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Extended Kalman Filter-Based Control of DC-DC Buck Converter 1

19th Control Instrumentation System Conference, CISCON 2022

作者： Mishra, Debarchita Mandal, Sharmistha Bag, Manojit Techno International Newtown Kolkata India Aliah University Kolkata India Department of Electrical & Electronics Engineering VELS Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

In this study, the output voltage of a DC-DC buck converter with pulse width modulator (PWM) has been regulated using extended Kalman filter (EKF) and proportional-integral (PI) controller. To minimize the effect of sensor and process noises on the output voltage of the buck converter, the EKF is designed, and to regulate the output voltage, proportional-integral controller is designed. The EKF and PI controller combination tracks the reference output voltage accurately and minimizes the effects of sensor and process noises. The robust performance of the controller is also verified by changing load resistance of the buck converter. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extended Kalman filters

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Using NSCTS and DCNN, Rapid Prediction and Forecasting of Breast Cancer 1

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Br...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Seethalakshmi, K. Bhagyalakshmi, A. Sangeetha, A. Vijitha, S. Palukuri, P. Suhas Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350316469

Utilizing the CAD system, which aids radiologists in various diagnostic procedures, a recent study has been conducted on the early identification of breast cancer. Next to lung cancer, breast cancer ranks among the most deadly diseases. Initially, detection is done based on screening tests done through Magnetic resonance imaging (MRI), subsequently Infrared thermography, and digital mammography (DM). Whenever a suspicion is raised, these tests might be recommended by the specialist. Region-based segmentation is used for the test picture in this work, while NSCTS (Non-subsample Contourlet transform segmentation) is used for preprocessing. ResNet-164 DCNN architecture performs feature extraction and classification between normal and aberrant tumor pictures. The diagnosis system adopts the deep learning technology to increase its performance rate. The approach to prediction starts with screening test photos, then preprocessing to enhance the image quality, ROI detection, ROI segmentation, and classification. The resulting images are examined using a variety of evaluation measures, including performance rate, sensitivity, specificity, and accuracy. When compared to other recent work, the outcome is exceptional. The obtained accuracy, sensitivity of the proposed system is 98.58% and 98.78%. The application takes advantage of the MIAS mammography image dataset. © 2023 IEEE.

关键词： Breast cancer contourlet segmentation DCNN Digital mammograms Image preprocessing MRI ResNet

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms 4

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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Automated Heart Disease Diagnosis and Monitoring System Using Similarity-Navigated Graph Neural Networks with Leopard Seal Optimization in IoT Healthcare Applications 4

Automated Heart Disease Diagnosis and Monitoring System Usin...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Raja, V. Sripathi Rawat, Ruchira Pandit, Karuna Dhumpati, Raghu Vekariya, Vipul Vijitha, S. B V Raju Institute of Technology Department of Biomedical Engineering Telangana Narsapur502313 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Nitte Karkala Karnataka Udupi District India Bahrain Polytechnic Isa Town Department of Computer Science and Engineering Isa Town3339 Bahrain Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Vels Institute of Science Technology and Advanced Studies Department of computer science and Engineering Pallavaram Tamil Nadu Chennai600043 India

ISBN: (纸本)9798331540364

One of the leading causes of death worldwide continues to be heart disease;therefore, early detection and precise diagnosis are essential to better patient outcomes. The conventional methods of diagnosing chemotherapy-induced nail changes involve a great deal of laboratory testing, which is more than exhaustive. In order to tackle these obstacles, this research introduces a novel methodology for the automated diagnosis and continuous tracking of heart diseases in the IoT -based health care system, integrated with deep learning algorithms. The proposed system includes several state-of-the art techniques to improve diagnostic reliability and speed. First of all, min-max normalization is used as a pre-processing technique that aims at pre-scaling the data of patients to a consistent level and, as a result, enhancing the performance of the model. The Mud Ring Algorithm is then used for feature selection, whereby the best features are chosen together with the reduction in dimensionality to decrease computational intensity, thus improving diagnostic accuracy. The core diagnostic engine is based on Similarity Navigated Graph Neural Networks (SNGNN) and has been enhanced with Leopard Seal Optimization (LSO), a new meta-method motivated by the hunting patterns of leopard seals. This combination enables the system to relate various features and give the correct prognosis for heart diseases. These techniques, applied within the IoT framework, enable real-time monitoring and diagnosis and therefore have the benefit of being a scalable solution for modern health care systems. The results show that the proposed method for providing diagnosis of heart disease is accurate, timely, and reliable and thus has the potential to revolutionize patient management in healthcare based on the IoT. © 2024 IEEE.

关键词： Chemotherapy

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Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication

Advancements in Speech Recognition: Exploring Feature Extrac...

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

作者： R. Bhavani Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy.

