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共 943 条记录，以下是71-80 订阅

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Rock / Mine Classification Using Supervised Machine Learning Algorithms

Rock / Mine Classification Using Supervised Machine Learning...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Sivachandra, K. Kumudham, R. Kumar, P Sathish Rajendran, V. Lakshmi, G.R.Jothi Vels Institute of Science Technology and Advanced Studies Department of ECE Chennai India Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Chennai India

ISBN: (纸本)9781665462631

Nowadays, Artificial Intelligence appears in the domain of goetechnics, underwater acoustics, tunneling, geomorphology engineering and also in several fields too. This paper focused on the prospectivefor machine learning approaches which are sub field of artificial intelligence especially in underwater acoustics domain. In this proposal, machine learning approaches such as light gradient boosting, logistic regression, and random forest classifier algorithms are used for categorizing rocks or mines from collected sonar dataset. Based on performance metrics such as precision, F-score, recall, execution time, accuracy and confusion matrix, evaluate overall performance of machine learning models. Here, the experimental results shows that among all classifier algorithms, light gradient boosting achieves greater validation accuracy as 95% also training accuracy as 100∗ moreover, random forest classifier achieves 100% accuracy during training phase. © 2023 IEEE.

关键词： Underwater acoustics

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support 2

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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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. Al Jabri, Jaber Saleh Salim 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 Kattankulathur Department of Computational Intelligence Tamil Nadu 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

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 IEEE.

关键词： Neurodegenerative diseases

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High-Performance Intelligent System for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Passive Islanding Detection and Load Shedding Techniques in Micro Grids: A Brief Review 1st

Passive Islanding Detection and Load Shedding Techniques in ...

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1st International Conference on Renewable Energy, Green Computing, and Sustainable Development, REGS 2023

作者： Reddy, Sareddy Venkata Rami Premila, T.R. Reddy, Ch. Rami Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai600117 India Electrical and Electronics Engineering Joginpally B R Engineering College Hyderabad500075 India

ISBN: (纸本)9783031586064

The passive islanding detection methodologies for the integrated DG approach are the primary subject of this study. Combined with fossil fuel sources and utilized in a cumulative manner, renewable energy generation and the grid are helping to satisfy the growing load demand. Unintentional islanding is the primary challenge of renewable arrangement coordination with grid connectivity. In the event of energy system islanding, the DG will detach from the main grid and begin supplying electricity to locally linked loads. According to many interconnections of DG systems, islanding will be recognized within 2 s using the techniques now in use for isolating the DG hardware from the grid. ‘Remote controlling procedures’ refer to islanding techniques that operate from the utility side, whereas ‘local controlling procedures’ are those that are implemented on the DG side. The methods for detecting passive islanding and the characteristics by which they may be evaluated are the topic of this study. The study provides a thorough discussion of the pros and cons, energy concerns, Zone of Non-Detection, islanding detection of balanced state, and merits of many different passive islanding detection approaches. For researchers developing advanced islanding detection systems, this paper’s thorough explanation of Passive islanding strategies is a valuable resource. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distributed power generation

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Shaik Abdul Yunus Basha K.Ulaga Priya Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

As artificial speech generates technological evolution; fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches.

关键词： Deepfakes Accuracy Audio systems Speech recognition Metadata Feature extraction Audio recording Real-time systems Fraud Bagging

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Artificial Neural Network-based Relay Selection in Underwater Wireless Sensor Network 2

Artificial Neural Network-based Relay Selection in Underwate...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sreeraj, Anoop Vijayalakshmi, P. Rajendran, V. Department of Electronics & Communication Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

As one of the most challenging and promising wireless communications, underwater communication has received much attention. According to the distance, there may be significant delay, limited bandwidth and attenuation, which reduce the system's overall performance. To solve these problems, Artificial Neural Network based Relay Selection (ANNRS) in Under Water Wireless Sensor Networks (UWSN) is introduced. This mechanism uses the Artificial Neural Network (ANN) algorithm to choose the relay nodes to pass the data from sender to receiver. The ANN algorithm picked out the relay using four input features: Energy, bandwidth, depth and packet received ratio (PRR). The UWSN may dynamically pick the optimum relay node depending on the current network circumstances by employing an ANN-based relay selection system, which improves network performance, lowers energy consumption, and increases data transmission reliability. The extensive simulation outcomes demonstrate that the ANNRS mechanism enhances the performance, such as PRR, and energy, and minimizes the delay and Assemble Propagation Distance in UWSN. © 2023 IEEE.

关键词： Artificial neural network Energy efficiency Relay selection Simulation analysis Underwater wireless sensor network

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Real-Time Identification of Medical Equipment Using Deep CNN and computer Vision 1st

Real-Time Identification of Medical Equipment Using Deep CNN...

