In aquatic ecosystems, the triadic relationship among phytoplankton, zooplankton, and their piscine predators constitutes a delicately balanced ecological continuum where nutrient cycling, toxin transfer, and spatial refugia collectively dictate population persistence. In this paper, the fractional-order dynamics of Nekton-Plankton Coupled systems are analyzed using intelligent computing driven autoregressive exogenous artificial neural networks trained by a hybrid second-order Bayesian Regularized Levenberg-Marquardt optimization algorithm, achieving remarkably low errors in the ranges of 10-04 to 10-12.
@article{sultan2026nekton,title={Neuro-Computational Surrogates for Aqueous Fractional-Order Nekton-Plankton Spatiotemporal Dynamics Under Toxicant Stress, Refuge Efficacy, and Nutrient Flux Modulation},author={Sultan, Adil and Chang, Chuan-Yu and Raja, Muhammad Junaid Ali Asif and Kiani, Adiqa Kausar and Shoaib, Muhammad and Raja, Muhammad Asif Zahoor},journal={Water Research},year={2026},volume={289},number={A},doi={10.1016/j.watres.2025.124754},}
2025
Environ. Monit.
Predictive Analysis of Plankton Population Dynamics in Marine Biosphere: A Nonlinear ARX Neural Network for the Carbon-Thermal-Nutrient-Plankton Asymmetric Multifactor System for Global Warming
Adil Sultan, Muhammad Junaid Ali Asif Raja*, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, Muhammad Shoaib, and Muhammad Asif Zahoor Raja
Dynamic nonlinear ARX neural network trained with Levenberg-Marquardt algorithm for predicting plankton population dynamics in the marine biosphere under global warming conditions.
@article{sultan2025env_monitoring,title={Predictive Analysis of Plankton Population Dynamics in Marine Biosphere: A Nonlinear ARX Neural Network for the Carbon-Thermal-Nutrient-Plankton Asymmetric Multifactor System for Global Warming},author={Sultan, Adil and Raja, Muhammad Junaid Ali Asif and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Shoaib, Muhammad and Raja, Muhammad Asif Zahoor},journal={Environmental Monitoring and Assessment},year={2025},volume={197},number={12},doi={10.1007/s10661-025-14818-5},}
IEEE TCBB
Stochastic-Deterministic Modeling of Immune Responses and Tumor Evolution Under Therapeutic Influence: Intelligent Predictive Supervised Exogenous Networks
Hassan Raza, Muhammad Junaid Ali Asif Raja*, Rikza Mubeen, Zaheer Masood, and Muhammad Asif Zahoor Raja
IEEE Transactions on Computational Biology and Bioinformatics, 2025
The incredible synergy between monoclonal antibodies and interferons in cancer chemotherapy signifies a stride forward in our battle against this inexorable disease. This study develops a precise and reliable application of numerical as well as artificial intelligence-based treatment methodology via predictive supervised exogenous networks for calculable understanding of the movement of the immune response to treatment such as timing, dosing and forecasting therapy retorts. The framework demonstrates impressive performance and accuracy, achieving a mean square error between 10-11 and 10-8 through iterative refinement.
@article{raza2025ieee_tumor,title={Stochastic-Deterministic Modeling of Immune Responses and Tumor Evolution Under Therapeutic Influence: Intelligent Predictive Supervised Exogenous Networks},author={Raza, Hassan and Raja, Muhammad Junaid Ali Asif and Mubeen, Rikza and Masood, Zaheer and Raja, Muhammad Asif Zahoor},journal={IEEE Transactions on Computational Biology and Bioinformatics},year={2025},volume={22},number={6},pages={2764--2773},doi={10.1109/TCBBIO.2025.3604337},}
CMES
Systematic Analysis of Latent Fingerprint Patterns through Fractionally Optimized CNN Model for Interpretable Multi-Output Identification
Mubeen Sabir, Zeshan Aslam Khan, Muhammad Waqar, Khizer Mehmood, Muhammad Junaid Ali Asif Raja*, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Muhammad Asif Zahoor Raja, Muhammad Farhan Khan, and Syed Sohail Ahmed
CMES-Computer Modeling in Engineering & Sciences, 2025
Fractionally optimized CNN model with channel-wise attention for fingerprint pattern analysis achieving 97.85%, 99.10%, and 99.29% accuracies for finger, gender, and hand classification.