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PSD Analysis of EGG Signal using Various Types of Windowing Methods 3

PSD Analysis of EGG Signal using Various Types of Windowing ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Gowtham, R. Chandrasekaran, R. Atchaya, J. Ravindran, Rekha Vels Institute of Science Technology and Advanced Studies Chennai India Indian Institute of Technology Guwahati India Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350343632

In this study, the effects of pre-processed foods in the gastric motility are analysed and described in detail. The electro gastro graphic signal tends to vary based upon the types of the food we intake and the body mass index of the person. In the modern era the consumptions of the pre-processed foods are increased among the 18 to 25 aged group of people. The preprocessed foods mainly consist of the high CHO, protein and fat content in it;hence the consumption of the pre-processed foods leads to the several gastric issues for the person who consumes it regularly. The gastric issues caused due to the consumption of the pre-processed foods are tachy-gastria and the brady-gastria. Tachy-gastria is the state at which the gastric myoelectric activity is increased and brady-gastria is the state at which the gastric motility is reduced. These types of gastric disorders may lead to the irritable bowel syndrome for the person with gastric issues. The acquired EGG signal is pre-processed using the advanced filters present in the BIOPAC system to remove the noises and the power spectral density is applied to the acquired signal to analyse the deviation in the power spectral values of the pr-processed EGG signal at the different states. In this study the changes in the PSD values of the gastro graphic signal after consumption of different type of pre-processed foods are explained in detail and the EGG power spectrum signal deviations for the various types of foods are shown in this study. © 2023 IEEE.

关键词： brady gastria Electro gastro graphic signal gastric disorders irritable bowel syndrome power spectral density tachy-gastria

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Harvesting Insights: Predicting Vegetable Prices with Hybrid CLA-Conv BiGRU Techniques 3

Harvesting Insights: Predicting Vegetable Prices with Hybrid...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Lal Ambashtha, Kanahaiya Praveen Kumar, Chatakunta Pandey, Vivekanand Sakthivel Padaiyatchi, S. Bharathi, A. Navaz, K. Gnsu Faculty of Information Technology Bihar Sasaram India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Amity University Bihar Patna India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering TN Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

The system delves into the important topic of agricultural market pricing, with a specific emphasis on the ever-changing realm of vegetable supply and prices. Stabilizing the supply and prices of vegetables becomes an enormous task when consider that the system are grown outdoors and that weather variables significantly impact harvests. Such unpredictability has far-reaching consequences for the country's economy, highlighting the critical need to develop efficient methods of precise forecasting. The government has made multiple attempts to stabilize vegetable prices and supplies, but these have all been unsuccessful due to the unpredictable weather patterns of late. This system uses state-of-the-art deep learning methods, notably the Hybrid CLA-Conv BiGRU model, which incorporates a novel preprocessing strategy based on the HP Filter, to conquer this obstacle. A strong prediction model for vegetable prices is constructed using the process, which includes feature extraction, scoring, and selection. © 2024 IEEE.

关键词： Vegetables

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Formability and Strength Enhancement of AA7075 Sheets by Cryorolling and Post-Treatment

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Journal of The Institution of Engineers (India): Series D 2024年 1-10页

作者： Radha Krishnan, B. Sudarsan, D. Sanjeevi, R. Parthiban, A. Eswara Prasath, N. Arun Prasath, K. Department of Mechanical Engineering Nadar Saraswathi College of Engineering and Technology Tamilnadu Theni India Department of Mechanical Engineering PSNA College of Engineering & ampTechnology Dindigul India SSM Institute of Engineering and Technology Tamil Nadu Dindigul India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India Department of Mechanical Engineering Velammal College of Engineering and Technology Tamilnadu Madurai India Department of Mechanical Engineering PSN College of Engineering and Technology Tamilnadu Tirunelveli India

Cryorolling, an approach involving severe plastic deformation, offers a means to fabricate aluminum alloy sheets (AA7075) with exceptionally fine grains, conferring greater strength and hardness than those achieved through conventional cold rolling methods. Diverging from established techniques, we introduced a post-cryorolling heat treatment to enhance both the strength and formability of the cryorolled sheets. The formability within the temperature range of warm working was evaluated using parameters such as limiting dome height (LDH) and forming limit diagrams for cryorolled AA7075 alloy sheets. We conducted an analysis of strain distributions and thickness alterations resulting from biaxial stretching. To infer the mechanical attributes of the produced specimens, we established a correlation between their hardness and ultimate tensile strength. Employing this hybrid approach led to elevated limit stresses and LDH compared to the traditional processing route involving annealing, cold rolling, and room temperature forming. As a result, this method enables the fabrication of aluminum alloy sheet metal components with exceptional formability and high strength. Notably, this alloy exhibits optimal performance for warm forming at 250 °C due to its enhanced formability and post-forming strength when contrasted with annealed sheets and the conventional cold-rolling process. © The Institution of Engineers (India) 2024.

关键词： Hardness

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Spectral Estimation of ElectroGastroGram Signal

Spectral Estimation of ElectroGastroGram Signal

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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Chandrasekaran, R. Vijayaraj, S. Jothi Lakshmi, G.R. Vels Institute of Science Technology & Advanced Studies Department of Electrical and Electronics Engineering Tamilnadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9781665454995

In this study the various spectral estimation of the Electrogastrogram Signals are discussed. The Main objective of the study is to identify the gastroparesis condition using various signal processing techniques. Gastroparesis is one of the pathetic condition in which the stomach fails to digest. Gastroparesis is also called as stomach paralysis. Due to damage or dysfunction of vagal nerve, kajal cells at stomach lining temporary stomach paralysis happen. The immediate stimulation can make the pacemaker of the stomach to get activated and back to digestion process. In this study the stomach signals are preprocessed and spectral estimation techniques are applied to study about the signal spectrum characteristics. The determination of dominant frequencies and features extracted from frequency domain gives vivid description about the abnormalities of the gastric signal. © 2022 IEEE.

关键词： Spectrum analysis

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