Jyothi Yadla

Dr. Reddy's Laboratories (United States)

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Co-Authors

Jonnalagadda VNRaju

Institute of Engineering

Venkata Lakshmi Narasimha

National Institute of Mental Health and Neurosciences

Pushpendra Kumar

Jawaharlal Nehru University

Valaparla Rohini

National Institute of Technology Tiruchirappalli

Boddapati Teja Vamsi

Institute of Engineering

V Priyadarshini

National Institute of Technology Andhra Pradesh

H. Manoj T. Gadiyar

Visvesvaraya Technological University

K. Amrutha

Kerala Agricultural University

Moinuddin Ahmed Shaik

Maulana Azad National Urdu University

Gujjarlapudi Varun Kumar

Maulana Azad National Institute of Technology

Cooperative Institutions

Institute of Engineering

National Institute of Mental Health and Neurosciences

All India Institute of Medical Sciences, Deoghar

CMR University

American Society of Addiction Medicine

All India Institute of Medical Sciences

All India Institute of Medical Sciences Bhopal

All India Institute of Medical Sciences Raipur

University of Cape Town

All India Institute of Medical Sciences Bhubaneswar

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Optimized Predictive Modelling for Chronic Kidney Disease: A Hybrid Ensemble Approach with Bacterial Foraging Optimization and SMOTE

Vibhu Kumar Jyothi Yadla Niranjan Kumar Parvatham Pushpendra Kumar Usha Tulluri

10.1109/icmcsi64620.2025.10883351

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Application of IP102 Dataset in Pest Detection by using Deep Learning Techniques

2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (2023)

Boddapati Teja Vamsi Jyothi Yadla Gujjarlapudi Varun Kumar

To ensure high biomass yields and reduce operating risks for farmers, lightning-fast and precise agricultural pest detection is essential. Conventional pest detection techniques, such as manual inspection and visual examination, take a lot of time, require a lot of work, and are prone to human mistake. Deep learning methods have become a potential tool for crop pest identification in recent years. An abstract of a work on agricultural pest identification using deep learning algorithm is presented here. The suggested method employs You Only Look Once (YOLO) algorithm to evaluate crop photos and accurately detect the presence of pests and their damage patterns. The model can identify pests in real time and notify farmers to immediately implement the necessary pest management measures. The method has the potential with precision of 87% to boost the effectiveness of pest identification and management while decreasing reliance on human labour, improving agricultural yields and enhancing food security. The problems and promise for future development of deep learning-based agricultural pest identification are covered in the paper's conclusion.

Identification

Mistake

10.1109/accai58221.2023.10199252

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Citations (1)

Optimizing Diabetes Prediction with Ensemble Learning with Voting and Cross-Validation: A Comprehensive Approach

Moinuddin Ahmed Shaik M. V. Rama Sundari Jyothi Yadla V Priyadarshini Venkata Lakshmi Narasimha

Diabetes, an enduring metabolic illness distinguished by heightened amounts of glucose in the bloodstream, poses a substantial health obstacle on a global scale. The present study aims to address the pressing requirement for precise predictive instruments in the identification and control of diabetes. The present study investigated two basic classifications of diabetes, namely Type 1 and Type 2, which are characterized by unique etiological variables. The impetus behind this research stems from the increasing prevalence of diabetes and its significant implications for public health and healthcare expenditures. The prompt detection and timely intervention of medical conditions have the potential to enhance patient outcomes and alleviate the burdens placed on society. The stated research need pertains to the necessity for the development of robust and scalable machine learning algorithms specifically designed for the purpose of predicting diabetes. The present study utilizes a robust ensemble learning methodology by integrating Random Forest, Support Vector Machine, and XGBoost classifiers within a Voting Ensemble Classifier. The models are assessed using both 10-fold cross-validation (CV-10) and without it. During the evaluation process, important metrics such as accuracy, precision, recall, F1-score, and AUC are examined. The findings suggest that the inclusion of CV-10 improves the performance of the model, resulting in increased accuracy, precision, and a more favorable tradeoff between precision and recall. The proposed methodology seeks to incorporate a broader spectrum of diabetes problems, such as hypoglycaemia and diabetic retinopathy. The major aim of this work is to provide an academic contribution to the field of healthcare analytics through the presentation of a comprehensive technique for the prediction and management of diabetes.

Ensemble Learning

10.1109/icscna58489.2023.10370697

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A Comparison of Supervised Learning Algorithms to Prediction Heart Disease

Kuchlpudi Prasanth Kumar Valaparla Rohini Jyothi Yadla Jonnalagadda VNRaju

Heart disease problems are rapidly increasing day to day. Humanslose their lives at an early stage. Consequently, themain purpose of this projectis to employ supervised machine learning methods for heart disease early prediction. For the prediction and diagnosis of cardiac diseases, different techniques are used like data mining and machine learning. This would be tremendously useful to human life since, owing to a lack of cardiovascular competency and quick development in improperly diagnosed instances, heart diseases in people might develop at an early stage. As a result, developing robust and effective early-stage cardiac illness prediction by using analytical decision-making and digital patient data might alleviate this problem. To predict heart diseases, numerous supervised machine-learning techniques were used to learn about the illness, and their efficiency and accuracy were evaluated. This study used a Kaggle dataset on heart disease and found that three classification methods-, K-Nearest Neighbor (KNN), Support Vector Classifier, and Multi-Layer Perception (neural network) could accurately classify heart disease. TheKNN is given 91.8% accuracy. As a result, we discovered that KNN results can more accurately forecast the chance of patients developing heart disease.

Supervised Learning

Statistical classification

10.1109/iceconf57129.2023.10084035

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Citations (6)