@article{sabir2025fingerprint,title={Systematic Analysis of Latent Fingerprint Patterns through Fractionally Optimized CNN Model for Interpretable Multi-Output Identification},author={Sabir, Mubeen and Khan, Zeshan Aslam and Waqar, Muhammad and Mehmood, Khizer and Raja, Muhammad Junaid Ali Asif and Chaudhary, Naveed Ishtiaq and Cheema, Khalid Mehmood and Raja, Muhammad Asif Zahoor and Khan, Muhammad Farhan and Ahmed, Syed Sohail},journal={CMES-Computer Modeling in Engineering \& Sciences},year={2025},volume={145},number={1},pages={807--855},doi={10.32604/cmes.2025.068131},}
Nonlinear Dyn.
Design of Stochastic Backpropagative Autoregressive Exogenous Neuroarchitectures for Predictive Analysis of Fractional-Order Nonlinear Rabinovich-Fabrikant Chaotic Attractors
Shahzaib Ahmed Hassan, Muhammad Junaid Ali Asif Raja*, Syed Zoraiz Ali Sherazi, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, Zeshan Aslam Khan, Muhammad Shoaib, and Muhammad Asif Zahoor Raja
@article{hassan2025rabinovich,title={Design of Stochastic Backpropagative Autoregressive Exogenous Neuroarchitectures for Predictive Analysis of Fractional-Order Nonlinear Rabinovich-Fabrikant Chaotic Attractors},author={Hassan, Shahzaib Ahmed and Raja, Muhammad Junaid Ali Asif and Sherazi, Syed Zoraiz Ali and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Khan, Zeshan Aslam and Shoaib, Muhammad and Raja, Muhammad Asif Zahoor},journal={Nonlinear Dynamics},year={2025},volume={113},number={25},pages={34451--34483},doi={10.1007/s11071-025-11827-4},}
J. Ind. Inf. Integr.
Machine Learning Knowledge Driven Investigation for Immunity Infused Fractional Industrial Virus Transmission in SCADA Systems
Kiran Asma, Muhammad Asif Zahoor Raja, Chuan-Yu Chang, Muhammad Junaid Ali Asif Raja*, Chi-Min Shu, and Muhammad Shoaib
Journal of Industrial Information Integration, 2025
NM-ARX-BR neurocomputational framework for fractional kinetics of immunity-based nonlinear industrial virus transmission in SCADA environments.
@article{asma2025scada,title={Machine Learning Knowledge Driven Investigation for Immunity Infused Fractional Industrial Virus Transmission in SCADA Systems},author={Asma, Kiran and Raja, Muhammad Asif Zahoor and Chang, Chuan-Yu and Raja, Muhammad Junaid Ali Asif and Shu, Chi-Min and Shoaib, Muhammad},journal={Journal of Industrial Information Integration},year={2025},volume={48},doi={10.1016/j.jii.2025.100940},}
Int. J. Inf. Secur.
A Machine Learning Approach Using Nonlinear ARX Neural Networks with Bayesian Regularization for Epidemic Malware Dynamics in Critical Network Infrastructures
Kiran Asma, Muhammad Asif Zahoor Raja, Chuan-Yu Chang, Muhammad Junaid Ali Asif Raja*, Muhammad Shoaib, and Chi-Min Shu
International Journal of Information Security, 2025
NARXNN-BR machine learning approach for modeling epidemic nonlinear malware propagation dynamics in critical network architectures.
