Elayaraja Aruchunan

@um.edu.my

Senior Lecturer and Department of Decision Science
Universiti Malaya

Elayaraja Aruchunan


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https://researchid.co/elayarajah
Dr. Elayaraja Aruchunan started his carrier at Curtin University Malaysia in March 2008. Based on his excellent track record in research, he was awarded a full scholarship to pursue his Ph.D. by the Australian Government in 2012. Upon completion of his Ph.D., he joined the University of Malaya in November 2020. He has vast experience in the fields of numerical analysis, computational applied mathematics, and machine learning. Currently, he is working on developing new algorithms for solving various complex scientific, engineering, and industrial mathematical models. Over the last 12 years, he has also conducted various mathematics courses at the undergraduate and postgraduate levels in Malaysian and Australian higher education institutions.

RESEARCH, TEACHING, or OTHER INTERESTS

Decision Sciences, Applied Mathematics, Numerical Analysis, Statistics, Probability and Uncertainty
59

Scopus Publications

827

Scholar Citations

14

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • HETEROGENEOUS GRAPH NEURAL NETWORKS FOR STOCK PRICE PREDICTION: MODELING TEMPORAL AND CROSS-STOCK DEPENDENCIES
    Hilmi Aziz Bukhori, Elayaraja Aruchunan, Syaiful Anam, Saiful Bukhori, Avin Maulana
    Barekeng, 2026
    Stock price prediction remains a challenging task due to the complex interplay of temporal trends and relational dependencies within financial markets. This study proposes the GNN-LSTM Hybrid model, a novel framework that integrates Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) units to simultaneously capture heterogeneous graph structures and temporal dynamics in stock data, leveraging GNNs to model relational dependencies and LSTMs to address long-term temporal patterns, with graph construction based on stock correlation and temporal edge features. Using a dataset covering 1,270 trading days from March 2015 to April 2020, we evaluate the model against traditional methods (ARIMA, LSTM) and modern graph-based approaches (T-GCN, GAT, Transformer-TS, Base GraphSAGE, SAGE-IS). The GNN-LSTM Hybrid achieves superior performance, with a Mean Absolute Error (MAE) of 0.740 (±0.13), Root Mean Squared Error (RMSE) of 1.100 (±0.21), Mean Absolute Percentage Error (MAPE) of 4.92% (±1.16), and Directional Accuracy (DA) of 67.0% (±2.7), and significantly outperforms all baselines, as confirmed by paired t-tests (p < 0.05). Hyperparameter analysis reveals that a configuration of 6 GNN layers and a hidden dimension size of 128 optimizes predictive accuracy, balancing computational efficiency (training time: 16.0 ± 0.7 s) and performance. Validation across 100 training epochs further confirms the model’s robust convergence across all metrics. With an inference time of 20.0 ± 1.0 ms, which is competitive compared to baselines like ARIMA (23.5 ± 1.1 ms) and GAT (20.5 ± 1.0 ms), the GNN-LSTM Hybrid demonstrates strong potential for practical financial forecasting, offering a scalable and accurate solution for capturing the multifaceted dynamics of stock markets, with implications for real-time applications and broader economic modeling.
  • CAN CHINA’S FOREIGN DIRECT INVESTMENT CAUSE ECONOMIC GROWTH IN ASEAN?
    LI PING, FUMITAKA FURUOKA, RAJAH RASIAH, ELAYARAJA ARUCHUNAN
    Singapore Economic Review, 2026
    This paper uses systematic panel data methods to scrutinize the impact of China’s foreign direct investment (FDI) on economic growth in eight Association of Southeast Asian Nations (ASEAN) countries from 2004 to 2018. The findings indicate a statistically significant causal association between these countries’ economic growth and Chinese investment, which shows that China’s FDI is not a cause but rather a result of the economic expansion. Specifically, the results show that there was a causal chain running from fixed capital to Chinese FDI, through trade openness, in the relatively wealthier ASEAN countries; also, there was a causal chain running from economic growth to Chinese FDI, through trade openness, in relatively poorer ASEAN countries.
  • Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach
    Cheng Cheng, Elayaraja Aruchunan, Muhamad Hifzhudin Noor Aziz
    Scientific Reports, 2025
    A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states. Our proposed hybrid SEIRV-DNNs model can also be applied to other infectious diseases such as influenza and dengue, with some modifications to the compartments and parameters in the model to accommodate the related control measures. This approach will facilitate improving predictive modeling and optimizing public health intervention strategies.
  • Modeling and analysis of a delayed fractional order COVID-19 SEIHRM model with media coverage in Malaysia
    Rui Hu, Muhamad Hifzhudin Noor Aziz, Elayaraja Aruchunan, Nur Anisah Mohamed
    Scientific Reports, 2025
