Polycystic ovary syndrome (PCOS) has been classified as a severe health problem common among women globally. Early detection and treatment of PCOS reduce the possibility of long-term complications, such as increasing the chances of developing type 2 diabetes and gestational diabetes. Therefore, effective and early PCOS diagnosis will help the healthcare systems to reduce the disease’s problems and complications. Machine learning (ML) and ensemble learning have recently shown promising results in medical diagnostics. The main goal of our research is to provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations. Feature selection methods with different types of ML models (logistic regression (LR), random forest (RF), decision tree (DT), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), xgboost, and Adaboost algorithm to get optimal feature selection and best model. Stacking ML models that combine the best base ML models with meta-learner are proposed to improve performance. Bayesian optimization is used to optimize ML models. Combining SMOTE (Synthetic Minority Oversampling Techniques) and ENN (Edited Nearest Neighbour) solves the class imbalance. The experimental results were made using a benchmark PCOS dataset with two ratios splitting 70:30 and 80:20. The result showed that the Stacking ML with REF feature selection recorded the highest accuracy at 100 compared to other models.

Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as passwords, credit card ids, and personal information. Enormous intelligent attacks are launched dynamically with the aim of tricking users into thinking they are accessing a reliable website or online application to acquire account information. Researchers in cyberspace are motivated to create intelligent models and offer secure services on the web as phishing grows more intelligent and malicious every day. In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. BERT was used to extract the URLs’ text from the Phishing Site Predict dataset. Then, the natural language processing (NLP) algorithm was applied to the unique data column and extracted a huge number of useful data features in terms of meaningful text information. Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. Then, the data were classified into legitimate and phishing URLs. To evaluate the proposed method, a famous public phishing website URLs dataset was used, with a total of 549,346 entries. However, three scenarios were developed to compare the outcomes of the proposed method by using similar datasets. The feature extraction process depends on natural language processing techniques. The experiments showed that the proposed method had achieved 96.66% accuracy in the results, and then the obtained results were compared to other literature review works. The results showed that the proposed method was efficient and valid in detecting phishing websites’ URLs.

Pomegranates are nutrient-rich fruits renowned for their vibrant ruby-red seeds and antioxidant properties. With a rich history rooted in various cultures, pomegranates have gained widespread popularity for their distinct flavor and potential health benefits. Timely detection and understanding of the growth stages of pomegranates can facilitate optimized resource allocation, targeted interventions, and efficient crop management. Additionally, early detection contributes to maximizing crop yield, ensuring product quality, and mitigating potential risks such as diseases and pest infestations. The primary goal of the present study is the early detection of pomegranate growth stages using an efficient approach. We conducted our experiments using standard image data of the pomegranate growth stages, comprising 5857 files categorized into five classes: Bud, Early-Fruit, Flower, Mid-growth, and Ripe. We propose a transfer learning-based CRnet approach to capture spatial features from pomegranate images depicting the five stages of pomegranate growth. The extracted spatial features serve as inputs for the random forest method, resulting in the creation of a new probabilistic feature set. These new probabilistic features assist the proposed model in performing the detection of pomegranate growth stages. To evaluate performance, we implemented state-of-the-art image classification techniques, including a Convolutional Neural Network (CNN), K-Neighbors Classifier (KNC), Gaussian Naive Bayes (GNB), and Logistic Regression (LR). To ensure the accuracy of applied machine learning methods, we utilized a hyperparameter optimization approach and a k-fold-based cross-validation technique. Additionally, computational complexity is determined. Extensive analysis of research results shows that by using the proposed features, the random forest model outperformed state-of-the-art methods with a high accuracy of 98% in predicting pomegranate growth stages. Our proposed scheme has the potential for the timely detection of pomegranate growth stages, assisting farmers in maximizing crop yield and mitigating potential risks.