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1st International Conference on Computational Science and Technology, ICCST 2022

作者： Subramanian, M. Shobha Rani, P. Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India R.M.D. Engineering College Department of Computer Science Engineering Chennai India

ISBN: (纸本)9781665476553

Skin Cancer of any type is the most deleterious disease of the world, among them melanoma is the precarious skin cancer type. The rate of survival of skin cancer affected patients can be increased by early detection. With the advancements in deep learning algorithms, the accuracy rate of early detection is improved drastically. This paper aims at a detailed literature survey of different deep learning algorithms, its features, classification and detection rates. © 2022 IEEE.

关键词： learning algorithms

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Utilizing Cloud Computing for distributed Training of deep learning Models 2

Utilizing Cloud Computing for Distributed Training of Deep L...

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2nd International Conference on data Science and Information System, ICdSIS 2024

作者： dhanasekaran, S. Rajput, Kapil Yuvaraj, N. Aeri, Manisha Shukla, Rishi Prakash Singh, Sandeep Kumar Sri Eshwar College Of Engineering Department Of Electronics And Communication Engineering Coimbatore India Era Hill University Computer Science And Engineering Graphic Uttarakhand Dehradun India Computer Science And Engineering Sri Shakthi Institute Of Engineering And Technology Tamil Nadu Coimbatore India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Ait Management Mohali India Dr. D. Y. Patil Vidyapeeth- Centre For Online Learning Pune India

ISBN: (纸本)9798350381665

Cloud computing has emerged as a powerful solution for lots of computational responsibilities, including system studying and deep getting to know. deep knowledge of models often requires big quantities of information and schooling time, making it challenging to educate on traditional computing infrastructure. In this summary, we propose utilizing cloud computing for the dispensed training of deep getting-to-know fashions. By leveraging the scalability and flexibility of cloud computing, we are able to distribute the education process throughout more than one machine, reducing the overall schooling time. Moreover, with the right to enter a large number of computational assets, cloud computing can cope with the large number of records required for deep knowledge of training. deploying deep learning fashions inside the cloud additionally offers benefits, which include cost optimization, accessibility, and reproducibility. Cloud computing vendors provide pay-according-to-use pricing models, permitting groups to optimize their training charges with the aid of simplest making use of assets when wished. Furthermore, with cloud-primarily based education, accessibility to the models and statistics has progressed, allowing for easy collaboration and entry to the contemporary updates. © 2024 IEEE.

关键词： Cloud computing

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Exploring the Potential of Machine learning and deep learning in ECG Image Analysis for Cardiovascular disease diagnosis 5

Exploring the Potential of Machine Learning and Deep Learnin...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sathiya Priya, S. Angel Jean Vincy, K. Bharathi, S. Gresa, P. Sasi Manivannan, d. Sathya, V. VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai600062 India Srm Institute of Science and Technology Department of Computer Science & Engineering Vadapalani Chennai26 India Velammal Engineering College Department of Information Technology Surapet Chennai India

ISBN: (纸本)9798350379945

This research proposes a novel machine learning approach to detect cardiovascular diseases (CVds) in electrocardiogram (ECG) images. The goal is to improve diagnostic accuracy and efficiency within the healthcare system. CVds remain the leading cause of death globally, emphasizing the importance of early and accurate diagnosis for effective treatment. ECG signals provide valuable information for assessing cardiovascular health. The methodology utilizes advanced machine learning algorithms for classification. First, it pre-processes ECG signals to extract relevant features. The results demonstrate the machine learning model's potential as a significant diagnostic tool by showcasing its accuracy in detecting various cardiovascular illnesses from ECG images. Specifically, this research leveraged the power of machine learning to predict four major heart abnormalities: arrhythmias, myocardial infarction (heart attack), heart failure, and hypertrophic cardiomyopathy. This investigation explores how different machine learning methods perform when analyzing ECG images to identify cardiovascular diseases. The primary objective is to identify the algorithm that delivers the highest accuracy in classifying ECG images for CVd detection. decision trees are best suited for working with data organized in tables, where each entry has specific numerical values or categories. Random forests, like decision trees, are primarily designed to handle data in a tabular format with features like numbers or categories. ECG image analysis necessitates techniques adept at recognizing intricate patterns within the pixels of an image. The ultimate goal is to identify the most effective machine learning approach for accurate CVd detection using ECG images, potentially leading to improved healthcare system efficiency and patient outcomes. In the proposed system the algorithm with the highest accuracy for CVd diagnosis using ECG image analysis is the decision Tree, achieving 59.36% accuracy. © 2024