    This paper proposed a delayed fractional-order SEIHR-M model incorporating media influence to investigate the transmission dynamics of COVID-19 in Malaysia. By integrating fractional-order dynamics and time-delay media influence into a unified epidemic framework, this novel structure more accurately captures both memory effects and behavioral response lags in the context of COVID-19. Theoretical analysis verified the existence, non-negativity, and boundedness of the solutions, ensuring the biological feasibility of the model. The basic reproduction number [Formula: see text] was derived using the next-generation matrix method, serving as a key metric for evaluating disease transmission and model stability. Furthermore, when [Formula: see text], the disease-free equilibrium is locally asymptotically stable regardless of the value of the delay parameter τ. When [Formula: see text], the stability of the endemic equilibrium exhibits two scenarios: if [Formula: see text], sufficient conditions for local asymptotic stability are provided; if [Formula: see text], there exists a critical delay [Formula: see text]. The endemic equilibrium remains locally asymptotically stable for [Formula: see text] but becomes unstable for [Formula: see text], undergoing a Hopf bifurcation at [Formula: see text], leading to periodic oscillations. The numerical simulation results not only validate the theoretical analysis but also show that as the fractional-order parameter increases, the system exhibits more pronounced oscillations; furthermore, longer delay times facilitate the emergence of these oscillatory behaviors, making the epidemic more prone to recurrent and periodic fluctuations. By fitting the model with early COVID-19 data from Malaysia, the feasibility and applicability of the model are further validated, and the superior fitting performance of the fractional-order delay model compared to the corresponding integer-order model is highlighted. Finally, sensitivity analysis results show that media interventions have a significant impact on epidemic spread, further demonstrating that timely and effective information dissemination plays a crucial role in reducing the peak of infections and controlling the epidemic.
  • A Novel Variant of Weighted Quadratic Mean Iterative Methods for Fredholm Integro-Differential Equations
    Wei Li Ng, Elayaraja Aruchunan, Zailan Siri
    Sains Malaysiana, 2025
    Integro-differential equations are critical for modelling real-world phenomena in physics, engineering, and biology. This paper introduces a Quadratic Mean iterative method to solve dense linear systems derived from the discretization of second- and fourth-order Fredholm integro-differential equations (FIDEs). The solution of the FIDEs is approximated using finite difference, composite trapezoidal, and composite Simpson’s 1/3 and 3/8 schemes. The quadratic mean iterative method then solves the discretized system with different mesh sizes. As the resulting systems are large, a complexity reduction approach is implemented on the quadratic mean method to develop the half-sweep quadratic mean iterative method. The newly proposed iterative method includes a novel theorem, comprehensive proofs, and a detailed convergence analysis. The numerical results indicate that the quadratic mean method significantly outperforms the Gauss-Seidel iterative method in terms of efficiency, making it a promising solution for FIDEs.
  • Can Chinese Investments Contribute to Accelerating Economic Growth in Europe?
    Ping Li, Rajah Rasiah, Fumitaka Furuoka, Elayaraja Aruchunan
    Institutions and Economies, 2025
    This study investigates the impact of Chinese outward foreign direct investment (FDI) on the economic growth of 27 European countries from 2004 to 2021, amid concerns about China’s increasing economic influence in Europe. This study employs systematic econometric methods, including the LLC and IPS tests for stationarity, Kao and Pedroni cointegration tests, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) for long-term effects, and the ARDL test for short- and long-term effects. The findings further supported by Panel Granger causality test, one-way and two-way fixed effect models, and dynamic panel models, suggest a significant positive impact of trade openness and fixed capital on longterm European economic development. The study also reveals that while Chinese FDI and trade openness primarily influence economic growth in the long run, fixed capital has both short and long-term effects. Moreover, a sensitivity analysis of rich and poor European nations confirms these patterns, emphasising the role of trade openness and fixed capital in promoting sustainable economic growth. The study suggests a balanced approach to leveraging FDI, highlighting the importance of policy measures that encourage trade openness and fixed capital investment to enhance economic development in Europe.
  • Forecasting NVIDIA Stock Prices Using LSTM and Random Forest: A Comparative Study with XAI-Based KernelSHAP Interpretation
