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An Intellectual Fusion Classification Prototypical for an Imbalanced Electrocardiogram data

SN Computer science 2023年第6期4卷 721页

作者： Ruth Isabels, K. Mrudula Devi, K. Anand, R. Athe, Ramesh Chowdhury, Sumit Singha Pund, Sachin S. Department of Mathematics Saveetha Engineering College (Autonomous) Chennai India BS&H Vignans Institute of Information Technology (A) Visakhapatnam India Department of MCA Acharya Institute of Technology Bangalore India Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad India Acharya Institute of Technology Bangalore India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

One of the greatest threats posed by progress around the world is cardiovascular diseases (CVD). Each year, CVD accounts for the vast majority of deaths worldwide. The vast majority of people who die from CVD do so suddenly, and their families have no hope of getting them medical help in time. Because of this, there is a pressing need for a real-time, intelligent system that can keep tabs on cardiac patients round the clock. Internet of Things (IoT)-enabled systems are a viable choice for real-time data monitoring. Internet-based smart healthcare has several potential uses, including monitoring physiological data, such as electrocardiograms (ECGs), heart rates (HRs), blood pressures (BPs), and more. IoT-based smart healthcare paradigms present significant issues, not the least of which is selecting the suitable model. This research proposes a heart rate sensor and a sophisticated hybrid classification algorithm as components of an IoT-based electrocardiogram (ECG) monitoring system. The goal of this study is to aid in the development of an automated key for social happiness by creating an intelligent hybrid classification model that can handle the class imbalance problem with more precision. No matter what validation criterion was used (twofold, fivefold, or tenfold cross-validation), the results of the empirical evaluation showed that the proposed hybrid model achieved better outcomes than the additional ingrained classification models in terms of accuracy and efficiency for both classes. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BP CVD ECG HR IoT

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Implementation of CBDC System for the Customised Development of Finance Service System

Implementation of CBDC System for the Customised Development...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Devansh Mehra A Sri Vamsi Prabhu Kavin Department of Data Science and Business Systems School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

CBDC–as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have – security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there; information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving.

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Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

arXiv

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arXiv 2023年

作者： Ji, Jiahao Zhang, Wentao Wang, Jingyuan Huang, Chao School of Computer Science and Engineering Beihang University Beijing China SCSE Beihang University Beijing China MIIT Key Laboratory of Data Intelligence and Management SEM Beihang University Beijing China Department of Computer Science Musketeers Foundation Institute of Data Science University of Hong Kong Hong Kong

Traffic prediction is essential for intelligent transportation systems and urban computing. It aims to establish a relationship between historical traffic data X and future traffic states Y by employing various statistical or deep learning methods. However, the relations of X → Y are often influenced by external confounders that simultaneously affect both X and Y, such as weather, accidents, and holidays. Existing deep-learning traffic prediction models adopt the classic front-door and back-door adjustments to address the confounder issue. However, these methods have limitations in addressing continuous or undefined confounders, as they depend on predefined discrete values that are often impractical in complex, real-world scenarios. To overcome this challenge, we propose the Spatial-Temporal sElf-superVised confoundEr learning (STEVE) model. This model introduces a basis vector approach, creating a base confounder bank to represent any confounder as a linear combination of a group of basis vectors. It also incorporates self-supervised auxiliary tasks to enhance the expressive power of the base confounder bank. Afterward, a confounder-irrelevant relation decoupling module is adopted to separate the confounder effects from direct X → Y relations. Extensive experiments across four large-scale datasets validate our model’s superior performance in handling spatial and temporal distribution shifts and underscore its adaptability to unseen confounders. Our model implementation is available at https://***/bigscity/STEVE_CODE. Copyright © 2023, The Authors. All rights reserved.

