Category Archives: Uncategorized

Authors: Akshay Surendra Pimple, guidance of Ar. Anand A. Pande

Abstract: Temporary urbanism plays a critical role in managing dynamic human movement, particularly during large-scale mass gatherings, pilgrimages like the Kumbh Mela, and disaster-relief scenarios. While these transient settlements must rapidly provide vital infrastructure, safety, and shelter for millions of people, traditional construction methods relying on bamboo, corrugated metal sheets, and plastic coverings face severe limitations in durability, environmental impact, comfort, and reusability. This research investigates the untapped potential of innovative alternative materials and modular construction techniques as environmentally responsive solutions to replace conventional, inefficient practices. Through rigorous experimentation and design exploration, this study aims to develop safer, highly adaptable, and affordable temporary architectural systems that ensure rapid assembly and structural utility while minimizing ecological footprints. Furthermore, the paper analyzes the socio-economic scope of these systems, highlighting future opportunities for local skill development, manufacturing employment, and sustainable material lifecycles. Ultimately, this research offers a progressive framework for temporary architecture that balances immediate human comfort with long-term sustainable growth.

DOI: https://doi.org/10.5281/zenodo.21273750

Published by: vikaspatanker

Authors: Egondi Obinga Patrick, Dr. Mwenda Paul, Dr. Ongalo Thomas

Abstract: This study examined the influence of financial transparency on the financial performance of Deposit-Taking Savings and Credit Cooperative Societies (DT-SACCOs) in Kenya, with a particular focus on the moderating role of SACCO size. Drawing on Agency Theory, Stakeholder Theory, and Resource Dependence Theory, the study adopted a mixed-method explanatory design involving 221 respondents drawn from 123 DT-SACCOs regulated by the SACCO Societies Regulatory Authority (SASRA). Quantitative data were analysed using correlation and regression techniques, while qualitative insights were used to contextualise statistical outcomes. Findings revealed that financial transparency has a strong and statistically significant positive effect on financial performance (r = 0.844, p < 0.001), explaining 71.1% of performance variation (R² = 0.711). However, interaction analysis indicated that SACCO size significantly moderates this relationship, with larger SACCOs exhibiting reduced marginal benefits from transparency due to increased structural complexity. The study concludes that financial transparency is a critical governance driver of financial performance, but its effectiveness depends on institutional scale. Policy implications emphasise differentiated governance frameworks based on SACCO size to enhance efficiency, accountability, and sustainability in Kenya’s cooperative financial sector.

DOI: https://doi.org/10.5281/zenodo.21258209

Published by: vikaspatanker

Authors: Maureen Nechesa Murambi, Daniel Khaoya Muyobo, Richard Rono

Abstract: Traditional meteorological models often face challenges in processing large volumes of real-time data and capturing complex nonlinear atmospheric relationships. Recent advances in Machine Learning (ML) have provided powerful tools for analysing weather patterns and improving forecasting accuracy. The paper discusses relevant literature on machine learning algorithms suitable for weather pattern analysis, identifies research gaps and proposes future research directions involving deep learning and hybrid forecasting systems. This paper presents an integrated Internet of Things (IoT) and Machine Learning (ML) model for analysing weather patterns in Bungoma County, Kenya. Historical weather data (2006–2025) from the Nzoia Sugar Factory Weather Station and simulated real-time IoT sensor observations were analysed using Random Forest (RF) and K-Nearest Neighbours (KNN). Data preprocessing included outlier detection using the IQR method, polynomial interpolation for missing values, Min-Max normalization, and feature engineering. The model was trained and evaluated with an Infinite Random Search hyperparameter optimiser (578 configurations, 3-hour window). Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²).. The overall average R² across all predicted weather targets was 0.495, with relative humidity at 15:00 achieving R² = 0.836 and maximum temperature achieving R² = 0.629. Comparative evaluation showed that RF consistently outperformed KNN in predictive accuracy, demonstrating the suitability of ensemble learning for nonlinear meteorological datasets. The integration of IoT enabled continuous monitoring and improved decision support for agriculture and disaster preparedness. These findings contribute to the growing body of knowledge on ML applications in meteorology and provide a foundation for developing localized weather forecasting systems in regions with similar climatic conditions.