关键词： Cardiovascular diseases (CVd) decision Tree(dT) deep learning Machine learning Random Forest(RF) Support Vector Machine(SVM)

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Cloud-Enabled Patient Acuity Prediction Utilizing decision Trees in Smart ICU Nurse Assignment Systems 4

Cloud-Enabled Patient Acuity Prediction Utilizing Decision T...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arul Prasath, A. Saravanan, S. Monikapreethi, S.K. Brindha, G. Maheshwari, S. Srinivasan, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Information Technology Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

This research introduces a new method for improving Intensive Care Units (ICUs) nurse assignment using cloud computing and decision Trees (dTs )-based patient acuity prediction models. Effective and timely care delivery is dependent on the efficient allocation of nursing resources, which is becoming more important due to the growing complexity of patients' situations in ICUs. To integrate patient data from many sources seamlessly the proposed system uses cloud infrastructure to process and analyze data in real-time. To proactively identify high-risk situations, use dTs algorithms to forecast patient acuity levels using a variety of clinical indicators. Optimizing staffing levels and improving patient outcomes which incorporates these predictions into the nurse assignment process. Through a case study performed in a real-world ICUs improves nurse efficiency and the quality of patient care. The findings of this study highlight the promise of decision support systems hosted in the cloud for improving the delivery of healthcare in ICUs. © 2024 IEEE.

关键词： Nursing

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Enhancing Cloud Storage Security with Homomorphic Encryption and Ml-Based Access Control 2

Enhancing Cloud Storage Security with Homomorphic Encryption...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Shnain, Ammar H. Varshney, Neeraj Afreen, Sana Suganya, M. Babu, Bulipe Sankara Aravind, T. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GLA University Department of Computer Engineering & Applications Mathura India India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GRIET Department of CSE Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350368109

In this research a new approach of improving security of cloud storage through the use of homomorphic encryption and machine learning access control is proposed. One of the most important deficiencies the proposed system relevant to the critical issue of protecting data during its life cycle while enabling computations on them in an encrypted form without decrypting them. Thus, the system minimizes both the unauthorized access rights and the user behavior affecting the data integrity and confidentiality relying on the approaches of the supervised learning. It uses partially homomorphic encryption based on the Paillier cryptosystem as well as RandomForest and NeuralNetwork models. Experimental evaluation performed in a cloud-like environment shows that a system achieves desired security while providing low latency and high accuracy under high working load. The provided research can be considered as a step to the enhancement of the cloud security technologies and presents efficient solution for the protection of the cloud data storage. © 2024 IEEE.

关键词： Cloud security

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The Accurate Prediction and Forecasting Strategy Implementation for Various Weather Conditions using different Algorithms 4

The Accurate Prediction and Forecasting Strategy Implementat...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Sivapriyan, R. Vidya Prabha, C.V. Santhakumar, d. Rao, Kolli Himantha Mohammed, Safaa Hallol Abdtawfeeq, Tareq Hafdhi Sir Mvit R & D Centre Department of Eee Bengaluru India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai India Institute of Cse Saveetha Engineering College Department of Artificial Intelligence and Machine Learning India National University of Science and Technology Collage of Pharmacy Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

this study presents a new regression method for forecasting the occurrence of rainfall. The method includes three main steps: feature extraction, classification, and preprocessing. In the feature extraction step, all weather samples are normalized for a uniform sampling frequency, after which signal noise is removed to improve data quality and each signal is then converted to a 1 × 34 feature vector, capturing subtle emotional nuances. Notably, normalization precedes classifier training to improve model performance. Performance metrics including f-score, sensitivity, precision, and accuracy are used to evaluate the efficiency of the model. The proposed methodology has the potential to advance our understanding of emotional responses to music, providing a promising avenue for future research in predicting rainfall and other natural phenomenon. © 2024 IEEE.

关键词： Prediction models

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Nonlinear Testing-Based EMI Characterization of Wireless Communication Transmitter with Microwave Power Amplifier

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Progress In Electromagnetics Research C 2024年 147卷 27-37页

作者： du, Hongyu Wan, Fayu Mordachev, Vladimir Sinkevich, Eugene Chen, Xiaohe Fontgalland, Glauco do, dinh-Thuan Ngoho, Samuel Ravelo, Blaise Jiangsu Nanjing210044 China R&D Laboratory of EMC BSUIR Minsk Belarus College of Information Science & Engineering China University of Petroleum Beijing China School of Engineering University of Mount Union Alliance44601 United States AFSCET 151 Bd de l’Hôpital Paris75013 France

