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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Kar, Siddheswar Wisemin Lins, A. Kavin, K.S. Patwa, Sharda Martin, G.W. Karthikeyan, D. Medi-Caps University Department of Electrical Engineering M.P India Vels Institute of Science Technology and Advanced Studies Department of Electrical and Electronics Engineering Chennai India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India Marthandam College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai Kattankulathur India

ISBN: (纸本)9798350393378

Photovoltaic (PV) organizations are one of the best prevalent types of renewable energy in use today. The battery charge controller is crucial to the effectiveness of freestanding PV systems. The maximum power point tracking (MPPT) responsibility is tracks the supreme vitality while managing the voltage level. Electric vehicles (EVs) are a more cost-effective alternative to gasoline-powered vehicles, as well as being more ecologically benign when it comes to human transport. The ability to charge of EVs is its distinguishing characteristic. Each charging station has a dissimilar level of control founded on the requirements, and every charger has a power converter. A modified zeta converter utilized to charge batteries for electric vehicles offers better outcomes in this investigation. The testing outcomes also support the proposed algorithm's to enhance performance. A demonstration of the suggested Artificial Neural Network (ANN-based MPPT) algorithm's qualities is given. Several effective power converters are examined and described in this work. The estimated efficiency and disadvantages are also covered. © 2023 IEEE.

关键词： Pulse width modulation

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Biomimetic hydrogels with mesoscale collagen architecture for patient-derived tumor organoids culture

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Bioactive Materials 2024年第8期38卷 384-398页

作者： Jiaxin Wang Zhilin Sui Wei Huang Zhentao Yu Ling Guo Shenzhen Key Laboratory of Epigenetics and Precision Medicine for Cancers National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital&Shenzhen HospitalChinese Academic of Medical Sciences&Peking Union Medical CollegeShenzhen518116China Center for Cell and Gene Circuit Design CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic BiologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055GuangdongChina Department of Biology Academy for Advanced Interdisciplinary StudiesSouthern University of Science and TechnologyShenzhenChina

Patient-derived tumor organoids(PDTOs)shows great potential as a preclinical ***,the current methods for establishing PDTOs primarily focus on modulating local properties,such as sub-micrometer ***,they neglect to capture the global millimeter or intermediate mesoscale architecture that have been demonstrated to influence tumor response to therapeutic treatment and tumor *** this study,we present a rapid technique for generating collagen bundles with an average length of 90±27μm and a mean diameter of 5±1.5μm from tumor tissue debris that underwent mechanical agitation following enzymatic *** collagen bundles were subsequently utilized for the fabrication of biomimetic hydrogels,incorporating microbial transglutaminase(mTG)crosslinked *** biomimetic hydrogels,referred to as MC-gel,were specifically designed for patient-derived tumor *** lung cancer organoids cultured in MC-gel exhibited larger diameters and higher cell viability compared to those cultured in gels lacking the mesoscale collagen bundle;moreover,their irregular morphology more closely resembled that observed in *** MC-gel-based lung cancer organoids effectively replicated the histology and mutational landscapes observed in the original donor patient’s tumor ***,these lung cancer organoids showed a remarkable similarity in their gene expression and drug response across different *** recently developed model holds great potential for investigating the occurrence,progression,metastasis,and management of tumors,thereby offering opportunities for personalized medicine and customized treatment options.

关键词： Extracellular matrix(ECM) Mesoscale architecture Collagen bundles Lung cancer organoids(LCOs) Biomimetic hydrogel

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Reinforcement Learning Technique Based Automated Feature Analysis of Gene Expression Data

Reinforcement Learning Technique Based Automated Feature Ana...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

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

Immunohistochemistry and reverse transcription polymerase chain reaction, it is feasible to find molecular markers that are specific to cancer; nevertheless, there are no pathognomonic molecular markers available for the vast majority of solid tumors at this time. In this paper, we develop a Reinforcement learning Technique based automated feature analysis of gene expression data. This provides support for the idea that, on average, all the validation findings and training data from each epoch were utilized. This proposed method helps to acquiring knowledge at a more leisurely pace is, in the end, what makes episodic deep RL possible as a potential choice for students to pursue. To put it another way, the slow but steady accumulation of knowledge paves the path for the rapid extension of one existing body of information. The proposed methods provide 98 percent of the accuracy result than existing ANN and DNN. The proposed framework outperformed existing methods in terms of increase accuracy rate and reduced execution time than other methods

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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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Web Based Cyber Attack Detection for Industrial System (PLC) Using Deep Learning

Web Based Cyber Attack Detection for Industrial System (PLC)...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： A. Yasir Kalaivani Kathirvelu M. K. Arif Department of Computer Science and Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Web apps are frequently targeted by cyber-attacks due to their network access and security flaws. Instances of attacks targeting online applications might pose significant risks. The issue of cyber security continues to pose a significant barrier in Industry 5.0 scenarios, since cyber-attacks have the potential to result in severe outcomes such as production disruptions, data breaches, and even physical injuries. In order to tackle this difficulty, this study suggests a novel deep-learning approach for identifying web-based assaults in the context of Industry 5.0. The investigation explores transformer models, which are methods used in deep learning, for their capacity to accurately classify attacks and detect unusual behavior. The results of this research demonstrated the higher performance of the suggested transformer-based system, surpassing earlier methods in terms of accuracy, precision, and recall. Deep learning plays a crucial role in effectively tackling cyber security issues in Industry 5.0 settings.

