Rainfall prediction plays a crucial role in raising awareness about the potential dangers associated with rain and enabling individuals to take proactive measures for their safety. This study aims to utilize machine learning algorithms to accurately predict rainfall, considering the significant impact of scarcity or extreme rainfall on both rural and urban life. The complex nature of rainfall, influenced by various atmospheric, oceanic, and geographical factors, makes it a challenging phenomenon to forecast. This research employs data preprocessing techniques, outlier analysis, correlation analysis, feature selection, and several machine learning algorithms such as Naive Bayes (NB), Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. The study focuses on developing the most accurate rainfall prediction model by utilizing machine learning and feature selection techniques. The Artificial Neural Network (ANN) achieves a maximum accuracy of 90% and 91% before and after feature selection, respectively. Furthermore, k-means clustering and Principal Component Analysis (PCA) are applied to examine regional rainfall patterns in Australia. Lastly, to make our proposed machine learning simpler and more usable for general people, we have formulated a web-based application system using Flask in our research paper. Overall, this research demonstrates the effectiveness of different machine-learning techniques in predicting rainfall using Australian weather data.

Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), Random Tree (RT), and Bagging Tree Model (BTM). Accuracy, Sensitivity, Specificity, and Kappa values were used to evaluate model performance.

The uncommon illness known as monkeypox is brought on by the monkeypox virus. The Orthopoxvirus genus belongs to the family Poxviridae, which also contains the monkeypox virus. The variola virus, which causes smallpox; the vaccinia virus, which is used in the smallpox vaccine; and the cowpox virus are all members of the Orthopoxvirus genus. There is no relationship between chickenpox and monkeypox. After two outbreaks of a disorder resembling pox, monkeypox was first discovered in colonies of monkeys kept for research in 1958. The illness, also known as “monkeypox”, still has no known cause. However, non-human primates and African rodents can spread the disease to humans (such as monkeys). In 1970, a human was exposed to monkeypox for the first time. Several additional nations in central and western Africa currently have documented cases of monkeypox. Before the 2022 outbreak, almost all instances of monkeypox in people outside of Africa were connected to either imported animals or foreign travel to nations where the illness frequently occurs. In this work, the most recent monkeypox dataset was evaluated and the significant instances were visualized. Additionally, nine different forecasting models were also used, and the prophet model emerged as the most reliable one when compared with all nine models with an MSE value of 41,922.55, an R² score of 0.49, a MAPE value of 16.82, an MAE value of 146.29, and an RMSE value of 204.75, which could be considerable assistance to clinicians treating monkeypox patients and government agencies monitoring the origination and current state of the disease.

Accurate colon polyp detection and segmentation in colonoscopy images remain a challenging task due to the variability in colon polyp appearance, size, shape, and location. In this paper, we propose EvoGrayNet, an automated evolutionary framework for colon polyp segmentation that facilitates detailed measurement of polyp boundaries, shape, size, and structural contrast. EvoGrayNet integrates a custom-designed feature extraction module with an evolutionary neural architecture search method to enhance spatial segmentation accuracy and model efficiency. First, we introduce a gray module that combines standard and depthwise separable convolutions, batch normalization, and dropout to capture both local details and global context in colonoscopy images. Second, a lightweight attention gate (AG) module is proposed to refine feature maps by focusing on low-contrast regions and improving localization accuracy. Third, we integrate the gray module and the AG module into a supernet along with a dilated feature extractor (DFE) and a feature recalibration (FR) module. The DFE captures multiscale spatial information using dilated convolutions, while the FR recalibrates channel importance for better feature representation. Finally, EvoGrayNet is optimized through an evolutionary process involving crossover and mutation, systematically identifying configurations that enhance boundary delineation accuracy. Performance is quantitatively assessed using the dice similarity coefficient (DSC), intersection over union (IoU), and the 95th percentile hausdorff distance (HD95), which together reflect both the precision of spatial measurement and boundary adherence. Experimental results show that EvoGrayNet outperforms 12 existing methods across four public colon polyp datasets, offering superior detection and segmentation accuracy, lower complexity, reduced FLOPS, faster inference speed, and improved generalization ability.