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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Jayanthi, M. Rao, K. Ram Mohan Sukanya, Vuppala Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda India Dept of Information Technology Vasavi College of Engineering Hyderabad India Dept of CSE Hyderabad India

Big data processing frameworks demands for scalable and efficient cluster management. Apache Spark has emerged as prominent big data processing framework providing high-speed data processing and analytics capabilities for multiple applications. This paper explores the integration of Kubernetes as a cluster manager for Apache Spark applications leveraging its containerization capabilities to improve resource utilization and simplify deployment. In this paper the challenges of deploying spark applications on traditional cluster managers and showcase the advantages of adopting Kubernetes are analysed. The experimental evaluation demonstrates the benefits of Kubernetes as a cluster manager for Apache Spark framework. To execute the multiple Apache Spark applications on Kubernetes a homogenous cluster on Google Cloud is created by History bucket and service account. Finally multiple applications are executed on Google Kubernetes Engine. Output can be shown as the number of executor pods created with the performance metrics can be viewed. In conclusion, this paper compares the performance metrics such as job execution time and resource utilization with the different cluster. © 2025 The Authors. Published by Elsevier B.V.

关键词： Information management

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Evaluating the Necessity of Cloud Computing in Modern Industrial Applications: A Comprehensive Analysis 2

Evaluating the Necessity of Cloud Computing in Modern Indust...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Singh, Abhiraj Soni, Manish Kumar Pratap, Ajay Dept.of Computer Sci. & Engineering Bansal Inst of Eng. & Technology Lucknow India Amity Institute of Info Technology Amity University Uttar Pradesh Lucknow India

ISBN: (纸本)9798331530389

This study thoroughly assesses the need for cloud computing across industries, examining the ways in which it improves scalability, flexibility, and affordability. Through the use of cloud computing, industries may update their IT infrastructure and move from outdated, capital-intensive models to more adaptable and effective cloud-based solutions. The report examines how cloud services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are integrated into key industries like manufacturing, healthcare, finance, and retail. Through the implementation of cutting-edge technologies like Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI), cloud computing stimulates innovation, optimizes operations, and improves decision-making via data-driven insights. The growing significance of hybrid and multi-cloud strategies-which offer increased flexibility and enable industries to optimize resources and enhance performance-is also covered in the study. The study discusses the difficulties that sectors encounter in implementing cloud computing, along with its advantages. These difficulties include worries about data security, regulatory compliance, and cloud management. Strong security frameworks and efficient governance models are highlighted as essential elements of successful cloud integration. This study presents a framework for adopting cloud-based solutions for industries and emphasizes the significance of cloud computing as a fundamental technology for contemporary industrial applications. © 2025 IEEE.

关键词： Cloud platforms

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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Evaluation of Multiple Apache Spark Applications using Kubernetes as a Cluster manager on Google Cloud

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Procedia computer Science 2025年 252卷 576-582页

作者： M. Jayanthi K. Ram Mohan Rao Vuppala Sukanya Assistant Professor Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda Professor Dept of Information Technology Vasavi College of Engineering Hyderabad Assistant Professor Dept of CSE OU Hyderabad

关键词： Homogenous Apache Spark Kubernetes Google Kubernetes Engine Containerization

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Evaluating the Necessity of Cloud Computing in Modern Industrial Applications: A Comprehensive Analysis

Evaluating the Necessity of Cloud Computing in Modern Indust...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Abhiraj Singh Manish Kumar Soni Ajay Pratap Dept.of Computer Sci. & Engineering Bansal Inst of Eng. & Technology Lucknow India Amity Institute of Info Technology Amity University Lucknow Uttar Pradesh India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This study thoroughly assesses the need for cloud computing across industries, examining the ways in which it improves scalability, flexibility, and affordability. Through the use of cloud computing, industries may update their IT infrastructure and move from outdated, capital-intensive models to more adaptable and effective cloud-based solutions. The report examines how cloud services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are integrated into key industries like manufacturing, healthcare, finance, and retail. Through the implementation of cutting-edge technologies like Internet of Things (IoT), machine learning (ML), and artificial intelligence (Al), cloud computing stimulates innovation, optimizes operations, and improves decision-making via data-driven insights. The growing significance of hybrid and multi-cloud strategies—which offer increased flexibility and enable industries to optimize resources and enhance performance—is also covered in the study. The study discusses the difficulties that sectors encounter in implementing cloud computing, along with its advantages. These difficulties include worries about data security, regulatory compliance, and cloud management. Strong security frameworks and efficient governance models are highlighted as essential elements of successful cloud integration. This study presents a framework for adopting cloud-based solutions for industries and emphasizes the significance of cloud computing as a fundamental technology for contemporary industrial applications.

