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

Authors: Assistant Professor Dr. Vijaykumar Kale, Ms.Rutuja Popat Chavan, Ms.Pratiksha Ashok Jaybhay, Dr. Mahesh Thakare, Mr. Vaibhav Narwade

Abstract: Herbal cosmetics have gained significant importance due to their safety, effectiveness, and minimal side effects compared to synthetic products. The present research project focuses on the formulation and development of herbal hair oil using Betel Leaf as the major active ingredient. Betel leaf is traditionally known for its antimicrobial, antifungal, antioxidant, and anti-inflammatory properties, which are beneficial for maintaining healthy hair and scalp conditions The herbal hair oil was prepared using betel leaf along with other natural ingredients such as coconut oil, curry leaves, hibiscus, and aloe vera. The formulation was developed by heating the herbal materials with the base oil to extract the active constituents effectively. The prepared oil was filtered and evaluated for various physicochemical parameters including color, odor, pH, viscosity, specific gravity, irritation test, and stability study. The formulated herbal hair oil showed satisfactory physical appearance, good stability, and acceptable consistency without causing skin irritation. The presence of betel leaf in the formulation may help reduce dandruff, scalp infections, and hair fall due to its medicinal properties. The study concludes that the prepared herbal hair oil can serve as a safe, economical, and natural alternative for hair care management. This research supports the growing demand for herbal cosmetic products and highlights the potential. [1]

Published by: vikaspatanker

Authors: By Mr. Basavaraj Swamy

Abstract: Techniques from Machine learning and deep learning are usually helpful in classification of data. A dataset is processed through a CNN before it is used for classification. Text mining, image processing, and score prediction techniques are very much important in the field of analytics. In paper, we used classification and data prediction methods to demonstrate image and numerical analysis. Analytics show that traditional backup methods have been improved with better ways of managing data. This process of supervised learning produces comparable present outcomes with accurate predicted values.

Published by: vikaspatanker

Authors: Amal prawin, Sanjay P K, Mrs.Haseena

Abstract: The surge in digital payment systems, facilitated through mobile phones, has revolutionized the financial landscape, promising convenience, accessibility, and efficiency. However, amid this rapid digital transformation, various challenges and problems have emerged, necessitating comprehensive examination. This study delves into the intricate fabric of mobile phone-based digital payment systems, aiming to identify and analyse the multifaceted hurdles impeding their seamless operation. Drawing upon extensive literature review and empirical research, this study navigates through the labyrinth of challenges encountered in digital payment ecosystems. From technological limitations to socio-economic disparities, from security concerns to regulatory complexities, the spectrum of impediments is diverse and far-reaching. The research employs both qualitative and quantitative methodologies to unravel the underlying dynamics and discern patterns amidst the chaos.

DOI: http://doi.org/10.5281/zenodo.20617421

Published by: vikaspatanker

Authors: Ms. Anisha S, Ms. Rathika R, Dr. N. Rajendran

Abstract: This study investigates the role of Corporate Social Responsibility (CSR) in marketing, highlighting its increasing significance as a strategic component in today’s business landscape. As consumers become more socially and environmentally conscious, companies are compelled to integrate CSR initiatives into their marketing strategies to align with evolving consumer expectations. The study aims to examine the influence of CSR on consumer purchasing behavior, evaluate its role in enhancing brand image and reputation, and identify the challenges businesses face in authenticating CSR within their marketing efforts. Through a comprehensive analysis, the study reveals that CSR initiatives positively impact consumer purchasing decisions, particularly among younger generations who prioritize ethical practices and sustainability. It emphasizes the importance of authenticity in CSR efforts, noting that companies that genuinely engage in responsible practices are perceived as more trustworthy and responsible, which enhances their brand reputation. The findings also indicate that the effectiveness of CSR marketing varies across industries, suggesting that tailored strategies are essential for resonating with target audiences. However, companies encounter challenges such as skepticism about insincere CSR activities and difficulties in effectively communicating their initiatives. Recommendations for effective CSR integration include ensuring authenticity, tailoring initiatives to industry specific needs, committing to long-term sustainability efforts, and actively engaging stakeholders. This study concludes that when strategically incorporated into marketing, CSR can strengthen brand loyalty, enhance corporate reputation, and contribute to positive social and environmental impacts, ultimately driving long-term business success.

DOI: http://doi.org/10.5281/zenodo.20614759

Published by: vikaspatanker

Authors: Ms. Anisha, Ms. Divyabharathi, Mrs. Jeya Padma Deepa I

Abstract: In the contemporary digital era, ethical commerce has emerged as a critical dimension of business strategy, extending beyond profit maximization to include social responsibility, environmental sustainability, and ethical governance. Corporate Social Responsibility (CSR) in a digital age is shaped by rapid technological advancements, e-commerce platforms, social media, data analytics, and increased stakeholder awareness. Businesses today are expected to operate transparently, protect consumer data, ensure fair digital practices, and contribute positively to society while leveraging digital tools for growth. This article examines the concept of ethical commerce and the evolving role of CSR in a technology-driven business environment. It explores how digital platforms influence CSR initiatives, enhance stakeholder engagement, and promote sustainable business practices. The study also highlights challenges such as digital inequality, data privacy concerns, and greenwashing. By adopting ethical digital strategies, organizations can build trust, strengthen brand reputation, and achieve long-term sustainability. The article aims to provide undergraduate students with a comprehensive understanding of ethical commerce and the significance of CSR in the modern digital business landscape.

DOI: http://doi.org/10.5281/zenodo.20614573

Published by: vikaspatanker