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检索条件"任意字段=7th IASTED International Conference on Advances in Computer Science and Technology"

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advances in Early Detection and Monitoring of Retinopathy in Preterm Infants Using CNN and MLP Models 7

Advances in Early Detection and Monitoring of Retinopathy in...

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7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Hubert, G. Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

this research showcases significant advancements in the timely identification and monitoring of retinopathy among preterm infants through the integration of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) models. We eradicate the necessity for subjective assessments by ensuring a precise and automated diagnosis through the implementation of these contemporary models. the CNN is capable of extracting robust features from retinal images, and the MLP model enhances the predictive precision and monitoring functionalities even further. By integrating these models, our approach enhances the precision and velocity of retinopathy diagnosis, thereby resulting in improved therapeutic outcomes. the proposed framework significantly improves neonatal care by incorporating cutting-edge technology that enables continuous and advanced detection of retinopathy in premature infants. © 2024 IEEE.

关键词： Convolutional neural networks

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Innovative Communication Solutions for Specially-Abled Individuals: A Machine Learning-Approach to EEG Interpretation 7

Innovative Communication Solutions for Specially-Abled Indiv...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： thakur, Anikait Girish Krithik, G.J. Vani, K. Kapilash, K. Ray, Saumarghya Parthasarathy, S. Psg College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641004 India

ISBN: (纸本)9798350305005

In this study, the use of electroencephalography (EEG) as a brain-computer interface (BCI) for people with communication difficulties is being investigated. the study aims to decode EEG data into words, providing an alternative means of communication for patients with ailments like ALS, stroke, or any physical brain injuries. Raw data were preprocessed, and features were extracted using 24 signal features to convert continuous time-series data to discrete features for word classification. Out of the total features, feature selection was performed using two techniques: K -best features using ANOVA f-score and recursive feature elimination using an SVM kernel. then various machine learning and deep learning techniques were used to analyse the data to classify the intended communication activities from the EEG signals. the results concluded by providing the best interclass accuracy of 46.10% using a multi-layered perceptron model with selected 200 features using recursive feature elimination. the findings of this research will aid in creating more effective and convenient BCI systems that can enhance the quality of life for individuals with communication disabilities. © 2023 IEEE.

关键词： Electroencephalography

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Improved Forecasting of Electric Vehicle Charging Load using Neural Architecture Search 7

Improved Forecasting of Electric Vehicle Charging Load using...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Kar, Subhadeep Das, Sayanee Chattopadhyay, Paramita Indian Institute of Engineering Science and Technology Department of Electrical Engineering Shibpur India

ISBN: (纸本)9798350305005

In the modern world, due to global climatic changes, the Automobile Sector is generally shifting towards Electric Vehicles due to its benefits over Internal Combustion Engines (ICE). this rapid increase in the Electric Vehicle Charging stations causes increase in overall Energy demand. thus, forecasting of power demand becomes a key ingredient for Electric Vehicle Charging Infrastructure Management. In this paper, two of the most popular Machine Learning forecasting models namely Bidirectional Long Short-Term Memory Network (Bi-LSTM) and Long Short-Term Memory Network (LSTM) are used for prediction of Electric Vehicle Charging Load demand whereby, both the network hyper parameters are tuned using traditional Grid Search Method and Neural Architecture Search (NAS) Method. the models are tested on the dataset provided by Electric Vehicle Supply Equipment (EVSE) using Adaptive Charging Networks (ACN) by California technology. the present work proposes a SARSA-based Neural Architecture Search (NAS) method to optimize the design of the forecasting models and results show satisfactory improvement in forecasting performance over models designed by conventional Grid Search-based approaches. © 2023 IEEE.

关键词： Long short-term memory

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Breadth-First-Search based Phase Unwrapping Algorithm for Quantitative Phase Imaging 7

Breadth-First-Search based Phase Unwrapping Algorithm for Qu...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Ghosh, Anik Arya, Deep Kulkarni, Rishikesh Indian Institute of Technology Department of Electronics and Electrical Engineering Assam Guwahati India Indian Institute of Technology Centre for Intelligent Cyber Physical Systems Assam Guwahati India

ISBN: (纸本)9798350305005

A breadth-first-search (BFS) based novel quality-guided phase unwrapping (PU) algorithm is proposed. the BFS algorithm is assisted with the non-uniformly segmented intervals over the range of phase quality values. On the contrary to common approach of unwrapping a pixel based on its quality value, the eligibility of a pixel to unwrap its neighbors is decided based on its quality value. the BFS algorithm provides high computational efficiency in the implementation of PU. the number of segments in which the phase quality range is divided into is appropriately selected to depending on the phase noise to achieve the robustness in the PU. Simulation examples and experimental results with phase images of A375 cancer cells and HeLa cells are reported to demonstrate the performance comparison among different PU algorithms. © 2023 IEEE.

