Authors: Assistant Professor Mrs. G. Rohini Phaneendra Kumari, Ravikrinda Hemanjali,, Manasa Kunduru, Yanamadala Naga Lakshmi,, Chundi Pallavi

Abstract: Increased demand of energy-efficient technologies has resulted in the creation of intelligent systems that would optimize the energy use in residential and commercial buildings. In this paper, the design and development of an automatic light control system of indoor environment that ensures that there is minimal energy wastage through the use of adaptation of illumination is presented. The system makes use of a set of sensors, such as motion sensors and light-dependent resistors (LDRs) to automatically control the lighting through occupancy and the intensity of the ambient light. A framework based on an IoT provides the ability to monitor and control remotely through the use of mobile devices, which makes it more convenient and flexible to the user. The proposed system will provide the optimal lighting conditions and produce a considerable reduction in the electricity consumption, and hence, it will lead to sustainable energy management and smart home automation.

DOI: https://doi.org/10.5281/zenodo.20648720

Published by: vikaspatanker

Authors: SK. Sharmila, Pavuluri Pavani,, Pavuluri Nandini,, Yarramneni Sushma, Onteru Keerthi

Abstract: Safe and potable water must be maintained, and the quality water should be checked frequently, especially due to the pollution and environmental shift. So as to analyse sensor-based method of monitoring water quality, this research integrated temperature reading, total dissolved solids (TDS) and turbidity. The scheme was aimed at the real-time data gathering and evaluation to identify alterations in the water parameters, which will prove the contamination or the quality decline. The results have proven that the combination of input of several sensors enhanced the accuracy and reliability of water quality determination, allowing to identify the possible dangerous situation in time. The paper brings to the fore the possibilities of automated sensor networks in streamlining the water management process and protecting human health

DOI: https://doi.org/10.5281/zenodo.20648454

Published by: vikaspatanker

Authors: Associate Professor P.Sandhya Krishna, Ala Nandini, Pavuluri Sri Lekha, Gumma Aparna, Patchava Pujitha

Abstract: Clean and safe drinking water is a crucial factor in the health of the population, but even now, delivery of contaminated drinking water remains one of the world issues. Water potability: a ML approach The use of ML models in Water Quality Assessment is a recent phenomenon in the past years, as it is now a highly promising tool that predicts the water potability in an efficient (more efficient than traditional) manner. The paper presents a smart machine learning system to anticipate the potability of water that is determined by undertaking a thorough review of diverse physico-chemical characteristics of water such as PH, Hardness, Solids, Chloramines, Sulfate and organic contaminants. State of art preprocessing methods are also applied to address missing values, outliers and feature stratification which enhance the quality and the strength of the data. There are several supervised learning processes, which include Random Forest, SVM, Gradient Boosting and ANN to determine the best predictive accuracy algorithm. The general performance is also justified with the premises of accuracy, precision, recall, F1-score and ROC-AUC performance parameters and demonstrates that the suggested framework implementation is reliable and efficient on actual water quality monitoring scenarios. Also, the work places emphasis on the effects of the feature selection and the hyperparmeter tuning on the enhancement of the prediction performance. Ensemble approach and cross-validation methods cut down on the framework and expand the generalization potential with different datasets.

DOI: https://doi.org/10.5281/zenodo.20648118

Published by: vikaspatanker

Authors: Niravkumar Mahendrabhai Panchal

Abstract: Customer churn has become a major challenge for global e-commerce businesses due to increasing market competition and changing consumer behaviour. This study examines the role of machine learning and big data analytics in predicting customer churn and improving customer retention strategies. A quantitative research design with secondary data sources was adopted to analyse customer behaviour patterns and predictive modelling techniques. The findings indicate that machine learning algorithms and predictive analytics significantly improve churn prediction accuracy and support personalised customer engagement strategies. The study highlights the importance of data-driven decision-making in international e-commerce and provides practical insights for improving customer loyalty, profitability, and long-term business sustainability.

DOI: http://doi.org/10.5281/zenodo.20646827

Published by: vikaspatanker

Authors: Associate Professor V. Pavani, Challagundla Amrutha, Palanati Sirisha, Ganjapu Sowmya, Gottipatti Tejaswini

Abstract: Precise prediction of rainfall is required in agriculture, management of water resources and mitigation of disasters. The nonlinear and uncertain characteristics of the meteorological data are usually difficult to capture by traditional statistical models. As a solution to this, a hybrid stacking ensemble model based on the combination of Random Forest (RF) and Support Vector Machine (SVM) and Logistic Regression as a meta-classifier is proposed. The model, when using the Rain in Australia data set, has the highest accuracy with a value of over 95% in the present version and the possible accuracy of over 96% with superior prepossessing, feature engineering, and class balancing. The suggested method provides a sure model of enhanced rainfall forecasting, which would be involved in planning the sustainability of agriculture and environmental decision-making.

