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All Journal Journal of Computing Theories and Applications Scientific Journal of Computer Science
Abdulrashid Abdulrauf
Federal Polytechnic Kaltungo
Computer Science & IT Decision Sciences, Operations Research & Management

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A Comparative Analysis of an Enhanced Hybrid Model for Predicting Dollar Against Naira Exchange Rate Using Deep Learning and Statistical Methods Philip O. Odion; Maaruf M. Lawal; Abdulrashid Abdulrauf
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12513

Abstract

In today’s global economy, accurately predicting foreign exchange rates or estimating their trends correctly is crucial for informed investment decisions. Despite the success of standalone models like ARIMA and deep learning models like LSTM, challenges persist in capturing both linear and nonlinear dynamics in highly volatile exchange rate environments. Motivated by the limitations of these individual models and the need for more robust forecasting tools, this study proposes a hybrid ARIMA-LSTM model that integrates ARIMA’s strength in modeling linear trends with LSTM’s capability to capture nonlinear dependencies, using historical USD/NGN exchange rate data from the Central Bank of Nigeria (CBN) spanning 2001 to 2024. The research hypothesis posits that the hybrid ARIMA-LSTM model will significantly outperform standalone models in forecasting accuracy. By comparing these models against state-of-the-art approaches, the study highlights the advantages of hybridizing statistical and deep learning methods. The findings demonstrate that the hybrid model achieved the lowest Root Mean Squared Error (RMSE) of 2.216 and the highest R² of 0.998, indicating superior forecasting performance. This study fills a critical research gap by demonstrating the effectiveness of hybrid deep learning in financial time series forecasting, providing valuable insights for investors, policymakers, and financial analysts. Future research will extend this work by incorporating the latest dataset and evaluating model robustness during the recent surge in the Naira/Dollar exchange rate from 2023 to 2024.
Fake News Detection Using Bi-LSTM Architecture: A Deep Learning Approach on the ISOT Dataset Maaruf M. Lawal; Abdulrashid Abdulrauf
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14235

Abstract

The proliferation of fake news across digital platforms has raised critical concerns about information reliability. A notable example is the viral rumour falsely claiming that the Nigerian Minister of the Federal Capital Territory, Nyesom Wike, had collapsed at an event and was rushed to an undisclosed hospital an entirely fabricated claim that caused public confusion. While both traditional machine learning and deep learning approaches have been explored for automated fake news detection, many existing models have been limited to topic-specific datasets and often suffer from overfitting, especially on smaller datasets like ISOT. This study addresses these challenges by proposing a standalone Bidirectional Long Short-Term Memory (BiLSTM) model for fake news classification using the ISOT dataset. Unlike multi-modal frameworks such as the MM-FND model by state-of-the-art model, which achieved 96.3% accuracy, the proposed BiLSTM model achieved superior results with 98.98% accuracy, 98.22% precision, 99.65% recall, and a 98.93% F1-score. The model demonstrated balanced classification across both fake and real news and exhibited strong generalization capabilities. However, training and validation performance plots revealed signs of overfitting after epoch 2, suggesting the need for regularization in future work. This study contributes to the growing body of research on fake news detection by showcasing the efficacy of a focused, sequential deep learning model over more complex architectures, offering a practical, scalable, and robust solution to misinformation detection
A Lightweight Stacking Ensemble Intrusion Detection Framework for Software-Defined Networking Using the InSDN Dataset Ubakaghinwa Paul Chigbu; Abdulrashid Abdulrauf; Ishaq Isa; Badamasi Usman Zuntu; Maryam Abubakar Sharif
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.511

Abstract

Software-Defined Networking (SDN) has become a key enabler of next-generation communication infrastructures because of its centralized control, programmability, and global network visibility. However, the centralized architecture also introduces significant security vulnerabilities, making SDN environments highly susceptible to attacks such as DoS, DDoS, probing, brute-force, and botnet activities. Although deep learning-based intrusion detection systems have achieved high detection accuracy, many existing approaches suffer from high computational complexity, long training time, and limited suitability for real-time deployment. This study addresses this gap by developing a lightweight stacking ensemble intrusion detection framework for SDN using the InSDN dataset. The proposed framework employs XGBoost, LightGBM, CatBoost, Random Forest, and Extra Trees as base learners, with Logistic Regression serving as the meta-learner. Experiments were conducted using 48-feature, 6-feature, and 4-feature configurations derived from previous feature-reduction studies. The results demonstrate consistently high detection performance, achieving accuracies above 99% across all feature subsets, with only marginal degradation under reduced feature dimensions. The framework showed excellent detection capability for major attack categories while maintaining reliable performance for most minority classes. These findings demonstrate that stacking ensemble learning is a practical and computationally efficient alternative to complex deep learning architectures for SDN intrusion detection, with strong potential for scalable and real-time cybersecurity deployment in modern network environments.
A Binary Firefly Optimized Stacking Ensemble Model for Customer Churn Prediction in the Telecommunications Industry Abdulrashid Abdulrauf; Maaruf Mohammed Lawal; Oluwatoyin Omoloba; Omolara Busayo Abodunrin; Minkail Mohammad
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.507

Abstract

Customer churn prediction remains a critical challenge in the telecommunications industry because subscriber attrition directly affects revenue generation, customer retention, and long-term business sustainability. Although numerous studies have employed machine learning and deep learning techniques for churn prediction, many existing approaches rely on standalone models that have limited ability to simultaneously capture complex nonlinear customer behaviour, exploit model diversity, and eliminate redundant features. Building upon our previous study, this research proposes an enhanced heterogeneous stacking ensemble framework that integrates Binary Firefly Algorithm (BFA)-based feature optimization with Deep Neural Network (DNN), Support Vector Machine (SVM), and Random Forest (RF) as base learners, while Logistic Regression serves as the meta-learner for final churn classification. To ensure a fair and controlled comparison, the same Maven Analytics Telecom Customer Churn dataset and preprocessing strategy adopted in the previous study were retained, including data cleaning, feature transformation, stratified data partitioning, and normalization. Model development further incorporated 5-fold cross-validation on the training dataset, while BFA was introduced to identify the most informative pre-churn features. Experimental results demonstrate that the proposed framework achieved strong and balanced predictive performance and demonstrated improvements in several classification metrics compared with the previously developed BFA-based Hybrid TabNet-DNN model. Furthermore, its performance was competitive with the Random Forest baseline, which exhibited comparable classification effectiveness. These findings show that optimized feature selection and heterogeneous ensemble learning improve prediction stability and generalization. The proposed framework provides an effective decision-support tool for proactive customer retention in the telecommunications industry.
Co-Authors Badamasi Usman Zuntu Ishaq Isa Maaruf M. Lawal Maaruf M. Lawal Maaruf Mohammed Lawal Maryam Abubakar Sharif Minkail Mohammad Oluwatoyin Omoloba Omolara Busayo Abodunrin Philip O. Odion Ubakaghinwa Paul Chigbu