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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Meenakshi Kandpal Bhisham Sharma rabindra K. Barik Subrata Chowdhury Sudhansu Shekhar Patra Imed Ben Dhaou School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Applications KIIT Deemed to be University Bhubaneswar India MCA Department Sri Venkateshwara College of Engineering and Technology (A) Chittoor AP India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because to integration. The advent of smart watch technology and deep learning techniques has engendered a potential increase in performance and accuracy of such tasks. Our paper explores the application of recurrent neural networks particularly LSTM for action recognition task using smart watch sensor data, i.e., gyroscope and accelerometer data. We have taken into consideration some general activities like standing, walking, sitting, etc. and implemented a LSTM network for the action recognition job using the time sequences sensor data. This model can be useful for other health-based applications which can monitor the health conditions of a user by keeping record of his activities.

关键词： Legged locomotion Deep learning Technological innovation Recurrent neural networks Smart cities Medical services Watches

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Equitable and Fair Performance Evaluation of Whale Optimization Algorithm

arXiv

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arXiv 2023年

作者： Hassan, Bryar A. rashid, Tarik A. Ahmed, Aram Qader, Shko M. Majidpour, Jaffer Abdalla, Mohmad Hussein Tayfor, Noor Hamarashid, Hozan K. Sidqi, Haval Noori, Kaniaw A. ramadhan, Awf Abdulrahmam Department of Computer Science College of Science Charmo University Chamchamal KRI Sulaimani46023 Iraq Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Computer Science Institute Sulaimani Polytechnic University Information Technology Department Sulaimani Iraq The Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq Department of Computer Science University of Raparin Sulaimani Iraq Department of Information Technology Kurdistan Technical Institute Sulaimani Iraq Information Technology Department Computer Science Institute Sulaimani Polytechnic University Sulaimani46001 Iraq Technical College of informatics Sulaimani Polytechnic University Sulaimani46001 Iraq Department of Database Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Public Health Department College of Health and Medical Techniques - Shekhan Duhok Polytechnic University Duhok Iraq

It is essential that all algorithms are exhaustively, somewhat, and intelligently evaluated. Nonetheless, evaluating the effectiveness of optimization algorithms equitably and fairly is not an easy process for various reasons. Choosing and initializing essential parameters, such as the size issues of the search area for each method and the number of iterations required to reduce the issues, might be particularly challenging. As a result, this chapter aims to contrast the Whale Optimization Algorithm (WOA) with the most recent algorithms on a selected set of benchmark problems with varying benchmark function hardness scores and initial control parameters comparable problem dimensions and search space. When solving a wide range of numerical optimization problems with varying difficulty scores, dimensions, and search areas, the experimental findings suggest that WOA may be statistically superior or inferior to the preceding algorithms referencing convergence speed, running time, and memory utilization. © 2023, CC BY.

关键词： Benchmarking

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Numerical Approach of Laser-Induced Spherical Microcavitation Bubbles: Effect of Enthalpy

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Bulletin of the Russian Academy of Sciences: Physics 2024年第Suppl 3期88卷 S287-S292页

作者： Ali F. Abu-Bakr Abu-Nab, Ahmed K. AbuShaeer, Zain Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Dolgoprudny Moscow141700 Russia Center for Photonics and 2D Materials Moscow Institute of Physics and Technology Dolgoprudny141700 Russia Department of Basic Science Higher Institute of Engineering and Technology Kafrelsheikh33511 Egypt

Abstract: Over the past decades, interest has increased in studying different techniques to detect the importance of microbubbles in many industrial and medical applications such as mechanisms of hydraulic machinery cavitation erosion, histotripsy, and lithotripsy. The behavior from early irregular oscillations to late regular oscillations of spherical microcavitation bubbles produced by lasers in water was studied by means of plasma photography. Repetition of such delayed oscillations results in quantities of gas/vapour inside the spherical bubbles. Using the modified Gilmore model of cavitation microbubble dynamics, calculations were made for the laser pulse energy, microbubble radius behavior, and microbubble growth rate. Additionally, the pressure evolution inside the bubble, and pressures distribution surrounding of microbubbles in liquid are investigated under the effect of different values of initial cavitation of microbubble radius. The effect of enthalpy is considered into account. The proposed model is solved numerically using numerical techniques as a finite difference method. In addition, the radius of the microbubbles is calculated as a function of time under the influence of the initial radius of these laser-induced microcavitations. Moreover, some physical properties and numerical calculations of the presented mathematical model for cavitation induced by lasers bubbles are deduced, analyzed and investigated. © Pleiades Publishing, Ltd. 2024.