DOI: https://doi.org/10.5281/zenodo.21257834

Published by: vikaspatanker

Authors: Md Juman Hussan

Abstract: Blood services collect a lot of data. They know who donates, when, how often, and what happens to every unit of blood after that. Most of this data still sits in spreadsheets and databases, doing very little beyond record-keeping. This paper looks at a simple, well-known framework for AI, the same one taught in introductory AI courses, and asks a plain question: what could a blood service actually do with it? Using Australian Red Cross Lifeblood as a case study, this paper walks through four uses of everyday AI: predicting which donors are about to stop donating, forecasting blood stock before shortages happen, supporting donor screening questions, and using generative AI to reach donors in their own language. Real figures from Lifeblood's published donor study, transplant program, and research investment reports are used throughout to ground the discussion in actual numbers rather than hypothetical ones. None of the ideas here need advanced or futuristic technology. They need clean data, a clear question, and a narrow tool built for one job.

DOI: https://doi.org/10.5281/zenodo.21257647

Published by: vikaspatanker

Authors: Tosif Raza Mansoori

Abstract: Customer retention has become one of the most significant challenges faced by modern banking organizations. Due to increasing competition in the financial sector, customers can easily switch from one bank to another if they are dissatisfied with the services provided. Therefore, predicting customer churn has become an important business problem, as retaining existing customers is generally more cost-effective than acquiring new ones. This project presents a Machine Learning-based Bank Customer Churn Prediction Dashboard developed using Python and Streamlit. The objective of the project is to analyze customer information and accurately predict whether a customer is likely to discontinue banking services. Along with prediction, the dashboard provides interactive visualizations and business insights that assist organizations in making informed decisions. The project begins with data collection and preprocessing, where duplicate records and unnecessary attributes are removed. Categorical variables are converted into numerical values using Label Encoding, and numerical features are standardized using StandardScaler. The cleaned dataset is then used to train multiple Machine Learning classification models. Three Machine Learning algorithms were implemented and compared, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier. Their performances were evaluated using Accuracy Score, Precision, Recall, F1-Score, and Confusion Matrix. Experimental results showed that the Random Forest classifier achieved the highest prediction accuracy of 86.25%, making it the final model selected for deployment. To improve usability, the trained model was integrated into an interactive Streamlit dashboard. Users can enter customer details and instantly receive churn predictions along with prediction confidence, customer risk level, and business recommendations. The dashboard also includes interactive data visualizations, customer analytics, dataset exploration, feature importance analysis, and model comparison charts. Overall, this project demonstrates how Machine Learning and Data Analytics can support banking organizations in reducing customer churn, improving customer retention strategies, and making data-driven business decisions.

Published by: vikaspatanker

Authors: Vaishnavi Tayade, guidance of Prof. Sudhir Dhomane and , Prof. Anand Pande

Abstract: Performing arts and cultural institutions are essential components of every society as they preserve traditions, encourage creativity, and strengthen social identity. Architecture plays a crucial role in shaping these cultural experiences by creating spaces that inspire artistic expression and meaningful interaction. Beyond providing functional infrastructure, architectural design influences how performers perform, how audiences perceive performances, and how communities engage with cultural activities. This research paper explores the relationship between architecture and performing arts, emphasizing the impact of spatial planning, acoustics, lighting, circulation, material selection, and cultural symbolism on human experience. The study examines how architectural spaces contribute to emotional engagement, creativity, cultural continuity, and community participation. The paper draws upon historical examples ranging from ancient Greek theatres and traditional Indian temple complexes to contemporary cultural institutions and performing arts centers. It investigates how architecture has continuously evolved to accommodate changing artistic practices while preserving cultural heritage. Special emphasis is placed on user-centered design, where the needs of performers, audiences, artists, and visitors are carefully considered. The research also discusses climate-responsive architecture, flexible performance spaces, sustainable design strategies, and the integration of public spaces that encourage social interaction. Several national and international case studies have been analyzed to identify successful architectural approaches that enhance performing arts and cultural experiences. These include iconic cultural landmarks, multi-functional performance venues, and community-oriented cultural centers. The study concludes that architecture serves not only as a physical setting for performances but also as an active medium that enriches artistic expression, strengthens cultural identity, and creates memorable experiences. By integrating functionality, sustainability, technology, and cultural values, architects can design environments that inspire creativity and preserve cultural heritage for future generations.

