Journal of Statistics and Mathematical Engineering

Factor Analytic Modelling of Empowerment and Financial Inclusion

Madhusmita Tripathy — 2026-05-16

The study uses factor analysis to explain the effect of financial inclusion on the lives of women and how it influences their ability to advance socially, economically and politically. A dataset of twelve variables is used to establish the underlying factors, and find out the interrelations between different measures of women empowerment. Before performing factor analysis, supplementary methods like the principal component analysis, Bartlett test of sphericity and the Kaiser Meyer Olkins measure had been used to verify the suitability of the analysis. Three main factors consider in this study like, economic empowerment, social empowerment, and political empowerment, followed by regression analysis to understand the influence of various aspects of empowerment on the degree of financial inclusion in a community; the results show that women who feel more in control of their economic, social and political lives are much more inclined to use the financial system. In turn, women empowerment is a key mechanism in promoting an inclusive community building, which has heavy implications on policy makers who strive to make a positive impact in the societies they operate.

Machine Learning Models for Climate Prediction: A Comparative Study with Classical Statistical Methods

Md. Tanvin Mahfuz Tuhin — 2026-05-22

This work investigates the role of advanced modeling techniques in improving the accuracy of climate prediction, which is vital for understanding environmental change and guiding mitigation efforts. Conventional statistical models, including Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA), have been extensively used due to their clarity, efficiency, and effectiveness in handling linear and stationary time series data. However, the growing complexity of climate systems has encouraged the adoption of machine learning approaches that can better capture nonlinear relationships and long-term dependencies. Techniques such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and ensemble learning models have shown strong potential in extracting meaningful patterns from large and complex climate datasets. In this study, a detailed comparison is conducted between traditional statistical methods and modern machine learning models for forecasting key variables such as temperature, rainfall, and atmospheric CO₂ concentration. Performance is assessed using widely accepted evaluation metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²), along with considerations of computational requirements and model interpretability. The findings suggest that machine learning models, especially LSTM and hybrid approaches, generally provide more accurate predictions for nonlinear and large-scale data. Nevertheless, statistical models remain reliable for short-term forecasts and relatively stable datasets, highlighting the value of combining both approaches for enhanced climate prediction.

Mathematical Modeling and Analysis of Channel Capacity in Shannon Information Theory

Md. Nurul Islam — 2026-05-02

This work investigates channel capacity as a fundamental concept in information theory, representing the maximum achievable data transmission rate over a communication channel with an arbitrarily low probability of error. The study develops a comprehensive mathematical and analytical framework, based on Shannon’s theory, to evaluate channel capacity across different communication models. Both discrete memory-less channels and continuous channel models are examined to provide a broad and systematic understanding of theoretical capacity limits. Particular attention is given to the Additive White Gaussian Noise (AWGN) channel, which serves as a standard model for practical communication systems due to its ability to accurately represent thermal noise and other random disturbances. The analysis incorporates key information-theoretic measures such as entropy and mutual information to derive channel capacity expressions. The Shannon–Hartley theorem is explored in detail to establish the relationship between channel capacity, bandwidth, and signal-to-noise ratio (SNR). To validate the theoretical findings, numerical simulations are performed to analyze the variation of channel capacity with respect to SNR under different bandwidth conditions. The results demonstrate that channel capacity increases logarithmically with SNR, highlighting the phenomenon of diminishing returns at higher signal power levels. Furthermore, the study examines the effects of noise characteristics, power constraints, and signal design on achievable data rates. The outcomes of this research provide important insights for the design and optimization of modern communication systems, including wireless and optical networks. By linking theoretical principles with practical considerations, this work contributes to a deeper understanding of efficient data transmission and the fundamental limits imposed by noise and bandwidth in real-world channels.