关键词： Cavitation

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Smart watering system based on framework of low-bandwidth distributed applications (LBDA) in cloud computing 12th

Smart watering system based on framework of low-bandwidth di...

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12th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2020

作者： Sirimorok, Nurdiansyah As, Mansur Yoshida, Kaori Köppen, Mario Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Creative Informatics Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9783030577957

The estimation of soil moisture content is required for agriculture, mainly to build the irrigation scheduling model. In this study, we present a smart watering system to deal with various factors derived from the stochastic information in the agricultural operational, i.e., air temperature, air humidity, soil moisture, soil temperature, and light intensity. The methodology consists of exploiting the Internet of Things (IoT) using Low-Bandwidth Distributed Applications (LBDA) in cloud computing to integrate the real data sets collected by the several sensor technologies. We conducted experiments for the watering system used two types of soil and different plant. Here, the Long Short Term Memory Networks (LSTMs) approach in deep learning techniques used to build smart decisions concerning watering requirements and deal with heterogeneous information coming from agricultural environments. Results show that our models can effectively improve the prediction accuracy for the watering system over various soil and plants. © Springer Nature Switzerland AG 2021.

关键词： Cloud computing

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Age-of-Information and Energy Optimization in Digital Twin Edge Networks

Age-of-Information and Energy Optimization in Digital Twin E...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yongna Guo Yaru Fu Yan Zhang Tony Q. S. Quek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Science and Technology Hong Kong Metropolitan University China Department of Informatics University of Oslo Oslo Norway Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.

关键词： Measurement Multi-access edge computing Costs Numerical analysis Power control Information age Digital twins Synchronization Resource management Optimization

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RSA Algorithm Using Performance Analysis of Steganography Techniques in Network Security

RSA Algorithm Using Performance Analysis of Steganography Te...

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International Conference on Communication, Computing and Electronics Systems, ICCCES 2020

作者： Sundaram, B. Barani Kannaiya raja, N. Sreenivas, Nune Mishra, Manish Kumar Pattanaik, Balachandra Karthika, P. ICT-CE Computer Science Department College of Informatics Bule Hora University Bule Hora Ethiopia College of Informatics Bule Hora University Bule hora Ethiopia School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Department of Computer Science University of Gondar Gondar Ethiopia Department of Electrical and Computer Engineering Blue Hora University Blue Hora Ethiopia Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Viruthunagar India

ISBN: (纸本)9789813349087

The stable network has now become a prerequisite for every relationship. The security hazards are expanding and rendering wired/remote organizations and Internet providers fast, shaky and problematic. Today, safety efforts are all the more important in meeting the cutting edge demands of current emerging businesses. The need is also discussed in areas such as guards, where the key points of concern are safe and checked admission of properties identified with data security. In this paper, the author described the important measures and limits for the establishment of a secure organization with regard to enormous industry / authoritative needs. In providing remote organization access to different assets and interfacing various gadgets remotely, Wi-Fi networks are extremely basic. To deal with Wi-Fi hazards and organizational hacking attempts, different prerequisites are required. This article discusses important protection efforts identified with different organizational conditions, so that an association may be set up with a fully assured organizational environment. Besides, a contextual investigation was examined to delineate the insignificant arrangement of measures necessary in any association to construct network security. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wi-Fi

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer Sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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Current Progress of Digital Twin Construction Using Medical Imaging

arXiv

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arXiv 2024年

作者： Zhao, Feng Wu, Yizhou Hu, Mingzhe Chang, Chih-Wei Liu, Ruirui Qiu, Richard Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Radiation Oncology University of Nebraska Medical Center OmahaNE United States

Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data—such as MRI, CT, PET, and ultrasound—with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Log-based Sparse Nonnegative Matrix Factorization for Data Representation

arXiv

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arXiv 2022年

作者： Peng, Chong Zhang, Yiqun Chen, Yongyong Kang, Zhao Chen, Chenglizhao Cheng, Qiang College of Computer Science and Technology Qingdao University China Department of Computer Science Harbin Institute of Technology China School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science University of Kentucky United States Institute of Biomedical Informatics University of Kentucky United States

Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose a new NMF method with log-norm imposed on the factor matrices to enhance the sparseness. Moreover, we propose a novel column-wisely sparse norm, named l2,log-(pseudo) norm to enhance the robustness of the proposed method. The l2,log-(pseudo) norm is invariant, continuous, and differentiable. For the l2,log regularized shrinkage problem, we derive a closed-form solution, which can be used for other general problems. Efficient multiplicative updating rules are developed for the optimization, which theoretically guarantees the convergence of the objective value sequence. Extensive experimental results confirm the effectiveness of the proposed method, as well as the enhanced sparseness and robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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