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.

Cancer is a disease where abnormal cells grow uncontrollably and spread to other body parts. It can originate anywhere in the human body, which consists of trillions of cells. These cells continually divide, replenishing the body’s needs. As cells age or sustain damage, they naturally undergo apoptosis, allowing new cells to take their place. Our research uses a secondary dataset from Kaggle, comprising over 130,000 images representing various cancer types. We have developed a novel Deep Learning(DL) model capable of detecting and classifying cancer at early stages with remarkable accuracy. The model classifies eight primary cancer types and 26 subtypes, each represented by 5,000 images. Our approach combines various computational tools, including pre-trained Convolutional Neural Networks (CNNs), Machine Learning (ML), and DL classifiers such as KNN(K-Nearest Neighbors) and Support Vector Machine (SVM), and innovative multimodal architectures of merged Convolutional Neural Network(CNN)-Long Short-Term Memory(LSTM) hybrids. We applied two distinct classification strategies. In our first approach, the main class and subclass are classified together. In the second approach, the model first predicts the main eight classes and then 26 subclasses concerning the main class classification, where the KNN model achieved higher accuracy for the Lymphoma class than CNNs. Finally, the X-OR(Exclusively-OR) Gate-based fusion technique applied after prediction significantly reduces misclassifications and enhances the certainty of cancer types. Our findings reveal great accuracy levels of 99.25% for primary cancer classifications and 97.80% for subclass classifications. The introduction of novel models, Vception (VGG + Inception) and Vmobilnet (VGG + MobileNet), integrated with LSTM, further advances diagnostic capabilities. Again, By utilizing an X-OR Gate post-prediction from Vmobilenet and Vception models, we achieved a main class accuracy of 99.95% and a subclass accuracy of 99.13%, significantly boosting model confidence. Moreover, individually, KNN achieved 97.14% accuracy for the Lymphoma class using Principal Component Analysis (PCA). This study not only sets a new benchmark in cancer detection but also promises to improve patient care and treatment outcomes significantly.

In this era of widespread information, combating fake news in less commonly represented languages like Bengali is a significant challenge. Fake news is a critical issue in Bangla, a language that a vast population uses but lacks adequate natural language processing tools. To address this, our research introduces RoBERTa-GCN, a cutting-edge model combining RoBERTa with a graph neural network (GCN) to accurately identify fake news in Bangladesh. The dataset we utilized comprises articles from 22 prominent Bangladeshi news portals covering diverse subjects such as politics, sports, economy, and entertainment. This comprehensive dataset enables the model to learn and adapt to the intricacies of the Bangla language and its news ecosystem, facilitating effective fake news detection across various content categories. Our approach integrates the RoBERTa model, adapted for Bangla, with GCN’s expertise in processing relational data, forming an effective means to differentiate between authentic and fake news. This study’s key achievement is the creation and application of the RoBERTa-GCN model to the Bangla language, an area not thoroughly explored in previous research. The findings show that RoBERTa-GCN surpasses existing methods, achieving impressive accuracy rates of 98.60%, highlighting its capability as a robust model for preserving news integrity in the digital era, especially for the Bangla-speaking population.

Medical text classification involves organizing medical documents into specific categories to streamline information retrieval and support clinical decision-making. Traditional deep learning (DL) techniques, including adapting pre-trained language models (PLMs), have demonstrated notable effectiveness in this domain. However, they require extensive domain-specific training data, often perform subpar across various languages, and involve high costs and complexities in deploying large-scale models. In this study, we employed four datasets: Clinical Trials on Cancer, encompassing 6 million statements from interventional cancer clinical trial protocols; the Illness-dataset, consisting of 22,660 categorized tweets from 2018 and 2019; the Multi-View Active Learning for Short Medical Text Classification in User-Generated Data, an extended version of the Illness-dataset including 22,660 documents from the same period; and the Symptom2Disease dataset, containing 1,200 data points used to predict diseases based on symptom descriptions. This study uses ChatGPT, particularly its ChatGPT-3.5 and ChatGPT-4 versions, as a viable alternative for classifying medical texts. We investigate essential aspects, including the construction of prompts, the parsing of responses, and the various strategic use of GPT models to optimize outcomes. Through comparative analysis with established methods like PLM fine-tuning and prompt-tuning, our findings indicate that ChatGPT addresses these challenges efficiently and matches the performance of traditional methods. Furthermore, the enhanced capabilities of the proposed MediGPT (Medical Generative Pre-Trained Transformers) have led to performance improvements of 10.5%, 15.1%, 10.8%, and 12.4% across the datasets, highlighting its adaptability and robustness in diverse medical text scenarios without the need for specialized domain adjustments. This research underscores the capability of ChatGPT to facilitate a versatile AI framework in medical text processing, which could revolutionize medical informatics practices.

Autism Spectrum Disorder (ASD) is a complex developmental condition marked by challenges in social interaction, communication, and behavior, often involving restricted interests and repetitive actions. The diversity in symptoms and skill profiles across individuals creates a diagnostic landscape that requires a multifaceted approach for accurate understanding and intervention. This study employed advanced machine-learning techniques to enhance the accuracy and reliability of ASD diagnosis. We used a standard dataset comprising 1054 patient samples and 20 variables. The research methodology involved rigorous preprocessing, including selecting key variables through data mining (DM) visualization techniques including Chi-Square tests, analysis of variance, and correlation analysis, along with outlier removal to ensure robust model performance. The proposed DM and logistic regression (LR) with Shapley Additive exPlanations (DMLRS) model achieved the highest accuracy at 99%, outperforming state-of-the-art methods. eXplainable artificial intelligence was incorporated using Shapley Additive exPlanations to enhance interpretability. The model was compared with other approaches, including XGBoost, Deep Models with Residual Connections and Ensemble (DMRCE), and fast lightweight automated machine learning systems. Each method was fine-tuned, and performance was verified using k-fold cross-validation. In addition, a real-time web application was developed that integrates the DMLRS model with the Django framework for ASD diagnosis. This app represents a significant advancement in medical informatics, offering a practical, user-friendly, and innovative solution for early detection and diagnosis.