Journal of Artificial Intelligence, Applications and Innovations

Psychological and academic problems in students rarely occur in isolation: depression, anxiety, low self-esteem and addiction proneness tend to appear together, and modelling each of them separately wastes the signal they share. This study introduces the STEM-Net framework. Data were collected from 1,713 high-school students using eight validated questionnaires; after synthetic minority oversampling (SMOTE) the set grew to 5,139 records, and labels were coded at several risk levels based on the official cut-off points of each instrument. This multi-task hybrid architecture predicts all eight problems simultaneously, each at the risk levels defined by its own instrument. The base layer contains five heterogeneous learners in three branches: a multi-task tabular transformer whose attention body is shared across the eight targets, gradient-boosted tree models trained per target, and a pre-trained network. Before fine-tuning, the body of the deep branch is pre-trained on roughly eighty-eight thousand public respondents to two psychometric instruments, and the outputs of the five learners are combined through an automatic choice between stacking and simple averaging. To prevent item-to-score leakage, the items of the target scale are removed from the feature space of that task. On the test set STEM-Net reaches a macro-average accuracy of 0.9277, an F1 of 0.9034 and a ROC-AUC of 0.9844. A four-method explainability analysis shows that in every one of the eight tasks the most informative source is another one of the scales, and that addiction proneness and suicidal ideation are mutually predictive. Building on these features, a web-based screening system with a short questionnaire was designed that reports, for each student, the predicted risk level together with the features that most influenced it.

Since its inception, the Forex market has attracted various market players, including central banks, financial institutions, and even academic researchers, due to its high volatility and potential profitability following the technical analysis saying that "history tends to repeat itself", RWBCPE captures the historical patterns to anticipate future changes (up or down) within certain possible boundaries to avoid signals that lack confidence thresholds. RWBCPE underwent testing on the EUR/USD currency pair in the context of three different simple machine learning models: Random Forests, Support Vector Machines, and Long Short-Term Memory networks to demonstrate its strength and efficiency, even using simple, conventional machine learning and deep learning models. The framework achieved a higher accuracy than the traditional technical indicators, such as RSI and MACD. To evaluate the practical feasibility of the framework, we conducted a 25-year training set from 2000 to 2025 and a 1-year backtest from 2025 to 2026 with an initial balance of $10,000. The RWBCPE-driven strategy achieved 65.96% accuracy and 67.29% precision, growing the account balance to $34,844.75. The system also recorded a profit ratio of 2.17 and a maximum drawdown of 4.6%, demonstrating strong predictive performance and low-risk trading through confidence-weighted position size.

Advances in artificial intelligence (AI) offer new opportunities to deliver scalable and personalized formative feedback in higher education, yet robust empirical evidence of their effectiveness remains limited. Although peer feedback can promote active learning, reflective thinking, and evaluative judgment, inconsistencies and inaccuracies in student assessments often undermine its impact.

This study proposes PeerReli-AI, an AI-supported peer feedback framework that integrates automated quiz administration, peer evaluation, and AI-generated formative guidance. A messaging-bot infrastructure was used to deliver quizzes and manage anonymous peer feedback, while the Gemini large language model generated context-aware feedback on students’ peer evaluations. The framework was implemented in an undergraduate Data Structures and Algorithms course across multiple iterative assessment cycles.

Results from Hotelling’s T² tests and linear mixed-effects models demonstrate a significant reduction in discrepancies between peer and instructor evaluations over successive iterations when AI guidance was provided. Survey findings further indicate that students perceived the AI-generated feedback as engaging, useful, and supportive of their learning.

Overall, the findings suggest that PeerReli-AI enhances the reliability and quality of peer feedback and offers a scalable approach for improving formative assessment practices in large higher education classrooms.

