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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Ruchitha, Inaganti Dhavale, Sunita Defence Institute of Advanced Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331527822

Height measurement is crucial across various domains including healthcare, ergonomics, security, and forensic analysis. Traditional methods, such as stadiometers, often require cooperation from the subject and can be intrusive, posing significant challenges especially with children, the elderly, or individuals with disabilities. This survey paper addresses the growing need for artificial intelligence (AI) based noninvasive, automated height measurement techniques that can effectively estimate height without physical interaction. In this paper, we examined different state of the art methodologies for height measurement application area, including AI, computer vision (CV), deep learning (DL), depth imaging, and 3D laser scanning. While 3D scanning and depth imaging methodologies provide precise measurements, but they require specialized equipments which leads to additional costs for implementation. Recently, DL and advanced CV models have shown great accuracy in height estimation. Promising solutions for uncooperative subjects can be found by utilizing facial recognition and anatomical traits, and incorporating biometric parameters which can improve prediction accuracy. Despite these advancements, challenges remain with methods requiring multiple images or additional equipment, underscoring the need for more practical and user-friendly solutions. This survey highlights the necessity for tailored approaches to address specific measurement challenges and proposes guidelines for future studies to improve efficiency of noninvasive height estimation technologies. © 2024 IEEE.

关键词： Laser applications

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Comparative Study for Performance Analysis of Machine Learning Algorithms for Survival of COPD Patients 21

Comparative Study for Performance Analysis of Machine Learni...

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21st International Learning and Technology Conference, L and T 2024

作者： Ray, Prabhudutta Sharma, Sachin Rawal, Raj Rizvi, Ahsan Z. Institute of Advanced Research Computer Science & Engineering Gandhinagar India Ganpat University Computer Science & Engineering Gandhinagar India Gujarat Pulmonary and Critical Care Medicine Gujarat Ahmedabad India Institute of Advanced Research Computer Science & Engineeringn Gandhinagar India

ISBN: (纸本)9798350393569

Major causes of mortality rate vary due to COPD disease rising considerably today due to fast lifestyle. Accurate analysis of cardio data and correct prediction is a major challenge to save lives at an early stage. Using critical data analysis by applying statistical techniques and providing correct features as input to the ML techniques to predict the chances of heart attack and save many lives. Finding overall risk using artificial intelligence techniques and ML algorithms is highly demanding. This paper focuses on the various ML techniques that are applied and their comparative analyses are observed to correctly identify the CVD diseases. © 2024 IEEE.

关键词： Deep learning

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Enhancing Plant Leaf Disease Prediction Through advanced Deep Feature Representations: A Transfer Learning Approach

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 469-482页

作者： Naralasetti, Veeranjaneyulu Bodapati, Jyostna Devi Department of Information Technology Vignan’s Foundation for Science Technology and Research Guntur India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India

Plant diseases present a considerable threat to the farming industry, causing significant economic losses by reducing crop yields. The emergence of deep neural network models in the realm of computer vision has brought about a groundbreaking transformation in the swift recognition and surveillance of plant diseases. In this study, we introduce an innovative approach for plant disease prediction, leveraging transfer learning techniques to enable accurate and early detection of plant diseases. The proposed leaf disease prediction framework encompasses multiple key stages, including preprocessing, feature extraction, classification, and postprocessing. Notably, our model excels at feature extraction, utilizing the pre-trained convolutional architecture of VGG16 to extract valuable information from leaf images. These features are then fed into a deep neural network model designed for disease prediction. The feature extraction method employed in our approach excels at capturing intricate disease-related details from plant images. When the deep features extracted are utilized for classification using our proposed classifier, the outcome demonstrates a substantial enhancement in disease prediction performance. Based on extensive experimentation with the benchmark plant village dataset, it is evident that our transfer learning-based approach outperforms existing methods, achieving an impressive accuracy rate of 96.56%. Besides accuracy, the proposed approach surpasses F1_score and Kappa scores for the benchmark plant village dataset. © The Institution of Engineers (India) 2024.

关键词： Forecasting

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An Enhanced Detection System of Autism Spectrum Disorder Using Thermal Imaging and Deep Learning

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SN computer science 2025年第2期6卷 1-11页

作者： Jegan Amarnath, J. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Tamil Nadu Chennai 600117 India

Autism Spectrum Disorder (ASD) is a brain-based condition characterized by social difficulties and repetitive behaviors. Traditional diagnostic methods can be subjective and time-consuming. Current diagnostic techniques for ASD often rely on behavioral assessments and clinical observations, which can be inconsistent and prone to subjectivity. Additionally, there is a need for more reliable and objective methods to differentiate between autistic and non-autistic individuals. Existing imaging techniques require further refinement to improve accuracy and efficiency in detecting ASD. The primary objective of this research is to develop and evaluate an enhanced detection system for ASD using thermal imaging and Improved Mask Faster Recurrent Convolutional Neural Network (IMFRCNN). This research focuses on enhancing the detection of ASD using Deep Learning (DL) methods and thermal imaging. Traditional diagnostic techniques for ASD often rely on subjective behavioral assessments, highlighting the need for more reliable methods. By examining cerebral imaging data from the thermal face image datasets collect facial thermal images using thermal cameras. Ensure the dataset is diverse, including varying expressions and environmental conditions. The study compares the effectiveness of IMFRCNN and the ResNet 50 system in classifying thermal images of 50 autistic and 50 non-autistic individuals. Emotions were evoked using audio-visual stimuli, and temperature variations in facial areas such as the nose, forehead, cheek, and eyes were measured. Anger showed the greatest temperature differences, with a 12.6% variation between autistic and non-autistic individuals. The IMFRCNN achieved a reliability of 96%, while the ResNet 50 achieved 90%. These findings indicate that computer-aided assessment techniques utilizing thermal imaging and DL can provide a consistent and objective method for identifying individuals with ASD. © The Author(s), under exclusive licence to Springer Nature Sing

关键词： Deep learning algorithms Diagnostic tool Improved mask faster recurrent convolutional neural network Performance measures

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A Comprehensive Exploration of Storage Attacks in Information-Centric Networking Environments 10

A Comprehensive Exploration of Storage Attacks in Informatio...

