Notice Board :

Call for Paper
Vol. 5 Issue 10

Submission Start Date:
Oct 01, 2024

Acceptence Notification Start:
Oct 10, 2024

Submission End:
Oct 25, 2024

Final MenuScript Due:
Oct 30, 2024

Publication Date:
Oct 30, 2024
                         Notice Board: Call for PaperVol. 5 Issue 10      Submission Start Date: Oct 01, 2024      Acceptence Notification Start: Oct 10, 2024      Submission End: Oct 25, 2024      Final MenuScript Due: Oct 30, 2024      Publication Date: Oct 30, 2024




Volume V Issue VIII

Author Name
Nitesh Kumar, Alka Thakur
Year Of Publication
2024
Volume and Issue
Volume 5 Issue 8
Abstract
As the cornerstones of the smart grid of the future, smart microgrids integrate various Internet of Things (IoT) designs and technologies for applications intended to establish, manage, keep an eye on, and safeguard the microgrid (MG), especially as the IoT grows and changes every day. Urban and rural communities, as well as institutional, commercial, and industrial users, find a smart microgrid (MG) to be a perfect solution. It is a tiny grid that can function independently or in conjunction with the power grid. A MG has two modes of operation: stand-alone and grid-connected. It can switch between these modes in response to various circumstances, such as planned maintenance, expansions, local grid faults, host system deficiencies and failures, and so on. Energy storage is the process of storing and converting energy that can be used for a variety of purposes, including voltage and frequency management, power backup, and cost optimization. IoT is designed to deliver solutions for optim
PaperID
2024/IJEASM/5/2024/2141

Author Name
Pooja Ballodia, Ashish Suryavanshi
Year Of Publication
2024
Volume and Issue
Volume 5 Issue 8
Abstract
This paper presents a cardiovascular disease detection model developed using three machine learning classification techniques: Logistic Regression, Random Forest Classifier, and K-Nearest Neighbors (KNN). The project predicts individuals with cardiovascular disease by extracting medical history data from a dataset, including attributes such as chest pain, sugar level, and blood pressure. The Heart Disease Detection System assists patients based on their clinical information and history of heart disease. The model achieves an accuracy of 87.5%, illustrating that incorporating more training data can enhance the model's predictive accuracy. Utilizing computer-aided techniques allows for quicker and more cost-effective patient predictions, surpassing traditional methods and benefiting both patients and doctors. Our project improves heart disease prediction by cleaning the dataset and applying Logistic Regression and KNN, achieving an average accuracy of 87.5%, which is higher than previous
PaperID
2024/IJEASM/5/2024/2142

Author Name
Abhilekh Tyagi, Ashish Suryavanshi
Year Of Publication
2024
Volume and Issue
Volume 5 Issue 8
Abstract
The integration of Artificial Neural Networks (ANNs), Machine Learning (ML), Deep Learning (DL), and Convolutional Neural Networks (CNNs) is revolutionizing medical imaging and diagnostics. ANNs utilize a layered architecture and iterative training to refine accuracy by adjusting weights based on errors. DL further enhances this by managing large datasets and abstracting complex features through deep structures. CNNs, a specialized form of DL, excel in image analysis through convolution and pooling layers, improving feature extraction and precision. Radiomics builds on these advancements by correlating imaging features with clinical outcomes, marking a shift from scalar to pixel-based data analysis. This evolution enhances predictive capabilities for treatment responses and outcomes. However, the adoption of AI in healthcare faces challenges such as data quality, workflow integration, and ethical considerations. Addressing these concerns—ensuring robust data management, patient confide
PaperID
2024/IJEASM/5/2024/2143

Author Name
Saurabh Sharma, Khemraj Beragi
Year Of Publication
2024
Volume and Issue
Volume 5 Issue 8
Abstract
This project focuses on improving the thermal efficiency of solar collectors through computational fluid dynamics (CFD) modeling and analysis using CATIA design software. The study examines fluid flow and thermal performance in both flat plate collectors (FPC) and parabolic trough collectors (PTC) with air and water as working fluids. The analysis was performed across different times of the day, specifically from 10:00 AM to 2:00 PM, and compared the performance of aluminum and copper materials. The simulations revealed that the output temperatures for both FPC and PTC peaked at 2:00 PM. The FPC reached a maximum temperature of 381K with an inlet water temperature of 334K, showing an increase of 47K, while the PTC achieved 398K with an inlet air temperature of 334K, indicating an increase of 64K. However, as time progressed, both the output temperatures and solar flux decreased. The comparative thermal analysis highlighted that copper exhibited a higher heat flux compared to aluminum,
PaperID
2024/IJEASM/5/2024/2144