Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.
Novel Coronavirus (Covid-2019), which first appeared in December 2019 in the Chinese city of Wuhan. It is spreading rapidly in most parts of the world and becoming a global epidemic. It is devastating, affecting public health, daily life, and the global economy. According to the statistics of the World Health Organization on August 11, the number of cases of coronavirus (Covid-2019) reached nearly 17 million, and the number of infections globally distributed among most European countries and most countries of the Asian continent, and the number of deaths from the Corona virus reached 700 thousand people around the world. . It is necessary to detect positive cases as soon as possible in order to prevent the spread of this epidemic and quickly treat infected patients. In this paper, the current literature on the methods used to detect Covid is presented. In these studies, the research that used different techniques of artificial intelligence to detect COVID-19 was reviewed as the convolutionary neural network (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) were proposed for the identification of patients infected with coronavirus pneumonia using chest X-ray radiographs By using 5-fold cross validation, three separate binary classifications of four grades (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) were introduced. It has been shown that the pre-trained ResNet50 model offers the highest classification performance (96.1 percent accuracy for Dataset-1, 99.5 percent accuracy for Dataset-2 and 99.7 percent accuracy for Dataset-2) based on the performance results obtained.
Recently, numerous researches have emphasized the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, different machine learning models for PV fault detection using a simulated 0.25MW PV power system were developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to-ground faults. Multiple electrical and environmental variables were measured and exploited as features, such as current, voltage, power, temperature, and irradiance. Four algorithms (Tree, LDA, SVM, and ANN) were tested using 5-fold cross-validation to identify errors in the PV system. The performance evaluation of the models revealed promising results, with all algorithms demonstrating high accuracy. The Tree and LDA algorithms exhibited the best performance, achieving accuracies of 99.544% on the training data and 98.058% on the testing data. LDA achieved perfect accuracy (100%) on the testing data, while SVM and ANN achieved 95.145% and 89.320% accuracy, respectively. These findings underscore the potential of machine learning algorithms in accurately detecting and classifying various types of PV faults. .