising environmental factors, medical history and epidemiologic information to demonstrate how machine learning influences this to abundance of data to enhance prediction accuracy. This study also enhances the major advancements in prior malaria detection that have been made feasible and practicable by machine learning driven symptomatic technologies such prompt analytical test and image analysis of blood taints. Concerns like model comprehensibility, data secrecy, and equitable distribution of healthcare assets are also concealed in this review study. © 2024 IEEE.

关键词： Forecasting

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Energy Management Strategy with a Hybrid Renewable Energy System for a Standalone House 16

Energy Management Strategy with a Hybrid Renewable Energy Sy...

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Mustata, Laurentiu-Alex Sorea, Gheorghe-Dan Helerea, Elena Transilvania University Electrical Engineering and Computer Science Faculty Electrical Engineering and Applied Physics Department Brasov Romania

ISBN: (纸本)9798350388107

In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system combining photovoltaic panels, a battery, and a Diesel generator is considered. A software application simulates the power flow in a standalone house with common AC loads, analysing summer and winter scenarios over 24 hours. The strategy employed provides an optimal energy management by using the Diesel generator only during peak demand when PV is available and to cover loads and maintain battery life when PV is not in operation. The simulation results provide valuable insights for designing such hybrid energy systems and for enhancing system responsiveness to dynamic energy demands. © 2024 IEEE.

关键词： Hybrid power

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Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection 6

Arabic Handwritten Document OCR Solution with Binarization a...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Waly, Alhossien Tarek, Bassant Feteha, Ali Yehia, Rewan Amr, Gasser Fares, Ahmed Egypt-Japan University of Science and Technology E-JUST Faculty of Engineering Computer Science and Engineering Departement Alexandria21934 Egypt Benha University Faculty of Engineering Electrical Engineering Department Cairo11629 Egypt

ISBN: (纸本)9798350378511

The problem of converting images of text into plain text is a widely researched topic in both academia and industry. Arabic handwritten Text Recognation (AHTR) poses additional challenges due to diverse handwriting styles and limited labeled data. In this paper we present a complete OCR pipeline that starts with line segmentation using Differentiable Binarization and Adaptive Scale Fusion techniques to ensure accurate detection of text lines. Following segmentation, a CNN-BiLSTM-CTC architecture is applied to recognize characters. Our system, trained on the Arabic Multi-Fonts Dataset (AMFDS), achieves a Character Recognition Rate (CRR) of 99.20% and a Word Recognition Rate (WRR) of 93.75% on single-word samples containing 7 to 10 characters, along with a CRR of 83.76% for sentences. These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR systems. © 2024 IEEE.

关键词： Arabic Optical Character Recognition convolutional neural networks line segmentation recurrent neural networks Scene text detection

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Helically symmetric equilibria for some ideal and resistive MHD plasmas with incompressible flows

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Applied Mathematics(A Journal of Chinese Universities) 2023年第2期38卷 192-209页

作者： S.M.Moawad O.H.El-Kalaawy H.M.Shaker Mathematics and Computer Science Department Faculty of ScienceBeni-Suef UniversityBeni-SuefEgypt Future High Institute of Engineering in Fyoum Ministry of High EducationFyoumEgypt

In this paper, the problem of finding exact solutions to the magnetohydrodynamic(MHD) equations in the presence of incompressible mass flows with helical symmetry is considered. For ideal flows, a similarity reduction method is used to obtain exact solutions for several MHD flows with nonlinear variable Mach number. For resistive flows parallel to a magnetic field, the governing equilibrium equation is derived. The MHD equilibrium state of a helically symmetric incompressible flow is governed by a second-order elliptic partial differential equation(PDE) for the helical magnetic flux function. Exact solutions for the latter equation are obtained. Also, the equilibrium equations of a gravitating plasma with incompressible flow are derived.

关键词： magnetohydrodynamics helical symmetry resistivity incompressible ows exact equilibria

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A Comprehensive Study on Plant Classification Using Machine Learning Models 1

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Karnan, A. Ragupathy, R. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Annamalainagar608002 India

ISBN: (数字)9789819994892

ISBN: (纸本)9789819994885

Plant classification is a critical task with many practical applications, including agriculture, environmental management, and biodiversity conservation. Traditional methods of plant classification can be time-consuming and require considerable expertise. In recent years, machine learning models have shown great promise for automating the plant classification process. This paper provides a comprehensive survey of the existing literature on plant classification using machine learning models. This paper discusses the most commonly used machine learning algorithms, feature extraction and selection techniques, publicly available datasets, and evaluation metrics, also examines the potential applications of plant classification using machine learning models, and identifies the major challenges and future directions for research in this field. Overall, this review highlights the importance of machine learning methods for plant classification and their potential to revolutionize this field in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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