Machine learning based COVID -19 disease recognition using CT images of SIRM database.

The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.

Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm.

Stock index price forecasting is the influential indicator for investors and financial investigators by which decision making capability to achieve maximum benefit with minimum risk can be improved. So, a robust engine with capability to administer useful information is desired to achieve the success. The forecasting effectiveness of stock market is improved in this paper by integrating a modified crow search algorithm (CSA) and extreme learning machine (ELM). The effectiveness of proposed modified CSA entitled as Particle Swarm Optimization (PSO)-based Group oriented CSA (PGCSA) to outperform other existing algorithms is observed by solving 12 benchmark problems. PGCSA algorithm is used to achieve relevant weights and biases of ELM to improve the effectiveness of conventional ELM. The impact of hybrid PGCSA ELM model to predict next day closing price of seven different stock indices is observed by using performance measures, technical indicators and hypothesis test (paired t-test). The seven stock indices are considered by incorporating data during COVID-19 outbreak. This model is tested by comparing with existing techniques proposed in published works. The simulation results provide that PGCSA ELM model can be considered as a suitable tool to predict next day closing price.