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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Thamizhvani, T.R. Hemalatha, R.J. Technology and Advanced Studies Department of Biomedical Engineering Vels Institute of Science Chennai India

ISBN: (纸本)9798350350951

Fast Fourier Transform (FFT) is the transform used for the determination of the frequency domain features and description of the signal or image. The frequency components of the images are analyzed using the FFT techniques in image processing. In the study, Three-dimensional FFT features are analyzed to diagnose Alzheimer's Disease (AD) at early stages. The medical images are obtained from the standardized database which is acquired using Magnetic resonance Imaging (MRI). MRI images of different AD stages are acquired. Fast Fourier Transform is applied to three-dimensional spatial images in MRI to analyze the frequency components in each domain. Different features like r-Value, Phi, Theta, Spectral coefficients, and Fourier coefficients. The significant feature difference between the two types of 3D MRI images is analyzed using statistical techniques. Thus, the FFT features possess a high efficiency in the detection of Alzheimer's Disease. © 2024 IEEE.

关键词： Fast Fourier transforms

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Performance investigation on novel MPPT controller in solar photovoltaic system

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Materials Today: Proceedings 2023年 80卷 3517-3522页

作者： Bhukya, Ramu Shanmugasundaram, N. Department of Electrical and Electronics Engineering VELS Institute of Science Technology and Advanced Studies Pallavaram Chennai600 117 India

This paper investigates the performance of solar PV system by implementing MPPT algorithm. The IV and PV characteristics of solar photovoltaic system have been analyzed for various parameters of temperature and irradiation. From the characteristics, the peak power variations with respect to physical parameters have been identified using algorithmic approach. To validate the MPPT operation, the P&O algorithm has been implemented and verified the operation under varying environmental conditions. And also, the same has been compared with theoretical approach in this paper. © 2021

关键词： Solar concentrators

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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

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SN Computer Science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 46-66页

作者： Jayakrishnan, R. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

The word “cancer” denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and understandable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analyse various methods for multiclass categorising cancer subtypes using deep structured reinforcement learning (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combining the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Cancer subtypes Deep neural networks Deep-learning Gene expression Machine-learning Reinforcement learning

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication 4

Advancements in Speech Recognition: Exploring Feature Extrac...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

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

ISBN: (纸本)9798350343670

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. © 2024 IEEE.

关键词： feature extraction Signal processing Signals Sound Speech recognition

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Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence

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npj Computational Materials 2024年第1期10卷 1231-1238页

作者： Shaomeng Xu Zhuyang Chen Mingyang Qin Bijun Cai Weixuan Li Ronggui Zhu Chen Xu X-D.Xiang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China School of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen518055China Shenzhen Polytechnic University Shenzhen518055China

The development of non-noble metal electrocatalysts for the Oxygen Evolution Reaction(OER)is advancing towards the use of multi-element *** reveal the complex correlations of multielement OER electrocatalysts,we developed an iterative workflow combining high-throughput experiments and AI-generated content(AIGC)*** increased number of 909(compared to 145 in previous literature)universal descriptors for inorganic materials science were constructed and used as Artificial Neural Network(ANN)input.A large number of statistical ensembles with each ANN individual ensemble having a reduced number of descriptors were integrated with a new Hierarchical Neural Network(HNN)*** algorithm addresses the longstanding challenge of balancing overwhelming descriptor numbers with insufficient datasets in traditional ANN approaches to materials science *** a result,the combination of AIGC and experimental validation significantly enhanced prediction accuracy,increase the R2 values from 0.7 to 0.98 for Tafel slopes.

关键词： materials artificial evolution

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A Comparative Analysis of Real-Time Sign Language Recognition Methods for Training Surgical Robots

A Comparative Analysis of Real-Time Sign Language Recognitio...

