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All Journal JOIV : International Journal on Informatics Visualization
Salamat, Mohamad Aizi
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Computer Science & IT

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A Hybrid Approach for Malicious URL Detection Using Ensemble Models and Adaptive Synthetic Sampling Sujon, Khaled Mahmud; Hassan, Rohayanti; Zainodin, Muhammad Edzuan; Salamat, Mohamad Aizi; Kasim, Shahreen; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4627

Abstract

Malicious URLs pose a significant cybersecurity threat, often leading to phishing attacks, malware infections, and data breaches. Early detection of these URLs is crucial for preventing security vulnerabilities and mitigating potential losses. In this paper, we propose a novel approach for malicious URL detection by combining ensemble learning methods with ADASYN-based oversampling to address the class imbalance typically found in malicious URL datasets. We evaluated three popular machine learning classifiers, including XGBoost, Random Forest, and Decision Tree, and incorporated ADASYN (Adaptive Synthetic Sampling) to handle the class-imbalanced nature of our selected dataset. Our detailed experiments demonstrate that the application of ADASYN can significantly increase the performance of the predictive model across all metrics. For instance, XGBoost saw a 2.2% improvement in accuracy, Random Forest achieved a 1.0% improvement in recall, and Decision Tree displayed a 3.0% improvement in F1-score. The Decision Tree model, in particular, showed the most substantial improvements, particularly in recall and F1-score, indicating better detection of malicious URLs. Finally, our findings in this research highlighted the potential of ensemble learning, enhanced by ADASYN, for improving malicious URL detection and demonstrated its applicability in real-world cybersecurity applications.
Systematic Literature Review: An Early Detection for Schizophrenia Classification Using Machine Learning Algorithms Azizi, Ainin Sofiya; Kamal, Marnisha Mustafa; Azizan, Nurzarifah; Zawawi, Rohaizaazira Mohd; Zakaria, Noor Hidayah; Salamat, Mohamad Aizi; Yulherniwati, -
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2446

Abstract

Schizophrenia is a complex mental health disorder that poses significant challenges in diagnosis and treatment due to its multifaceted symptoms, such as hallucinations, delusions, and cognitive impairments. Early detection is crucial for effective intervention, yet traditional diagnostic methods often fail in precision and scalability. This systematic literature review investigates the application of machine learning (ML) algorithms in the early detection and classification of schizophrenia. By synthesizing findings from 40 primary studies, the review highlights the effectiveness of diverse ML models, including Random Forests, Support Vector Machines (SVM), and advanced deep learning techniques like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Key datasets such as clinical records, EEG signals, and neuroimaging data were analyzed to evaluate model performance across metrics like accuracy, precision, and sensitivity. Studies demonstrated that hybrid approaches, integrating multiple data sources and deep learning architectures, achieved classification accuracies exceeding 90%, with notable advancements in early-stage diagnosis. However, the review identifies critical challenges, including data quality issues, biases, and limited external validation, which hinder the widespread clinical application of these models. Through a comparative analysis of ML methods and traditional supervised approaches, the study underscores the transformative potential of ML in enhancing diagnostic accuracy and facilitating personalized treatment plans. Addressing current limitations, such as expanding data diversity and improving model interpretability, is essential for translating these findings into practical healthcare solutions. This research contributes to the growing knowledge in ML-driven diagnostics, advocating for its integration into clinical workflows to optimize schizophrenia management.
Co-Authors Alde Alanda, Alde Azizan, Nurzarifah Azizi, Ainin Sofiya Kamal, Marnisha Mustafa Rohayanti Hassan, Rohayanti Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Yulherniwati, - Zainodin, Muhammad Edzuan Zakaria, Noor Hidayah Zawawi, Rohaizaazira Mohd