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共 6586 条记录，以下是121-130 订阅

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Improving data Integrity and Security in Cloud Environments: A Blockchain-based Method 15

Improving Data Integrity and Security in Cloud Environments:...

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： John, Mahima Raipurkar, Abhijeet R. Department of Computer Science & Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Despite the years' rapid advancements, valid computing and data security continue to be ongoing challenges. dynamic cloud infrastructure, surpassing manual or reactive approaches. Unfortunately, many obstacles, including overly complex computations and issues with scalability, hinder the efficiency of many suggested fixes. This article has discussed the addition of block chain approaches as a possible solution to these problems. A promising way to improve cloud computing's processing speed and storage security is through the decentralized distributed computing structure known as block chain. The study presents the use of remote agent technology to deploy a distributed virtual machine (VMA) model over the cloud. The VM agent mechanism effectively handles tasks pertaining to monitoring, verification, and dependable data storage, providing a crucial basis for creating the reliability and safeguards for block chain technology. In order to reduce the computational burden and quantum computing during the user verification phase, this work presents the lattice signature algorithm, which contains a cuckoo filter. It is suggested that the decentralized ledger network take the role of conventional centralized audits in order to improve security and transparency. © Grenze Scientific Society, 2024.

关键词： Cloud security

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A Multi-Class Classification Framework with SMOTE Based data Augmentation Technique for Alzheimer's Disease Progression

A Multi-Class Classification Framework with SMOTE Based Data...

引用

2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Saketh, C.H. Ramasamy, Gayatri Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350371567

The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders. © 2024 IEEE.

关键词： Classification (of information)

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Application of Machine Learning Algorithms in Predicting the Heart Disease in Patients 3

Application of Machine Learning Algorithms in Predicting the...

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3rd international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2023

作者： Razia Sulthana, A. Jaithunbi, A.K. Sunraja, P. University of Greenwich Department of Computing and Mathematical Sciences Old Royal Naval College London United Kingdom RMD Engineering College Department of Computer Science and Engineering Kavarapettai India SRM Institute of Science and Technology Department of Computer Science Kattankulathur India

ISBN: (纸本)9781665494007

Healthcare services save the life of human beings by making timely effective decisions. The use of data mining tools is crucial for decision making, forecasting, and disease prediction. In this study, data mining algorithms are applied to predict heart disease. The dataset contains 14 attributes such as age, gender, blood pressure, blood fat, etc. These parameters are analyzed to predict the probability of patients prone to heart disease in future. Initially, the relationship between the parameters is analyzed. Following which Naïve Bayes, decision trees and Naïve Bayes with k-means clustering are applied over it for classification and prediction. These algorithms were employed to train the dataset and create a binary classification. The proposed system shows a better prediction of heart disease. The performance measures of the system are measured, and the obtained results illustrate the system can forecast the probability of developing the heart diseases. © 2023 IEEE.

关键词： Forecasting

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

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2024 international conference on advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Ghosh, Tanumay Singal, Omisha Lodh, Namit Suchitra, S. School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350364828

Using a cutting-edge neural network framework, 'PulseSync BP' estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare. © 2024 IEEE.

关键词： Blood pressure

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Parkinson's Disease Diagnosis using Neutral Matrix and Machine Learning

Parkinson's Disease Diagnosis using Neutral Matrix and Machi...

引用

2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Grover, Priyanshu Kadyan, Suryaveer Singh Chakravarthy, Deeban Srm Institute Of Science And Technology Dept Of Computing Technologies Chennai India

ISBN: (纸本)9798350371567

This research explores the potential of the Neutral Matrix (NM) to diagnose Parkinson's disease (PD). It builds upon current diagnostic methods by incorporating imaging, genetic markers, and advanced clinical data analysis. The findings highlight the effectiveness of NM in achieving accurate and consistent disease identification, potentially improving patient satisfaction and enabling early intervention. This study emphasizes the significance of NM as a contributor to the ongoing development of PD diagnostic strategies. By enhancing the accuracy and reliability of diagnosis, NM offers valuable data-driven insights for timely identification of this debilitating condition. Ultimately, this research paves the way for improved PD diagnosis and potentially improved quality of life for affected individuals, leveraging the valuable resource of NM. Additionally, the study delves into the application of game-based assessments in PD diagnosis. By demonstrating the potential of a data-driven approach for accurate and early PD detection, the research contributes significantly to the scientific field. The methodology, trends, operational techniques, results, discussion, and implications are outlined in detail within the study. © 2024 IEEE.

