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

作者： Katariya, Jitendra Kumar Nivesh Vivekananda Global University Department of Computer Science Engineering India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The Internet of Things (IoT) has significantly impacted many facets of contemporary life, and several sophisticated IoT applications and services are already being developed. Recently, the concept of federated learning has been proposed in order to train a globally shared model on IoT devices utilising a large number of user-generated data samples while stopping data leak. Due to the widespread heterogeneities in device, statistics, and model in complex IoT contexts, traditional federated learning is not suitable for instant implementation. In this paper, we present a cloud-edge architecture for intelligent IoT applications that includes a customised federated learning framework. To address the heterogeneity worries in IoT conditions, we examine imaginative altered united learning calculations that could diminish the unfortunate results brought about by heterogeneities in various aspects. The advantages of edge registering empower smart IoT applications to meet the models for high handling pace and low inertness. At long last, we give a contextual investigation of human movement recognition in light of Web of Things to show the advantages of customisable combined learning for intelligent IoT applications. © 2023 IEEE.

关键词： Internet of things

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Deep Learning-Based Detection and Severity Analysis of Corona and Pneumonia Using CNN with Wolf Optimization Algorithm 9

Deep Learning-Based Detection and Severity Analysis of Coron...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Kutti, M. Beula Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Datascience And Business System Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Effective identification and severity analysis of two serious respiratory diseases-pneumonia and corona-are desperately needed, and this study attempts to fill that gap. Early diagnosis and treatment of many conditions depend critically on prompt intervention. Diagnoses are made slowly and subjectively by existing procedures, which frequently rely on human interpretation of medical imaging data. The existing methods for severity analysis do not have a cohesive structure. Our study presents an all-encompassing strategy to get around these restrictions. First, to improve the quality of the images, grayscale-based pre-processing is applied. Next, marker-based watershed segmentation is used to precisely locate anomalies. Feature extraction and categorization are done using a convolutional neural network, or CNN. Specifically, for severity analysis, we optimize the CNN parameters by including the Wolf Optimization Algorithm (WOA). Grayscale pre-processing guarantees the best possible picture quality;marker-based watershed segmentation improves the accuracy of lesion identification;CNN streamlines feature extraction;and WOA supports the study of disease severity. Python is used in the study's implementation, which takes advantage of its many machine learning frameworks and tools. The outcomes show how well the suggested methodology works for precisely identifying and evaluating the levels of corona and pneumonia. By integrating WOA with CNN, the shortcomings of current techniques are addressed and diagnostic accuracy and severity evaluation are improved. When compared to alternative approaches like CNN-SVM, Bayesian CNN, DCNN with BPNN, the suggested CNN with WOA (99.3%) shows an average accuracy gain of about 11.68%, demonstrating higher performance in disease diagnosis and severity analysis. This innovative method offers a methodical and effective framework for better patient treatment, which has the potential to advance the field of respiratory disease diagnostics. © 2

关键词： Extraction

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

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2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Ghazal, Taher M. Iqbal Janjua, Jamshaid Abbas, Sagheer Fatima, Areej Saleem, Muhammad Khan, Muhammad Adnan Alqarafi, Abdulrahman Khalifa University Centre for Cyber Physical Systems Computer Science Department Abu Dhabi United Arab Emirates Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia National College of Business Administration and Economics School of Computer Sciences Lahore Pakistan Al-Khawarizmi Institute of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan LGU Department of Computer Science Lahore Pakistan Minhaj University School of Computer Science Lahore Pakistan Skyline University College School of Computing Sharjah United Arab Emirates Riphah International University Riphah School of Computing and Innovation Lahore Pakistan Taibah University College of Computer Science and Engineering Medina42353 Saudi Arabia

ISBN: (纸本)9798350385144

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-Ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner. © 2024 IEEE.

关键词： Energy management systems

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High-Frequency Transformer Design for Smart Microgrids

High-Frequency Transformer Design for Smart Microgrids

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Nachappa, M.N. Kumar, Ajay Choudhary, Deeksha Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Those magnetic linkages might eventually replace conventional buses in micro-grids due to recent developments in switching devices and material with magnetic properties. By varying the windings' turn ratio in response to the source voltage level, multilinking transistors (MWTs) function as efficient magnetic connections and cut down on the overall number of converter stages within solar power plants. Benefits of this method include instantaneous power transmission across many ports, two-way power transfer capabilities, and isolation from the environment. Notwithstanding these advantages, cross-coupling issues and structural complexity make MWT design and characterization challenging. The processes of numerical design, prototypes, and measurement of a MWT over microgrid systems are described in detail in this study. In order to minimize computing time and achieve adequate accuracy in reaching target parameters, a resistant network approach is used in the course of design. Amorphous Polymers magnetic compounds are used for the prototype something, and the magnetic properties of the core are measured by a battery of scientific experiments. Its parameters are determined by using both open-circuit testing and series coupling tests. An extensive cross-check of simulation and experimental data at different loads in the medium region confirms the efficiency of the development and modeling methods. © 2023 IEEE.