    Zhafira Oktaviani, Nughthoh Arfawi Kurdhi, Zailan Siri, Elayaraja Aruchunan
    2025 International Conference on Artificial Intelligence and Technological Solutions for Good Health Well Being and Sustainable Water Management in Support of Sdgs 3 6 and 9 Icaitech 2025 Proceeding, 2025
    A fundamental objective in the field of computational finance is the precise forecasting of market behavior, which provides considerable benefits for investors and financial analysts. The primary focus is forecasting the stock price of NVIDIA (NVDA), a key player in the technology sector. We develop and compare two machine learning approaches: Long Short-Term Memory (LSTM), a deep learning model adept at capturing temporal dependencies in time-series data, and Random Forest, a robust ensemble learning method. Historical daily stock data, including Open, High, Low, Close (OHLC), and Volume, were used for training and testing the models. Performance was quantitatively evaluated using standard metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination. An Explainable AI (XAI) framework, specifically Kernel SHapley Additive exPlanations (KernelSHAP), is employed to provide transparency and interpretability to these “black box” models. The SHAP analysis revealed the key features influencing the forecasts of both models. By integrating high-performance forecasting with robust explainability, this research provides a more transparent, reliable, and actionable framework for financial analysts and investors.
  • Predictive and Interpretative Analysis of Gold Price Using Long Short-Term Memory and Opti-LIME
    Agrippina Nicola Putra, Nughthoh Arfawi Kurdhi, Zailan Siri, Elayaraja Aruchunan
    2025 International Conference on Artificial Intelligence and Technological Solutions for Good Health Well Being and Sustainable Water Management in Support of Sdgs 3 6 and 9 Icaitech 2025 Proceeding, 2025
    Gold is one of the most popular investment instruments and is sought after by various groups. Its p opularity stems from the perception that gold is a relatively safe and stable asset, especially during times of economic uncertainty or financial market volatility. However, the price of gold itself cannot be predicted completely simply, as it is influenced by various complex, interrelated factors that are often difficult to explain directly. Recognizing the need for more accurate information for investment decision-making, we conducted this study with the aim of forecasting future gold prices using a machine learning-based approach. The model we used was Long Short-Term Memory (LSTM), a variant of artificial neural networks specifically designed to handle time series data. However, one challenge in using models such as LSTM is the lack of transparency in the decision-making process. Often, the model’s results appear as a “black box,” without a clear explanation of how and why they were obtained. To address this challenge, we integrated the LSTM model with an Extensible Artificial Intelligence (XAI) approach, Opti-LIME (Optimized Local Interpretable Model-Agnostic Explanations), which allows us to forecast prices while locally explaining the contribution of each feature to the model’s results. By combining the LSTM’s ability to predict price trends with the interpretive power of Opti-LIME, we have successfully produced an approach that is not only accurate but also transparently explainable. This approach is expected to help investors and other stakeholders understand gold price movements and make more informed decisions, based on reliable data and reasoning.
  • Forecasting Tesla Stock Price Using XGBoost, Random Forest, and CatBoost: A Comparative Study with TreeSHAP Interpretation of the Best Model
    Iqbal Ghani Assaduddiari, Nughthoh Arfawi Kurdhi, Zailan Siri, Elayaraja Aruchunan
    2025 International Conference on Artificial Intelligence and Technological Solutions for Good Health Well Being and Sustainable Water Management in Support of Sdgs 3 6 and 9 Icaitech 2025 Proceeding, 2025
    The rapid development of electric vehicles (EV) has significantly increased investor interest in Tesla Inc., one of the world’s largest and most influential EV manufacturers. Accurate stock price forecasting has thus become a valuable tool for supporting investment decisions. This study leverages recent advancements in artificial intelligence to develop predictive models for Tesla’s stock price using historical stock data and related commodity market indicators. We construct and evaluate three machine learning models namely XGBoost, Random Forest, and CatBoost. Their performance is rigorously assessed using key metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination $\left(\mathbf{R}^{\mathbf{2}}\right)$. The results demonstrate that the CatBoost model outperformed the others, achieving the lowest MAE and RMSE and the highest $\mathbf{R}^{\mathbf{2}}$ value, proving it to be the most accurate model for forecasting Tesla’s stock price in this study. Furthermore, to enhance transparency and interpretability, we integrate Explainable Artificial Intelligence (XAI) techniques, specifically Tree-based SHapley Additive exPlanations (TreeSHAP), which elucidates the reasoning behind the model’s predictions. By combining predictive modeling with interpretability tools, this study provides both accurate and explainable forecasts, offering practical insights for investors, analysts, and financial decision-makers. The research confirms that machine learning, when augmented with explainability techniques, constitutes a powerful framework for stock price forecasting in dynamic markets such as the EV industry.
