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

Authors: Tushar Hingmire

Abstract: Gold has long been regarded as a safe-haven asset, yet its price is subject to intense volatility driven by a complex interplay of global economic conditions, currency fluctuations, and commodity market dynamics. Traditional forecasting methods often fail to capture these non-linear dependencies, motivating the development of data-driven approaches. This paper presents GoldMind AI, a machine learning framework designed to forecast gold prices using four key macro-financial indicators: the S&P 500 Index (SPX), the United States Oil Fund ETF (USO), the iShares Silver Trust ETF (SLV), and the EUR/USD currency pair exchange rate. Two supervised learning models — Linear Regression and Random Forest Regressor — are trained on historical financial data and evaluated using standard regression metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The Random Forest model achieves an R² score of 0.92, RMSE of 1.07 USD, and MAE of 0.94 USD, significantly outperforming Linear Regression with a 23% reduction in error rates. The trained model is deployed as an interactive web application built with Streamlit, enabling real-time gold price forecasting from user-supplied market inputs. GoldMind AI demonstrates that ensemble machine learning methods can effectively capture complex market relationships, providing actionable insights for investors and financial analysts.

DOI: https://doi.org/10.5281/zenodo.20640504

Published by: vikaspatanker

Authors: Parekh Jay Alpeshkumar, Manavsinh Maheshkumar Mahida, Anil Patidar, Shah Shubham Rameshbhai, Ajmeri Shifa, Dhruv Jayeshbhai Rathod, Prof. Sohil Govindbhai Parmar

Abstract: The game development industry has evolved rapidly over the last few decades, becoming one of the most signif-icant sectors of the global entertainment market. Modern games are no longer limited to entertainment purposes but are increasingly utilized in education, healthcare, military training, business simulations, and virtual learning envi-ronments. The growing demand for high-quality gaming experiences has encouraged developers to adopt advanced technologies and innovative development methodologies. As a result, game development has transformed into a multidisciplinary field that integrates software engineer-ing, computer graphics, artificial intelligence, storytelling, animation, sound design, and user experience design. Unlike traditional software systems, game development involves highly dynamic and continuously changing re-quirements throughout the production lifecycle. Develop-ers frequently modify gameplay mechanics, visual assets, and system features based on testing results and player feedback. This flexibility creates unique challenges related to project management, communication, resource alloca-tion, quality assurance, and deadline management. Tra-ditional Software Development Life Cycle (SDLC) models often fail to address these challenges effectively due to the creative and iterative nature of game production. There-fore, specialized Game Development Life Cycle (GDLC) models have emerged to better support the requirements of modern game projects. This study investigates contemporary game develop-ment methodologies and design techniques used in the gaming industry. The research examines important con-cepts such as Agile Development, Model-Driven Game Development (MDGD), iterative prototyping, continuous testing, and collaborative development workflows. Fur-thermore, the study analyzes the role of modern game en-gines, including Unity and Unreal Engine, in accelerating development processes and improving production quality. The impact of emerging technologies such as artificial in-telligence, cloud computing, procedural content genera-tion, and automated testing systems is also explored. A qualitative research methodology based on a liter-ature review and comparative analysis was employed to Evaluate existing development models and identify their strengths and limitations. Various academic publications, industry reports, and research studies were analyzed to understand common development challenges and modern solutions adopted by professional game studios. Based on the findings, an optimized Game Development Life Cy-cle framework is proposed to improve development effi-ciency, flexibility, communication, scalability, and overall game quality while maintaining the creative freedom nec-essary for successful game production. The results indi-cate that integrating Agile practices, iterative prototyping, continuous feedback mechanisms, and collaborative work-flows significantly enhances the effectiveness of game de-velopment projects. The proposed framework provides a balanced approach that combines structured software en-gineering principles with creative design processes. This study contributes to the understanding of modern game development and design techniques and offers practical recommendations for indie developers, researchers, and game studios seeking to improve production workflows and deliver engaging, high-quality gaming experiences. Key-words— Game Development, Game Design, Game En-gines, Artificial Intelligence, User Experience, Agile Devel-opment, Interactive Entertainment, Cross-Platform De-velopment.