An effective empirical method of EMI analysis for transceiver (Tx-Rx) system implemented with nonlinear (Nl) microwave power amplifier (MPA) dedicated to wireless communication is developed. The nonlinearity is experimentally quantified by the MPA gain, P1dB, and third order intermodulation component via spectral response around 2.4 GHz 802.11b IEEE frequency band. The proof-of-concept represents the Tx-Rx system environment for wireless communication. The considered test signal emulates synchronization and physical broadcast different channels of downlink communication signals under QPSK modulation. The error vector magnitude (EVM) and signal-to-noise-ratio (SNR) due to the microwave Tx-Rx transmission undesirable EMI effect are assessed. Without MPA, the EVM and SNR of various channels fluctuate within a small range. Because of MPA nonlinearity, EMI becomes awfully significant due to the intermodulation generating SNR 20-dB decrease. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Intermodulation

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A Survey on Byzantine Attack using Secure Cooperative Spectrum Sensing in Cognitive Radio Sensor Network 6

A Survey on Byzantine Attack using Secure Cooperative Spectr...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Patil, Siddarama R Rajashree, Rajashree Agarkhed, Jayashree P.D.A College of Engineering Department of Electronics and Communication Engineering Karnataka Kalaburagi585102 India P.D.A College of Engineering Department of Computer Science and Engineering Karnataka Kalaburagi585102 India

ISBN: (纸本)9781665410281

The strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio Sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN. © 2022 IEEE.

关键词： Cognitive radio

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MYSTERIOUS ACTION dETECTION by SPEEd and dISPlACEMENT ANAlYSIS in IMAGE PROCESSING 1

MYSTERIOUS ACTION DETECTION by SPEED and DISPLACEMENT ANALYS...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vishnupriya, S. darwin Nesakumar, A. Mounika, S. Pavithra, K.N. R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering India R.M.K. Engineering College Department of Electronics and Communication Engineering India R.M.D.Engineering College Department of Computer Science and Business System India R.M.D.Engineering College Department of Computer Science Engineering India

ISBN: (纸本)9781665456876

There are now three moving target detection algorithms: The optical flow methodology, the background difference technique, and the inter-frame difference method are all employed. The optical flow technique has the most complex algorithm and takes the longest to process data compared to other approaches. The background subtraction(BS) approach, on the other hand, is recognised for its high sensitivity to light changes. Finally, while the frame difference approach is very straightforward and quick to use, the findings are insufficiently accurate due to backdrop brightness fluctuations causing mistakes. In this paper, the objective is to discover strange actions in image processing using speed and displacement analysis. Motion is detected using Incremental and Multi-feature Tensor Subspace learning (IMTSl) Applied for Background Modeling and Subtraction. Following IMTSl Background Subtraction, different image analysis were performed on the resulting pictures for displacement estimate, followed by speed computation. The results demonstrate improved backdrop modelling and action prediction. © 2022 IEEE.

关键词： Optical flows

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Hybrid deep learning Model for Cyber-Attack detection 7

Hybrid Deep Learning Model for Cyber-Attack Detection

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Premkumar, M. lakshmi, R. Velrajkumar, P. Priya, S.Gayathri Tanguturi, Rama Chaithanya Murali, S. Sivaramkrishnan, M. Department of Information Technology Namakkal Mallasamudharam India RNS Institute of Technology Bengaluru Affiliated to VTU Department of Computer Science and Engineering Belgaum India CMR Institute of Technology Bengaluru India R. M. D. Engineering College Kavaraipettai Department of Electronics and Communication Engineering Chennai India PACE Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Karpagam College of Engineering Department of EEE Coimbatore India

ISBN: (纸本)9798350397253

Better detection and avoidance of intrusions solutions are in high demand because of the worldwide upsurge in hacking on computer networks. New technologies like fog computing, cloud computing, and the Internet of Things have dramatically increased the potential for cyberattacks and other forms of cyber risk. These attacks can compromise computer network infrastructures, web services, and social media platforms, resulting in economic and reputation loss. Because of its usefulness in detecting and halting malicious actions, intrusion detection systems (IdS) play a crucial part in network defence mechanisms. In this study, a hybrid deep learning (dl) network was used to identify the cyberattack. This effort began with the collection and processing of cyber-attack data from the NSl-Kdd. Convolutional Neural Networks (CNNs), Bidirectional long Short-Term Memory (Bi-lS TMs), and hybrid CNN+Bi-lSTMs are the dl models trained with 80% of processed data. The remaining 20% of the data is used for testing the models after they have been trained. Both positive and negative metrics are used to assess the results of the testing phase. When compared to other networks, the recommended CNN+Bi-lSTM model achieves the highest score for positive metrics and the lowest score for negative metrics. © 2023 IEEE.

关键词： Feature extraction

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