关键词： Urban transportation

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Smart Personal Protective Equipment in Ambulance Services with IoT Integration for Safety 2

Smart Personal Protective Equipment in Ambulance Services wi...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Senkamalavalli, R. Merlin Livingston, L.M. Guruva Reddy, Elangovan Basavaraddi, Chethan Chandra S. Latha, N. Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Karnataka Tumkur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Internet of Things (IoT) technology has improved ambulance safety and efficiency. In ambulance services, Smart PPE (Personal Protective Equipment) improves patient and healthcare worker safety via IoT connectivity. In crises, ambulances are vital. However, healthcare professionals risk contagious infections and physical damage. Though vital, traditional PPE lacks real-time monitoring and communication, leaving healthcare workers exposed. These issues are addressed with Smart PPE using IoT. This novel invention adds sensors and communication devices to paramedic and EMT PPE. These sensors measure air quality, temperature, and the wearer's heart and body temperature. A centralized system can remotely monitor healthcare personnel's safety and well-being using real-time data. Smart PPE has two-way communicators and emergency alert systems. Emergency coordination is improved by ambulance crews and dispatch centers communicating seamlessly. The Smart PPE can automatically inform the team and dispatch in urgent situations, increasing response times and patient outcomes. IoT enables predictive maintenance and data analytics. Trends and dangers may be identified from gathered data, allowing proactive risk mitigation. Smart PPE can measure use and remind users to maintain and replace equipment, assuring dependability when required most. By continually monitoring conditions, allowing real-time communication, and enabling data-driven decision-making, this unique technology improves ambulance services and reduces dangers to devoted personnel. © 2023 IEEE.

关键词： Ambulance Services Efficiency Improvement Emergency Healthcare Real-time Alerts Safety Enhancement

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Human-Computer Interface Challenges in Mobile data Mining

Human-Computer Interface Challenges in Mobile Data Mining

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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Padmanaban, K. Sathiya, A. Jeevitha, K. Rosaline, R. Anto Arockia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350333602

The continuous advancement of computer software, hardware, and artificial intelligence, has allowed for tremendous progress in the development of human-machine communication systems in recent years. The development of such methods provides more accurate results than humans. The core of the human-computer interface is the Natural Language Understanding (NLU) component. The success of Human-Computer Interaction (HCI) and the time it takes to accomplish tasks are both directly affected by the results of this module's semantic comprehension. In this research study, a neural network and HCI-based engine is developed for understanding and interacting with natural languages. data from the appropriate literature is used to comprehend the necessary understanding of HCI. Next, the interactive engine is used to develop a model of HCI based on natural language comprehension using a neural network, and also the model's underlying neural network reasoning structure is assessed. © 2023 IEEE.

关键词： Engines

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Cloud Based Remote File Access from PC to Mobile Using File Directory 1

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Proceedings of the 6th IFIP TC 12 International Conference on Computational Intelligence in data science, ICCIDS 2023

作者： Kanimozhi, J. Bharathi, Sv. Shri Kathiroli, Panimalar Sharmila, P. Ayshwariya, S. Abi Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Electronics and communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sagunthala Engineering College Chennai India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

People can share their digital data or information with others through networks with the aid of file sharing techniques. Peer-to-peer, cloud storage, and removable storage are the three ways that file sharing can be accomplished, according to the examination of file sharing methods. The most popular option for remote file sharing today is using a cloud storage services. Therefore, by utilizing cloud storage services, a file access method is established, in which the user can access a copy of a file that is located on their PC device using a mobile phone with additional security services offered. It is possible because JSON is utilized to render the file structure in the mobile application and NodeJS is used to index the local machine’s available files. The user can choose the necessary file from the rendered file system using their mobile device, and the URL will download the file to their device. Security is provided in this file access method by a key issued by the NodeJS application, which uses the JSON Web Token (JWT) technique for confirmation of the user. Result of the developed work is compared with file sharing services provided by Google drive, Gmail and others, and the proposed work is performing better in terms of all metrics. © 2023, IFIP International Federation for Information Processing.

