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Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military expenditure, and rice production. The study's findings reveal the significant influence of economic factors on rice production in Sri Lanka. Machine learning models, including Linear Regression, Support Vector Machines, Ensemble methods, and Gaussian Process Regression, demonstrate strong predictive accuracy in forecasting rice production based on economic indicators. These results underscore the importance of economic indicators in shaping rice production outcomes and highlight the potential of machine learning in predicting agricultural trends. The study suggests avenues for future research, such as exploring regional variations and refining models based on ongoing data collection.

Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.

Codebook-based feature encodings are a standard framework for image recognition issues. A codebook is usually constructed by clusterings, such as the k-means and the Gaussian Mixture Model (GMM). A codebook size is an important factor to decide the trade-off between recognition performance and computational complexity and a traditional framework has the disadvantage to image recognition issues when a large codebook; the number of unique clusters becomes smaller than a designated codebook size because some clusters converge to close positions. This paper focusses on the disadvantage from a perspective of the distribution of prior probabilities and presents a clustering framework including two objectives that are alternated to the k-means and the GMM. Our approach is first evaluated with synthetic clustering datasets to analyze a difference to traditional clustering. In the experiment section, although our approach alternated to the k-means generates similar results to the k-means results, our approach is able to finely tune clusters for our objective. Our approach alternated to the GMM significantly improves our objective and constructs intuitively appropriate clusters, especially for huge and complicatedly distributed samples. In the experiment on image recognition issues, two state-of-the-art encodings, the Fisher Vector (FV) using the GMM and the Vector of Locally Aggregated Descriptors (VLAD) using the k-means, are evaluated with two publicly available image datasets, the Birds and the Butterflies. For the results of the VLAD with our approach, the recognition performances tend to be worse compared to the original VLAD results. On the other hand, the FV using our approach is able to improve the performance, especially in a larger codebook size.