@article{asma2025malware_arxnn,title={A Machine Learning Approach Using Nonlinear ARX Neural Networks with Bayesian Regularization for Epidemic Malware Dynamics in Critical Network Infrastructures},author={Asma, Kiran and Raja, Muhammad Asif Zahoor and Chang, Chuan-Yu and Raja, Muhammad Junaid Ali Asif and Shoaib, Muhammad and Shu, Chi-Min},journal={International Journal of Information Security},year={2025},volume={24},number={5},doi={10.1007/s10207-025-01104-1},}
J. Therm. Anal.
Prandtl-Eyring Hybrid Nanofluidic Thermal Flow Model in Solar Aircrafts: A Novel Design of Dual-Layered Nonlinear Autoregressive Exogenous Neural Architecture
Maryam Pervaiz Khan, Muhammad Junaid Ali Asif Raja*, Adil Sultan, Chuan-Yu Chang, Muhammad Shoaib, Zeshan Aslam Khan, Adiqa Kausar Kiani, Chi-Min Shu, and Muhammad Asif Zahoor Raja
Dual-layered NARX neural architecture with Bayesian regularization for Prandtl-Eyring hybrid nanofluidic thermal analysis in solar aircraft design.
@article{khan2025prandtl,title={Prandtl-Eyring Hybrid Nanofluidic Thermal Flow Model in Solar Aircrafts: A Novel Design of Dual-Layered Nonlinear Autoregressive Exogenous Neural Architecture},author={Khan, Maryam Pervaiz and Raja, Muhammad Junaid Ali Asif and Sultan, Adil and Chang, Chuan-Yu and Shoaib, Muhammad and Khan, Zeshan Aslam and Kiani, Adiqa Kausar and Shu, Chi-Min and Raja, Muhammad Asif Zahoor},journal={Journal of Thermal Analysis and Calorimetry},year={2025},volume={150},number={13},pages={10031--10055},doi={10.1007/s10973-025-14396-1},}
Chaos Solit.
A Hybrid Neural-Computational Paradigm for Complex Firing Patterns and Excitability Transitions in Fractional Hindmarsh-Rose Neuronal Models
Muhammad Junaid Ali Asif Raja*, Shahzaib Ahmed Hassan, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, Muhammad Shoaib, and Muhammad Asif Zahoor Raja
This study presents a hybrid neural-computational paradigm for analyzing complex firing patterns and excitability transitions in fractional Hindmarsh-Rose neuronal models.
@article{raja2025hybrid,title={A Hybrid Neural-Computational Paradigm for Complex Firing Patterns and Excitability Transitions in Fractional Hindmarsh-Rose Neuronal Models},author={Raja, Muhammad Junaid Ali Asif and Hassan, Shahzaib Ahmed and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Shoaib, Muhammad and Raja, Muhammad Asif Zahoor},journal={Chaos, Solitons \& Fractals},year={2025},doi={10.1016/j.chaos.2025.116149},}
Appl. Soft Comput.
Design of Deep Learning Networks for Nonlinear Delay Differential System for Stuxnet Virus Spread in an Air-Gapped Critical Environment
Muhammad Junaid Ali Asif Raja*, Zaheer Masood, Ijaz Hussain, Aneela Zameer, and Muhammad Asif Zahoor Raja
A deep learning framework for modeling nonlinear delay differential systems analyzing Stuxnet virus propagation in air-gapped critical infrastructure.
@article{raja2025stuxnet,title={Design of Deep Learning Networks for Nonlinear Delay Differential System for Stuxnet Virus Spread in an Air-Gapped Critical Environment},author={Raja, Muhammad Junaid Ali Asif and Masood, Zaheer and Hussain, Ijaz and Zameer, Aneela and Raja, Muhammad Asif Zahoor},journal={Applied Soft Computing},year={2025},doi={10.1016/j.asoc.2025.113091},}
Eng. Appl. AI
Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics Under Global Warming and Climatic Perturbations
Muhammad Junaid Ali Asif Raja*, Adil Sultan, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, and Muhammad Asif Zahoor Raja
Engineering Applications of Artificial Intelligence, 2025
Bayesian-regularized cascaded neural networks for studying fractional asymmetric carbon-thermal nutrient-plankton dynamics under global warming scenarios.
