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Analysis of Green computing Models on AWS Using Machine Learning Algorithms 1

Analysis of Green Computing Models on AWS Using Machine Lear...

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Reddy, T. Sudeep Beena, B.M. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering India

ISBN: (纸本)9798331518943

The increasing demand for computational resources has led to a surge in energy consumption, posing significant environmental challenges. This study analyzes green computing models on Amazon Web Services (AWS) by leveraging machine learning algorithms to optimize resource utilization and minimize energy consumption. Using a dataset of virtual machine (VM) metrics-such as CPU and memory usage, network traffic, and power consumption-various machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, are employed to predict energy efficiency and identify key factors influencing sustainable computing. The analysis focuses on understanding the relationship between resource utilization and power consumption and how task characteristics like type and priority impact energy efficiency. By exploring AWS services, such as EC2 and Auto Scaling, and applying machine learning techniques for real-time resource allocation, this research provides insights into reducing the carbon footprint of cloud operations. The findings contribute to the development of intelligent resource management strategies, promoting eco-friendly computing practices in cloud environments while maintaining operational efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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FER-Net: Leveraging Attention Mechanisms in EfficintNetB5 model for Enhanced Emotion Recognition in Augmented Images 5

FER-Net: Leveraging Attention Mechanisms in EfficintNetB5 mo...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Sulochana, Balla Charishma Pragada, Bhavya Sri Reddy, Gaddam Anvith Singh, Rimjhim Padam Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Facial emotion recognition is a critical component in the development of advanced human-computer interaction systems, with applications spanning security, healthcare, and social robotics. This study explores the efficacy of advanced deep learning models, specifically EfficientNetB5 model in identifying emotions from facial images. Leveraging with augmented data, our approach integrates attention mechanisms, batch normalization, convolutional layers, global average pooling layers, and dropout techniques to enhance model performance. The proposed model demonstrated significant performance accuracy of 87.5%, highlighting the importance of emphasizing relevant image regions for better emotion detection. Additionally, we conducted a comparative analysis with other prominent deep learning architectures, including AlexNet, XceptionNet, ResNet, MobileNet and EfficientNetV2M and proved the superiority of the proposed model against them. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparative Analysis of Machine Learning Classifiers for Audio-Based Disease Classification 15

A Comparative Analysis of Machine Learning Classifiers for A...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sai Varun, B.P. Jonnalagadda, Ishita Vishnu Vardhan, Ch. Srinivas, Marimganti Ullas, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In order to address the increasingly significant role played by the monitoring of health issues during the times of growing healthcare challenges, this research paper will discuss more about the use of audio signals for diagnosing respiratory diseases by the means of analyzing of cough sounds, breathing patterns, and other audio cues. However, the vision of this work comes from the desire to make a non-invasive and efficient diagnostic process to overcome the difficulty of the traditional invasive ways of imageries. Combining the machine learning techniques using Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction we aimed to construct a powerful tool that can do health state labels using the audio signals with accuracy. In the end, our multi-classifier approach in conjunction with accuracy measurements reveal the promising applications of machine learning algorithms to accurately differentiate between various respiratory diseases, thereby opening the avenue for more robust health monitoring systems. © 2024 IEEE.

关键词： MFCC Random Forest SVM XG Boost

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An Investigation into YOLO-v8 Model Optimization for Small Object Detection in UAVs using Attention Mechanism 15

An Investigation into YOLO-v8 Model Optimization for Small O...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rupak, N.S. Rayvanth, N. Kushank, Pulipati Singh, Rimjhim Padam Amrita School of Computing Bengaluru Department of Computer Science & Engineering Amrita Vishwa Vidyapeeetham India