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10th International Conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Sethu, S. Manikandan, A. Vels Institute of Science Technology & Advanced Studie Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

To help users overcome the limitations of current IP networks, Data Networking (NDN) and Content-Centric Network (CCN) architectures have been created to move communication from host-centric to content-centric. NDN is essentially a network for the dissemination of content. In NDN, a user's request for material will be the only reason for contact. The descriptive capabilities of NDN technology leverage the globally unique names that encryption and content-based security provide. As part of its design, it upholds content validity and reliability. We examine several storage risks that NDN caches face, including content poisoning attacks, cache pollution attacks, and man-in-the-middle attacks. © 2024 IEEE.

关键词： Internet protocols

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Digital twin empowered lightweight and efficient blockchain for dynamic internet of vehicles

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Digital Communications and Networks 2024年第6期10卷 1698-1707页

作者： Haoye Chai Supeng Leng Jianhua He Ke Zhang School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu611731China Shenzhen Institute for Advanced Study UESTCShenzhen518000China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

The Internet of Vehicles(Io V)has great potential for Intelligent Transportation Systems(ITS),enabling interactive vehicle applications,such as advanced driving and *** is crucial to ensure the reliability during the vehicle-to-vehicle interaction *** the emerging blockchain has superiority in handling security-related issues,existing blockchain-based schemes show weakness in highly dynamic Io *** the transaction broadcast and consensus process require multiple rounds of communication throughout the whole network,while the high relative speed between vehicles and dynamic topology resulting in the intermittent connections will degrade the efficiency of *** this paper,we propose a Digital Twin(DT)-enabled blockchain framework for dynamic Io V,which aims to reduce both the communication cost and the operational latency of *** address the dynamic context,we propose a DT construction strategy that jointly considers the DT migration and blockchain computing ***,a communication-efficient Local Perceptual Multi-Agent Deep Deterministic Policy Gradient(LPMA-DDPG)algorithm is designed to execute the DT construction strategy among edge servers in a decentralized *** simulation results show that the proposed framework can greatly reduce the communication cost,while achieving good security *** dynamic DT construction strategy shows superiority in operation latency compared with benchmark *** decentralized LPMA-DDPG algorithm is helpful for implementing the optimal DT construction strategy in practical ITS.

关键词： Digital twin Blockchain Internet of vehicles Multi-agent DDPG

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System 3

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Preetha, P. Packialatha, A. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350374643

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics. © 2024 IEEE.

关键词： Prediction models

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model 5

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

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

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

ISBN: (纸本)9798350379945

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. © 2024 IEEE.

关键词： Audio recordings

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Automated Detection and Classification of Defects in Pcb Using Deep Learning Techniques: A Comparison of Resnet50v1 and Efficient Det D1 9

Automated Detection and Classification of Defects in Pcb Usi...

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9th IEEE India International Conference on Power Electronics, IICPE 2023

作者： Sharma, Sachin Patel, Dhruv Shah, Dharmesh Indrashil University Computer Science and Engineering Mehsana India Institute of Advanced Research Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9798350307252

To keep pace with the fast-growing Printed Circuit Board (PCB) industry, automated processes are necessary for producing flawless products. Automated visual PCB inspections have become indispensable for addressing the challenges posed by conventional manual inspection systems, as they offer faster, more quantitative, and objective assessments. The aim of this research is to determine an appropriate and effective Convolutional Neural Network (CNN) algorithm for detecting and classifying different PCB defects. Two CNN algorithms, namely Single Shot Detector-Residual Neural Network 50 V1 Model (SSD ResNet50v1) and Efficient Object Detector Model D1 (EfficientDet D1), are evaluated for their classification performance, and their results are compared. The training of these models is conducted on Google Colaboratory, a cloud-based service that provides a free online Jupyter notebook environment for deep learning model training on NVIDIA's Graphical Processing Unit (GPU). The PCB dataset comprises 10,668 images of size 600 × 600 with six common PCB defects frequently found in the industry. The study calculates the classification performance of both algorithms and presents their results. The accuracy achieved by SSD ResNet50v1 is 81.68%, while EfficientDet D1 reaches 71.75%. A comparison of both architectures' accuracies shows that SSD ResNet50v1 performs better than EfficientDet D1. © 2023 IEEE.

关键词： Neural network models

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Classification of Soil Moisture Content with the Application of Deep Learning

Classification of Soil Moisture Content with the Application...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Ansari, Mohammad Shaharyar Gautam, Prateek Raj Centre for Advanced Studies AKTU Department of Computer Science and Engineering Lucknow India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798350364088

India is an agricultural-oriented nation where soil moisture plays a vital role in the growth of plants and crops. Measuring soil moisture and maintaining a balance in the soil moisture content help sustain water resources. The traditional method for calculating the soil moisture content is to calculate the weight of moisture soil and dry soil differences. With time passage, different techniques are introduced based on different sensors. Recently, machine learning and deep learning have emerged as powerful tools and can be used for the classification of different levels of soil moisture. Convolution Neural Network is a technique used in the field of deep learning for the classification of images. This algorithm is used to analyze the images on various factors like texture, color, hue, and shapes. In this research, images have been collected from the internet image search and stored in separate folders according to their class. CNN is applied to the created dataset and after tuning the hyperparameters model reaches an accuracy of 73%. © 2024 IEEE.

关键词： Soil moisture

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