@article{raja2025bayesian,title={Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics Under Global Warming and Climatic Perturbations},author={Raja, Muhammad Junaid Ali Asif and Sultan, Adil and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Raja, Muhammad Asif Zahoor},journal={Engineering Applications of Artificial Intelligence},year={2025},doi={10.1016/j.engappai.2025.110739},}
Water Res.
Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures
Muhammad Junaid Ali Asif Raja*, Adil Sultan, Chuan-Yu Chang, Chi-Min Shu, Muhammad Shoaib, Adiqa Kausar Kiani, and Muhammad Asif Zahoor Raja
In this study, nonlinear autoregressive exogenous neural network coupled with Levenberg-Marquardt is efficaciously selected to model the fractional-order toxin-plankton system asserting the phytoplankton and zooplankton dynamics in aquatic environments under the influence of environmental toxins. The fractional differential ecological system incorporates density population of phytoplankton, zooplankton, and environmental toxins exacted by fractional Adams multistep predictor-corrector method. Rigorous analysis on single-step and multistep ahead predictors with error of order 10-5 further highlights the efficacy of the employed neurocomputing design.
@article{raja2025toxin,title={Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures},author={Raja, Muhammad Junaid Ali Asif and Sultan, Adil and Chang, Chuan-Yu and Shu, Chi-Min and Shoaib, Muhammad and Kiani, Adiqa Kausar and Raja, Muhammad Asif Zahoor},journal={Water Research},year={2025},doi={10.1016/j.watres.2025.123640},}
CNSNS
Novel Intelligent Exogenous Neuro-Architecture-Driven Machine Learning Approach for Nonlinear Fractional Breast Cancer Risk System
Afshan Fida, Muhammad Asif Zahoor Raja, Chuan-Yu Chang, Muhammad Junaid Ali Asif Raja*, Zeshan Aslam Khan, and Muhammad Shoaib
Communications in Nonlinear Science and Numerical Simulation, 2025
Intelligent exogenous neuro-architecture-driven ML approach for analyzing nonlinear fractional breast cancer risk systems.
@article{fida2025breast,title={Novel Intelligent Exogenous Neuro-Architecture-Driven Machine Learning Approach for Nonlinear Fractional Breast Cancer Risk System},author={Fida, Afshan and Raja, Muhammad Asif Zahoor and Chang, Chuan-Yu and Raja, Muhammad Junaid Ali Asif and Khan, Zeshan Aslam and Shoaib, Muhammad},journal={Communications in Nonlinear Science and Numerical Simulation},year={2025},doi={10.1016/j.cnsns.2025.108955},}
Biomed. Signal
Supervised Autoregressive eXogenous Networks with Fractional Grünwald-Letnikov Finite Differences: Tumor Evolution and Immune Responses Under Therapeutic Influence Fractals Model
Hassan Raza, Muhammad Junaid Ali Asif Raja*, Rikza Mubeen, Zaheer Masood, and Muhammad Asif Zahoor Raja
Supervised autoregressive exogenous networks with fractional finite differences for tumor evolution and immune response modeling.
@article{raza2025tumor,title={Supervised Autoregressive eXogenous Networks with Fractional Gr{\"u}nwald-Letnikov Finite Differences: Tumor Evolution and Immune Responses Under Therapeutic Influence Fractals Model},author={Raza, Hassan and Raja, Muhammad Junaid Ali Asif and Mubeen, Rikza and Masood, Zaheer and Raja, Muhammad Asif Zahoor},journal={Biomedical Signal Processing and Control},year={2025},doi={10.1016/j.bspc.2025.107871},}
Comput. Biol. Med.