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

作者： Rubi, Jaya Hemalatha, R.J. Janney, Bethanney Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9783031350801

Sign language is a way of communication in which hand gestures and symbols are used to connect with each other. Communication provides interaction among people to exchange feelings and ideas. Similarly, when it comes to the handling of medical equipment using a robot, sign language should not be a barrier to carrying out such applications. The purpose of this work is to provide a real-time system that can convert Sign Language (ISL) to text format. Most of the work is based on the handcrafted feature. This paper concentrates on introducing a deep learning approach that can classify the signs using the convolutional neural network. First, we make a classifier model using the signs, then using Kera’s implementation of convolutional neural network using python we analyze those signs and identify the surgical tools. Then we process another real-time system that uses skin segmentation to find the Region of Interest in the frame. The segmented region is fed to the classifier model to predict the sign. The predicted sign would gradually identify the surgical tool and convert the sign into text. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Surgical equipment

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Hyperledger Blockchain and Lightweight Bcrypt Symmetric Key Encryption to Boost Cloud Computing Security Effectiveness

Hyperledger Blockchain and Lightweight Bcrypt Symmetric Key ...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Banushri, A. Karthika, R.A. Vels Institute of Science Technology & Advanced Studies Dept. of Computer Science & Engineering Pallavaram Chennai India SRM Institute of Science & Technology Dept. of Computational Intelligence Kattangulathur Chennai India

ISBN: (纸本)9798350333244

One of the key business models of contemporary information technology is cloud computing. It offers users a range of services (hardware, software) with little user engagement and at a reasonable price. Utilising a cloud environment raises several challenges, chief among them being security and privacy. This study explores the issues and proposes a Lightweight Bcrypt Symmetric Key (LBSK) data encryption and decryption technique with key rotation. A variety of algorithms and Blockchain technologies are used to protect the information. Encryption is one of the most fascinating features of data security technology. An Aggregated Authority Certificate Provider (AACP) is recommended with blockchain authentication for secured cloud data audit, which lessens the burden on the data owner. The suggested AACP continuously runs on a cloud server and tracks user requests. For the non-trusted provider, the Hyperledger blockchain proxy re-encrypts data to increase protection and privacy. The Blockchain algorithm was used to encrypt the data from beginning to end, and it was then saved in the cloud. Without storing the data locally, a security degree, public verification, and performance factors must be taken into consideration. Compared to current methods, the proposed strategy is intended to deliver a better result. The Blockchain technique was used to encrypt the data from beginning to end, and it was then saved on the cloud. Public verification and performance considerations raise the security level for remote data verification without storing the data locally. In comparison to current methods, the proposed method offers a better result. © 2023 IEEE.

关键词： Blockchain

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Internet of Things (IOT) based Smart Nebulizer System 5

Internet of Things (IOT) based Smart Nebulizer System

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

作者： Gowtham, R. Chandrasekaran, R. Harendharan, B. Inakollu, Sai Amrutha Vels Institute of Science Technology & Advanced Studies Chennai India Sri Venkateswara College of Engineering Chennai India Parul University Vadodara India

ISBN: (纸本)9798350379945

The prevalence of asthma and other respiratory diseases is increasing due to continuous inhalation of contaminated air. To address this issue, an IoT-based smart nebulizer has been developed with advanced features to aid individuals suffering from chronic pulmonary conditions. Nebulizers are typically used to deliver medication as a mist to patients with respiratory diseases. The proposed smart nebulizer incorporates a high-efficiency jet motor capable of nebulizing 500 ml of air per cycle at a rate of 15 respiratory cycles per minute. It also includes a respiration rate sensor and IoT sensors to monitor patients' respiratory flow during nebulization. This technology is physician-friendly, enabling healthcare providers to remotely set and control the nebulizer's flow rate. The developed IoT-enabled smart nebulizer utilizes Wi-Fi for data transmission and reception, allowing real-time analysis and monitoring of patients' respiratory patterns. This innovation offers a more efficient and controlled way to manage respiratory treatments. © 2024 IEEE.

关键词： Pulmonary diseases

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Maximum Energy Transfer Efficiency in Solar-Powered Bilateral Dual-Stage AC to DC Converters for Vehicle-to-Home and Vehicle-to-Grid Applications 4

Maximum Energy Transfer Efficiency in Solar-Powered Bilatera...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Rubavathy, S. Jaanaa Bharathiraja, M. Sasikala, K. Gomathi, S. Saveetha University Saveetha School of Engineering Simats Tamilnadu Chennai India Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India

ISBN: (纸本)9798331508845

Transition to alternative sources of energy is essential to halting global warming and lessening its effects on biodiversity. In this research, the beneficial environmental effects of integrating powered by solar energy multiple stage bidirectional AC to DC converters in Vehicle-to-Home (V2H) and Vehicle-to-Grid (V2G) systems are investigated. By enabling efficient energy transmission between electric vehicles (EVs), the grid, and household energy systems, these converters enhance the handling of energy and long-term sustainability. Due to its simple construction and customizable properties, 2 stages isolated AC/DC converters are frequently employed in the rapidly expanding renewable energy vehicle industry. Here, we construct and assess a highly effective dual-stage in-car charger that operates effectively across a broad voltage range for both I/P and O/P. The main portion comprises PFC (power factor correction) converters with bridgeless buck-boost completely soft switching design, while the battery end converter is powered by an isolated filter-in-built direct current converter. The primary stage's principal benefit is the elimination of electromagnetic interference (EMI) caused by sudden voltage fluctuations by creating an I/p and O/P common ground. This lessens the requirement for EMI filtering in common mode. Both the common mode (CM) noise and the total leakage current are maintained at a reasonable level. Greater efficiency and power density are the main advantages of the second stage. In order to make sure the safe charging and discharging of batteries, a converter that offers controls battery current and galvanic isolation is needed. © 2024 IEEE.

关键词： Vehicle-to-grid

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