DOI: https://doi.org/10.5281/zenodo.21256703

Published by: vikaspatanker

Authors: Published by Vaishali Darda under , Dr Sudhir Dhomane and , Dilip Jade

Abstract: This paper investigates how globalisation reshapes architecture and environment in two contrasting settings: the corporate curtain-wall office towers of Gurugram, India, and the tourism-driven villa economy of Bali, Indonesia. The central research question is how the wholesale transplantation of foreign building typologies and the selective extraction of vernacular aesthetics generate measurable thermodynamic, hydrological, and cultural costs when detached from climatic and cosmological context, producing what geographers term architectural placelessness. The study combines a comparative literature review of vernacular and post-liberalisation architecture with a quantitative synthesis of published energy performance, groundwater, and land use data, modeled and triangulated against real anchor datasets (Delhi-NCR peak power demand and Bali land conversion statistics) where direct metered series are unavailable. Findings show that unoptimized glass curtain-wall towers in Gurugram's composite climate consume roughly fourteen times the cooling energy of traditional masonry havelis, with modeled cooling energy use intensity rising by an estimated 38.6% between 2015 and 2025, compounding an already acute groundwater deficit now exceeding twice the sustainable recharge threshold. In Bali, aesthetic extraction without the retention of Tri Hita Karana cosmology and Subak irrigation governance has driven sustained conversion of sawah rice terraces and skewed water allocation toward tourism. The paper concludes that architectural globalisation is not environmentally or culturally neutral: it substitutes climatic responsiveness and cultural meaning with mechanical and infrastructural dependency. It recommends regionally calibrated performance codes, hybrid typologies that reintroduce thermal mass and passive strategies, and governance models that protect indigenous land and water institutions.

DOI: https://doi.org/10.5281/zenodo.21256263

Published by: vikaspatanker

Authors: Sampada Wasudeo Dhore has published a paper in guidance of, Dr. Sudhir V. Dhomane and , Prof. Anand Pande

Abstract: Agricultural Interpretation Centres serve to educate the public about farming and ecology through immersive experiences. However, their success depends heavily on architectural design that engages visitors. This study examines how architecture can be used as a medium of experiential learning in such centres. A literature review and case study analysis identify strategies linking spatial design to interactive knowledge. Key findings show that features like clear wayfinding, sensory-rich exhibits, and integration with the landscape can significantly enhance visitor engagement. For example, balanced natural light and natural materials improve comfort and focus, while tactile displays and environmental sounds strengthen understanding. The aim is to propose architectural strategies-covering site planning, flexible layout, accessibility, and sustainability-that make agricultural knowledge tangible. In conclusion, the study finds that architecture is not just a container for exhibits but an active teacher: thoughtful design can transform Agriculture Centres into compelling, multi-sensory learning environments.

DOI: https://doi.org/10.5281/zenodo.21256149

Published by: vikaspatanker

Authors: Kiran Paul Kanikaram

Abstract: Software testing is among the most time-consuming and challenging stages of the development lifecycle to automate. Effective testing requires contextual awareness, including an understanding of code semantics and the identification of edge cases. The advancement of Large Language Models (LLMs) has enhanced the feasibility of automated testing by enabling unit, end-to-end, and exploratory testing as well as supporting a more agentic Quality Assurance (QA) process. The following paper will discuss the current use of large language models in software testing, with an emphasis on test case creation, bug detection, and self-healing automated systems based on natural language prompts. Although LLMs are providing novel ways to improve testing efficiency, there are certain obstacles that require special attention. They include incorrect output due to hallucinations, incomplete test coverage, and decreased reliability as the software grows. To better understand the obstacles, the example of a checkout module taken from the existing literature is discussed. The results show that while LLM-based testing methods can achieve useful test coverage, they do not necessarily outperform search-based methods. The analysis concludes that the value of LLMs in Quality Assurance is maximized through a human-in-the-loop approach, supported by a five-layer governance framework. As a result, the role of the QA professional is evolving toward that of a test-quality engineer and AI supervisor, requiring an expanded skillset.

DOI: https://doi.org/10.5281/zenodo.21217705

Published by: vikaspatanker

Authors: Research Scholar Mr. Narender Kumar, Associate Professor Dr. Pramod Kumar

Abstract: The primary objective of this research is to develop and evaluate an effective machine learning-based framework for the early detection of ransomware attacks. The study investigates a range of machine learning techniques, including supervised classification, anomaly detection, and clustering methods, to distinguish ransomware activities from legitimate system behavior. It focuses on extracting and analyzing critical behavioral features such as file access patterns, process execution characteristics, and network communication activities to train predictive models capable of achieving high detection accuracy while minimizing false positive rates.

DOI: https://doi.org/10.5281/zenodo.21217459

Published by: vikaspatanker