  • Dynamic analysis and optimal control of a fractional-order epidemic model with nucleic acid detection and individual protective awareness: A Malaysian case study
    Rui Hu, , Elayaraja Aruchunan, Muhamad Hifzhudin Noor Aziz, Cheng Cheng, Benchawan Wiwatanapataphee, , and
    Aims Mathematics, 2025
    In this paper, we present a Caputo fractional-order COVID-19 model that incorporates nucleic acid testing and individual protective awareness to capture memory effects and the interaction of non-pharmaceutical interventions. We proved the existence, non-negativity, and boundedness of solutions and derived the basic reproduction number $R_{0}$ using the next-generation matrix method. Stability analysis showed that the disease-free equilibrium is globally asymptotically stable when $R_{0} < 1$, and the endemic equilibrium is globally asymptotically stable when $R_{0}>1$. Numerical simulations using the PECE scheme of the Adams–Bashforth–Moulton method validate the theoretical results and demonstrate the role of the fractional-order parameter $\alpha$ in capturing transmission memory. Model parameters were estimated using a hybrid genetic algorithm-least squares approach calibrated with Malaysian COVID-19 data. The proposed model outperformed both integer-order and simplified fractional SEIR models in replicating real-world dynamics. Sensitivity and uncertainty analyses identified protective awareness and testing intensity as key factors in mitigating epidemic severity. We also formulated an optimal control problem, applying Pontryagin's maximum principle to derive six intervention strategies. Cost-effectiveness analysis showed that combined interventions are superior to single strategies, proving effective and economically viable under Malaysia's healthcare constraints.
  • Comparative Study of LSGAN and WGAN-GP for Data Augmentation in Aircraft Damage Detection
    Adib Haidar Zaky, Nughthoh Arfawi Kurdhi, Zailan Siri, Elayaraja Aruchunan, Iftitahu Ni'mah
    2025 10th International Conference on Informatics and Computing Icic 2025, 2025
  • Antecedents of purchase intentions in digital marketing: A case of TikTok shop
    Ainiezean Awang Jual, Anbukkarasu Paramasivam, Silllalee S. Kandasamy, Parvathi Wajindram, Elayaraja Aruchunan
    African Journal of Science Technology Innovation and Development, 2025
  • The Authentic Socioculture of Malay and Malaysian Indian Communities
    Authentic Socioculture of Malay and Malaysian Indian Communities, 2025
  • New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores
    Dineswary Nadarajan, Elayaraja Aruchunan, Noor Fadiya Mohd Noor
    Plos One, 2024
  • A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis
    Fumitaka Furuoka, Luis A. Gil-Alana, OlaOluwa S. Yaya, Elayaraja Aruchunan, Ahamuefula E. Ogbonna
    Empirical Economics, 2024
  • Intelligent Approximation for Climate Differential Equations
    Jackel Vui Lung Chew, Elayaraja Aruchunan, Andang Sunarto
    Studies in Systems Decision and Control, 2024
  • Intelligent System Design for the Solutions of Nonlinear Diffusion in the Two-Dimensional Porous Medium
    Jackel Vui Lung Chew, Elayaraja Aruchunan, Andang Sunarto, Jumat Sulaiman
    Intelligent Systems of Computing and Informatics, 2024
  • Effectiveness of Cooperative Learning Strategies in Improving Performance for Large Mathematics Classes
    Mohana Sundaram Muthuvalu, Majid Khan Majahar Ali, Elayaraja Aruchunan, Kogila Vani Annammala, Jumat Sulaiman
    Lecture Notes in Educational Technology, 2024
  • Intelligent LASSO Regression Modelling for Seaweed Drying Analysis
    Pei Yeen Ng, Elayaraja Aruchunan, Fumitaka Furuoka, Samsul Ariffin Abdul Karim, Jackel Vui Lung Chew, Majid Khan Majahar Ali
    Studies in Systems Decision and Control, 2024
  • Intelligent Application of Partial Least Square Algorithm in Developing Model of Fat Depth Measurement
    Shrley Chan Suet Yee, Elayaraja Aruchunan, Nur Anisah Mohamed A. Rahman, Kohilavani Naganthran, Adilah Abdul Ghapor, Jayaseelan Marimuthu, Graham Edwin Gardner, Samsul Ariffin Abdul Karim
    Intelligent Systems of Computing and Informatics, 2024
  • Intelligence Predictive Model for Lamb Carcass C-Site Fat Depth Using Support Vector Machine
