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Optimization enabled LeNet for big data classification using MapReduce framework on COVID-19 data

Australian Journal of Electrical and Electronics engineering 2024年第4期21卷 409-424页

作者： Patle, Bhagyashree R. Vijayarajan, V. VIT Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

The big data clustering is a requisite for generating the data in the digitalised globe. The old-fashioned clustering approaches are not large sized and highly unorganised big data. Thus, to obtain the efficiency of big data clustering, a new architecture is required. This work introduces the Jaya African Vulture Optimization Algorithm-based LeNet (JAVO-based LeNet) for big data classification on COVID-19. The big data clustering is detected by using the LeNet model, which is tuned by the JAVO algorithm. Here, the JAVO algorithm is the integration of the Jaya algorithm and AVOA algorithm. Further, using the Deep Embedded Clustering (DEC) technique for partitioning the smaller data to improve the performance of big data classification. The classification process for COVID-19 is established in the MapReduce (MR) framework. For each mapper, the pre-processing is completed based on Min-Max normalisation and the feature fusion is performed based on the Hellinger distance, which is measured with a deep residual network (DRN). Finally, the classification process for COVID-19 is done by considering the fused data acquired from each mapper with LeNet, which is tuned by the JAVO algorithm. Furthermore, the analysis of the proposed method attains the testing accuracy of 0.948, specificity of 0.939 and sensitivity of 0.950. ©, Engineers Australia.

关键词： Cluster analysis

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Reliable Fetal Health Assessment through Ensemble Techniques in Machine Learning 5

Reliable Fetal Health Assessment through Ensemble Techniques...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajkumar, M. Madhan, E.S. Karthikeyan, D. Gopal, Mohanraj Nithish, G. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore Campus Tamil Nadu India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632014 India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore Campus Tamil Nadu India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore632104 India Vellore Institute of Technology Vellore Campus Tamil Nadu India

ISBN: (纸本)9798350389609

Ensuring the well-being of foetuses during pregnancy is crucial for preventing child and maternal mortality, aligning with the United Nations' Sustainable Development Goals. Utilizing a dataset of 2126 records from Cardiotocography (CTG) exams, obtained from the Kaggle repository, the study employs machine learning ensemble techniques, specifically stacking with 10x10 validations, to increase the classification of fetal health's precision and dependability. To address class imbalance, the dataset is resampled to 150%, and filter-based feature selection using the chi-squared statistical test is applied. Base models, including Decision Trees (C4.5), Random Forest (100 trees), Logistic Regression, and Sequential Minimal Optimization (SMO), are utilized. With the help of the Weka Machine Learning software tool, the ensemble model, after hyper parameter tuning and employing diverse techniques, with 98.68% accuracy, the model effectively classifies fetal health, indicating robust performance across various evaluation metrics. © 2024 IEEE.

关键词： Electronic health record

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Leveraging Convolutional Neural Network and Explainable AI for Enhanced Pneumonia Detection 1

Leveraging Convolutional Neural Network and Explainable AI f...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Murugan, Ramalingam Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331520540

Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification, particularly in detecting pneumonia from chest X-ray images. Despite their high accuracy, CNNs face challenges in clinical adoption due to their lack of interpretability. Medical practitioners require a clear understanding of the decision-making process to trust AI-generated diagnoses. To address the aforementioned challenge, we propose pneumonia classification model incorporating CNN with a combination of the model and Grad-CAM, for enhanced interpretability. Grad-CAM visually outlines critical regions, influencing the prediction, so the clinician can easily confirm AI-based diagnoses with a more confident and credible approach. It is exclusively for pediatric pneumonia, an accurate and explainable machine for early interventions that can also work in resource-scarce regions. Our research uses a set of 5,863 pediatric chest X-ray images, divided into Pneumonia and Normal classes. The dataset was acquired from Guangzhou Women and Children's Medical Center and divided into three subsets: training, validation, and test. By improving the accuracy and transparency of the diagnosis, this model can help bridge the gap between AI and the real world. This can help facilitate quicker and more reliable pneumonia diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Smart Surveillance Object Detection System 3

Smart Surveillance Object Detection System

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chaudhary, Gaurav Pathak, Shreya Choudhary, Vikas Chauhan, Love Tripathi, Abhishek N School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350372120

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM's Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities. © 2024 IEEE.

关键词： computer vision

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer Learning 6th

Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer...

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6th International Conference on information systems and Management science, ISMS 2023

作者： Bhattacharya, Varun Tripathy, Balakrushna School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9783031664090

The detection of gender, age, and ethnicity using machine learning techniques holds significant promise for applications in marketing, healthcare, and law enforcement. Previous methods relied on conventional machine learning and manual feature engineering, which often struggled to capture the complex relationships between facial features and these demographic factors. To overcome these limitations, our novel approach leverages the powerful YOLOv5 framework, integrating transfer learning and deep neural networks. This cutting-edge method excels at accurately classifying gender, age, and ethnicity from facial images, thanks to its deep neural network architecture with advanced object detection capabilities. Our work represents a substantial advancement in gender, age, and ethnicity prediction, showcasing the potential of advanced machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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Advancing Plant Disease Prediction Accuracy with ViT Based Deep Learning Model

Advancing Plant Disease Prediction Accuracy with ViT Based D...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent systems and Communications, COSMIC 2024

作者： Sam, Felicita Kavitha, B.R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu Vellore India

ISBN: (纸本)9798331517892

Agriculture is the key source for many people's livelihoods and an important contributor to a country's economy. There is a huge demand to streamline the process in the field of agriculture by integrating Computing into the traditional way of farming. Computing plays a key role in handling this sector with many newer revolutions that would automate the various processes involved in overall farming. Particularly, machine and deep learning based models have shown remarkable results in smart farming. Rice crop is considered in this work which is the major crop in India. It is necessary to meet the demands of the growing population in terms of sufficient production of this crop. One of the key issues in this crop is the plant diseases that impact the overall crop yield production considerably. Among the various diseases, the most commonly occurring disease is White stem Borer. Diagnosing the disease at an early stage could treat the problem at the earliest to ensure the appropriate amount of yield production. In this paper, a Vision transformer (ViT) based deep learning model is proposed to predict the white stem borer disease on the PlantDoc dataset and the results obtained are compared with the other promising CNN based pre trained models like VGG19, InceptionV3, Resnet50. The experimental output shows that Vision transformer based model outperforms the other proven models with an accuracy of 96%. © 2024 IEEE.

关键词： Plant diseases

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Architectural framework for multiplayer cooperative cloud gaming to optimise quality of service

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International Journal of Networking and Virtual Organisations 2024年第2期30卷 174-205页

作者： Srilakshmi, Nimmagadda Kannan, Naresh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-intensive tasks within the cloud. In this paper, an architectural model for supporting cooperative gaming towards multiplayer is proposed to improve the quality of service in terms of bandwidth and latency compared to existing architectural models. According to this study, the time it takes to change a video has grown by 95% compared to how well ad hoc mobile cloudlets and the cloud do at sharing videos. Although P-frames, I-frames, and B-frames account for 23%, 78%, and 94% of the resource consumption, density in intra-stream P-frames is also considered. Similarly, resource and CPU use based on skewness are effective compared to mobile devices' 95% faster video sharing and 77% shorter link delays. © 2024 Inderscience Enterprises Ltd.

关键词： Quality of service

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