DOI: http://doi.org/10.5281/zenodo.20637746

Published by: vikaspatanker

Authors: Ayushi Dixit , Dr. Brij Mohan Singh

Abstract: Accurate automated diagnosis of visually similar scalp diseases represents one of the most challenging problems in clinical dermatology. Conditions such as Psoriasis, Seborrheic Dermatitis, Tinea Capitis, Alopecia Areata, Folliculitis, and Eczema share overlapping visual characteristics: including redness, scaling, and patchy hair loss, making misclassification clinically dangerous and common even among trained dermatologists. The global shortage of specialist dermatologists, particularly in rural and resource-limited settings in India, further amplifies the need for reliable automated diagnostic tools. This comprehensive research proposes ScalpViT, a novel hybrid deep learning architecture that combines a 16×16 Patch Vision Transformer (ViT) with a Convolutional Neural Network (CNN) backbone connected via a bidirectional cross-attention fusion module. The ViT branch processes the scalp image by dividing it into 256 non-overlapping 16×16-pixel patches, embedding each as a 768-dimensional token, and applying multi-head self-attention across the full token sequence to capture global spatial distribution and morphological patterns. Concurrently, the CNN branch extracts local texture details. The bidirectional cross-attention enables texture features to query spatial features and vice-versa, avoiding the pitfalls of simple feature concatenation. Trained on a meticulously curated multi-source dataset of approximately 7,000 dermoscopic and clinical scalp images drawn from DermNet NZ, ISIC 2018, HAM10000, and SD-198, ScalpViT achieves 94.3% accuracy, a macro F1-score of 0.93, and an AUC of 0.97. It significantly outperforms conventional baselines like ResNet-50 (83.1%), EfficientNet-B3 (87.4%), standard ViT-B/16 (90.8%), Swin-Tiny (91.2%), and DINOv2-B (93.5%). Furthermore, to bridge the interpretability gap for clinical deployment, ScalpViT utilizes GradCAM for CNN texture heatmapping and Attention Rollout for ViT patch mapping, delivering dual visual explainability to clinicians. The paper extensively details the methodology, dataset construction, architectural innovations, and clinical relevance for point-of-care mobile deployments.

DOI: https://doi.org/10.5281/zenodo.20637980

Published by: vikaspatanker

Authors: Mr. Ashish Kumar, Dr. Satender Kumar

Abstract: The study of anomaly detection in time series has become one of the key topics in intelligent monitoring systems such as industrial automation, cybersecurity, healthcare, finance, IoT. The traditional approaches to anomaly detection primarily focused on detecting any signs of anomalous behaviour following their occurrence. However, in many cases, reactive anomaly detection does not allow for timely response to detected anomalies. Recently, some researchers have suggested the novel idea of Precursor-of-Anomaly (PoA) detection to detect and analyse warning signs prior to anomalies' occurrence. The present paper provides a review and experimental framework of PoA detection in time series. The paper outlines approaches to traditional anomaly detection, deep learning based forecasting models, uncertainty-aware models, and early warning approaches. Also, the paper outlines a practical framework of PoA analysis using industrial SWaT dataset and Isolation Forest approach. Experimental results prove that uncertainty-aware PoA detection is capable of delivering early warning signals before critical anomalies occur. The paper considers modern limitations and challenges in designing proactive anomaly prediction systems.

DOI: https://doi.org/10.5281/zenodo.20631084

Published by: vikaspatanker

Authors: Associate Professor Mahesh Thakare, Pooja Choudhary, Sakshi Harihar, Vijaykumar Kale, Associate professor Vaibhav Narwade

Abstract: Approximately 85% of teenagers suffer from acne vulgaris, which can last until adulthood. Teenagers see doctors approximately two million times a year, and the US spends more than $1 billion on acne treatments directly. There are many different therapy options for acne vulgaris, such as hormonal, anti- androgen, or anti seborrheic medications, as well as retinoids, isoprenoids, keratolytic soaps, alpha hydroxy acids, azelaic acid, and salicylic acid. All of these techniques do have some negative effects, though, and it's unclear exactly how they fit into therapy. This paper not only presents the potential causes of acne vulgaris, medications that can treat it, and recently released research on the usage of medicinal herbs to treat the condition were examined. Topical formulations (herbal cream) have been developed containing Ocimum sanctum (Tulsi) extract, Aloe barbadensis miller (Aloe-vera Gel), Melaleuca Oil (Tea Tree Oil). These medicinal herbs and essential oil (TTO) show anti-bacterial activity against acne causing bacteria like Propionibacterium and staphylococcus aures. Various batches containing above Herbs and Essential oil are prepared and their comparative studies are performed. Certain evaluation tests are performed like Irritancy, Washability, pH, Greasiness to check whether cream is suitable for human skin. In the end anti-bacterial activity of the cream was carried out using agar well diffusion method against staphylococcus aures.

DOI: http://doi.org/

Published by: vikaspatanker