    Wan Yu Jinq, Elayaraja Aruchunan, Nur Anisah Mohamed A. Rahman, Kohilavani Naganthran, Mohana Sundaram Muthuvalu, Jackel Vui Lung Chew, Jayaseelan Marimuthu, Graham Edwin Gardner, Samsul Ariffin Abdul Karim
    Intelligent Systems of Computing and Informatics, 2024
  • Intelligence Random Forest Application in Developing Regression Model from Lamb Carcass C-Site Fat Depth Data
    Sin Jie, Elayaraja Aruchunan, Nur Anisah Mohamed A. Rahman, Majid Khan Majahar Ali, Suhana Mohezar Ali, Muhammad Ashraf Khalid, Jayaseelan Marimuthu, Graham Edwin Gardner, Samsul Ariffin Abdul Karim
    Intelligent Systems of Computing and Informatics, 2024
  • Assessing the sustainability of the homestay industry for the East Coast of Malaysia using the Delphi approach
    Fatin Amira Zamzuki, Muhamad Safiih Lola, Elayaraja Aruchunan, Mohana Sundaram Muthuvalu, Ribed Vianneca W. Jubilee, Nurul Hila Zainuddin, Abdul Aziz K. Abdul Hamid, Nor Aieni Mokhtar, Mohd Tajuddin Abdullah
    Heliyon, 2023
  • Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model
    Noor Ilanie Nordin, Wan Azani Mustafa, Muhamad Safiih Lola, Elissa Nadia Madi, Anton Abdulbasah Kamil, Marah Doly Nasution, Abdul Aziz K. Abdul Hamid, Nurul Hila Zainuddin, Elayaraja Aruchunan, Mohd Tajuddin Abdullah
    Bioengineering, 2023
  • Identifying Multiple Outliers in Linear Functional Relationship Model Using a Robust Clustering Method
    Adilah Abdul Ghapor, Yong Zulina Zubairi, Sayed Md. Al Mamun, Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar
    Sains Malaysiana, 2023
  • Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022
    Wan Imanul Aisyah Wan Mohamad Nawi, Abdul Aziz K. Abdul Hamid, Muhamad Safiih Lola, Syerrina Zakaria, Elayaraja Aruchunan, R. U. Gobithaasan, Nurul Hila Zainuddin, Wan Azani Mustafa, Mohd Lazim Abdullah, Nor Aieni Mokhtar, Mohd Tajuddin Abdullah
    Plos One, 2023
  • Thermocapillarity in Cross Hybrid Nanofilm Flow Past an Unsteady Stretching Sheet
    Kohilavani Naganthran, Ishak Hashim, Roslinda Nazar, Dian Adline Jalaluddin, Elayaraja Aruchunan
    Coatings, 2023
  • Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022
    Abdul Aziz K Abdul Hamid, Wan Imanul Aisyah Wan Mohamad Nawi, Muhamad Safiih Lola, Wan Azani Mustafa, Siti Madhihah Abdul Malik, Syerrina Zakaria, Elayaraja Aruchunan, Nurul Hila Zainuddin, R.U. Gobithaasan, Mohd Tajuddin Abdullah
    Diagnostics, 2023
  • Complexity Reduction Approach for Solving Second Kind of Fredholm Integral Equations
    Mohana Sundaram Muthuvalu, Elayaraja Aruchunan, Majid Khan Majahar Ali, Jackel Vui Lung Chew, Andang Sunarto, Ramoshweu Lebelo, Jumat Sulaiman
    Symmetry, 2022
  • Efficiency Evaluation of Half-Sweep Newton-EGSOR Method to Solve 1D Nonlinear Porous Medium Equations
    Jackel Vui Lung Chew, Elayaraja Aruchunan, Jumat Sulaiman
    Studies in Systems Decision and Control, 2022
  • Quarter-Sweep Successive Over-relaxation Approximation to the Solution of Porous Medium Equations
    Iaeng International Journal of Applied Mathematics, 2022
  • Efficient Iterative Approximation for Nonlinear Porous Medium Equation with Drainage Model
    Jackel Vui Lung Chew, Jumat Sulaiman, Elayaraja Aruchunan, Andang Sunarto
    Studies in Systems Decision and Control, 2022
  • Numerical Solution of Nonlinear Diffusion in One Dimensional Porous Medium Using Hybrid SOR Method
    Kyungpook Mathematical Journal, 2022
  • Solution of Peak Junction Temperature with Crank-Nicolson and SOR Approach
    Elayaraja Aruchunan, Zailan Siri, Muhamad Hifzhudin Bin Noor Aziz, Muhammad Hakimi Bin Ab Wahab, Mohana Sundaram Muthuvalu, Jumat Sulaiman
    Studies in Systems Decision and Control, 2022
  • Thermal Analysis of VLSI System using Successive Over Relaxation (SOR) Method
    Elayaraja Aruchunan, Zailan Siri, Kohilavani Naganthran, Muhamad Hifzhudin Bin Noor Aziz, Shahirah Akma Ghazali, Mohana Sundaram Muthuvalu, Jumat Sulaiman
    Studies in Systems Decision and Control, 2022
  • Examination of Half-Sweep Closed Newton–Cotes Quadrature Schemes in Solving Dense System
    Elayaraja Aruchunan, Mohana Sundaram Muthuvalu, Zailan Siri, Sachin Sharma Ashok Kumar, Jumat Sulaiman, Jackel Vui Lung Chew, Majid Khan Majahar Ali
    Studies in Systems Decision and Control, 2022
  • Iterative method for solving one-dimensional fractional mathematical physics model via quarter-sweep and PAOR
    Andang Sunarto, Praveen Agarwal, Jumat Sulaiman, Jackel Vui Lung Chew, Elayaraja Aruchunan
    Advances in Difference Equations, 2021
  • Improved of forecasting sea surface temperature based on hybrid arima and support vector machines models
    Wan Imanul Aisyah Wan Mohamad Nawi, Muhamad Safiih Lola, Razak Zakariya, Nurul Hila Zainuddin, Abd. Aziz K. Abd Hamid, Elayaraja Aruchunan
    Malaysian Journal of Fundamental and Applied Sciences, 2021
  • A newton-modified weighted arithmetic mean solution of nonlinear porous medium type equations
    Elayaraja Aruchunan, Jackel Vui Lung Chew, Mohana Sundaram Muthuvalu, Andang Sunarto, Jumat Sulaiman
    Symmetry, 2021
