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All Journal JOIV : International Journal on Informatics Visualization Journal of Applied Data Sciences Journal of Digital Market and Digital Currency
Armoogum, Vinaye
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Unveiling Criminal Activity: a Social Media Mining Approach to Crime Prediction Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.350

Abstract

Social media platforms have become breeding grounds for abusive comments, necessitating the use of machine learning to detect harmful content. This study aims to predict abusive comments within a Mauritian context, focusing specifically on comments written in Mauritian Kreol, a language with limited natural language processing tools. The objective was to build and evaluate four machine learning models—Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine (SVM)—to accurately classify comments as abusive or non-abusive. The models were trained and tested using k-fold cross-validation, and the Decision Tree model outperformed others with 100% precision and recall, while Random Forest followed with 99% accuracy. Naïve Bayes and SVM, although achieving 100% precision, had lower recall rates of 35% and 16%, respectively, due to imbalanced data in the training set. Pre-processing steps, including stop-word removal and a custom Kreol spell checker, were key in enhancing model performance. The study provides a novel contribution by applying machine learning in a Mauritian context, demonstrating the potential of AI in detecting abusive language in underrepresented languages. Despite limitations such as the absence of a Kreol lemmatization tool and incomplete coverage of Kreol spelling variations, the models show promise for wider application in social media crime detection. Future research could explore expanding this approach to other languages and domains of social media crimes.
A Comprehensive Review of Cyber Hygiene Practices in the Workplace for Enhanced Digital Security Armoogum, Sheeba; Armoogum, Vinaye; Chandra, Anurag; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Bappoo, Soodeshna; Mohd Salikon, Mohd Zaki; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3787

Abstract

In today's digital age, cybercrime is increasing at an alarming rate, and it has become more critical than ever for organizations to prioritize adopting best practices in cyber hygiene to safeguard their personnel and resources from cyberattacks. As personal hygiene keeps one clean and healthy, cyber hygiene combines behaviors to enhance data privacy. This paper aims to explore the common cyber-attacks currently faced by organizations and how the different practices associated with good cyber hygiene can be used to mitigate those attacks. This paper also emphasizes the need for organizations to adopt good cyber hygiene techniques and, therefore, provides the top 10 effective cyber hygiene measures for organizations seeking to enhance their cybersecurity posture. To better evaluate the cyber hygiene techniques, a systematic literature approach was used, assessing the different models of cyber hygiene, thus distinguishing between good and bad cyber hygiene techniques and what are the cyber-attacks associated with bad cyber hygiene that can eventually affect any organization. Based on the case study and surveys done by the researchers, it has been deduced that good cyber hygiene techniques bring positive behavior among employees, thus contributing to a more secure organization. More importantly, it is the responsibility of both the organization and the employees to practice good cyber hygiene techniques. Suppose organizations fail to enforce good cyber hygiene techniques, such as a lack of security awareness programs. In that case, employees may have the misconception that it is not their responsibility to contribute to their security and that of the organization, which consequently opens doors to various cyber-attacks. There have not been many research papers on cyber hygiene, particularly when it comes to its application in the workplace, which is a fundamental aspect of our everyday life. This paper focuses on the cyber hygiene techniques that any small to larger organization should consider. It also highlights the existing challenges associated with the implementation of good cyber hygiene techniques and offers potential solutions to address them.
Deep Learning-Based Loan Approval Prediction Using Artificial Neural Network (ANN) and Feature Importance Analysis Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.55

Abstract

The increasing demand for efficient and objective credit evaluation has motivated the adoption of artificial intelligence in financial decision-making. This study proposes a deep learning-based loan approval prediction model using an Artificial Neural Network (ANN) combined with feature importance analysis to enhance interpretability. The dataset, consisting of 2,000 loan application records with both financial and demographic attributes, was preprocessed through normalization and one-hot encoding to ensure consistent feature representation. The ANN model was trained using three hidden layers (64–32–16 neurons) with the ReLU activation function and optimized using Adam with early stopping to prevent overfitting. Experimental results demonstrate that the proposed ANN model achieves an accuracy of 92%, with a precision of 0.91, a recall of 0.93, and a ROC-AUC of 0.95, indicating excellent classification capability. The Permutation Feature Importance analysis revealed that Credit Score, Income, and Loan Amount are the most significant predictors influencing loan approval decisions. These findings confirm that the ANN model can capture complex non-linear relationships among financial attributes while maintaining transparency through explainable AI techniques. The proposed approach contributes both theoretically and practically by combining predictive power with interpretability, offering a reliable and explainable framework for automating loan evaluation in modern financial institutions.
Co-Authors Alde Alanda, Alde Armoogum, Sheeba Bappoo, Soodeshna Chandra, Anurag Dewi, Deshinta Arrova Kurniawan, Tri Basuki Melanie, Nicolas Mohd Salikon, Mohd Zaki