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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

共 195250 条记录，以下是421-430 订阅

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HealthGuard: A Blockchain-Based Framework for Secure EHR Management

HealthGuard: A Blockchain-Based Framework for Secure EHR Man...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Jaiswal, Vandit Singh, Roshan Srinivas, B.V. Kumar, Shreyash B.M.S. Institute of Technology & Management Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331538538

Traditional Electronic Health Record (EHR) systems experience centralized vulnerabilities which explain how healthcare data breaches reach about 45% of cases. Operability requirements reveal patient data to multiple entities which creates breakable data structures that compromise security. Blockchain technology gives patients an alternative system that operates with decentralized security while maintaining transparency. HealthGuard exists to provide medical patients complete control over their healthcare records with built-in privacy features and educational decision solutions. The authentication security comes from Ethereum combined with Metamask services while decentralized storage solutions create revolutionary methods to protect sensitive information. The system operates more efficiently because smart contracts simplify connections between patients and their healthcare providers including lab technicians. Healthcare facilities can achieve advanced data protection through blockchain storage which improves patient care interconnectivity with individualized care plans. The combination of smart contracts together with decentralized applications helps reduce healthcare administration costs which leads to efficiency growth. Through blockchain technology the healthcare system develops better security and changes toward patient-centered care which enables people to manage their health information actively. © 2025 IEEE.

关键词： Electronic health record

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LoRa WAN Architecture with Collusion Management and Collusion Avoidances Schemes in MAC Layer

LoRa WAN Architecture with Collusion Management and Collusio...

引用

international conference on computing and Machine Learning, CML 2024

作者： Saravanan, N. Kayalvizhi, R. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India

ISBN: (纸本)9789819778386

Long Range (LoRa) is the most widely used technology for significant attention in recent years due to their ability to enable long-range, low-power connectivity, making it an ideal communication system for Internet of Things applications. LoRa radio is considered as the physical layer, MAC layer which was specifically developed for low power IoT devices. LoRa is one of the technologies in LPWAN, which demonstrated remarkable potential in various domains used for IoT applications. This research paper presents a comprehensive study of LoRa technology, covering its LoRa principles (Physical Layer, MAC Layer, Network layer) Communication Mechanism, Application usage. Through an in-depth analysis of the key features and advantages of LoRa, this paper aims to provide a holistic understanding of the technology and its potential implications for the future of IoT in Collusion management and collision avoidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： information management

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Data Analytics on Blitz E-sports: Enhancing Gaming Experience through Personalization and Prediction 3

Data Analytics on Blitz E-sports: Enhancing Gaming Experienc...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Kusuma, T. Shruthi, G. Avanish Rao, B.S. Anand, Nikhil Abhishek, M. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (纸本)9798331515911

This paper delves into the comprehensive data analytics of Blitz E-sports, a dynamic gaming cafe, to enhance operational efficiency and customer satisfaction. The study focuses on three pivotal objectives: revenue optimization, game recommendations, and churn prediction. Through meticulous analysis of customer data, gaming trends, and operational metrics, this proposal equips Blitz E-sports with action able insights to enhance profitability, customer engagement, and long-term sustainability. The potential of data-driven decision-making in the competitive e-sports industry is demonstrated by leveraging advanced machine learning techniques and big data technologies. This paper aims to reveal significant improvements in personalized user experiences, optimized revenue strategies, and effective customer retention measures. © 2025 IEEE.

关键词： Sales

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Blockchain-Enabled Supply Chain Management with Role-Based Access Control and AES Encryption

Blockchain-Enabled Supply Chain Management with Role-Based A...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dharshan, J.Y. Amarnath, Arjun R. Reddy, Pothamsetti Sai Srujan Rajagopal, Shinu M. Prashanth, Mrudula Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9798331538538

Blockchain-Enabled Supply Chain Management with Role-Based Access Control and AES Encryption will use blockchain technology in order to enable safe, transparent, and decentralized supply chain management. Using smart contracts deployed on Polygon Amoy Testnet, the system ensures data integrity, confidentiality, and traceability while implementing role- based access control (RBAC) in order not to give unauthorized access to data. AES encryption is set forth for secretive treatment of information, thus affording a double-layered security measure for protection against on-chain as well as off-chain transactions. The solution is intended for deployment and testing in both localhost environments for rapid prototyping and the Polygon Amoy Testnet for scalable and distributed implementation. The Polygon Amoy Testnet provides an inexpensive development environment: developers can acquire test tokens (0.807 POL) via the Polygon Faucet for complete testing purposes. Deployment and interactions of smart contracts can be checked via the Polygon Testnet Explorer. The project combines blockchain's immutability with cryptographic security to address traditional supply chain inefficiencies, enhancing trust, scalability, and data accuracy. © 2025 IEEE.