关键词： Industries Cloud computing Technological innovation Adaptation models Computational modeling Software as a service Finance Medical services Manufacturing Internet of Things

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

arXiv

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arXiv 2025年

作者： Hao, Luoying Hu, Yan Yue, Yang Wu, Li Fu, Huazhu Duan, Jinming Liu, Jiang Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Birmingham United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester United Kingdom MGI Tech Co. Ltd. China Singapore

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition → step recognition → action and instrument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://***/Aurorahao/HCT. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Exploring the Potential of DeepSeek-R1 Model in Transforming Healthcare Solutions: An Overview

Exploring the Potential of DeepSeek-R1 Model in Transforming...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Raheel Raza Shahbaz Ahmed Fahad Ahmed Khokhar Asaf Varol Peter Faber Business School Australian Catholic University North Sydney Australia Department of Computer Science Universitat Koblenz Koblenz Germany Department of Informatics & Mathematics University of Florence Firenze FI Italy Department of Engineering Management & Technology College of Eng. & Comp. Science Uni. of Tennessee at Chattanooga Chattanooga TN US Dept. of Computer Engineering College of Eng. & Nat. Sciences Maltepe University Istanbul Turkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Over the past few decades, artificial intelligence (AI) has become more integrated into healthcare, with Large Language Models (LLMs) being a key component in improving healthcare decision-making. These LLMs' capacity to produce and interpret human-like texts has the potential to revolutionize healthcare procedures, where efficient data processing and communication are crucial. DeepSeek-Rl, created by DeepSeek AI, has drawn notice globally for its reasoning-based design that uses reinforcement learning (RL) to enhance problem-solving skills. Keeping this in view, the paper emphasizes the significance of the DeepSeek- Rl model and its application in the healthcare industry. Highlighting some hypothetical potential scenarios, we believe that DeepSeek models can playa crucial role in transforming existing medical approaches and revolutionizing the future of AI in health care.

关键词： Industries Biological system modeling Large language models Digital forensics Decision making Medical services Reinforcement learning Data processing Security Problem-solving

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Real-time detection of moving objects in video sequences

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Journal of Systems engineering and Electronics 2005年第3期16卷 687-691页

作者：宋红石峰 Dept .of Computer Science and Engineering Beijing Inst .of Technology

An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.

关键词： object detection video surveillance region-based frame difference adjusted background subtraction.

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Virtual laboratories in education - A cheap way for schools to obtain laboratories for all courses, by using the computer Laboratory

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technology Enhanced Learning Workshop held at the18th IFIP World computer Congress

作者： Alexiou, A Bouras, C Giannaka, E Research Academic Computer Technology Institute Greece and Computer Engineering and Informatics Dept. Univ. of Patras Greece

ISBN: (纸本)0387240462

Practical experience is an important component of the educational process. However, the time and economical resources often required for the setting up and construction of scientific laboratories is outside the scope of many institutions. A solution to this problem could be found in the adaptation of the Virtual Reality technology, which could allow the creation of Virtual Laboratories, which will simulate the processes and actions that could take place in real laboratories. In particular, this paper, based on the expertise and motivation gained by the VirRAD-IST project, proposes and describes such an educational virtual laboratory, which aims to meet the requirements of a teal laboratory and furthermore to support communication and collaboration services. We propose a web-based system, which allows users to perform experiments on educational fields, such as Physics or Chemistry in 3D multi-user worlds where users are represented by avatars and they are offered a wide range of communication and collaboration services in. order to simulate efficiently a real learning experimental process.

关键词： distance learning virtual reality multi-user virtual environments Virtual Laboratories Educational Virtual Environments

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