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International Conference on ent computing and Control Systems (ICICCS)

作者： G. Kalaivani S. Kamalakkannan P. Kavitha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

Air pollution is the most important contributor to a variety of serious health problems as well as weather transformation. Air quality interpreters are needed to design human activity at a particular environmental location and lessen the adverse pollution detrimental impacts. The authors have examined the problem in difficulty of forecasting the classification of pollutant concentrations in the future and have proposed a novel method based on Long Short-Term Memory (LSTM) model. This is a Deep Neural Network (DNN) model that is identified to work well with consecutive prediction difficulties. Through the data obtained, this approach produces a prediction model that reliably forecasts the Air Quality Index (AQI) from Central Pollution Control Board (CPCB) website. LS TM is experimented eight number of times to choose the best possible function using Python’s LeakyRelu package to assess hyperparameter optimizations. The comparative analysis of accuracy metrics such as R Squared, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are measured for different models. The proposed model discovered R Squared is more than 1, indicating that the hyper parameter tuned model is the best fit model, based on the concurrent experiments. This dataset has the highest prediction accuracy and less prediction loss.

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Bioinspired sensing-memory-computing integrated vision systems: biomimetic mechanisms, design principles, and applications

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science China(Information sciences) 2024年第5期67卷 5-27页

作者： Yujie HUANG Yinlong TAN Yan KANG Yabo CHEN Yuhua TANG Tian JIANG Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology College of Advanced Interdisciplinary Studies National University of Defense Technology Institute for Quantum Science and Technology College of ScienceNational University of Defense Technology

With the explosion of sensory data in the Internet of Things(Io T) era, conventional machine vision systems are becoming increasingly difficult to meet the requirements of high efficiency, low energy consumption, and low latency due to their inherent shortcomings of separate sensing, memory, and computing units. Inspired by the retina and neuromorphic computing, the sensing-memory-computing integrated vision system(SMCVS) that features low power consumption, low latency, and high parallelism has been considered a promising technology to surpass the von Neumann architecture and realize strong artificial ***, novel materials like two-dimensional semiconductors and quantum dots with novel optoelectronic performance provide hardware carriers for implementing integrated sensing-memory-computing architectures,attracting considerable attention. This paper reviews the recent research progress in bioinspired vision systems in terms of biomimetic mechanisms, design principles, computational architectures, and ***, the biomimetic mechanisms are illustrated to guide the design of high-performance artificial visual perception systems. Then the research progress of optoelectronic-synapse-based bioinspired vision systems in the device principles and applications including image filtering, color recognition, visual adaptation, and motion detection are summarized. Finally, the challenges and future developing directions of the SMCVS are provided regarding bionic application, architecture design, and device fabrication.

关键词： sensing-memory-computing architecture neuromorphic computing retinomorphic device image recognition visual system

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An IoT based Voice Assisted Smart Residential Appliance Control Mechanism using Intelligent Node MCU Controller 5

An IoT based Voice Assisted Smart Residential Appliance Cont...

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

作者： Cherian, Aswathy K. Mercy, H. Reshma, R. Sivakami, K. Krishnamoorthy, R. Thiagarajan, R. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Jerusalem College of Engineering Department of Computer Science and Engineering Chennai India Justice Basheer Ahmed Sayeed College for Women Dept of Computer Science Chennai India Nehru Institute of Engineering and Technology Department of Ece Coimbatore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Chennai India Prathyusha Engineering College Department of It Tamilnadu Chennai India

ISBN: (纸本)9798350379945

The idea of a gadget operated by a remote or switch is today considered antiquated. This is the technology of the future. The article presents a novel approach of managing smart appliances with voice commands, which is referred to as Voice Assisted Smart Appliance Control (VASAP). In order to assess the efficacy and performance of the suggested system, it is compared against the traditional GPRS Assisted Smart Appliance Control (GPRSSAC). An Android app requires the user to speak commands. Voice instructions may be recognized and transmitted to the system using this program. These commands are decoded by a receiver and decoder, which then send them over a serial connection to the Arduino controllers. The system's software and hardware are developed in accordance with this plan, and the system's wireless communication technique, which integrates Wi-Fi and RF (radio frequency), is also established. The systems functional needs inform the design of two sets of control schemes-one for voice control and one for button control-with the goal of making the system more compassionate. For the voice control solution to work, the user must first read out from the smart terminal the control instructions that have been pre-programmed into the system's voice library. Once the recognition is confirmed, the user will be able to command their household appliances. Under the key control scheme, smart terminal keys are used to operate household appliances. In order to collect and store audio, verify family members, and recognize voice commands, the upper computer uses an algorithm that was studied in this study. The lower computer collects environmental information in real-time and wirelessly controls home facilities. Upon startup, family members are asked to confirm their voice commands. Following this, the system uses real-time environmental information and the results of voice command recognition to formulate a control command. Through the serial port, the command to operate the ass