  • Application of implicit scheme with AGE iterative method for solving fuzzy parabolic equation
    A. A. Dahalan, M. S. Muthuvalu, E. Aruchunan, J. Sulaiman, W. R. W. Din
    Aip Conference Proceedings, 2017
  • A new variant of arithmetic mean iterative method for fourth order integro-differential equations solution
    E. Aruchunan, Y. Wu, B. Wiwatanapataphee, P. Jitsangiam
    Proceedings Aims 2015 3rd International Conference on Artificial Intelligence Modelling and Simulation, 2016
  • A comparative study of iterative methods for solving first kind Fredholm integral equations with the semi-smooth kernel
    M. S. Muthuvalu, E. Aruchunan, M. K. M. Ali, J. Sulaiman
    2015 International Symposium on Mathematical Sciences and Computing Research Ismsc 2015 Proceedings, 2016
  • A New Algorithm of Geometric Mean for Solving High-Order Fredholm Integro-differential Equations
    E. Aruchunan, N. Khajohnsaksumeth, B. Wiwatanapataphee
    Proceedings 2016 IEEE 14th International Conference on Dependable Autonomic and Secure Computing Dasc 2016 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing Picom 2016 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing Datacom 2016 and 2016 IEEE Cyber Science and Technology Congress Cyberscitech 2016 Dasc Picom Datacom Cyberscitech 2016, 2016
  • Performance Analysis of Half-Sweep Successive Over-Relaxation Iterative Method for Solving Four-Point Composite Closed Newton-Cotes Quadrature System
    Mohana Sundaram Muthuvalu, Thaw Zin Htun, Elayaraja Aruchunan, Majid Khan Majahar Ali, Jumat Sulaiman
    Proceedings International Conference on Intelligent Systems Modelling and Simulation Isms, 2016
  • Preconditioned Jacobi-type iterative methods for solving Fredholm integral equations of the second kind
    Mohana Sundaram Muthuvalu, Elayaraja Aruchunan, Majid Khan Majahar Ali, Jumat Sulaiman
    Aip Conference Proceedings, 2016
  • Quarter-sweep iteration concept on conjugate gradient normal residual method via second order quadrature - Finite difference schemes for solving Fredholm integro-differential equations
    Elayaraja Aruchunan, Mohana Sundaram Muthuvalu, Jumat Sulaiman
    Sains Malaysiana, 2015
  • Application of four-point MEGMSOR method for the solution of 2D helmholtz equations
    Mohd Kamalrulzaman Md Akhir, Jumat Sulaiman, Mohamed Othman, Zanariah Abdul Majid, Mohana Sundaram Muthuvalu, Elvaraja Aruchunan
    International Journal of Mathematical Analysis, 2015
  • Valuing option on the maximum of two assets using improving modified Gauss-Seidel method
    Wei Sin Koh, Mohana Sundaram Muthuvalu, Elayaraja Aruchunan, Jumat Sulaiman
    Aip Conference Proceedings, 2014
  • Preliminary investigation on electrochemical parameters of lake waters in and around Miri city, Malaysia
    Pertanika Journal of Science and Technology, 2014
  • Enrichment pattern of leachable trace metals in roadside soils of Miri City, Eastern Malaysia
    R. Nagarajan, M. P. Jonathan, Priyadarsi D. Roy, M. V. Prasanna, A. Elayaraja
    Environmental Earth Sciences, 2014
  • Drip water Geochemistry of Niah Great Cave, NW Borneo, Malaysia: A base line study
    M. V. Prasanna, R. Nagarajan, S. Chidambaram, S. Manikandan, A. Elayaraja
    Carbonates and Evaporites, 2014
  • An iterative solution for second order linear Fredholm integro-differential equations
    Malaysian Journal of Mathematical Sciences, 2014
  • Numerical performance of AOR methods in solving first order composite closed Newton-Cotes quadrature algebraic equations
    Mohana Sundaram Muthuvalu, Elayaraja Aruchunan, Wei Sin Koh, Mohd Kamalrulzaman Md Akhir, Jumat Sulaiman, Samsul Ariffin Abdul Karim
    Aip Conference Proceedings, 2014
  • Solving first kind linear Fredholm integral equations with semi-smooth kernel using 2-point half-sweep block arithmetic mean method
    Mohana Sundaram Muthuvalu, Elayaraja Aruchunan, Jumat Sulaiman
    Aip Conference Proceedings, 2013
  • Application of quarter-sweep iteration for first order linear Fredholm integro-differential equations
    Elayaraja Aruchunan, Mohana Sundaram Muthuvalu, Jumat Sulaiman
    Aip Conference Proceedings, 2013
  • Evaluation of water quality pollution indices for heavy metal contamination monitoring: A case study from Curtin Lake, Miri City, East Malaysia
    M. V. Prasanna, S. M. Praveena, S. Chidambaram, R. Nagarajan, A. Elayaraja
    Environmental Earth Sciences, 2012
  • Assessment of metals distribution and microbial contamination at selected lake waters in and around Miri City, East Malaysia
    M. V. Prasanna, R. Nagarajan, S. Chidambaram, A. Elayaraja
    Bulletin of Environmental Contamination and Toxicology, 2012
  • Half-Sweep Conjugate Gradient method for solving first order linear fredholm integro-differential equations
    Australian Journal of Basic and Applied Sciences, 2011
  • Numerical solution of second-order linear fredholm integro-differential equation using generalized minimal residual method
    Aruchunan
    American Journal of Applied Sciences, 2010