ISBN: (纸本)9798350370249

The growth of UAV technology has led to advancements in aerial surveillance, resulting in the development of specialized algorithms for object detection in complex environments like university campuses, beaches etc. This study proposes to leverage the recent YOLOv8 model with various sophisticated attention mechanism like Spatial attention, channel attention, squeeze and excitation block attention, Efficient channel attention network, etc. to efficiently detect the tiny person objects in images captured using Unmanned aerial vehicles (UAVs) like drones. For the purpose of this analysis, the work utilizes a recent dataset, Manipal-UAV dataset, that focuses on detecting tiny objects in the form of person. The work also provides a comprehensive comparison of recent YOLO models for model selection and provides a detailed analysis of state-of-art attention mechanism when incorporated into the best baseline YOLOv8x model. The findings from this study will suggest that tailored YOLO models are effective for UAV-based person detection tasks, potentially driving advancements in aerial surveillance technology, particularly in scenarios requiring accurate identification of small objects like individuals. The research over-all aims to highlight the importance of dataset-specific optimizations to enhance the performance of object detection models in UAV imagery with mAP50 of 50.2%, thereby, promoting more reliable surveillance applications in various real-world settings. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Crop Irrigation Advisory System Using Federated Logistic Regression 7th

Crop Irrigation Advisory System Using Federated Logistic Reg...

引用

7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Gardas, Deepthi Karthi, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031699856

Federated learning (FL) is a collaborative learning algorithm that builds ML models using knowledge from distributed clients at various locations. Federated learning is valuable when data is collected from heterogeneous environments, and a unifiedML model needs to be built with minimal sharing of data. In this study, a federated irrigation advisory system is developed for an agricultural application that helps the farmer to water their field based on agricultural field parameters. The application of machine learning techniques in the agricultural domain is still in its infancy. Agricultural farms employ different sensors and have natural variations in soil type and environmental conditions. A federated model framework is suitable for the development of an irrigation advisory system as heterogeneous data is gathered using a variety of sensors spread across several farms. FL-based Logistic Regression is designed to predict whether to water the field or not based on the field parameters: temperature, humidity, soil moisture content, number of days since planting the crop, and crop type. Federated learning algorithms are designed with a client-server architecture. Flower, an open-source federated framework, which used to build and test the irrigation advisory application. Two clients and a server are used to build the FL-based Logistic Regression model by exchange of model parameters. Weights and bias of the logistic regression model are aggregated by the server from multiple clients. These aggregated model parameters are sent to clients to build the regression model with its own data. Experiments were conducted to understand the performance of FL in learning the model parameters for prediction. The model’s performance is evaluated using metrics to understand the converge patterns and parameters that affect FL. © IFIP International Federation for Information Processing 2024.

关键词： Crop Irrigation crop Irrigation scheduling dataset Federated Learning Irrigation Advisory system Machine Learning

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Detection of Real and Manipulated Videos using the Transfer Learning Approach of Deep-Learning Models 5

Detection of Real and Manipulated Videos using the Transfer ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Reddy, Bandi Rupendra Rup, Daka Chandra Rohith, Mathi Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Deepfake (manipulated images and videos) identification is critical in multimedia forensics, as it can spread misinformation, threaten privacy, and have legal implications. To address this problem, researchers and practitioners are developing deepfake detection techniques that use deep-learning models and computer vision algorithms to analyse the authenticity of images and videos. This study proposes a novel approach providing robust and scalable deepfake detection by using transfer learning in feature extraction and deep-learning models to train features extracted from the video dataset. The transfer-learning has been experimented with the ResNet, XceptionNet, and VGG16 models for feature extractions. Further, the deep-learning models Long short-term memory (LSTM) and Bidirectional long short-term memory (Bi-LSTM) have been experimented with to train the extracted features. To rigorously test the proposed approach, the data set under the experiment combined the three benchmark datasets - The Deepfake Detection Challenge (DFDC), Celeb-DF, and Face-Forensics++ datasets. The prediction rate achieved was 96 % and above on average by the proposed approach for the datasets under consideration for the experiments. The proposed approach performs better than the state-of-the-art techniques, gaining a higher accuracy of 96% and above. © 2024 IEEE.

