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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Kumudham, R. Kumar, Sathish Megalan, Leo L Sivakumar, S. Venkateshkanna, T. Velmurugan, V. Chennai India Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Chennai India Department of ECE School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Chennai126 India JAYA Engineering College Department of Electronics and Communication Engineering Chennai India R&D Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Department of ECE Chennai India

ISBN: (纸本)9781665462754

In this proposed work, we trained the neural network models for feature extraction and categorize the underwater seafloor sonar images into sediments, sands, mines, rocks, mud, gravels, etc using Convolutional Neural Network such as ResNet50, Inceptionv3 and VGG-19. Moreover, the performance of neural network is estimating by metrics measures such as validation accuracy, accuracy, loss, and accuracy loss upto 10 epochs. Hence assessment had performed among all three neural network models. The experimental output signifies that ResNet50 method achieves 94.4% appreciable accuracy with loss as 0.01 in distinguishing sonar imagery into sediments, sands, mud © 2022 IEEE.

关键词： Convolutional neural networks

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Innovative Approaches to Dental Age Estimation: A Comprehensive Review of CNN Technologies and Ethical Issue

Innovative Approaches to Dental Age Estimation: A Comprehens...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Domilin Shyni Sheeja Herobin Rani C I. Rexiline Sheeba Mahizha S I Dept. Of Computer Science and Engineering DMI Engineering College Aralvoimozhi India Dept. Of Electronics and Communication Engineering St.Xavier’s Catholic College of Engineering Nagercoil India Dept. Of Electronics and Communication Engineering Sathyabama Institute of Technology and Science Chennai India Dept. Of Information Technology St.Xavier’s Catholic College of Engineering Nagercoil India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Estimating dental age is crucial in multiple disciplines, such as forensic science, orthodontics, and pediatric dentistry. Traditional methods, while effective, often involve manual processes and subjective judgment. Recent advancements in Convolutional Neural Networks (CNNs) have introduced innovative approaches that promise greater accuracy and efficiency. This comprehensive review examines the latest CNN technologies applied to dental age estimation, highlighting their methodologies, performance, and advancements. Key aspects covered include the development and optimization of CNN architectures, the role of data preprocessing and augmentation, and the impact of these technologies on predictive accuracy. Additionally, the review addresses critical ethical issues associated with the implementation of CNNs in dental age estimation, such as data privacy, potential biases, and the implications of automated decision-making in sensitive contexts. By integrating insights from current research and practical applications, this review provides a thorough understanding of both the technological innovations and ethical considerations driving progress in determining dental age .

关键词： Ethics Technological innovation Accuracy Reviews Neural networks Estimation Manuals Dentistry Convolutional neural networks Optimization

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Enhanced Particle Swarm Optimization based Node Localization Scheme in Wireless Sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Singh, Shashank Poonkuzhali, R. Nithya, G. Kumar, R. Anil Kartigeyan, J. Ramya, S. Department of Computer Science and Engineering Integral University Kurshi Road Uttar Pradesh Lucknow India Department of Information Technology K.S.Rangasamy College of Technology Tamilnadu Tiruchengode India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Telangana Secunderabad India Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Telangana Hyderabad India Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu Thalavapalayam India

ISBN: (纸本)9781665489621

Wireless sensor networks (WSNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WSNs generally must be known. WSN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. Some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle Swarm Optimization based Node Localization Scheme (EPSONLS) in WSN. The presented EPSONLS technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPSONLS model applies the PSO algorithm to identify the node position. In addition, the EPSONLS model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPSONLS model over recent approaches. © 2022 IEEE.

关键词： Sensor nodes

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Protecting Data from DDOS Attack in a Cloud Based Intrusion Detection System Security through Enhanced RSA Algorithm 2

Protecting Data from DDOS Attack in a Cloud Based Intrusion ...

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2nd IEEE International Conference on communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Naik, Pramod Kumar Divya, S. Mohan, M. Velusamy, Jayaraj Radhakrishnan, P. Rajasekaran, Arun Sekar Dayananda Sagar University Bengaluru Department of Cse India Biomedical Engineering Dr. N.G.P. Institute of Technology India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India School of Cs and Ai Sr University Telangana Warangal India Sr University Department of Ece Telangana Warangal India

ISBN: (纸本)9798350369960

Since cloud computing platforms have become an essential component in a range of different industries, it is still challenging to ensure that user data and system resources will remain secure inside these settings. This paper proposes a methodology to identify and mitigate unwanted traffic and packets, specifically duplicate packets, in cloud computing envi- ronments. The procedure requires the creation of an IntrusionFinder and Detector (IF-AD) system that allots secondary storage and keeps user information in a secure manner. This technique does a comparison between the sizes of downloaded files and the sizes of the original files, searching for any variations between the two that would suggest a probable distributed denial of service assault (DDoS). The RSA cryptography procedure is employed in order to further strengthen the degree of security that is provided by any future file transfers that may take place. The problem that has been identified and a potential solution for has been proposed with the intention of enhancing the safety of cloud-based systems by identifying and preventing unauthorised access to files as well as modifications to them. © 2023 IEEE.

