Notice Board :

Call for Paper
Vol. 6 Issue 3

Submission Start Date:
March 01, 2025

Acceptence Notification Start:
March 10, 2025

Submission End:
March 25, 2025

Final MenuScript Due:
March 31, 2025

Publication Date:
March 31, 2025
                         Notice Board: Call for PaperVol. 6 Issue 3      Submission Start Date: March 01, 2025      Acceptence Notification Start: March 10, 2025      Submission End: March 25, 2025      Final MenuScript Due: March 31, 2025      Publication Date: March 31, 2025




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
Vaishali Prakash Gawaral, Ishwar Prasad Yadu
Year Of Publication
2024
Volume and Issue
Volume 5 Issue 8
Abstract
yah adhyayan, jisaka uddeshy bhaarat mein baal shram kee samasyaon kee gahanata ko samajhana aur unake samaadhaan ke lie prabhaavee upaayon kee pahachaan karana hai. bhaarat mein laakhon bachche baal shramik ke roop mein kaaryarat hain, jo apanee shaareerik, maanasik aur shaikshik kshamataon ko nasht karate hue kaam mein lage rahate hain.
PaperID
2024/IJEASM/5/2024/2141a

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