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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics
Belinda Ndlovu
National University of Science and Technology
Computer Science & IT

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Transformer-Based Abstractive Text Summarisation for Real-Time Web Applications: A Browser-Integrated System with REST API Architecture Zvinodashe Revesai; Belinda Ndlovu; Kudakwashe Maguraushe
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12559

Abstract

The exponential growth of digital textual content has intensified the need for efficient, accessible summarisation tools that support information processing across academic, professional, and research domains. While Transformer-based abstractive summarisation models have demonstrated strong performance in benchmark settings, their real-world deployment remains limited due to computational complexity and lack of user accessibility. This study presents a lightweight Transformer-based abstractive text summarisation system, operationalised as a Google Chrome extension and supported by a REST API, enabling seamless integration into everyday user workflows. The proposed system employs an encoder–decoder framework leveraging a pre-trained Transformer-based encoder and a sequence-to-sequence decoder with attention, fine-tuned on the CNN/Daily Mail dataset. Quantitative evaluation on the benchmark dataset achieved ROUGE-1, ROUGE-2, and ROUGE-L scores of 38.21, 16.54, and 35.12, respectively, demonstrating competitive performance relative to established neural baselines. To address the limitations of lexical evaluation metrics, a complementary human evaluation was conducted using a Likert-scale assessment across coherence, informativeness, and fluency, yielding mean scores above 4.0, thereby confirming the qualitative effectiveness of the generated summaries. In addition to model performance, system-level evaluation assessed functional correctness, latency, scalability, and usability within a real-world deployment context. The system demonstrated stable performance under concurrent usage, with an average response time of 4.2 seconds per request and positive user feedback, validating its practical applicability. The findings demonstrate that high-quality abstractive summarisation can be effectively operationalised within a lightweight, browser-integrated architecture, thereby bridging the gap between research-stage neural models and accessible end-user applications. This work contributes to deployment-oriented natural language processing by emphasising usability, modularity, and real-world integration as critical dimensions of system design.
Deep Learning and XGBoost for Pancreatic Cancer Survival Prediction: A Real-World Evaluation in a Resource-Constrained African Healthcare Setting Zvinodashe Revesai; Kudakwashe Maguraushe; Belinda Ndlovu
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12570

Abstract

Pancreatic cancer remains one of the most lethal malignancies worldwide, with persistently low survival rates and a pressing need for reliable prognostic tools to support treatment planning in resource-constrained healthcare environments. This study presents a structured comparative evaluation of Artificial Neural Network (ANN) and XGBoost classifiers for predicting 12-month survival using real-world clinical data from 569 pancreatic cancer patients treated at a public hospital in Zimbabwe between 2018 and 2023. The Cross-Industry Standard Process for Data Mining (CRISP-DM) framework guided data understanding, preprocessing, model development, and evaluation. A comprehensive preprocessing pipeline incorporating missing value imputation, outlier management, encoding, feature selection, and normalisation was applied, with all transformations derived exclusively from the training set to prevent data leakage. Models were trained using an 80/20 stratified split with cross-validated hyperparameter optimisation and evaluated on a strictly held-out test set using accuracy, precision, recall, F1-score, ROC analysis, and McNemar’s test. On the test dataset, the ANN model achieved 99% overall accuracy and 99% F1-score, outperforming XGBoost, which attained 90% accuracy and 90% F1-score. The performance difference was statistically significant (p < 0.05). Computational analysis demonstrated inference times below 3 milliseconds per sample, supporting feasibility for clinical deployment. While results indicate strong discriminative capacity within this single-centre dataset, external validation across multi-institutional cohorts is necessary to confirm generalisability. These findings suggest that supervised machine learning can provide clinically meaningful support for survival prediction in African tertiary healthcare settings. This study uniquely contributes a deployment-oriented, real-world evaluation of machine learning models within a resource-constrained African healthcare context, addressing a critical gap in the current oncology informatics literature.
Temporal Deep Learning for Probabilistic Mutation Forecasting in SARS-CoV-2 Spike Protein Sequences Robert Selemani; Belinda Ndlovu; Amazing Maphosa
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12650

Abstract

Modelling the evolution of biological sequences under temporal and probabilistic constraints remains a complex computational challenge. This study investigates longitudinal deep learning for probabilistic modelling of mutation patterns in the SARS-CoV-2 Spike Protein. A stacked Long Short-Term Memory (LSTM) network is trained on temporally ordered amino acid sequences to estimate residue-level substitution probabilities and rank plausible future mutations. Unlike deterministic classification approaches, the proposed framework treats mutation prediction as a probabilistic ranking task, accounting for the inherent uncertainty of viral evolution. The model is evaluated using metrics suitable for imbalanced sequence data, including Top-K accuracy, precision, recall, F1-score, and ROC-AUC. Results indicate strong ranking performance, with Top-3 accuracy of 94.6% and ROC-AUC of 0.91. In comparison, the overall accuracy (93.1%) is interpreted cautiously, given the dominance of conserved residues. Error analysis shows that difficult predictions are concentrated in low-frequency, rapidly evolving residue positions. A comparison with a frequency-based baseline demonstrates that the LSTM captures temporal dependencies beyond static substitution patterns. Predicted mutation distributions exhibit a structured alignment with known functional regions of the Spike Protein, as supported by the established literature, providing qualitative biological validation. This study contributes a temporally structured and probabilistic framework for mutation modelling, emphasising ranking-based evaluation and biologically contextualised interpretation. The findings demonstrate the feasibility of probabilistic mutation forecasting under controlled experimental conditions and provide a methodological foundation for future research on AI-assisted genomic surveillance.
Explainable Deep Learning for Diabetic Retinopathy Detection: A Quantitatively Validated Framework Tinashe Ngwazi; Belinda Ndlovu; Kudakwashe Maguraushe
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12687

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness, where early and accurate detection is critical for effective intervention. While deep learning models have demonstrated strong performance in DR classification, their limited interpretability and inconsistent evaluation practices hinder clinical trust and deployment. This study proposes an explainable deep learning framework for DR detection based on MobileNetV2, complemented by Integrated Gradients for feature attribution. A curated dataset of 4,464 retinal images was constructed from publicly available sources through systematic preprocessing, including quality filtering, deduplication, and class balancing across five DR stages. To ensure robust evaluation, a multi-level validation strategy was employed, incorporating stratified train–validation–test splits and k-fold cross-validation. The proposed framework achieved 87.0% accuracy and an F1-score of 0.868, outperforming baseline models including EfficientNet-B0, DenseNet121, and VGG16. Beyond predictive performance, explainability was quantitatively evaluated using deletion and insertion metrics, demonstrating that Integrated Gradients provides more faithful feature attribution compared to Grad-CAM and LIME. Error analysis further reveals that misclassifications are concentrated between adjacent DR stages, reflecting the inherent difficulty of fine-grained disease progression modelling. The findings highlight that combining rigorous validation with quantitative explainability evaluation can improve the reliability and transparency of deep learning models for medical imaging. While results are promising, the framework is validated on publicly available datasets and requires further external clinical validation before real-world deployment.
Co-Authors Amazing Maphosa Caroline Mhlanga Charnelle Razo Fortunate Mutanda Frederick Mlungisi Dandure Hazel Chamboko Hillary Muzenda Kudakwashe Maguraushe Kudzaishe Lawal Chizengwe Musawenkosi Moyo Pride Dube Robert Selemani Tinashe Ngwazi Zvinodashe Revesai Zvinodashe Revesai