Category Archives: Uncategorized

Authors: Swaminathan Mani

Abstract: This paper introduces the Universal Substrate (US), a dis-crete, non-local information-processing architecture that serves as the ontological basis for the phys-ical universe. This model proposes that the observable cosmos is an Emergent User Interface (UI), where the laws of physics are not fundamental constants but identified as algorithmic protocols optimized for systemic stability. By reinterpreting spacetime as a Topological Information-Braiding manifold, this model provides a unified resolution – reconciling the discrete nature of Quantum Mechanics with the geometric curvature of General Relativity through a single, self-correcting Au-todidactic Meta-Algorithm.

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Authors: Jasjit Singh Samagh, Tushar Sharma, Sumit Kharra

Abstract: AI and wearable sensors are revolutionizing the modern workout routine, offering real-time health tracking, tailored exercise plans, and intelligent performance optimization. The present research provides an extensive survey of AI-driven smart fitness systems with focus on upcoming machine learning and deep learning techniques that could be incorporated with wearable gadgets for instant wellness measurement and guidance. It explores cutting-edge techniques such as Convolutional Neural Network, Recurrent Neural Network, Spatio-temporal Graph Convolutional Network, Transformer-based model, and Virtual Fitness Assistants powered by Large Language Model, and delves into the applications of these models for posture correction, activity recognition, adaptive training, physiological recovery analysis, injury-risk prediction, and personalized wellness management. The paper also explores major technical hurdles like multimodal sensor data fusion, computational efficiency on the edge, privacy-preserving federated learning, explainable AI, and long-term personalization. Finally, new research trends such as digital twins, generative AI, and intelligent coaching with context are discussed to pave the way to the future of AI-powered fitness ecosystems. This research offers a technical foundation and insights to computer science researchers, practitioners and students on next generation intelligent fitness systems.

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Authors: Swaminathan Mani

Abstract: Generative Artificial Intelligence has changed the way we make images from text. Now we can make quality images from what we write. This is because of models that use special architectures. These models are really good at making images that look real. Are about the right thing. We can use these models to make art, design and ads. They are also useful in education, healthcare and gaming. This saves us time and money because we do not have to make images by hand. This paper is about how we can make images from text using Generative Artificial Intelligence. We look at how the models work and what's new about them. We talk about models like Stable Diffusion, DALL·E and Imagen. We look at how the whole process works, from getting the text ready to making the image. We also think about how to make the images better by using the words. We discuss what is good and bad about the models we have now. We also look at what other people have found out about making images from text. We compare the ways to do it and talk about what is new and interesting. We think about how we can make images that're just what we want and how we can make the models work better and faster. Generative Artificial Intelligence models that use diffusion are good at making images that look real. Are about the right thing. They open up possibilities, for art and industry. This paper ends by talking about what we need to do to make the models better and more responsible.

DOI: https://doi.org/10.5281/zenodo.21248944

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Authors: Teju M , Assistant Professor Divakar K M

Abstract: Generative Artificial Intelligence has changed the way we make images from text. Now we can make quality images from what we write. This is because of models that use special architectures. These models are really good at making images that look real. Are about the right thing. We can use these models to make art, design and ads. They are also useful in education, healthcare and gaming. This saves us time and money because we do not have to make images by hand. This paper is about how we can make images from text using Generative Artificial Intelligence. We look at how the models work and what's new about them. We talk about models like Stable Diffusion, DALL·E and Imagen. We look at how the whole process works, from getting the text ready to making the image. We also think about how to make the images better by using the words. We discuss what is good and bad about the models we have now. We also look at what other people have found out about making images from text. We compare the ways to do it and talk about what is new and interesting. We think about how we can make images that're just what we want and how we can make the models work better and faster. Generative Artificial Intelligence models that use diffusion are good at making images that look real. Are about the right thing. They open up possibilities, for art and industry. This paper ends by talking about what we need to do to make the models better and more responsible.

DOI: https://doi.org/10.5281/zenodo.21274709

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Authors: Dhanashree Sanjay Kale, Guidance of Ar. Dilip Jade, Ar. Radhika Raut

Abstract: Traditional architectural typologies for neuro-trauma rehabilitation heavily rely on institutionalized, clinical frameworks. While satisfying baseline medical and physical accessibility codes, these spaces often inadvertently induce spatial alienation, reinforcing a patient's perceived systemic limitations. This research paper investigates the intersection of environmental psychology and neuro-architecture to propose an alternative paradigm: an integrated rehabilitation and para-sports training facility structured around a "Gradient of Autonomy." Utilizing a qualitative and comparative spatial analysis methodology, this study examines how progressive spatial thresholds, sensory calibration, and intentional sightlines accelerate the psychological transition from a passive patient to an empowered, elite para-athlete. The findings demonstrate that replacing sterile, clinical aesthetics with calibrated acoustic zoning, circadian lighting systems, and dignified tactile wayfinding significantly mitigates sensory overload while fostering spatial agency. The paper concludes by presenting a programmatic matrix and architectural guidelines for future universally empowering athletic environments.

DOI: https://doi.org/10.5281/zenodo.21274067

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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

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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

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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

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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

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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.

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