关键词： Contrastive Learning

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Sea Surface Temperature Prediction using Sample Convolution and Interaction Network 15

Sea Surface Temperature Prediction using Sample Convolution ...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gayathri, P. Yalavarthi, Rahul Varma, Jayanth Santosh Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Predicting oceanic phenomena plays a very important role in ensuring maritime safety, managing coastal areas and preparing them for any disasters like cyclone or tsunami. There are several factors that contribute to these severe weather events among which Sea Surface Temperature(SST) is a crucial one. Warmer oceans can often lead to stronger storms or even frequent heavy downpours, highlighting the importance of accurately forecasting SST. This study explores the performance of a deep learning model called Sample Convolution and Interaction Network (SCINet) in forecasting SST. This model follows a decompose-downsample-convolve-interact architecture that helps in learning complex trends in the data. On comparing SCINet’s performance with well established time-series models like Long-Short Term Memory (LSTM) network, it was able to achieve almost 4% - 20% lower Mean Absolute Error (MAE) than LSTM on various testing points. This highlights SCINet’s potential effectiveness in time-series forecasting, contributing to the ongoing development and revolutionary advancements in deep learning models for time series prediction. ©2024 IEEE.

关键词： Long short-term memory

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Epilepsy Classification using Multi-Sequence MRI's: An Ensemble Approach 15

Epilepsy Classification using Multi-Sequence MRI's: An Ensem...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajasekhar, Alamanda Hima Naga Kavya, Gurram Sriya, Kota Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Epilepsy is a major threat, when it comes to human health. Classifying brain anomalies on MR images as a pathological clue of Epilepsy is crucial for diagnosis. Electroencephalography (EEG) and Magnetic resonance (MR) imaging are only two of the diagnostic tools that have been proposed for use in screening for epileptic seizures. The diagnosis can be made with the use of many MR images taken at different times by a neuroradiologist. The dataset under consideration is comprised of axial multi-sequences of MR images, and it features both epileptic and nonepileptic aberrant brain structures from a wide range of people. This article performs a comparative study among the models - CNN, MobileNet, InceptionV3, VGG16, VGG19, LSTM, GRU and also the Ensemble models - 'CNN + MobileNet', 'VGG16 + LSTM', 'VGG16 + GRU', 'VGG19 + LSTM' and 'VGG19 + GRU'. The Epilepsy prediction for user upload images is achieved through an ensemble approach. We have demonstrated the effectiveness of these models in correctly detecting epileptic patients based on multi-sequence MRI data by achieving extremely accurate and robust classification findings through extended experimentation. The experiments demonstrated the classification best accuracy of 97.16% for 'VGG19 + LSTM', while the lowest score was 69.93 for InceptionV3 among all the twelve models. © 2024 IEEE.

关键词： Electroencephalography

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Enhancing Medical Record Security: Using Role-based Access Control, Digital Signatures, and RSA Encryption 8

Enhancing Medical Record Security: Using Role-based Access C...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Vijjapu, Aditya Alagawadi, Anusha P Maddi, Anish Kavitha, C.R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350367904

Medicare, the proposed web application provides a unique way to store medical records in a secure manner by encrypting and providing role-based access control, where doctors can view the records of all patients but patients can view only their respective record and doctors also have the option of accessing RSA encrypted chat and also a unique digital signature is embedded to each record for ensuring the integrity of records. This provides a better approach for medical records management. Session management and secure authentication has also been implemented for tracking the actions that occurred in the website. The encrypted records are then stored in MongoDB. Therefore, this web-application provides a better and secure way to manage and share healthcare records. © 2024 IEEE.

关键词： Electronic health record

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Real-Time Object Detection using Mobile Robot Captured Images: A Deep Learning Approach 3

Real-Time Object Detection using Mobile Robot Captured Image...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Hema Srivarshini, Ch. Preeti, N. Marri, Sreekar Praneeth Palaniswamy, Suja Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

Robots are becoming more and more prevalent in a variety of industries, such as transportation, agriculture, healthcare, and so forth. Deep learning is an area of study that is rapidly expanding and is applied in many fields including robots. For a robot to understand its environment and carry out tasks, object recognition is essential. This paper proposes a deep learning method that leverages both pre-existing data and real-time data for robotic object recognition which will enable robots to operate in challenging conditions. Using real-time data, CNN with decision tree is tested after being trained on both pre-existing and real-time data which yields 96% accuracy. A model has been created for reliable and accurate object recognition for robots. © 2024 IEEE.

关键词： Object detection

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