RECENT SCHOLAR PUBLICATIONS

  • Heterogeneous Graph Neural Networks for Stock Price Prediction : Modeling Temporal and Cross-Stock Dependencies
    HA Bukhori, E Aruchunan, S Anam, S Bukhori, A Maulana
    BAREKENG: Jurnal Ilmu Matematika dan Terapan 20 (2), 0981–1000-0981–1000 , 2026
    2026
  • Forecasting NVIDIA Stock Prices Using LSTM and Random Forest: A Comparative Study with XAI-Based KernelSHAP Interpretation
    Z Oktaviani, NA Kurdhi, Z Siri, E Aruchunan
    2025 IEEE International Conference on Artificial Intelligence and … , 2025
    2025
  • Forecasting Tesla Stock Price Using XGBoost, Random Forest, and CatBoost: A Comparative Study with TreeSHAP Interpretation of the Best Model
    IG Assaduddiari, NA Kurdhi, Z Siri, E Aruchunan
    2025 IEEE International Conference on Artificial Intelligence and … , 2025
    2025
  • Predictive and Interpretative Analysis of Gold Price Using Long Short-Term Memory and Opti-LIME
    AN Putra, NA Kurdhi, Z Siri, E Aruchunan
    2025 IEEE International Conference on Artificial Intelligence and … , 2025
    2025
  • Antecedents of purchase intentions in digital marketing: A case of TikTok shop
    A Awang Jual, A Paramasivam, SS Kandasamy, P Wajindram, ...
    African Journal of Science, Technology, Innovation and Development, 1-11 , 2025
    2025
    Citations: 1
  • Comparative Study of LSGAN and WGAN-GP for Data Augmentation in Aircraft Damage Detection
    AH Zaky, NA Kurdhi, Z Siri, E Aruchunan, I Ni'mah
    2025 IEEE International Conference on Artificial Intelligence and … , 2025
    2025
  • Modeling and analysis of dynamical behavior in a fractional-order COVID-19 epidemic model with media coverage: A case study of Malaysia
    R Hu, MHN Aziz, NA Mohamed, E Aruchunan
    Alexandria Engineering Journal 127, 1081-1095 , 2025
    2025
    Citations: 5
  • Modeling and analysis of a delayed fractional order COVID-19 SEIHRM model with media coverage in Malaysia
    R Hu, MHN Aziz, E Aruchunan, NA Mohamed
    Scientific Reports 15 (1), 25305 , 2025
    2025
    Citations: 7
  • Can Chinese Investments Contribute to Accelerating Economic Growth in Europe?
    L Ping, R Rasiah, F Furuoka, E Aruchunan
    Institutions and Economies 17 (2), 87-119 , 2025
    2025
  • Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach
    C Cheng, E Aruchunan, MH Noor Aziz
    Scientific Reports 15 (1), 2043 , 2025
    2025
    Citations: 18
  • A Novel Variant of Weighted Quadratic Mean Iterative Methods for Fredholm Integro-Differential Equations
    WL Ng, E Aruchunan, Z Siri
    Sains Malaysiana 54 (9), 2301-2313 , 2025
    2025
    Citations: 1
  • Dynamic analysis and optimal control of a fractional-order epidemic model with nucleic acid detection and individual protective awareness: A Malaysian case study
    R Hu, E Aruchunan, MHN Aziz, C Cheng, B Wiwatanapataphee
    AIMS Math 10, 16157-16199 , 2025
    2025
    Citations: 5
  • Intelligent LASSO regression modelling for seaweed drying analysis
    PY Ng, E Aruchunan, F Furuoka, SA Abdul Karim, JVL Chew, MKM Ali
    Intelligent systems modeling and simulation III: Artificial intelligent … , 2024
    2024
    Citations: 3
  • Intelligent Approximation for Climate Differential Equations
    JVL Chew, E Aruchunan, A Sunarto
    Intelligent Systems Modeling and Simulation III: Artificial Intelligent … , 2024
    2024
  • New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores
    D Nadarajan, E Aruchunan, NF Mohd Noor
    Plos one 19 (7), e0305146 , 2024
    2024
  • Intelligent System Design for the Solutions of Nonlinear Diffusion in the Two-Dimensional Porous Medium
    JVL Chew, E Aruchunan, A Sunarto, J Sulaiman
    Intelligent Systems of Computing and Informatics, 177-191 , 2024
    2024
  • Intelligence random forest application in developing regression model from lamb carcass C-site fat depth data
    S Jie, E Aruchunan, NAMA Rahman, MKM Ali, SM Ali, MA Khalid, ...
    Intelligent Systems of Computing and Informatics, 133-150 , 2024
    2024
    Citations: 1
  • Intelligence predictive model for lamb carcass C-Site fat depth using support vector machine
    WY Jinq, E Aruchunan, NAMA Rahman, K Naganthran, MS Muthuvalu, ...
    Intelligent Systems of Computing and Informatics, 80-97 , 2024
    2024
    Citations: 1
  • Intelligent Application of Partial Least Square Algorithm in Developing Model of Fat Depth Measurement
    SCS Yee, E Aruchunan, NAMA Rahman, K Naganthran, AA Ghapor, ...
    Intelligent Systems of Computing and Informatics, 12-22 , 2024
    2024
    Citations: 1
  • A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis
    F Furuoka, LA Gil-Alana, OOS Yaya, E Aruchunan, AE Ogbonna
    Empirical Economics 66 (6), 2471-2499 , 2024
    2024
    Citations: 13