关键词： Domestic appliances

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Enhanced Proof of Authentication(E-PoA) Consensus Blockchain-Based Secured Health Care Data Transmission in Internet of Things

Enhanced Proof of Authentication(E-PoA) Consensus Blockchain...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： T. Keerthana D.R. Krithika Department of Computer Application Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Blockchain and the Internet of Things (IoT) are revolutionizing healthcare, presenting the potential for transformation. The IoT is achieving more and more confidence as the infrastructure for future applications. Therefore, based on the comprehensive integration of Blockchain (BC) technology with IoT, security requirements are provided through security frameworks built into BC. Healthcare IoT applications in BC technology are revolutionizing industries. It also analyzes the potential benefits of BC implementation in IoT and healthcare for securely transferring medical data. Blockchain's decentralized and immutable nature of medical data sharing offers secure ways to store and transmit data. However, it is necessary to protect the data's privacy stored and shared securely. This review paper evaluates the algorithm based on Rivest, Shamir, and Adelman (RSA), advanced Encryption Standard (AES), Lionized Golden Eagle-based Homomorphic Elapid Security (LGE-HES), and BC-Assisted Secure Data Management Framework (BSDMF). This survey paper describes Blockchain technology-based medical data transfer based on IoT medical systems. The performance evaluation regarding Root Mean Square Error (RMAE), MSE, time complexity, and security performance compared to standard technologies can be analyzed.

关键词： Surveys Privacy Data privacy Medical services Blockchains Security Internet of Things Time complexity Standards Systematic literature review

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： V. Ramya K.S. Thirunavakkarasu Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

SARS-CoV-2, also known as the Covid-19 Corona Virus, has caused widespread damage globally, and the situation is only becoming worse. Officials from all across the world are using a number of COVID-19 outbreak prediction models to help them make choices and implement pertinent control measures. Simple epidemiological and statistical models are well-liked by the media and have garnered more attention from authorities among the conventional models for COVID-19 global pandemic prediction. It's a pandemic illness that spreads daily from person to person. As a result, it's critical to monitor the total patient population impacted. The way that the computerised data is now provided by the system makes it extremely difficult to analyse and forecast the spread of disease both locally and globally. An enhanced Sprint algorithm, Enhanced Random forest algorithm, Customized sequential Deep learning Model (CSDLM), and HMC-19HRP model is proposed in this study and analysed each with two dataset that is Covid dataset and OMICRON dataset. Finally results obtained from all the method are compared. On overall comparison HMC-19HRP outperforms other methods on both the dataset with precision of 94.92 and 98.26, Recall of 99.03 and 95.86, F1-score of 96.93 and 97.04, Specificity of 78.71 and 97.06 and Accuracy of 94.98 and 96.30 on covid data and Omicron dataset respectively.

关键词： COVID-19 Measurement Deep learning Accuracy Pandemics Computational modeling Predictive models Prediction algorithms Data models Random forests

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Machine Learning based Prediction of Parkinson's Diseases

Machine Learning based Prediction of Parkinson's Diseases

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Arockiya Selvi S T. Kamalakannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Parkinson's disease(PDs) is a neurological disorder characterized by accidental or uncontrollable movements such as shaking, rigidity, and trouble with balance and coordination. Symptoms are usually minor at first and intensify over time. PD datasets tend to be compact and inadequately diversified. Several datasets contain incorrect values or discrepancies as a result of diagnostic procedure inconsistencies. Datasets frequently have fewer PD patients than healthy individuals, resulting in biased models. SVM is a common method for PD prediction due to its ability to handle tiny, high-dimensional data sets and its robust classification skills. SVM performs effectively even with small medical datasets, unlike deep learning models, which require enormous volumes of data. SVM minimizes overfitting, particularly when working with tiny datasets, making it more dependable for use in real-world medical settings. The study employed SVM algorithms to predict Parkinson's disease. Additionally, disease data is classified and compared using SVM with different kernel parameters. The study compares the performance of four alternative SVM models Linear SVM (LSVM), Quadratic SVM (QSVM), Cubic Gaussian SVM (CGSVM), and Medium Gaussian SVM (MGSVM) using five assessment metrics: accuracy, sensitivity, specificity, precision, and F1-Score. MGSVM is the most effective model for predicting PDs, with the highest precision and consistency.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy Sensitivity Parkinson's disease Predictive models Prediction algorithms Classification algorithms Medical diagnostic imaging

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A prognostic thyroid disorder analysis based on thyroid test measure with ECG 1

A prognostic thyroid disorder analysis based on thyroid test...