关键词： Pixels

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Tunable Sparing of Disks in a Cloud Data Center 7

Tunable Sparing of Disks in a Cloud Data Center

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Malawat, Rohit Modi, Shrey Vishwakarma, Rahul Indian Institute of Technology Roorkee Department of Material Science and Metallurgy Roorkee India California State University Long Beach Department of Computer Engineering & Computer Science Long Beach United States California State Uni. Long Beach Department of Computer Engineering & Computer Science Long BeachCA United States

ISBN: (纸本)9798350305005

Disk Drive failure tolerance and optimum utilization of disks for storage are two of the most important challenges in the Cloud based data center. Addressing both these problems simultaneously can be tricky. To make the system failure tolerant, more storage needs to be added;this results in redundancy of storage. Providing a safety net for the disk is important to make sure that the disk can still operate. therefore, we propose a novel drive sparing mechanism which not only provides failure tolerance by maintaining the optimal number of spare drives in the storage system, but also deals with over-provisioned spare. the method first identifies the critical disks that may fail in the RAID group based on the Venn-ABERS framework which helps us to know about the conditional probability of the disk failure, and then implements a variable sparing mechanism where a few disks act as dual-purpose disks. We demonstrate variable sparing using two cases, first when minimum spare pool is sufficient, and second, when minimum spare pool is not sufficient for failure tolerance. By proactively addressing potential failure points, the drives are able to extend their lifespan and enhance reliability when the variable sparing is utilized. In this paper, we explain the mechanism of variable sparing of disks and share results of an evaluation experiment using an open-source dataset by utilizing our prototype system. © 2023 IEEE.

关键词： Digital storage

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Sentiment Analysis for Stock Prediction Using Mass Media Sources 7th

Sentiment Analysis for Stock Prediction Using Mass Media Sou...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Billade, Kunal Kishor Patel, Jeel Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

ISBN: (纸本)9783031699818

"Sentiment Analysis for Stock Prediction Using Mass Media Sources" introduces a groundbreaking approach to forecasting stock movements by harnessing sentiment analysis applied to economic news gathered from a diverse range of mass media sources. the project encompasses the creation of a comprehensive system that systematically scrapes economic news websites to gather information relevant to specific companies. this data is then meticulously analyzed to discern the sentiments expressed in the news articles. Subsequently, cutting-edge Machine learning techniques are put to use to anticipate potential fluctuations in the stock prices of the target companies. this holistic approach capitalizes on the amalgamation of Natural language processing which combined with machine learning techniques, and real-time news data, delivering invaluable insights for both investors and traders. © IFIP international Federation for Information Processing 2024.

关键词： Prediction models

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SiC Based 3-Phase Vienna Rectifier Using Hybrid SVPWM Strategy for EV Charging Station 7

SiC Based 3-Phase Vienna Rectifier Using Hybrid SVPWM Strate...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Kumar, Uppalapati Sudheer Halder, Sukanta Bittu, Sk Bhattacharjee, Soumava Yalla, Naveen Nakka, Jayaram Sardar Vallabhbhai National Institute of Technology Surat Department of Electrical Engineering Surat India Vancouver Canada Indian Institute of Technology Bhu Department of Electrical Engineering Varanasi India National Institute of Technology Department of Electrical Engineering Andhra Pradesh Tadepalligudem India

ISBN: (纸本)9798350305005

Vienna rectifiers are widely used because of their advantages towards the input side harmonic reduction, stable output voltage with low stress on power switches, and high-power density. However, Vienna rectifiers also lag with a distortion in zero-crossing currents and potential imbalance at midpoint. this paper presents a modified hybrid space vector pulse width modulation (SVPWM) approach where midpoint voltage distortion is reduced along with improved current crossing distortion. A discontinuous PWM strategy is designed for suppressing the small vector mutation which further helps in current distortion. the total harmonic distortion (thD) is improved with the proposed hybrid modulation strategy. Furthermore, the efficiency is improved by using the wide band gap (WBG) based silicon carbide (SiC) power switches. Loss analysis for the proposed methodology is performed using the PLECS software and is presented in the paper. © 2023 IEEE.