关键词： Cloud computing decryption duplicate packets encryption Intrusion-Finder and Detector RSA

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A Robust Network Connection Across Internet of Things Devices Inside the Room with Visible Light communication

A Robust Network Connection Across Internet of Things Device...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： N. Banupriyal Suresh G R. Nandhini Anu Mary Mathew L. Saravanan Anitha G Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Communication Engineering Mar Baselios Christian College of Engineering and Technology Kerala Department of Electronics and Communication Engineering PERI Institute of Technology Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Chennai

In order to better monitor and engage with the communal assets of smart cities, the amount of Internet of Things (IoT) devices that have been installed has been continuously rising. This might result in a reduction in network efficiency as a consequence of the increasing volume of network traffic and connections brought about by different Internet of Things devices. In order to address these challenges, one approach that shows promise is to take advantage of the close physical proximity of communicating devices and to facilitate communication between individual devices in order to achieve low latency, efficient use of bandwidth, and reliable service. Taking use of new technologies like visible light and ultrasound, our study aims to improve the performance of indoor Internet of Things (IoT) connectivity in settings such as smart homes and small office and home offices (SOHO). This method expands the capacity of the network, ensures that network connections among IoT devices are stable, and offers effective ways to allow distance-bounding services. We have constructed communication modules for visible light and ultrasonic using off-the-shelf components, and we are now evaluating their network performance as well as their energy usage.

关键词： Performance evaluation Ultrasonic imaging Smart cities Telecommunication traffic Smart homes Internet of Things Reliability

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Exploration of Inverter's Efficiency and Lifespan at Various Phases of the Evolution using Wide Band Gap Power Devices 3

Exploration of Inverter's Efficiency and Lifespan at Various...

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3rd International Conference on Smart electronics and communication, ICOSEC 2022

作者： Ramesh, S. Senthilkumar, C. Nirmala, P. Ahila, S. Sobitha Geetha, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

In any electric vehicle, electric motors and power electronic components, such as dc-to-ac converters, are essential. Improved electric machine and power electronic components efficiency may be achieved by novel materials, topologies as well as new control systems. During the study, the electromagnetic turbo compressors component for energy storage applications is fully explored. Strong single frequency as well as variable switching speed inverter is needed for the built electrical machine. The efficiency of the inverter and its expected lifespan at various phases of development are examined in this study. Power density and efficiency may be studied using both simulations and measurements throughout the design phase for wide band gap devices in discrete packaging. Surface-mount silicon carbide MOSFETs are used in the final design. By adopting three-level architecture instead of silicon devices, the power density of the inverter has been greatly boosted. Electrical and electronic devices have a finite lifetime because power semiconductors have a finite life cycle. It is possible to estimate the inverter's lifespan depending on loss calculations and thermal gradients associated with virtual junctions. © 2022 IEEE.

关键词： Silicon carbide

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Intelligent System for Vehicles License Plate Recognition Using a Hybrid Model of GAN, CNN and ELM 2

Intelligent System for Vehicles License Plate Recognition Us...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Nirmala, B. Nithya, S. Vidhiya, R. Sunalini, K.K. Kumar, Buddha Hari Varadharajan, Bhoopathy CSE Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Tamilnadu Chennai India SRMIST Electrical and Electronics Department RAMAPURAM Tamilnadu Chennai India KPR Institute of Engineering and Technology Department of English Tamilnadu India Koneru Lakshmaiah Education Foundation Department of English Andhra Pradesh Guntur India Vignan's Institute of Engineering for Women Department of Electronics and Communication Engineering Andhra Pradesh Visakhapatnam India Malla Reddy College of Engineering Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350320923

The scientific community has given license plate recognition systems a lot of consideration. The current methods for vehicle identification need to be improved due to the swift increase in vehicle numbers. In order to lessen reliance on labor, a fully automated system is needed. With the growth of Intelligent Transportation Systems, demand for license plate recognition has increased significantly. License Plate Recognition (LPR) is susceptible to environmental factors such as a complex image background, angle view, and shift in illumination, it is still difficult to correctly recognize the digit letters on license plates. When reading license plates automatically, license plate recognition uses character recognition and image processing to identify the vehicles. The license plate detection and identification subsystems are typically combined into the vehicle license recognition system in order to locate the vehicle and identify the license plate. The Extreme Learning Machine (ELM) is used for categorization, identification, and training. This research suggests a GANCNN-ELM-based technique for detecting vehicle license plates. This method produces an accuracy of about 98.94% which outperforms the GAN-ELM, GAN-SVM, and GAN-CNN models. © 2023 IEEE.

关键词： Support vector machines

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Analysis and Prognosis of Covid-19 using Machine Learning Algorithms and Visualization using Tableau 7

Analysis and Prognosis of Covid-19 using Machine Learning Al...

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7th International Conference on communication and electronics Systems, ICCES 2022

作者： Swaminathan, Aravind Surendran, R. Benjamin, J. Joel Thiyagarajan, Swaminathan Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Digital Transformation Consultant Tata Consultancy Services Chennai India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.

关键词： Coronavirus

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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Performance of CoAP in the Limited Resource Environment: An Empirical Study

Performance of CoAP in the Limited Resource Environment: An ...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Sudhir K. Routray M. Pappa Mahesh K. Jha K P Sharmila Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

Several low bandwidth services have become very popular in the recent years. Wireless Sensor Networks (WSN) and the Internet of things (IoT) are two of them which need fewer resources than the typical modern communication systems. In order to handle the challenges associated with the limited resources, there are appropriate protocols designed for these low resource consuming systems. In case of IoT, where the resources are very limited these protocols play the crucial roles to provide the services without interruption. Constrained Application Protocol (CoAP) is one of them. It handles the resource limited scenarios very efficiently. In this study, features and characteristics of CoAP are analyzed. This protocol has the potential to maintain the long term sustainability of the low bandwidth IoT and WSN.

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