Prognostication of Zooplankton-Driven Cholera Patho-Epidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture
Muhammad Junaid Ali Asif Raja*, Adil Sultan, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, Muhammad Shoaib, and Muhammad Asif Zahoor Raja
Cholera outbreaks pose significant health concerns, particularly through freshwater contamination through zooplankton serving as reservoirs for Vibrio Cholerae. This study develops Bayesian-regularized deep nonlinear autoregressive exogenous neural networks to examine zooplankton-mediated cholera transmission dynamics. The methodology employs synthetic data generated via the Adams-Bashforth-Moulton numerical scheme processed through the novel BRDNARX framework, achieving mean square error outcomes ranging from 10-9 to 10-11 with comparative absolute error analysis adhering to diminutive disparities of range 10-3 to 10-9.
@article{raja2025cholera,title={Prognostication of Zooplankton-Driven Cholera Patho-Epidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture},author={Raja, Muhammad Junaid Ali Asif and Sultan, Adil and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Shoaib, Muhammad and Raja, Muhammad Asif Zahoor},journal={Computers in Biology and Medicine},year={2025},doi={10.1016/j.compbiomed.2025.110197},}
Chaos Solit.
A Novel Fractional Parkinson’s Disease Onset Model Involving α-Syn Neuronal Transportation and Aggregation: A Treatise on Machine Predictive Networks
Roshana Mukhtar, Chuan-Yu Chang, Aqib Mukhtar, Muhammad Junaid Ali Asif Raja*, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, and Muhammad Asif Zahoor Raja
Fractional Parkinson’s disease onset model with alpha-synuclein neuronal transportation and aggregation dynamics.
@article{mukhtar2025parkinson,title={A Novel Fractional Parkinson's Disease Onset Model Involving {$\alpha$}-Syn Neuronal Transportation and Aggregation: A Treatise on Machine Predictive Networks},author={Mukhtar, Roshana and Chang, Chuan-Yu and Mukhtar, Aqib and Raja, Muhammad Junaid Ali Asif and Chaudhary, Naveed Ishtiaq and Khan, Zeshan Aslam and Raja, Muhammad Asif Zahoor},journal={Chaos, Solitons \& Fractals},year={2025},doi={10.1016/j.chaos.2025.116269},}
Chaos Solit.
Machine Learning-Driven Exogenous Neural Architecture for Nonlinear Fractional Cybersecurity Awareness Model in Mobile Malware Propagation
Kiran Asma, Muhammad Asif Zahoor Raja, Chuan-Yu Chang, Muhammad Junaid Ali Asif Raja*, and Muhammad Shoaib
ML-driven exogenous neural architecture for fractional cybersecurity awareness modeling in mobile malware propagation.
@article{asma2025malware,title={Machine Learning-Driven Exogenous Neural Architecture for Nonlinear Fractional Cybersecurity Awareness Model in Mobile Malware Propagation},author={Asma, Kiran and Raja, Muhammad Asif Zahoor and Chang, Chuan-Yu and Raja, Muhammad Junaid Ali Asif and Shoaib, Muhammad},journal={Chaos, Solitons \& Fractals},year={2025},doi={10.1016/j.chaos.2024.115948},}
Biomed. Signal
Design of Intelligent Bayesian-Regularized Deep Cascaded NARX Neurostructure for Predictive Analysis of FitzHugh-Nagumo Bioelectrical Model in Neuronal Cell Membrane
Muhammad Junaid Ali Asif Raja*, Shahzaib Ahmed Hassan, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, Muhammad Shoaib, and Muhammad Asif Zahoor Raja
Intelligent Bayesian-regularized deep cascaded NARX neurostructure for FitzHugh-Nagumo bioelectrical neuronal membrane model.
