Automated detection of paddy (Oryza sativa) leaf diseases can support timely field intervention, but public datasets for this task are not always shipped in a directly usable form. This paper documents a reproducible object-detection pipeline built on a 480-image, 6-class Roboflow paddy-disease dataset whose raw export placed all annotated pairs under a single partition and provided only placeholder numeric class labels. We first validate every annotation, then construct a seeded (42), dominant-class-approximate-stratified 70/15/15 train/validation/test split with a post-split integrity check, and fine-tune a COCO-pretrained YOLO11m detector (80 epochs, patience 15, image size 640, AdamW) on the resulting train/validation partitions. Evaluated once on the held-out test split (72 images, 257 instances), the model reaches precision 0.570, recall 0.597, mAP50 0.585, and mAP50-95 0.269, at 46.65 ms/image on a Tesla T4 GPU. Per-class analysis shows AP50 ranging from 0.342 to 0.878 across the six classes, with the numerically dominant class exhibiting the lowest AP50 despite having the most training examples, an effect traced to a high rate of missed (background-classified) detections in the confusion matrix. We report this as a documented, reproducible baseline rather than a state-of-the-art claim, given open questions about class semantics and possible image-level leakage that the available metadata cannot rule out.
IEEE · 2026
This study investigates the associations between weekly generative artificial intelligence (GenAI) tool usage and three student-level academic outcomes — post-semester GPA,skill retention score, and burnout risk level — using a large cross sectional observational dataset of N = 50,000 undergraduate and graduate students spanning five academic disciplines. Employing covariate-adjusted ordinary least squares regression (ANCOVA), ordered logistic regression, quadratic dose-response modelling, bootstrap mediation analysis, and inverse probability weighting (IPW) with doubly robust correction, we examine whether GenAI tool adoption improves or undermines academic outcomes after controlling for pre-semester GPA, traditional study hours, institutional AI policy, and individual skill characteristics. Bivariate correlation between weekly GenAI hours and post-semester GPA is small and negative (r = −0.019), yet attenuates substantially
after adjustment for pre-semester GPA — the dominant predictor
(r = 0.927). Perceived AI dependency and examination anxiety
partially mediate the GenAI–GPA pathway. Prompt engineering
skill level and institutional policy moderate the relationship,
with advanced-skill users under permissive policies demonstrat
ing attenuated adverse effects. Non-linear modelling reveals an
inverted-U pattern with diminishing or adverse returns beyond
moderate weekly use thresholds. High-intensity GenAI users
report meaningfully elevated exam anxiety (mean 4.99 vs. 3.82
for low users) and 25% higher perceived AI dependency scores.
Debugging and troubleshooting use-cases are associated with
the strongest GPA gains (∆ = +0.249), while direct-answer
generation shows the weakest benefit (∆ = +0.133). These
findings carry direct implications for institutional AI governance,
curriculum re-design, and student wellbeing programming.
IJIRT · 2026
The proliferation of artificial intelligence (AI) tools across professional, educational, and social domains has prompted urgent scholarly inquiry into their long-term effects on human cognitive and psychological development. This study investigates whether measurable dimensions of AI engagement — including usage intensity, dependency score, tool proficiency, and scenario context — combined with individual cognitive and emotional attributes, can predict discrete mental growth trajectories across a global population spanning five decades (2024–2075). Using a dataset of 52,000 records across nine regions, five demographic cohorts, and seven AI developmental eras, we train and evaluate four multiclass classifiers: Logistic Regression, Random Forest, XGBoost, and LightGBM. All models achieve remarkably high performance, with Logistic Regression attaining the highest validation accuracy of 99.71%, macro-averaged F1 of 0.9973, and ROC-AUC of 0.9999 on the held-out test set. SHAP-based explainability analysis identifies Mental Wellbeing Score, Social Interaction Quality, and Emotional Intelligence as the dominant predictors of outcome class membership. These findings suggest that psychological and social dimensions of AI exposure are more strongly associated with mental growth trajectories than raw usage metrics. Implications for AI policy, digital mental health interventions, and human-centered AI design are discussed.
IJIRT · 2026
Artificial intelligence (AI) is restructuring the global labor market through simultaneous displacement of routine-cognitive tasks and the creation of high-skill occupational demand. This study employs a cross-sectional dataset of 3,000 occupational records spanning 10 countries, 10 industries, and 20 distinct job titles to investigate the structural determinants of AI replacement risk and near-term hiring trajectories. Using a combination of descriptive statistics, Spearman rank correlation, one-way ANOVA, Mann-Whitney U, Kruskal-Wallis tests, Ridge regression, and Random Forest modeling, we find that the observed dataset exhibits a notably uniform distribution of AI replacement risk (x̄ = 0.503, σ = 0.261), with no statistically significant differences across automation levels, education strata, upskilling engagement, or AI tool usage in the as-generated synthetic cross-section. Future demand score is near-orthogonal to replacement risk (ρ = 0.008, p = 0.677), suggesting the dataset reflects a balanced design rather than a population drawn from actual occupational risk distributions. Predictive models (Random Forest) are expected to achieve high held-out R² on this dataset given its tabular structure, and multinomial classifiers achieve balanced accuracy for three-class hiring trend prediction. These results are interpreted within a theoretical framework of labor-market polarization, and we draw direct implications for workforce policy, organizational upskilling investment, and occupational re-design strategies. We also document where the synthetic dataset departs from empirical expectations and identify avenues for replication with real-world data.
IJIRT · 2026