关键词： Deep learning Instruments Production Transformers Robustness Security Fifth Industrial Revolution

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Latha Mary, S Angel Ulagapriya, K. Poonguzhali, A. Menaha, R. David, Beaulah Priyadharshini, T.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350334050

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection. © 2023 IEEE.

关键词： Optical radar

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Driver Health Monitoring System to Identify Cardiac Problems in Drivers using Live ECG Signal

Driver Health Monitoring System to Identify Cardiac Problems...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. K. Arif Kalaivani Kathirvelu A. Yasir Department of Computer Science and Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

According to contemporary estimations, a significant proportion of elderly drivers are prone to experiencing accidents or collisions due to their diverse health states during the act of driving. Currently, individuals in the middle-age and younger demographics are encountering comparable challenges. Research indicates that drivers who experienced crashes caused by medical emergencies are not influenced by vehicle design or the integrity of the road, as seen by the sort of crashes and the manner in which they occurred. This research presents a comparison and measurement of heart rate and heart variability data for the occurrence of heart disease, stroke, or any emergency situation. The methods employed include the use of electrocardiogram (ECG) and Photo Plethysmo Graphy (PPG) *** enhanced performance of deep learning over conventional machine learning methods in addressing various artificial intelligence tasks, including image processing, computer vision, object detection, speech recognition, and medical imaging, has propelled deep learning to become the dominant term in the field of Artificial Intelligence applications

关键词： Deep learning Wrist Technological innovation Speech recognition Electrocardiography Task analysis Monitoring

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Influence of Control Parameters during Drilling of Aluminum Reinforced with HNT Hybrid Matrix Composites

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Advances in Materials Science and engineering 2023年第1期2023卷

作者： Kumar, Patnala Padma Ruban, M. Daniel, Ajith Arul Pugazhenthi, R. Santhosh, K. Department of Mechanical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Automobile Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Mechatronics Wollo University Kombalcha Institute of Technology P.O. Box: 208 Kombolcha Ethiopia

The current paper aims to find the optimum control parameters such as weight percentage of HNT, weight percentage of boron nitride, drilling speed, and feed rate in drilling of aluminum hybrid metal matrix composites. The aluminum matrix material are reinforced with different weight percentage of HNT (3%, 5%, and 7%) and BN (2%, 3%, and 4%). The hybrid matrix are fabricated through the sir casting technique. The prediction of process parameter are optimized using Taguchi assisted grey relation analysis. Also the ANN model is developed to predict the output parameter. Through Taguchi S/N ratio method the effect of each control parameters are optimized. The results show that the weight percentage of HNT and feed rate are the most influencing parameters which affect the drilling of developed aluminum composites. The GRA results show that the optimum parameters are 3% of HNT, 4% of BN, 500 rpm drilling speed, and 20 mm/min in combination, which has minimum surface roughness, lower temperature, highest cutting force, and highest material removal rate. Copyright © 2023 Patnala Padma Kumar et al.

关键词： III-V semiconductors

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Investigation on the response parameters in electric discharge machining of developed aluminium metal matrix composites

Investigation on the response parameters in electric dischar...

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作者： Hemadri, Kadapa Ajith Arul Daniel, S. Parthiban, A. Vinod kumar, T. Department of Mechanical Engineering Sri Venkateswara Institute of Science and Technology Andhra Pradesh Kadapa516003 India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai600117 India

The present research work shows the influence on process parameters of electric discharge machining in newly developed aluminium composites. The Molybdenum Disulphide (MoS2) particulate was added in various proportions of 2%, 4% and 6% on mass fraction and also added Tungsten carbide particulates at 3%, 6% and 9%.The prepared samples were fabricated through stir casting technique. On the response parameter of MRR, Machining timing, and surface roughness, pulse on time, pulse off time, and gap voltage (Ra). According to the findings of the experiments, the best surface finish and machining time was achieved by using a moderate level pulse on time, a higher level pulse off time, and a smaller gap voltage. MRR was achieved by using a relatively low level pulse on time, maximum pulse off time and a medium voltage. © 2022

关键词： Sulfur compounds

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Optimizing E-Commerce Fraud Detection with BiGRU and Capsule Network Architectures 2

Optimizing E-Commerce Fraud Detection with BiGRU and Capsule...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Vii, Suman Rede, Ganeshkumar D. Ramesh, P. Kumar A, Rajesh Bharathi, A. Anand, M. Clement Joe School of Management and Commerce Studies Shri Guru Ram Rai University Uttarakhand Dehradun India Nagpur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India N.S.N College of Engineering and Technology Department of Cse Karur India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India Department of Mathematics Bengaluru India

ISBN: (纸本)9798350373110

Computers and enterprises have proliferated to the point that most financial transactions may now be conducted through electronic commerce systems. This includes systems for credit, telephones, healthcare insurance, etc. The truth is that these solutions are used by both law-abiding citizens and criminals. Con artists also tried a variety of techniques to break into the e-commerce platforms. To provide adequate security for the e-commerce platforms, fraud prevention systems (FPSs) disappoint. Preprocessing, feature selection, and training the model are the three stages that make up the suggested method. Preprocessing includes discretization and min max normalization. Discretization is used to shorten attribute intervals and normalization is used to divide attribute values. Improving the efficacy of machine learning (ML) models in the intrusion detection system domain by GA-based feature selection. When training the model, a BiGRU-A-CapsNet was utilized. The suggested method outperforms BiGRU and CapsN et with an average accuracy of 95.44 percent. © 2024 IEEE.

关键词： Feature Selection

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