Asian Journal of Research in Computer Science

AI and Telepsychology Workforce Models to Address School Psychologist Shortages in High-Need Districts

Christopher Ugbong Akeke — 2026-06-12

School psychologist shortages remain a critical challenge in high-need districts, where ratios often exceed 1:1,200, limiting timely mental health support for students. This desk-based study developed hybrid AI and telepsychology workforce models to address these shortages. A systematic literature review synthesized best practices for telepsychology delivery and artificial intelligence tools in school mental health, identifying effective triage, consultation, and service expansion strategies. Agent-based modeling and Monte Carlo simulations, parameterised from NASP and NCES data, projected a 52.6% improvement in caseload ratios, reducing the average from 1:1,211 to 1:574 while enhancing equity and maintaining strong accuracy (AUC = 0.96) and cost-effectiveness. The models integrate AI triage with telepsychology to augment limited personnel and improve access in rural districts. Findings support scalable, technology-driven frameworks aligned with the NASP Practice Model. Policy recommendations emphasise licensure portability, infrastructure investment, and phased implementation for sustainable adoption. This interdisciplinary approach offers practical, replicable solutions for mitigating workforce shortages and advancing equitable mental health services in under-resourced schools.

Neural Network-Based DDoS Attack Detection in the Context of Unbalanced Classes: A Comparative Study of Resampling Methods

Kalala Kanyinda Norbert — 2026-06-04

Cyberattacks have been steadily increasing for the past twenty years. DDoS attacks, in particular, represent one of the greatest threats to organizations. A DDoS attack aims to render an information system's resources unavailable by overwhelming them with numerous requests originating from networks known as "botnets."

In this study, we propose an automatic DDoS attack detection model based on neural networks. The objective is to classify network traffic into two categories: normal traffic and abnormal traffic. However, in real-world scenarios, abnormal cases are often marginal within network traffic, leading to class imbalance. Most machine learning algorithms tend to predict the majority of normal cases more accurately. To mitigate the negative effects of this imbalance, methods exist to rebalance the classes before training the model. Among these, we can mention: 1) the choice of evaluation metrics, 2) data resampling, and 3) algorithm tuning. In this work, we opted for the resampling method to balance the data.

The study was conducted within the Department of Mathematics and Computer Science at the National Pedagogical University of the Democratic Republic of Congo.

In this study, we used the "unbalaced_20_80_dataset.csv" data from the Kaggle platform. This data shows an imbalance between normal (benign) cases, representing 80%, and DDoS attacks, representing 20%. Resampling was performed to rebalance the classes. After training the model using oversampled, undersampled, and hybrid sampling data, the results revealed few differences in the outcomes obtained. However, the model trained with oversampled data using the SMOTE technique demonstrated better learning and generalization capabilities to new data.

Conclusion: Detecting DDoS attacks presents a significant challenge for organizations. A neural network-based DDoS detection model appears to be a reliable and effective solution to this threat. By using unbalanced class data to train the model, we simulated real-world conditions where anomalous cases are rare and in the minority. Therefore, resampling was necessary to avoid bias. The model trained with oversampled data using the SMOTE technique yielded better results in terms of learning speed and its ability to generalize to unknown data.

An Interpretable Machine Learning Framework for Diabetes Prediction Using SMOTE-ENN Resampling and Feature Importance Analysis

Mustafa Hammad — 2026-06-15

Diabetes is a major global health challenge, making early and accurate prediction essential for improving patient outcomes and reducing healthcare burdens. This study presents an integrated framework for diabetes risk prediction using the Pima Indians Diabetes Dataset. The novelty of the proposed approach lies in combining class imbalance handling, comparative machine learning analysis, causal inference, and feature importance evaluation to achieve both high predictive performance and improved model interpretability.

To address class imbalance, the Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN) was applied during data preprocessing. Several machine learning algorithms were trained and evaluated, including Logistic Regression, KNN, Decision Tree Classifier, SVC, Random Forest Classifier, Gradient Boosting Classifier, and Extra Tree Classifier. Furthermore, LightGBM-based feature importance analysis and causal inference techniques were employed to identify the most influential factors associated with diabetes risk and enhance the explainability of the predictive models. The experimental results demonstrated that the KNN classifier achieved the best performance, attaining an accuracy of 94.33% and an AUC-ROC score of 98.47%.

These findings indicate that integrating advanced data balancing techniques with interpretable machine learning methods can improve both predictive accuracy and the understanding of diabetes-related risk factors, thereby supporting the development of reliable clinical decision-support systems.