In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.

Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border Collie Optimization (BBCO) is introduced as an updated version of Border Collie Optimization (BCO) for feature reduction based on maximizing classification accuracy. The proposed computer aid diagnosis tool merges transfer deep learning ResNet101, BBCO feature reduction, and Support Vector Machine (SVM) classifier to form a hybrid model ResNet101-BBCO-SVM an accurate and fast model for classifying WBCs. As a result, the ResNet101-BBCO-SVM scores the best accuracy at 99.21%, compared to recent studies in the benchmark. The model showed that the addition of the BBCO algorithm increased the detection accuracy, and at the same time, decreased the classification time consumption. The effectiveness of the ResNet101-BBCO-SVM model has been demonstrated and beaten in reasonable ratios in recent literary studies and end-to-end transfer learning of pre-trained models.

Prediction using machine learning has evolved due to its impact on providing valuable and intuitive feedback. It has covered a wide range of areas for predicting student’ performance. Instructors can track student’s dropout in a particular course at an early stage and try to improve students’ performance. The problem of students’ future performance prediction using advanced statistics and machine learning is a hard problem due to the imbalanced nature of the student data where the number of students who passed the exam is generally much higher than the number of students who failed the exam. This paper proposes a new type of crossover operator called Even-Odd crossover to generate new instances into the minority class to handle the imbalanced data problem. The experiments are implemented using three machine learning (ML) algorithms: random forest (RF), support vector machines (SVM), and K-Nearest-Neighbor (KNN) to ensure the efficiency of the proposed technique. The performance of the classifiers is evaluated using several performance measures. The efficient ability of the proposed method on solving the imbalance problem is proved by performing the experiments on 22 real-world datasets from different fields and four students’ datasets. The proposed Even-Odd crossover shows superior performance compared to state-of-the-art resampling techniques.