关键词： Cloud storage

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Decoding AI: Experimental Analysis of Artificial Intelligence based Wine Quality Prediction Logic using Convoluted Deep Classification Strategy 9

Decoding AI: Experimental Analysis of Artificial Intelligenc...

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

作者： Geetha, C. Arunsundar, B. Vasumathi, G. Thamizhazhakan, K. Sudhakar, C. Venkata R.MK. Engineering College Department of Cse Chennai Kavarapettai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Department of Electronics And Communication Engineering Tirupati517102 India

ISBN: (纸本)9798350365092

Wine quality prediction is essential for optimizing production and ensuring consumer satisfaction. This study introduces a novel Convoluted Deep Classification (CDC) approach for accurate wine quality assessment. Leveraging convolutional neural networks (CNNs), our method effectively extracts hierarchical features from diverse wine data. The process begins with dataset preprocessing, encompassing chemical properties and sensory attributes. Subsequently, a hierarchical CNN architecture is tailored to capture intricate features such as acidity levels and volatile compounds. Techniques like data augmentation and transfer learning are employed to bolster model generalization. Evaluation on a benchmark dataset demonstrates outstanding performance, achieving a remarkable accuracy of 97%. This surpasses traditional methods and baseline CNN architectures, affirming the efficacy of our approach. Additionally, interpretability analyses unveil the key features influencing wine quality predictions, providing invaluable insights for producers and connoisseurs alike. Our CDC framework offers a robust and interpretable solution for wine quality prediction, empowering stakeholders with actionable insights for production optimization and quality assurance in the wine industry. © 2024 IEEE.

关键词： Quality assurance

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Intelligent Refrigerator using Machine Learning and IoT

Intelligent Refrigerator using Machine Learning and IoT

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Rajeswari, D. Srinivasan, R. Ramamoorthy, S. Pushpalatha, M. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9781665495295

Machine Learning and Internet of Things (IoT) play a vital role in the current electronics world. Electronic appliances operated by humans are automated and accessed remotely via IoT and 5G networks. Smart Cars, Drone Deliveries, and Intelligent Refrigerators are some of IoT and machine learningmajor applications. Potentially, with the aid of computing devices Intelligent Refrigerators can be accessed from anywhere and anytime. In Intelligent Refrigerators,a single tap on the computing devices predicts the quality of food items kept in the refrigerator. Besides, in order to work smartly, intelligent IoT devices deployed in intelligent refrigerators predict food quality anonymously using machine learningtechniques. This paper uses ensemble machine learning technique with naive bayes, support vector machine and decision tree to classify the considered fruits are spoiled or not. © 2022 IEEE.

关键词： Support vector machines

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Deep Learning Based Intrusion Detection System with Integration of Blockchain Technology and Internet of Things

Deep Learning Based Intrusion Detection System with Integrat...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Uganya, G. Rajalakshmi, D. Kumar, K. Ashok Ravi, S. Sunheriya, Neeraj Kanan, Mohammad Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India St Joseph's college of Engineering Department of Information Technology OMR Chennai India Department of Vlsi Microeelctronics Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan

ISBN: (纸本)9798331543617

The number of people and objects linked to the Internet of Things (IoT) has expanded dramatically due to its fast development, giving consumers more opportunities to interact with and manage their physical surroundings. IoT is susceptible to a number of security risks, because of its openness, connectedness, and constrained device resources. It is essential to guarantee the dependability and security of data transmitted in IoT networks. In order to solve these security problems, this work provides intrusion detection system that combines IoT with and Blockchain technology. As well as, deep learning method is integrated to improve the security. This work uses the SHA-256 algorithm to secure the data, the Adaptive Optimization algorithm for generating the keys, Rivest, Shamir, and Adleman (RSA) algorithm for encryption. and Deep Neural Network (DNN) for identifying intrusions in Internet of Things. Through identification and mitigation of possible risks, this framework successfully protects IoT networks and ensures the integrity of communications, as demonstrated by a comparison analysis with existing models. © 2024 IEEE.

关键词： Network intrusion

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