Natural Language Processing has experienced significant advancements in recent years, leading to the widespread adoption of Large Language Model-based chatbots. These chatbots are popular due to their ability to engage in context-aware conversations. However, deploying LLM-based chatbots can be re-source-intensive, making them less suitable for smaller applications or focused tasks. To address this issue, we propose a robust and flexible approach to intent classification for chatbots using STraVEns (Sentence Transformer Voting Ensemble), which includes both hard voting and soft voting ensembles of sentence transformers. Our proposed method aims to improve accuracy and versatility in intent-based chatbots model. We use five sentence transformer models for this ensemble framework: RoBERTa, DistilRoBERTa, MPNet, MiniLM L6, and MiniLM L12, and evaluated our approach by training and testing using four distinct datasets: ATIS, IDE, Small Talk, and CLINC150 which cover a range of scenarios from general conversation to specific tasks and out-of-scope intent classification. The results demonstrate that the STraVEns approach is a promising solution for intent classification-based chatbot model. Results show that our ensemble models outperformed previous benchmarks, achieving the highest accuracy and F1-scores across all datasets. The soft voting method provided flexibility and robustness, while hard voting ensured stability in specific contexts. Overall, our study suggests that ensemble-based approaches can enhance the performance of intent classification chatbots model, providing a scalable solution for various applications.

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

Rainfall is one of the climatic factors that influence various human activities and affect decision making in daily life activities. High intensity of rainfall can turn into a threat and cause serious problems such as causing various natural disasters. Therefore, it is essential to conduct rainfall forecasting to anticipate and enable preventive actions and can be used as a decision consideration in increasing the productivity and mobility of human activities. The aim of this study is to compare rainfall accuracy between the Gregorian and the lunar calendars using the bidirectional long short-term memory (Bi-LSTM) machine learning model through the grid search approach. This method was used because it can capture patterns arising from the simultaneous effects of two asynchronous calendars, Gregorian and lunar, which were used in this study by finding the right parameters. Monthly rainfall data from Bogor City, Indonesia, were used from the period of 2001 to 2022. The results show that the MAPE of the lunar calendar is relatively smaller at 14.82% which indicates the better forecasting ability than the Gregorian calendar which is 35.12%.

Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.

In Bayesian statistics, prior specification has an important role in determining the quality of posterior estimates. We use expert judgments to quantify uncertain quantities and produce appropriate prior distribution. The aim of this study was to quantify the uncertainty of life insurance claims, especially on the policy owner’s age, as it is the main factor determining the insurance premium. A one-day workshop was conducted to elicit expert judgments from those who have experience in accepting claims. Four experts from different insurance companies were involved in the workshop. The elicitation protocol used in this study was The Sheffield Elicitation Framework (SHELF), which produces four different statistical distributions for each expert. A linear pooling method was used to aggregate the distributions to obtain the consensus distribution among experts. The consensus distribution suggested that the majority of policy owners will make a claim at the age of 54 years old.

The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels using various machine learning methods. The methods employed included long short-term memory (LSTM) and gated recurrent unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally provided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. The lunar calendar model demonstrated higher accuracy in its prediction, exhibiting smaller errors (MAPE and MBE values), whereas the Gregorian calendar model yielded somewhat larger errors and tended to underestimate the values. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems while also opening new opportunities for further research into lunar calendar applications across various domains.