关键词： Silicon carbide

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A Study of Generative Large Language Model for Healthcare 7

A Study of Generative Large Language Model for Healthcare

引用

7th international conference on Information technology, InCIT 2023

作者： Park, Seong-Gyeol Kim, Ahtae Yoon, Taewoong Kamyod, Chayapol Kim, Cheong Ghil Namseoul University Dept of Computer Scicence Choongnam 31020 Korea Republic of Mae Fah Luang University Excellence Ctr. of Indust. 4.0 TextitComputer and Commun. Eng. for Capacity Building Research Center School of Information Technology Chiang Rai57100 Thailand

ISBN: (纸本)9798350358698

the expansion of the 4th Industrial Revolution is making it easier to collect and analyze big data through cloud technology. In addition, advances in semiconductor design and manufacturing technology are accelerating the development of artificial intelligence due to improvements in computer processing speed and performance. these developments in artificial intelligence technology are expected to be applied to the healthcare industry and create new value. Recently, ChatGPT, which has evolved one step further, has emerged, bringing about the development of a new technology called 'Generative AI'. Research is being actively conducted on how AI can replace fields that were considered the exclusive domain of humans with specialized knowledge, such as medicine and law. In this study, we aim to take the opportunity to identify trends in the healthcare field of generative AI. © 2023 IEEE.

关键词： Artificial intelligence

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A Blockchain and IoT-Enabled Secure Health Data Handling Framework 7

A Blockchain and IoT-Enabled Secure Health Data Handling Fra...

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7th international conference on advances in Biomedical Engineering, ICABME 2023

作者： Elkhodr, Mahmoud Gide, Ergun Farid, Farnaz Ahamed, Farhad School of Engineering and Technology Central Queensland University Sydney Australia Western Sydney University Department of Social Science Parramatta Australia Western Sydney University Department of Computer Mathematics and Data Science Parramatta Australia

ISBN: (纸本)9798350325850

Secure data sharing while preserving user privacy is a critical challenge in Biomedical applications. this paper presents the Secure Health Data Handling Framework (SHDHF), a comprehensive and multi-layered solution designed to address this challenge. SHDHF leverages blockchain technology and a hierarchical architecture consisting of four primary layers: User, Gateway, Blockchain, and Hospital Cloud. Each layer incorporates sublayers to provision secure data handling and user-controlled access. the framework enables real-time data collection from wearable IoT devices, secure data transmission, decentralised access control, and robust encryption for storage. SHDHF also provides a comprehensive solution for privacy-preserving biomedical data sharing, supporting personalised healthcare and biomedical research. © 2023 IEEE.

关键词： Blockchain

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A Systematic Review of Various Deep Learning Techniques for Network Intrusion Detection System 7th

A Systematic Review of Various Deep Learning Techniques for ...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Sasikumar, A.N. Lilly, Sheeba S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600 089 India

ISBN: (纸本)9783031699856

the collective frequency and intricacy of attacks on computer networks remain a threat to information security within computer schemes. In order to solve this, researchers exploited network intrusion detection systems (NIDSs) to safe guard networks and information. Because of its lively nature of malware and continuous change in attacks, these systems normally recognize intrusions by the examination of network traffic. this survey demonstrates several Deep Learning (DL) approaches for the identification and categorization of unexpected attacks. the constant variations in the activities of the network make it essential to examine several datasets by dynamic and static techniques. Here 25 research papers are analyzed and surveyed. A complete assessment of many DL classifiers was exposed on various benchmark malware datasets. First, it offers a classification of computer intrusions with an explanation of categorized methods. Next, a general outline of the NIDS is described with its basic features. then, the classification of NIDS based on their categorized methods is explained. After that, the challenges faced by the categorized methods are deliberated in the section of research gaps and issues. Lastly, the analysis in the survey is performed in terms of publication year, category analysis, tools used and performance metrics. © IFIP international Federation for Information Processing 2024

关键词： Network intrusion

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