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1st IEEE International Conference on Advances in Electrical, computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Deepika, M. Department of Computer Science School of Computing Science Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9781728157900

Clinically the most prevailing relevant findings explaining the hyperthyroidism as well as the hypothyroidism are the anatomic symptoms of Thyroid disease. The concerned with cardiovascular system of cellular pathway for thyroid hormones will display changes to cardiac rate, heart contractility, blood pressure, vascular resistance and rhythms associated to thyroid dysfunction. The instruction to identify post - treatment and diagnostic methods required for the recovery of people with diagnosed hypothyroidism, the related thyroid dysfunction criteria are described in this article. Atrial fibrillation is a common type of heart disease. This paper looked at the threat of arrhythmia in thyroid problems verified patients aged 20 to 89 years, respectively. In a limited number of patients chosen, the interaction between hyperthyroidism and Atrial fibrillation has until now been evaluated. The report also identifies differences of tyrosine levels affecting chronic cardiac failure and the co-ordination of thyroid disease interventions in provisional modification situations. The results are obtained in all patients with a UCI hyperthyroidism event with logistic regression analysis for the estimation of odd ratios between sex, age group, cardiovascular disease and atrial or flutter-like effect. © 2021 IEEE

关键词： Blood pressure

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models 5

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Ramya, V. Thirunavakkarasu, K.S. Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

关键词： COVID-19

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Score-Level Fusion Approach for Traffic Flow Prediction in Intelligent Transportation System

Score-Level Fusion Approach for Traffic Flow Prediction in I...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Axcellin. T S. Kamalakkannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Traffic flow prediction (TFP) for intelligent transportation system is essential for effective transportation planning and city development. Conventional models often have difficulty with the complex nature of traffic data, which includes nonlinear patterns, spatial dependencies, and external influences such as weather or road conditions. Prediction accuracy may be significantly increased by utilizing an ensembling strategy, which combines the capabilities of many models. The goal of the study is to improve TFP accuracy by integrating the results of different machine learning (ML) models. The study uses Random Forest(RF) Naïve Bayes(NB), K-Nearest Neighbor(KNN), Extreme Gradient Boosting(XGBoost) and Support vector machine(SVM) as prediction models and based on the prediction probabilities and the score level fusion is performed with RF and XGBoost ensembling. The new fusion procedure uses a weighted average technique in which weights are assigned dynamically depending on individual model performance criteria including accuracy, precision, recall, and F1-score. Higher-performing algorithms are given greater significance in the final forecast, resulting in a dynamic and data-driven assembly strategy. The findings show that the efficiency of the ensemble of RF-XGBoost is higher compared to the contrasted methods. The model also uses feature selection approaches like mutual information to find the most significant traffic-related characteristics, lowering computational cost and increasing forecast efficiency.

关键词： Radio frequency Accuracy Computational modeling Heuristic algorithms Transportation Forestry Predictive models Feature extraction Vectors Data models

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Optimized Hybrid Cryptographic Technique Using Symmetric and Asymmetric Encryption for Cloud computing 5

Optimized Hybrid Cryptographic Technique Using Symmetric and...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Suresh, R. Dayana, T.R. Nisha Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Cloud computing (CC) is an expanding computing concept in which computing resources are given as Internetbased services. Security and confidentiality of cloud storage services are critical and pose a difficulty in cloud computing owing to loss of control over data and reliance on the cloud computing provider. While there is a big number of transmitting data in the cloud system, the risk of data access by attackers raises. CC requires encryption to maintain the privacy and security of data stored, processed, and transported on the cloud. Encryption ensures that confidential information saved in the cloud stays private and accessible only to authorized parties. The field of cryptography is continuously evolving to deal with current challenges in cybersecurity, confidentiality, and computing power. Optimizing encryption algorithms entails enhancing security, performance, and efficiency while reducing resource usage. This study suggests a optimized framework for encryption using RSA, an asymmetric key cryptography for key exchange and advanced / Encryption Standard (AES), a symmetric key cryptography for bulk message encryption to improve the efficiency of encryption. The suggested models's performance evaluated with RSA with Data Encrytpion Standard(DES) and RSA with BlowFish algorithms and the efficiency of RSA with AES is found to more in terms of Execution time, Throughput, memory usage and latency. © 2025 IEEE.

关键词： Sensitive data

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