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The flipped classroom is considered an instructional strategy and a type of blended learning instruction that focused on active learning and student engagement. Over the years, flipped classroom studies have focused more on the advantages and challenges of flipped instruction and its effectiveness, but little is known about the state of self-regulation in flipped classrooms. This study investigates the self-regulation strategies as well as the supports proposed for self-regulated learning in flipped classrooms. Findings show that relatively few studies have focused on self-regulated learning in flipped classrooms compared to the overall research and publication productivity in flipped classrooms. Also, the existing solutions and supports have only focused on either self-regulation or online help-seeking, but have not focused on other specific types of self-regulation strategies. Our study proposed some future research recommendations in flipped classrooms.

Cervical cancer is one of the leading causes of premature mortality among women worldwide and more than 85% of these deaths are in developing countries. There are several risk factors associated with cervical cancer. In this paper, we developed a predictive model for predicting the outcome of patients with cervical cancer, given risk patterns from individual medical records and preliminary screening. This work presents a decision tree (DT) classification algorithm to analyze the risk factors of cervical cancer. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) feature selection techniques were fully explored to determine the most important attributes for cervical cancer prediction. The dataset employed here contains missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features from RFE and SMOTETomek has better results with an accuracy of 98.72% and sensitivity of 100%. DT classifier is shown to have better performance in handling classification problems when the features are reduced, and the problem of high class imbalance is addressed.

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Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to noncommunicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called ''genetically optimized Bayesian adaptive resonance theory mapping'' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS. INDEX TERMS Metabolic syndrome, adaptive resonance theory, Bayesian ARTMAP, genetic algorithm.

Business firms and households sometimes seek for extra-funding to fulfill certain needs. The demand which arises from the need of extra funds is fulfilled by the credit market. Banks and others financial lending institutions are the key players in this market (Gaigaliene and Cesnys, 2018). Loan is one of the most important products of most financial institutions. All financial lenders try to find effective business strategies for persuading customers to apply for loans. However, there are some borrowers who default in loan payments (Begum and Deniz, 2019). During a loan term, default may occur when the borrower fails to make required payments. Therefore, an assessment of a borrower's default risk over time is essential to enable timely risk management. Credit officers determine whether borrowers can fulfill their requirements using manually analysis of borrower's credit history. In the last decade, this trend has changed over time with technological advancement (Rehman, 2017).In recent years, financial lending institutions are using automated loan default models as credit risk scoring tools when granting loans to potential borrowers (Bao et al., 2019). Machine Learning (ML) algorithms have been applied to assess the credit risk of borrowers in financial lending institutions (Djeundj and Crook, 2018). Reliable models for credit risks play an important role in loss control and revenue maximization (Luo and Nie, 2016). Earlier research treated loan default prediction as a binary classification problem, where a loan is classified as either creditworthy or non-creditworthy (Rosenberg and Gleit, 1994). Linear Discriminant Analysis (LDA) and logistic regression (LR) are two most popular tools for constructing credit scoring models (Wiginton, 1980). Subsequently, other classification algorithm such as, Artificial neural networks (ANN) Gulsoy and Kulluk (2019) support vector machines (SVM) Alaka et al. (2018), decision trees (DT) Liu et al. (2015), and Bayesian classifier (BC) Carta et al. (2020), have been used to estimate borrowers' probability of default. Recently, time-to-default modeling has attracted increasing research interest (Dirick et al., 2017). Time-to-default data fall into the category of lifetime data in general, which is commonly analyzed by survival analysis (SA) (Malekipirbazari and Aksakalli, 2015). In loan prediction, two types of errors inevitably lead to inefficiency in prediction

Conflict analysis plays an important role in the fields of politics, military operations, economics, business management, games, urban planning, management negotiations and etcetera. Computational intelligence model such as rough set theory has been used in managing conflict situations which have the ability to handle uncertainties. However, there is a great concern in the computational time of the rough set approach in determining strength, certainty and coverage of conflicts. Motivated by the fact that every rough set approach can be represented using soft set theory, we derived an alternative method based on the concept of co-occurrence from multi-soft sets to handle conflict situations. We first used an illustrative example of a movie selection problem to demonstrate the proposed approach and provide an extensive elaboration using a publicly available dataset. Our motivation is to provide a new measure based on support, strength, certainty and coverage of soft set on movie selection problem. Our findings have revealed that the proposed approach achieved less computational time when compared with the rough set-based approach of up to 8.05%. One potential application of the proposed approach is the domain of recommendation systems. The proposed approach can be used to easily identify users/items nearest neighbours based on support, strength, certainty and coverage, which is crucial for the success of recommendation systems.