关键词： Authentication

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Review of Self-driving Car Based on NEAT Algorithm 5th

Review of Self-driving Car Based on NEAT Algorithm

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5th international conference on Advances in information Communication technology and computing, AICTC 2024

作者： Hotkar, Om Nambiar, Prahas Dhumane, Amol Chiwhane, Shwetambari Sharma, Aditi Dharrao, Deepak Meshram, Vishal Symbiosis Institute of Technology Pune India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9789819761050

In current meta of Artificial Intelligence and Machine Learning, Self-Driving has gained a lot of popularity. Due to one-on-many usages of the technology, it becomes a massive challenge to deliver edge-driven technology. Self-Driving Car has various sections to it, like Lane Detection, Object Detection, Autonomous Driving Control, etc. Instead of opting for a traditional method for Machine Learning algorithms, we focused on Reinforcement Learning. In this paper, we discuss about various reinforcement learning challenges, solutions and results for it. This paper also talks about the Evolutionary Algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Intrusion Detection System for Multiclass Classification Across Multiple Datasets in Industrial IoT Using Machine Learning and Neural Networks Integrated with Edge computing 2

An Intrusion Detection System for Multiclass Classification ...

引用

2nd international conference on Data, information and computing Science, CDICS 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Baumuratova, Dilaram Ali, Mushtaq Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9781643685762

With the rapid expansion of industrial IoT (IIoT), maintaining robust cybersecurity is essential for the smooth operation of industrial processes. Industrial environments require adaptive solutions to effectively mitigate evolving cyber threats and protect sensitive operations. This research aims to improve the cybersecurity of industrial IoT environments. The research intends to design and implement an adaptive and real-time intrusion detection system with edge computing integration that improves the reliability of the operations in industrial IoT. We incorporated machine learning approaches to classify cyber threats using XGBoost and Deep Neural Networks (DNN). A comparative analysis of results obtained from two datasets shows that the XGBoost model was slightly more accurate than the DNN model, with an accuracy of 79% for dataset D1 and approximately 99.42% for data set D2. This analysis also clearly demonstrates the usefulness of these machine learning approaches and the need to select a model depending on the requirements for detecting particular attacks. Confusion matrix analysis shows that both models have several advantages in terms of recognizing different types of cyber threats. © 2025 The Authors.

关键词： Network intrusion

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HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in Parallel computing Programs 20th

HASLB: Huge Page Allocation Strategy Optimized for Load-Bal...

引用

20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin Zhang, Wenzhe School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628292

Equitable resource allocation is currently considered as a key enabler for achieving complete parallelism in parallel computing. Optimized memory management mechanisms are crucial for load balance in parallel tasks. In some cases, the performance of Linux’s huge page management mechanism deteriorates when handling parallel tasks due to the nonhomogeneous allocation of memory resources among parallel processes. This research introduces HASLB, a huge page allocation strategy optimized for parallel programs. HASLB evaluates the status of parallel processes based on the count of huge pages acquired by each process and the number of page faults it encountered. Following the results of the analysis, huge pages are allocated using a method conducive to load balance. The problems in the Linux transparent huge page mechanism are optimized through this strategy, which limits the greedy allocation mechanism in some situations. HASLB achieves a reasonable allocation of memory resources among parallel processes with low overhead, benefiting the load balance of parallel computing programs. In addition, contiguous memory would be protected because of limiting the greedy allocation of huge pages. The parallel benchmark experiments show a maximum performance improvement of 6.4% compared to Linux in the BFS benchmark. In the IS benchmark program, huge page consumption exhibits a 9% reduction. © IFIP international Federation for information Processing 2025.

