Quality education is necessary as it provides the basis for equality in society. It is also significantly important that educational institutes be focused on tracking and improving the academic performance of each student. Thus, it is important to identify the key factors (i.e., diverse backgrounds, behaviors, etc.) that help students perform well. However, the increasing number of students makes it challenging and leaves a negative impact on credibility and resources due to the high dropout rates. Researchers tend to work on a variety of statistical and machine learning techniques for predicting student performance without giving much importance to their spatial and behavioral factors. Therefore, there is a need to develop a method that considers weighted key factors which have an impact on their performance. To achieve this, we first surveyed by considering experts’ opinions in selecting weighted key factors using the Fuzzy Delphi Method (FDM). Secondly, a geospatial-based machine learning technique was developed which integrated the relationship between students’ location-based features, semester-wise behavioral features, and academic features. Three different experiments were conducted to prove the superiority and predict student performance. The experimental results reveal that Long Short-Term Memory (LSTM) achieved higher accuracy of 90.9% as compared to other machine learning methods, for instance, Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), and Decision Tree (DT). Scientific analysis techniques (i.e., Fuzzy Delphi Method (FDM)) and machine learning feature engineering techniques (i.e., Variance Threshold (VT)) were used in two different experiments for selecting features where scientific analysis techniques had achieved better accuracy. The finding of this research is that, along with the past performance and social status key factors, the semester behavior factors have a lot of impact on students’ performance. We performed spatial statistical analysis on our dataset in the context of Pakistan, which provided us with the spatial areas of students’ performance; furthermore, their results are described in the data analysis section.

The early detection of COVID-19 is essential for decision-makers to develop effective containment and treatment plans. Traditionally, researchers interpret computer tomography (CT) scans or X-ray images in order to diagnose this disease. This study aims to demonstrate that deep learning models can be applied to three common medical imaging modes: X-rays, ultrasounds, and CT scans. This study employs and enhances four convolutional neural networks for coronavirus detection, including DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2. In this study, two main experiments were carried out. In the first experiment, a model was developed by combining imagery data to detect this virus. In order to determine which model performed the best, separate models were trained using different datasets in the second experiment. Because there were only so many photos accessible, data augmentation techniques were used to enhance the amount artificially. The results indicate that the proposed models effectively accomplished the task of classifying COVID-19. The accuracy rates achieved by the combined model, utilizing DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2, were 88.21%, 93.02%, and 88.89% respectively. When using the combined imaging dataset, the CNN model employing ResNet101v2 exhibited superior accuracy compared to NASNetMobile, DenseNet121, and MobileNetV2 models. [Manuscript received: 23 January 2024 | Accepted: 26 March 2024 | Published: : 30 April 2024]

Accurate and early classification of brain tumors using magnetic resonance imaging (MRI) plays a vital role in guiding timely diagnosis and therapeutic intervention. Traditional convolutional neural networks (CNNs) excel at capturing local anatomical patterns but often struggle with contextual reasoning, while Vision Transformers (ViTs) model long-range dependencies but may overlook fine-grained tumor details. To bridge this gap, we propose a hybrid deep learning framework that fuses ResNet and ViT backbones through a Cross-Gated Multi-Path Attention Fusion (CG-MAF) mechanism. CG-MAF performs dynamic, pixel-wise integration of local and global features using cross-attention and learnable gating, thereby adapting to the heterogeneous texture and spatial complexity of brain tumors. To further enhance interpretability and predictive stability, a gate-consistency loss is introduced, which enforces alignment between dual-branch attentional cues. Evaluated on a composite four-class brain MRI dataset, our model achieves state-of-the-art accuracy of 99.81% and an AUC of 0.9999. Ablation studies confirm the critical role of CG-MAF and gate-consistency loss, while Grad-CAM and Integrated Gradients provide multi-scale visual explanations that validate the model’s attention on clinically relevant tumor regions. These findings highlight the potential of our approach for robust and interpretable brain tumor classification. All source code, trained models, and experimental results are publicly available at: https://github.com/MuhammadAhmad7171/Advance-brain-tumor to support full reproducibility.