关键词： High-Frequency Transformer Measurement Multilinking Transistors Prototypes Resistant Network Approach Smart Microgrids Soft Ferrite Components and Experimental Testing

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Correction to: Optimized auxiliary physics neural network based multi‑target detection for edge‑cloud in smart internet of things

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Peer-to-Peer Networking and Applications 2025年第3期18卷 1-2页

作者： Prabha, B. Yaramadhi, Mallikarjun Sharma, Abhishek Padmanaban, K. School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Department of Computer Science Engineering SVIIT ShriVaishnavVidyapeethVishwavidyalaya Indore India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram India

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Enhanced Diabetes Prediction Using Deep Autoencoder Framework and Electronic Health Records 2

Enhanced Diabetes Prediction Using Deep Autoencoder Framewor...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sivakumar, T.B. Malakar, Antara Lekshmi, S. Shailaja, G. Kalaivani, E. Babu, Dhananjaya K. Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering The Assam Royal Global University Guwahati India Basic Science and Humanities Department Mangalam College of Engineering Kottayam Ettumanoor India Department IT CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Bannari Amman Institute of Technology sathyamangalam India Rajampet India

ISBN: (纸本)9798350383867

Type II diabetes, which nowadays has become one of the most common chronic metabolic illnesses, needs cloud-based information systems with great predictive models to ensure timely intervention and proper management. Given the fact that EHRs and deep learning are quite advanced serves as a solution to the given problem. The study is about the application of the improved diabetes prediction model that makes use of prevailing deep learning architecture and EHR data. The framework constitutes a three-step process: It encapsulates a preprocessing step, a feature extraction stage, and a training procedure for a deep autoencoders that learns levels of representation. Demonstration of the system' s effectiveness becomes evident when it shows its improved ability of prediction compared to baseline models which in turn outperform in basic indicators such as accuracy, precision, recall, F1 score, and AUC-ROC. The result of the sensitivity check demonstrates that the model is able to perform well even under different hyperparameter arrangements. Latent feature analysis shows some unequivocal ways of hidings that reflect diabetes risk factors thus making it easy to understand. The clinical validation in a healthcare setting presents high accuracy, the specificity and the predictive values that are known in indicating those who are at the risk of diabetes onset. This research aims at deepening the understanding for the diagnosis of diabetes through the implementation of deep learning methods and generating real-world medical data. It attempts to provide better prognosis, lower healthcare expenditures and alleviate diabetes burden on patients and society. © 2024 IEEE.

关键词： Electronic health record

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Recognition of Data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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An experimentation of objective functions used for multilevel thresholding based image segmentation using particle swarm optimization

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1717-1732页

作者： Ahmed, Saifuddin Biswas, Anupam Khairuzzaman, Abdul Kayom Md Department of Computer Science and Engineering National Institute of Technology Silchar Cachar 788010 India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati 522237 India

For image segmentation, multilevel thresholding is treated as one of the widely used approach. However, this approach has a major issue that is it suffers of high computational complexity problem with the increase threshold levels. This paper presented a comparative performance analysis of some objective functions used for image segmentation using the concept of multilevel thresholding and a modified version of adaptive inertia weight Particle Swarm Optimization (PSO) technique. The PSO algorithm is applied to multilevel thresholding based image segmentation using either Otsu’s inter class variance, Kapur’s entropy or Masi entropy as an objective function. Each method is tested over various standard image dataset like Berkeley image database, USC-SIPI image dataset etc. The evaluated result of each method has been compared and the overall experimentation is performed in three different ways such as, PSO and Otsu’s method, PSO and Kapur’s entropy and PSO with Masi entropy. The performance and quality of the segmented images are measured using the following parameters such as, average Mean Structural Similarity Index (MSSIM), average Peak Signal to Noise Ratio (PSNR) values, average mean objective functions values and average CPU rum time values. The experimental analysis shows that the performance of Otsu’s inter-class variance function shows comparatively a better result than Kapur’s and Masi’s entropic method. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Kapur’s entropy Masi entropy Meta-heuristic Multilevel thresholding Non-parametric Otsu’s method Parametric Particle swarm optimization

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A Long Short-Term Memory Based Neural Network Approach for Numerical Solutions of Integral Equations

A Long Short-Term Memory Based Neural Network Approach for N...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Priyadharshini, A.R. Singh, Soumyendra Kumar, R. Prasanna Chaudhary, Dharminder Shirvastav, Pratik Yadav, Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai601103 India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai601103 India School of Computer Science Engineering and Technology Bennet University Greater Noida India

ISBN: (纸本)9798350310061

In this paper, neural networks with long short-term memories have been used to solve integral equations. The integral equations have been discretized using a Monte-Carlo method. While neural networks have been widely used for various applications, their application to integral equations, especially in conjunction with LSTMs, is relatively unexplored which is filled by this paper. By discretizing the stochastic integral equations into a system of linear algebraic equations, the proposed method provides a practical solution that can be easily implemented and computationally efficient. This addresses a practical gap in solving integral equations, as traditional methods can often be complex and time-consuming. The use of Monte-Carlo methods allows for a probabilistic approach, which can handle the inherent randomness in some integral equations. A numerical example of the Fredholm Integral equation has been provided to show how adaptable our suggested systems are. Adam optimization has been used to create a neural network model based on long short-term memory in order to find solutions. Loss function graphs, actual and predicted function value graphs, and other visualizations have also been described to provide a better grasp of the behavior of the solutions based on our suggested methodology. © 2023 IEEE.

关键词： Monte Carlo methods

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Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

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computers, Materials & Continua 2022年第5期71卷 2497-2513页

作者： Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed Abdullah Al-Hagery Ali Mohammed Al-Sharafi Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication *** this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed *** researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English ***,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any *** has been implemented using PHP with VS code *** robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and *** SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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