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Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Rubi, Jaya Hemalatha, R.J. Infant Francis Geo, I. Marutha Santhosh, T. Josephin Arockia Dhivya, A. Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9789819955206

This project proposes a real-time robot that can interact with humans based on the gestures fed to it as input. The proposed proposal aims to develop a constructive design of a robot that has computer vision and is trained to read human gestures. There is a need for intelligent robots in the healthcare industry. The impact of this project will be on sophisticated healthcare systems, especially the surgical system. The implementation is achieved by training the robot using deep CNN and making the robot perform certain functions like moving the arm upwards and downwards as well as opening and closing the robot grippers. It is also important to mention that a comparative analysis has been made with the existing system and advanced technology called MediaPipe framework for the acquisition of input signals. The comparative analysis will give us a clear picture of the usage of different types of classifiers for training robotic models. With the impact of this project, it would be easy for the physicians to pick and place the medical equipment in a correct manner and provide assistance to the surgeon during surgery. This device can also be very useful in robot-assisted surgeries as it can be further developed to perform actions like drilling and making incisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Robotic surgery

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Characteristics and Analysis of ElectroGastroGram Signal 1st

Characteristics and Analysis of ElectroGastroGram Signal

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1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

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

ISBN: (纸本)9783031350771

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. © 2023, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Spectrum analysis

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Convolutional and Graph Neural Networks for the prediction of Papulosquamous Diseases (PSDs) 3

Convolutional and Graph Neural Networks for the prediction o...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nagarajan, G. Raaza, Arun Vels Institute of Science Technology and Advanced Studies Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

The precise identification of papulosquamous diseases (PSDs), including psoriasis and seborrheic dermatitis, is a challenge for dermatologists due to their complicated and heterogeneous clinical presentations. In the discipline of dermatology, artificial intelligence (AI) has recently made major advancements in image classification and malignancy prediction. Deep learning is becoming increasingly popular for treating a variety of diseases. This study proposes a CNN (Convolutional Neural Networks)+GNN (Graph Neural Networks) architecture and evaluates and compares the performance of multiple deep learning models, including LSTM (Long Short-Term Memory), Autoencoders (AEs), and Recurrent Neural Networks (RNNs), for the automatic categorization of Papulosquamous Diseases(PSDs).. A hybrid strategy combining CNN and GNN was selected as the most efficient model, achieving the maximum performance across all measures with 93% accuracy, 92% precision, 94% recall, and 93% F1-Score. © 2025 IEEE.

关键词： Convolutional neural networks

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Construction of chiral through-space luminophores via symmetry breaking triggered by sequenced chlorination

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Science China Chemistry 2023年第7期66卷 2083-2090页

作者： Yufeng Xie Huaying Liu Zhongliang Li Xin Zhang Lijuan Song Kang Zhou Feng Yuan Hengzhi You Engui Zhao Zikai He School of Science Harbin Institute of TechnologyShenzhen 518055China School of Chemical Engineering and Technology Harbin Institute of Technology HarbinHarbin 150001China Academy for Advanced Interdisciplinary Studies and Department of Chemistry Southern University of Science and TechnologyShenzhen 518055China Shenzhen Bay Laboratory Shenzhen 518055China Hoffmann Institute of Advanced Materials Shenzhen PolytechnicShenzhen 518055China

The construction of molecular chirality is crucial for exploring novel luminophores with chiroptical *** asymmetric synthesis of chiral center or axial is not powerful enough on through-space *** methodologies for breaking molecular symmetry could be promising but remain less ***,we report a novel methodology for constructing chiral through-space luminophores via simple chlorination on bridged carbazole *** chlorination breaks the molecular symmetry and thus results in molecular chirality by eliminating the mirror plane or rotating ***,continuous multiple chlorinations can rebuild and break the symmetry of the skeleton in *** chiral and achiral isomeric analogues are synthesized and characterized with impressive chiroptical *** of chiral high performance liquid chromatography(HPLC),single-crystal X-ray diffraction,kinetic racemization,and chiroptical property investigation demonstrate the effectiveness of our rational design *** provides a feasible methodology for exploring novel chiral luminescent materials based on versatile though-space skeletons.

关键词： symmetry breaking chiral through-space luminophore circularly-polarized luminescence kinetic racemization

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