The National Education Policy (NEP) 2020 marks a transformative shift in India’s education system, aiming to create a more inclusive, flexible, and multidisciplinary framework for learning. This paper presents an in-depth analysis of NEP 2020 and its implications for various stakeholders, including students, teachers, educational institutions, policymakers, and society at large. The policy emphasizes holistic development, skill-based learning, digital education, and the integration of vocational and higher education through a competency-driven approach. For students, NEP 2020 promotes experiential and flexible learning pathways; for teachers, it enhances professional development and autonomy. Educational institutions are encouraged to adopt outcome-based curricula and innovative pedagogical methods. The paper also discusses challenges in implementation, such as infrastructure gaps, digital divide, and resource allocation. Overall, the study concludes that NEP 2020 lays a strong foundation for reimagining education in India by aligning it with global standards and future workforce needs, while ensuring equity, accessibility, and lifelong learning opportunities.

Keywords : NEP 2020, Education policy, Stakeholders, Higher education, Educational reform

Authors : K. Srinivas Rao Patnaik

Title : An In-Depth Analysis of the New Education Policy (NEP) 2020- effect on different Stakeholders

Volume/Issue : 2021;3(2 (March - April))

Page No : 23 - 29

Diabetic Retinopathy (DR) is a severe eye disease caused by long-term diabetes, leading to vision impairment and potential blindness if not diagnosed early. Automated detection of DR using Deep Learning techniques has gained significant attention for improving screening efficiency and accuracy. This paper presents a study on detecting Diabetic Retinopathy in retinal images using Convolutional Neural Networks (CNNs). CNNs are highly effective in extracting hierarchical visual features from medical images, enabling precise classification of disease severity levels. The proposed approach involves preprocessing retinal fundus images for noise reduction, contrast enhancement, and normalization, followed by training CNN models to detect abnormalities such as microaneurysms, hemorrhages, and exudates. The architecture demonstrates high accuracy and robustness in differentiating between healthy and diseased retina images. Comparative analysis with traditional image processing and machine learning methods shows that CNN-based models significantly outperform earlier approaches in both sensitivity and specificity. This study concludes that deep learning using CNNs provides a reliable, scalable, and automated solution for early DR diagnosis, helping prevent blindness through timely medical intervention.

Keywords : Diabetic Retinopathy, Deep Learning, CNN, Medical Imaging, Disease Detection

Authors : Ch. Pavan Sateesh

Title : Detecting Diabetic Retinopathy in Retina Photos Blindness with Deep Learning using CNNs

Volume/Issue : 2021;3(2 (March - April))

Page No : 8 - 22

Agriculture plays a vital role in India’s economy, and accurate crop yield prediction is essential for ensuring food security, efficient resource allocation, and policy formulation. Traditional prediction methods often fail to account for the complex interactions between climatic, soil, and management factors. This paper presents a comprehensive survey on the use of machine learning (ML) techniques for predicting Indian crop yield. Various supervised and unsupervised learning algorithms—such as Linear Regression, Random Forest, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks (ANN)—have been analyzed for their performance in forecasting yield across diverse agro-climatic regions. The survey highlights the importance of feature selection, data preprocessing, and the integration of remote sensing and weather data for improving prediction accuracy. Additionally, it discusses challenges such as data inconsistency, regional variability, and model generalization. The study concludes that machine learning-based models offer a promising approach for building intelligent, data-driven agricultural systems that support sustainable farming and policy planning in India.

Keywords : Crop Yield Prediction, Machine Learning, Agriculture, Data Analytics, India

Authors : K Varaprasad

Title : Improvement on Quality of Service in WIMAX using OPNET Simulator

Volume/Issue : 2021;3(2 (March - April))

Page No : 1 - 7