Cervical cancer remains a significant global health concern, ranking as the fourth most common cancer among women. Early detection through Pap smear screening is vital for improving treatment outcomes. Computer-aided detection systems can support clinical decision-making by providing accurate and timely diagnoses. This paper proposes a deep learning model for automated cervical cancer detection using Pap smear images. Pre-trained Convolutional Neural Networks (CNNs), InceptionV3, InceptionResNetV2, and MobileNetV2, are fine-tuned with additional layers to extract specialized features through transfer learning. The extracted feature vectors are concatenated to form a unified representation, which is then used as input to multiple classification algorithms. Among these, the Bagging classifier with Random Forest as the base estimator achieves the highest performance. The model attained 97.25% accuracy, 97.26% precision, 97.28% recall, and a 97.26% F1-score on the SIPaKMeD dataset. It also achieved 96.72% accuracy on the Herlev dataset and 99.47% on the Mendeley Liquid-Based Cytology dataset. The results show that the proposed approach consistently outperforms individual CNN baselines as well as several state-of-the-art methods reported in the literature.

The growing complexity and volume of digital advertising have made recommender systems essential for enhancing user engagement and campaign performance. However, existing models predominantly rely on behavioral data, neglecting critical psychological and subconscious dimensions of user perception. This study introduces a novel hybrid recommender system that integrates multidimensional inputs, personality traits (Big Five model), subconscious associations (captured via ZMET), customer inspiration scores, and ad content tags, to deliver more psychologically aligned advertising recommendations. Using a sample of 549 participants exposed to four distinct ads from a pool of 625, data were collected through NEO personality inventories, inspiration scales, ZMET-based image selection, and expert ad tagging. The model was evaluated using standard classification metrics, achieving an accuracy of 91.5% and an AUC of 0.957, substantially outperforming conventional approaches. Key personality traits, especially Openness and Extraversion, were identified as significant predictors of recommendation relevance. This research demonstrates the value of combining behavioral, psychological, and subconscious data to build more intelligent, human-aware recommender systems. The findings offer practical insights for designing personalized ad campaigns and improving marketing efficacy in digital environments.

This study was conducted with the purpose of “developing and validating a model of the convergence of artificial intelligence and content marketing in the digital transformation of businesses.” The research design followed a sequential exploratory mixed-method approach. In the qualitative phase, semi structured interviews were conducted with experts in communication, digital marketing, and artificial intelligence. The data were analyzed using the thematic network approach at three levels—components, dimensions, and core concepts. The output of this phase yielded five principal concepts: “content creation,” “use of automation tools,” “efficiency of data analysis,” “effective interaction and content distribution,” and “enhancement of decision making,” which formed the basis for developing the quantitative instrument. The content validity of the items was confirmed through expert judgment and the Content Validity Ratio (CVR) with a threshold of 0.62. In the quantitative phase, the final questionnaire was distributed online, and 489 valid responses were collected. A confirmatory factor analysis (CFA) was performed; nine items with low factor loadings, as well as the “search engine optimization” factor, were removed to improve model fit. The measurement model fit indices and chi square ratio were found to be satisfactory. The final outcome presents a coherent and reliable framework that clarifies the linkage between the technical capacities of artificial intelligence and the content strategic needs of startups, offering a practical roadmap for designing and assessing content quality, implementing automation, optimizing distribution, and strengthening data driven decision making.

Modeling and optimizing the blasting process in mining has always been one of the fundamental challenges in mining engineering due to its multidimensional and nonlinear nature. This study aimed to develop a hybrid framework based on artificial intelligence for predicting rock fragmentation and simultaneously optimizing blasting cost and quality. In the first stage, the XGBoost machine learning algorithm was employed to model and predict the mean fragmentation size (P₅₀) based on blasting design parameters. The results indicated that the proposed model could accurately capture complex relationships among variables, achieving R² values of 0.97 for the training dataset and 0.92 for the testing dataset, thus demonstrating remarkable predictive performance. Variable importance analysis revealed that the specific charge (q) and the burden distance (B were the most influential factors controlling fragmentation, while the rock mass quality (T) also played a decisive role in altering the fracture mechanism. In the next step, the NSGA-II evolutionary algorithm was applied for multi-objective optimization between blasting costs and the P₅₀ index. The optimization outcomes generated a set of Pareto solutions, allowing engineers to flexibly select blasting patterns in accordance with either economic or operational priorities. The findings demonstrate that the proposed hybrid framework not only enhances prediction accuracy compared to empirical models but also provides a powerful data-driven decision-making tool for blast design. By introducing an integrated approach based on XGBoost and NSGA-II, this study makes a significant contribution to the blasting engineering literature and paves the way for developing sustainable and efficient blast designs in various mining operations.