Formal predictive analysis remains limited in tobacco-dependent economies, where forecasting has largely relied on ARIMA-type time-series models. While widely used, these models impose linearity assumptions that restrict their ability to capture key structural drivers of production. This limitation is evident in recent studies where an ARIMA (1,1,0) model projected Zimbabwean tobacco yield at 1,511.78 kg/ha for 2023, underestimating the observed yield of 2,278 kg/ha by approximately 50.7% [1]. Export forecasting is even less developed, with most existing studies remaining descriptive rather than predictive. The paper reviewed the literature related to tobacco yield forecasting, agricultural export modelling and the application of Machine Learning in crop prediction and trade intelligence systems. Data from across the fields confirm that the Machine Learning techniques Ridge Regression, Random Forest and Gradient Boosting offer superior results to statistical models. The analysis points out three main gaps. First, Machine Learning methods have not been widely applied to tobacco production in sub-Saharan Africa. Second, there is no formal export forecasting model for Zimbabwe that accounts for its multi-year shipment patterns. Third, there is no integrated framework that jointly modelsyield and exports within a unified decision-support system. These gaps highlight the need for more comprehensive, data-driven approaches to forecasting in the tobacco sector.
The World Wide Web has become one of the most valuable resources for data recovery and information releases since it has the largest collection of data and numerous pages or reports. Advances in web mining are the key to unlocking information on the Internet. There are three types of web mining: web content, web structure, and web usage. One of these classes, Web structure mining is the focus of this research. A significant role in the web mining process is played by web structure mining. This paper discusses the experimental results for Link Based Ranking Algorithms and clarifies Web Mining techniques and certain well-known tactics used in Web structure mining.
In this study, we analyzed average sleep durations across 61 countries to investigate the impact of Daylight Saving Time (DST) practices. We identified key metrics influencing sleep and employed statistical correlation analysis to explore relationships among these factors. Countries were categorized based on DST observance, and visualizations were generated to compare sleep patterns between DST and non-DST regions. Our findings indicate that, on average, countries that observe DST tend to have better sleep durations compared to those that do not. However, a more nuanced pattern emerged when accounting for latitude: DST- observing countries at lower latitudes reported shorter sleep durations than their non-DST counterparts, whereas at higher latitudes, DST-observing countries demonstrated longer aver- age sleep durations. These results suggest that the effectiveness of DST in improving sleep may be moderated by a country’s geographical location.
The integration of Sentiment Analysis (SA) and Natural Language Processing (NLP) assists companies in improving customer service by examining actionable insights from unstructured data sources like social media tweets and customer reviews. RoBERTa acts as a sophisticated transformer-based model that is capable of analyzing complex customer sentiment data. This paper assesses the performance of RoBERTa for sentiment classification while investigating solutions to class imbalance issues, sarcasm detection difficulties, and ethical issues. The discussion proposes optimization techniques for RoBERTa on different data sets to register high accuracy and outline future research directions to improve fairness and explainability. The paper concludes its material by introducing mathematical frameworks as performance metric tools in addition to optimization and evaluation procedures.
This study evaluates the effectiveness of a median sector rotation strategy within the Nikkei 500 component sectors, building on prior research that demonstrated superior risk-adjusted returns by selecting midperforming assets. Unlike traditional momentum-based investing, which focuses on winners or losers, the median strategy systematically reallocates capital to sectors with moderate past performance, reducing volatility while maintaining steady growth. Our findings reveal that quarterly and semi-annual rebalancing optimize returns in Japan, differing from U.S.-based studies where monthly rebalancing was more effective. Unlike buy-and-hold investing, the median strategy tends to outperforms total return and drawdown reduction, making it a viable alternative for public investors. By applying structured sector rotation rather than passive indexing, investors gain exposure to Japan’s strongest industries while mitigating downside risk. The results highlight the strategy’s adaptability across markets and suggest broader applications in global equities, fixed income, and multi-asset portfolios for enhanced portfolio resilience.