DOI: https://doi.org/10.5281/zenodo.20646431

Published by: vikaspatanker

Authors: Associate Professor K.V.S.S.Rama Krishna, Jakka Venkata Lahari, Gurram Yasaswini, Marri Lakshmi Poojitha, Changa Nagalakshmi, Udayagiri Bhavani

Abstract: IoT-Rain Sense is an innovative and state-of-the-art solution for rain prediction on demand and continual weather monitoring based-on Internet of things (IOT) systems and cloud-based Neural Networks designed to predict precise, hyper localised forecasts. The architecture of the system consists of three main components: Data Acquisition, Feature Processing, and Weather Prediction. In phase 1, sensors being IoT based and ESP32 microcontrollers keep on monitoring temperature, humidity and light intensity over the environment of an application. The measurements are displayed in real time on a built local LCD interface. These sensors are cheap and energy-friendly, which means they could be sprinkled around agriculture and cities and institutions, without bothering anyone, and can scale up as needed. The second level is focused on feature processing, including preprocessing which aims to clean, filter and normalize raw data in order to control the quality of them. There is more weather related information added to

DOI: https://doi.org/10.5281/zenodo.20646110

Published by: vikaspatanker

Authors: Mamta Bhardwaj

Abstract: Polycystic Ovary Syndrome (PCOS) is a multifactorial endocrine disorder affecting a significant proportion of women of reproductive age, often leading to metabolic, hormonal, and reproductive complications such as infertility, insulin resistance, and cardiovascular risks. Early and accurate diagnosis of PCOS remains a major clinical challenge due to its heterogeneous symptoms, variability across patients, and reliance on subjective diagnostic criteria such as the Rotterdam guidelines. In recent years, machine learning (ML) techniques have shown promising potential in improving diagnostic accuracy; however, their lack of interpretability has limited their adoption in real-world healthcare settings. This study proposes a comprehensive Explainable Artificial Intelligence (XAI)-based risk prediction framework for PCOS diagnosis that combines robust machine learning algorithms with interpretable techniques to enhance clinical trust and usability. The proposed model utilizes a publicly available PCOS dataset comprising clinical, hormonal, and ultrasound features. A systematic preprocessing pipeline is implemented, including missing value imputation, feature scaling, and class imbalance handling using Synthetic Minority Oversampling Technique (SMOTE). Feature selection methods such as correlation analysis and Recursive Feature Elimination (RFE) are applied to identify the most significant predictors contributing to PCOS. Multiple machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), are evaluated. A stacking ensemble model is then developed to leverage the strengths of individual classifiers and improve overall predictive performance. To address the critical challenge of model interpretability, ex-plainability techniques such as SHapley Additive explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) are integrated into the framework. These methods provide both global and local explanations, enabling the identification of key features such as menstrual cycle irregularity, Body Mass Index (BMI), follicle count, and hormonal imbalance, which are consistent with established clinical knowledge.

DOI: http://doi.org/10.5281/zenodo.20643863

Published by: vikaspatanker

Authors: Amandeep Sharma, Dr. Sahil Nazir

Abstract: Investment decisions are primarily influenced by the relationship between risk and return. Investors seek investment avenues that provide maximum returns while maintaining an acceptable level of risk. Equity shares and fixed-income securities are among the most widely preferred investment instruments. Equity shares offer opportunities for capital appreciation and dividend income but involve higher market risk. Fixed-income securities such as government bonds, corporate bonds, and debentures provide stable returns with comparatively lower risk. The present study examines the risk-return characteristics of equity shares and fixed-income securities using both primary and secondary data. Primary data were collected from 200 investors through a structured questionnaire, while secondary data were obtained from stock market reports, company annual reports, and financial databases. Statistical tools including percentage analysis, mean, and standard deviation, coefficient of variation, correlation analysis, chi-square test, t-test, and regression analysis were employed. The findings indicate that younger investors prefer equity investments due to higher return expectations, whereas older investors favor fixed-income securities for capital preservation and income stability. The study further reveals that equity shares generate higher average returns but are associated with greater volatility. Fixed-income securities exhibit lower returns but provide greater consistency and lower risk exposure. The research concludes that a balanced portfolio containing both asset classes can optimize risk-adjusted returns and achieve long-term financial objectives.

DOI: http://doi.org/10.5281/zenodo.20643891

Published by: vikaspatanker

Authors: Ajay Soni, Hina Thakre, Hitesh Chouksey, Krishna Kumar, Mohit Bunkar, Rahul Kadam, Priyank Srivastava

Abstract: The rapid growth of electric vehicles (EVs) has increased the demand for sustainable and convenient charging infrastructure. Conventional wired charging systems require physical connectors that suffer from wear, maintenance requirements, and user inconvenience. This paper proposes a Solar Wireless EV Charging System that combines solar photovoltaic generation with wireless power transfer technology. Solar energy is harvested using photovoltaic panels and stored in a battery bank through a charge controller. The stored energy is converted into high-frequency AC power using an inverter and transferred wirelessly through resonant inductive coupling. A receiver coil mounted on the electric vehicle captures the transmitted energy, which is rectified and used for battery charging. The proposed system reduces dependency on fossil-fuel-based electricity, enhances charging convenience, and promotes renewable energy utilization. The design improves safety by eliminating exposed charging cables and supports future smart transportation infrastructure. Performance factors such as coil alignment, transfer distance, efficiency, and energy management are discussed. The study concludes that integrating solar energy with wireless charging provides an environmentally friendly and practical solution for future electric mobility.

DOI: http://doi.org/10.5281/zenodo.20641205

Published by: vikaspatanker

Authors: Shilsa. K V

Abstract: Mobile health technologies are increasingly transforming healthcare delivery by enabling personalized, accessible, and cost-effective services. This study presents the design, development, and evaluation of Healthy Food, an Android-based nutrition consultation platform that connects users with qualified nutritionists through a digital environment. The application integrates personalized nutrition guidance, online communication, health education resources, and nutrition plan management. The system was developed using Android Studio and Firebase following the waterfall software development methodology. Feasibility analysis, system design, implementation, and testing were conducted to evaluate operational effectiveness. Results indicate that the platform enhances accessibility to nutrition advice, reduces consultation barriers, and supports preventive healthcare practices. The findings highlight the growing significance of mobile applications in promoting healthy lifestyles and improving healthcare communication.

DOI: https://doi.org/10.5281/zenodo.20640714

Published by: vikaspatanker