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.

Cross-domain recommendation systems are essential for delivering personalized experiences across diverse content areas. However, aligning user preferences across distinct domains, especially in cold-start scenarios where users have no interaction history in the target domain remains a significant challenge. To bridge this gap, we propose UTAM (Unified Thematic Alignment and Mapping), a novel framework that leverages thematic clustering of user preferences in a source domain to enable recommendations in a target domain. By capturing semantically aligned user-topic engagement patterns, UTAM effectively transfers user intent across domains. We evaluate UTAM on Amazon Movies and Books datasets using ten different experimental configurations that explore various dimensionality reductions, clustering thresholds, and engagement metrics. In cold-start scenarios, UTAM outperforms its single-domain variants. Specifically, UTAM achieves a precision of 0.770, recall of 0.854, and F1-score of 0.783. In comparison, the single-domain Movies model records precision 0.7005, recall 0.2506, F1-score 0.3174, while the single-domain Books model achieves precision 0.6309, recall 0.3142, F1-score 0.3649. These results highlight UTAM’s robustness and effectiveness in alleviating the cold-start problem while delivering accurate, scalable, and interpretable cross-domain recommendations.

Early identification of depression risk from social media text can support large-scale screening and timely follow-up. However, posts are often emotionally complex and linguistically ambiguous, which makes robust detection challenging. This paper proposes RST-DS (Rhetorical Structure Theory-based Depression Scanning), a discourse-aware framework that integrates Rhetorical Structure Theory (RST) signals with lexical evidence for classifying posts as Depressed/Non-Depressed (D/ND). Using Reddit posts, we compute an RST-derived score capturing rhetorical relationships and coherence and fuse it with lexical features represented via Term Frequency–Inverse Document Frequency using two strategies: feature addition (+) and feature concatenation ($\left|\right|$). We evaluate K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), along with a soft-voting ensemble model named MLPBoostReg (an ensemble of LR, XGBoost, and MLP). Using a 5-fold cross validation, the concatenation strategy consistently outperforms the addition strategy across accuracy, precision, recall, and F1-score. The best-performing configuration, MLPBoostReg$\left|\right|$ achieves the highest accuracy, precision, recall, and F1-score of 0.970, indicating a strong balance between identifying depression-related content and limiting false alarms. These findings suggest that combining rhetorical structure with lexical evidence improves depression detection from social media text, while noting that social-media labels do not substitute for clinical diagnosis.