关键词： Matrix algebra

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Intelligent Management Information System: Web Approach 15

Intelligent Management Information System: Web Approach

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Sujatha, E. Devi, S. Renuka Subha, S. Sree Raju, D. Naveen Loganathan, V. Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Aims to develop web application for information management and sharing which helps educational institutions broadcast information to the students and their faculties. A college generates and uses a large amount of data where it needs to be stored in the centralized repository. This data must be communicated appropriately from the authorities to students, faculty, and HOD. This web application resolves to store, manage, and distribute this information. It is intended for information management and sharing in a way that can manage and manipulate data in a centralized place which helps us reduce the time required for updating, storing, sharing the information a college is generating. And eliminates duplication and maintains consistency in data and using one of the latest and powerful concepts called session which helps us store the user information across multiple web pages. The goal of the of web application for information management and sharing is to achieve centralized data management and accessibility. © Grenze Scientific Society, 2024.

关键词： Information hiding

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Analysis Of data Forwarding Issues In Wireless Networks Aimed For Railway Signaling Systems 4

Analysis Of Data Forwarding Issues In Wireless Networks Aime...

引用

4th international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2024

作者： Katragadda, Jahnavi Shaik, Mubeena Seetha Ramanjaneyulu, B. Vignan's Foundation for Science Technology and Research Department of Ece Guntur India

ISBN: (纸本)9798350343670

Due to the difficulties faced with wire-based signaling systems, wireless communication based signaling systems are gaining importance worldwide. While the cellular communication based systems like GSM-R and LTE-R were proposed in majority of the cases, the non-cellular communication systems that are based on adhoc networks and wireless sensor networks have also become equally important. In this later case, routing protocols like AODV are needed to forward the data packets when destination is not reachable directly from the source. In this context, if some improvements are made to these protocols to make it more suitable to these environments, it can help to have better performance. These aspects are studied and analyzed in this work by carrying out the simulation studies for the model network considered for railway signaling application. Results suggest that making use of GPS and keeping the alternate route on hand can improve the performance considerably. Omnet++ simulator with INET framework is used to carry out the simulations. © 2024 IEEE.

关键词： CBTC ETCS Fixed Block GPS Moving Block Train Communications Wireless Signaling

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 international conference on advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Extending the Progression Model of Alzheimer's Disease Combining KNN, LDA and SMOTE 5

Extending the Progression Model of Alzheimer's Disease Combi...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Reddy, Tatireddy Subba Gopisetti, Sowmya Kumar, Cheviti Sanjay Prasad, Gunnala Krishna Vaishnavi, Dommati B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur Telangana Medak India

ISBN: (纸本)9798350356236

Alzheimer's disease (AD) is a challenging condition that significantly impacts quality of life and leads to cognitive impairment. As the most prevalent form of dementia, AD causes severe damage to nerve cells. Individuals affected by AD struggle with cognitive functions such as memory, thinking, and behaviour, which in turn disrupts their daily activities. Current methods for creating medical concept structures from various data sources - such as ICU and outpatient clinical records - often fall short in capturing the full complexity of disease progression. The primary issues include limitations in attention weight learning and restricted handling of data sources. To address these challenges, this study introduces a novel approach that combines three key techniques: Linear Discriminant Analysis (LDA) for reducing dimensionality, the (SMOTE) Synthetic Minority Over-sampling Technique for enhancing data through augmentation and addressing class imbalance, and K-Nearest Neighbours (KNN) for classification purposes. The proposed method was empirically tested and achieved a high accuracy rate of 98.6%, demonstrating its effectiveness in representing AD progression more comprehensively. © 2025 IEEE.

关键词： k-nearest neighbors

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A Comprehensive Exploration of Digital Twins in Smart City Applications 15

A Comprehensive Exploration of Digital Twins in Smart City A...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Menon, Remya R.K. Saikrishna, S. Sai Ganesh, P.V. Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amrita School of Computing Amritapuri India

ISBN: (纸本)9798331300579

The notion of a Digital Twin (DT), acting as a virtual replica interconnected with its physical counterpart through real-time data exchange, finds extensive applications in real world scenarios. As industries experience a surge in interest in DT, companies and other organisations who are interested in DT define their own methods to leverage the technology. This paper aims to consolidate and summarise these varied definitions of DT prevalent in the literature. The exploration spans from the foundational concepts to the anticipated future of DT, emphasising the need for a standardised architecture to guide its effective application across diverse ***, the incorporation of Machine Learning (ML) into the DT framework and the combination of DT and The interconnected network of smart devices, known as the Internet of Things are examined, emphasising the revolutionary possibilities of these technologies. This study provides a thorough overview of the changing DT landscape by demonstrating the architecture, variants, kinds, and integration of several communication protocols and data representation. The use of DT in the development of smart cities is examined in the paper's last section. The digital twin city, characterised by the integration of dynamic digital technology and static 3D models, emerges as a critical element for achieving comprehensive urban intelligence, planning, and related services. © Grenze Scientific Society, 2024.

关键词： Smart city

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