关键词： Memory management

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Contrast Memory for Unsupervised Anomaly Detection

Contrast Memory for Unsupervised Anomaly Detection

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Jiahao Chen, Yiqiang Xing, Yunbing Gu, Yang Lan, Xiangyuan Institute of Computing Technology Chinese Academy of Sciences Beijing China Pengcheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China Guangzhou China

ISBN: (纸本)9798350368741

Real-world multivariate time series unsupervised anomaly detection is a challenging problem due to intricate temporal correlations. Recently, impressive progress have been made in tackling this issue through the design of large-scale models, facilitated by the growing model parameters. However, in resource-constrained scenarios such as ubiquitous computing and edge computing, the large-scale models suffer from issues like high parameter complexity and expensive training overheads. Existing methods can only strive for a direct tradeoff between model size and performance. To address this challenge, we propose DiMER (Diminutive Memory-Enhanced Reconstruction), a model with parameters of the order of 0.1M. In DiMER, we introduce a novel contrast memory mechanism to learn normal patterns with diminutive network and propose a temporal reconstruction loss nto add the autocorrelation information. In addition, we introduce a multi-space composite detection criterion, an anomaly score calculation that takes into account both memory space and data space. Extensive experiments on real-world datasets across various domains demonstrate that the proposed model achieves comparable or even superior performance to large-scale models while maintaining lightweight. © 2025 IEEE.

关键词： Anomaly Timeseries Unsupervised Learning

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Deep Learning - Based Prediction Models for Colon and Rectum Cancer Disease: Enhancing the Precision Diagnosis

Deep Learning - Based Prediction Models for Colon and Rectum...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Suman, Sunil Kumar Kumaraswamy, D. Sahu, Sunil Kumar Akella, Prasanna Lakshmi Santhoshkumar, S.P. Karthika, R. Supaul College of Engineering Department of Computer Science and Engineering Supaul India Ellenki College of Engineering and Technology Telangana Sangareddy India Hyderabad India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331538538

Colon and rectal cancer are among the leading causes of cancer-related illness and death worldwide. The detection and treatment of colon cancer are viewed as social and economic issues due to the high fatality rates. Every year, around half a million people worldwide develop cancer, including colon cancer. Although improving patient outcomes requires an accurate and timely diagnosis, traditional diagnostic methods usually suffer from subjectivity and variability problems. An extensive summary of colon cancer diagnosis is given here. Deep learning, a branch of artificial intelligence, has become a powerful tool for increasing cancer detection accuracy by utilizing large datasets and complex algorithms. This study combines genomic data, medical imaging, histopathological analysis, and clinical decision-making tools to better comprehend the creation and application of deep learning-based prediction models in the diagnosis of colon and rectal cancer. Through the use of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and hybrid models, deep learning can improve the accuracy of cancer detection, anticipate the course of a disease, and assist with customized treatment planning. By automating and enhancing diagnostic precision, these models have the potential to revolutionize healthcare workflows, reduce diagnostic errors, and ultimately increase patient survival rates. This research also discusses the challenges and possible advantages of using deep learning to clinical practice for colon and rectal cancer. Last but not least, the problems that exist today and possible directions for future research are examined. © 2025 IEEE.

关键词： Diseases

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Medical Diagnosis through Machine Learning: Detecting Heart Disease, Lung and Breast Cancer 6

Medical Diagnosis through Machine Learning: Detecting Heart ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Satya Naga Deep, G. BalaKrishna, G. Sathya Bama krishna, R. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Medical diagnostics for heart disease, lung cancer, and breast cancer using ML algorithms. The ML algorithms used in medical research have received considerable attention, motivated by their potential to revolutionize early analysis and enhance patient care. The relevancy of machine learning techniques to medical research in three significant conditions—breast cancer, lung cancer, and heart disease—is examined in this paper. This approach can improve the prompt diagnosis with better patient outcomes. In this work, it has adopted several machine learning techniques like naive Bayes, decision tree classification, k-nearest neighbor, support vector machines, linear discriminant analysis, and logistic regression. For heart disease, lung cancer, and breast cancer datasets, we evaluate all algorithms regarding their unique characteristics. We carefully assess the effectiveness of these algorithms utilizing a wide range of experimental conditions and comparison analysis. The review analyzes the processes of data preprocessing, feature selection, and model validation. Additionally, it offers a thorough analysis of current studies, providing information on earlier machine learning research on medical subjects. © 2025 IEEE.

关键词： Lung cancer

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