Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the backbone of computer‐aided diagnostic tools for more accurately forecasting heart disease risk levels. The datasets which contain 13 attributes such as gender, age, blood pressure, and chest pain are taken from the Cleveland clinic. In total, there were 303 records with 6 tuples having missing values. To clean the data, we deleted the 6 missing records through the listwise technique. The size of data, and the fact that it is a purely random subset, made this approach have no significant effect for the experiment because there were no biases. Salient features are selected using the boosting technique to speed up and improve accuracies. Using the train/test split approach, the data is then partitioned into training and testing. SVM is then used to train and test the data. The C parameter is set at 0.05 and the linear kernel function is used. Logistic regression, Nave Bayes, decision trees, Multilayer Perceptron, and random forest were used to compare the results. The proposed boosting SVM performed exceptionally well, making it a better tool than the existing techniques.

Lending institutions face key challenges in making accurate predictions of loan defaults. Large sums of money given as loans are defaulted and this causes a substantial loss in business. This study addresses loan default in online peer-to-peer lending activities. Data for the study was obtained from the online lending club on the Kaggle platform. The loan status was chosen as the dependent variable and was classified discretely into “default” and “fully paid” loans. The dataset is preprocessed to eliminate all irrelevant instances. Due to the imbalanced nature of the dataset, the adaptive synthetic (ADASYN) oversampling algorithm is used to balance the data by oversampling the minority class with synthetic data instances. Deep neural network (DNN) is used for prediction. A prediction accuracy of 94.1% is realized and this emerged as the highest score from several trials with variations in batch sizes and epochs. The result of the study clearly shows that the proposed procedure is very promising.

Summary: Fast and accurate detection of a facial data is crucial for both face and facial expression recognition systems. These systems include internet protocol video surveillance systems, crime scene photographs systems, and criminals' databases. The aim for this study is both improvement of accuracy and speed. The salient facial features are extracted through Haar techniques. The sizes of the images are reduced by Bessel down‐sampling algorithm. This method preserved the details and perceptual quality of the original image. Then, image normalization was done by anisotropic smoothing. Multilayer feed‐forward neural network with a back‐propagation algorithm was used as classifier. A detection accuracy of 98.5% with acceptable false positives was registered with test sets from FDDB, CMU‐MIT, and Champions databases. The speed of execution was also promising. An evaluation of the proposed method with other popular detectors on the FDDB set shows great improvement.

Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.