Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and machine learning perspective and how LR can be applied to imbalanced and rare events data.

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

Lean is a philosophy that helps organizations develop a continuous improvement culture by eliminating waste and minimizing the use of resources. On the other hand, Industry 4.0 is a complex high-tech model that contributes to flexible organizations using different technologies such as IoT, cloud computing, blockchain, virtual reality, and Artificial Intelligence. The concept of integrating Lean Principles and Industry 4.0 is still very recent and limited, and the literature lacks a bibliometric perspective. The integration is challenging in terms of contentment-wise and is a broad multi-dimensional research area. This study aims to analyze Lean Industry 4.0 or Lean 4.0 and Lean digital transformation literature using bibliometrics, identifying its state of art, benefits, challenges, and scientific gaps. In this study, articles published between 2010 and 2022 from the Scopus database are analyzed to identify significant publications, journals, authors, institutions, and countries based on the number of publications and frequently used keywords and subject areas. This study contributes to developing the state of art of Lean Industry 4.0 and Lean digital transformation and helps researchers and practitioners direct their future studies. The literature performance is measured based on both the number of publications and the number of citations; this bibliometric analysis covers most of the published papers in both research domains.

Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.