MOST CITED SCHOLAR PUBLICATIONS

  • Evaluation of water quality pollution indices for heavy metal contamination monitoring: a case study from Curtin Lake, Miri City, East Malaysia
    MV Prasanna, SM Praveena, S Chidambaram, R Nagarajan, A Elayaraja
    Environmental Earth Sciences 67 (7), 1987-2001 , 2012
    2012
    Citations: 340
  • Half-sweep conjugate gradient method for solving first order linear Fredholm integro-differential equations
    E Aruchunan, J Sulaiman
    Australian Journal of Basic and Applied Sciences 5 (3), 38-43 , 2011
    2011
    Citations: 42
  • Assessment of metals distribution and microbial contamination at selected Lake waters in and around Miri city, East Malaysia
    MV Prasanna, R Nagarajan, S Chidambaram, A Elayaraja
    Bulletin of environmental contamination and toxicology 89 (3), 507-511 , 2012
    2012
    Citations: 38
  • Numerical solution of second-order linear fredholm integro-differential equation using generalized minimal residual method
    E Aruchunan, J Sulaiman
    American Journal of Applied Sciences 7 (6), pp. 780-783 , 2010
    2010
    Citations: 38
  • Iterative method for solving one-dimensional fractional mathematical physics model via quarter-sweep and PAOR
    A Sunarto, P Agarwal, J Sulaiman, JVL Chew, E Aruchunan
    Advances in Difference Equations 2021 (1), 147 , 2021
    2021
    Citations: 34
  • Improved of forecasting sea surface temperature based on hybrid arima and support vector machines models
    W Nawi, MS Lola, R Zakariya, NH Zainuddin, AAK Abd Hamid, ...
    Malaysian Journal of Fundamental and Applied Sciences 17 (5), 609-620 , 2021
    2021
    Citations: 27
  • Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach
    C Cheng, E Aruchunan, MH Noor Aziz
    Scientific Reports 15 (1), 2043 , 2025
    2025
    Citations: 18
  • A New Variant of Arithmetic Mean Iterative Method for Fourth Order Integro-differential Equations Solution
    E Aruchunan, Y Wu, B Wiwatanapataphee, P Jitsangiam
    2015 IEEE International Conference on Artificial Intelligence, Modelling … , 2015
    2015
    Citations: 17
  • Enrichment pattern of leachable trace metals in roadside soils of Miri City, Eastern Malaysia
    R Nagarajan, MP Jonathan, PD Roy, MV Prasanna, A Elayaraja
    Environmental Earth Sciences, 1-9 , 2014
    2014
    Citations: 17
  • Application of the Central-Difference with Half-Sweep Gauss-Seidel Method for Solving First Order Linear Fredholm Integro-Differential Equations
    E Aruchunan, J Sulaiman
    International Journal of Engineering and Applied Sciences 6, 296-300 , 2012
    2012
    Citations: 17
  • Half-sweep quadrature-difference schemes with iterative method in solving linear fredholm integro-differential equations
    E Aruchunan, J Sulaiman
    Progress in Applied Mathematics 5 (1), 11-21 , 2013
    2013
    Citations: 16
  • Assessing the sustainability of the homestay industry for the East Coast of Malaysia using the Delphi approach
    FA Zamzuki, MS Lola, E Aruchunan, MS Muthuvalu, RVW Jubilee, ...
    Heliyon 9 (11) , 2023
    2023
    Citations: 15
  • Drip water geochemistry of Niah Great Cave, NW Borneo, Malaysia: a base line study
    MV Prasanna, R Nagarajan, S Chidambaram, S Manikandan, A Elayaraja
    Carbonates and evaporites 29 (1), 41-54 , 2014
    2014
    Citations: 15
  • Improvement of time forecasting models using machine learning for future pandemic applications based on COVID-19 data 2020–2022
    AA K Abdul Hamid, WIA Wan Mohamad Nawi, MS Lola, WA Mustafa, ...
    Diagnostics 13 (6), 1121 , 2023
    2023
    Citations: 14
  • A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis
    F Furuoka, LA Gil-Alana, OOS Yaya, E Aruchunan, AE Ogbonna
    Empirical Economics 66 (6), 2471-2499 , 2024
    2024
    Citations: 13
  • Quarter-Sweep Iteration Concept on Conjugate Gradient Normal Residual Method via Second Order Quadrature - Finite Difference Schemes for Solving Fredholm Integro-Differential …
    E ARUCHUNAN, MS MUTHUVALU, J SULAIMAN
    Sains Malaysiana 44 (1), 139-146 , 2015
    2015
    Citations: 13
  • An Iterative Solution for Second Order Linear Fredholm Integro-Differential Equations
    E Aruchunan, S Muthuvalu, J Sulaiman, WS Koh, MKM Akhir
    Malaysian Journal of Mathematical Sciences 8 (2), 158-170 , 2014
    2014
    Citations: 11
  • Quarter-Sweep Gauss-Seidel Method for Solving First Order Linear Fredholm Integro-differential Equations
    E Aruchunan, J Sulaiman
    MATEMATIKA 27 (2), 199-208 , 2011
    2011
    Citations: 11
  • Developing forecasting model for future pandemic applications based on COVID-19 data 2020–2022
    WIA Wan Mohamad Nawi, AA K. Abdul Hamid, MS Lola, S Zakaria, ...
    PloS one 18 (5), e0285407 , 2023
    2023
    Citations: 9
  • Enhancing COVID-19 classification accuracy with a hybrid SVM-LR model
    NI Nordin, WA Mustafa, MS Lola, EN Madi, AA Kamil, MD Nasution, ...
    Bioengineering 10 (11), 1318 , 2023
    2023
    Citations: 8