@article{raja2025fitzhugh,title={Design of Intelligent Bayesian-Regularized Deep Cascaded NARX Neurostructure for Predictive Analysis of FitzHugh-Nagumo Bioelectrical Model in Neuronal Cell Membrane},author={Raja, Muhammad Junaid Ali Asif and Hassan, Shahzaib Ahmed and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Shoaib, Muhammad and Raja, Muhammad Asif Zahoor},journal={Biomedical Signal Processing and Control},year={2025},doi={10.1016/j.bspc.2024.107192},}
Process Saf.
Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian-Regularized Deep Cascaded Exogenous Neural Networks
Adil Sultan, Muhammad Junaid Ali Asif Raja*, Chuan-Yu Chang, Chi-Min Shu, Adiqa Kausar Kiani, and Muhammad Asif Zahoor Raja
Bayesian-regularized deep cascaded exogenous neural networks for fractional plankton-assisted cholera propagation dynamics.
@article{sultan2025plankton,title={Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian-Regularized Deep Cascaded Exogenous Neural Networks},author={Sultan, Adil and Raja, Muhammad Junaid Ali Asif and Chang, Chuan-Yu and Shu, Chi-Min and Kiani, Adiqa Kausar and Raja, Muhammad Asif Zahoor},journal={Process Safety and Environmental Protection},year={2025},doi={10.1016/j.psep.2025.106819},}
Chaos Solit.
Design of Fractional Innate Immune Response to Nonlinear Parkinson’s Disease Model with Therapeutic Intervention: Intelligent Machine Predictive Exogenous Networks
Roshana Mukhtar, Chuan-Yu Chang, Muhammad Asif Zahoor Raja, Naveed Ishtiaq Chaudhary, Muhammad Junaid Ali Asif Raja*, and Chi-Min Shu
Fractional innate immune response design for nonlinear Parkinson’s disease with therapeutic intervention.
@article{mukhtar2025parkinson2,title={Design of Fractional Innate Immune Response to Nonlinear Parkinson's Disease Model with Therapeutic Intervention: Intelligent Machine Predictive Exogenous Networks},author={Mukhtar, Roshana and Chang, Chuan-Yu and Raja, Muhammad Asif Zahoor and Chaudhary, Naveed Ishtiaq and Raja, Muhammad Junaid Ali Asif and Shu, Chi-Min},journal={Chaos, Solitons \& Fractals},year={2025},doi={10.1016/j.chaos.2024.115947},}
Comput. Biol. Med.
Generalized Fractional Optimization-Based Explainable Lightweight CNN Model for Malaria Disease Classification
Zeshan Aslam Khan, Muhammad Waqar, Muhammad Junaid Ali Asif Raja*, Naveed Ishtiaq Chaudhary, Abeer Tahir Mehmood Anwar Khan, Farrukh Aslam Khan, Iqra Ishtiaq Chaudhary, and Muhammad Asif Zahoor Raja
Over the past few decades, machine learning and deep learning have incredibly influenced a broader range of scientific disciplines. This research proposes a generalized fractional order-based explainable lightweight convolutional neural network model for malaria disease classification from cell images, achieving 95 percent accuracy on the NIH dataset, 92 percent on the MP-IDB dataset, and 90.4 percent on the M5 test set while maintaining computational efficiency and providing explainability through evaluation metrics.
@article{khan2025malaria,title={Generalized Fractional Optimization-Based Explainable Lightweight CNN Model for Malaria Disease Classification},author={Khan, Zeshan Aslam and Waqar, Muhammad and Raja, Muhammad Junaid Ali Asif and Chaudhary, Naveed Ishtiaq and Khan, Abeer Tahir Mehmood Anwar and Khan, Farrukh Aslam and Chaudhary, Iqra Ishtiaq and Raja, Muhammad Asif Zahoor},journal={Computers in Biology and Medicine},year={2025},doi={10.1016/j.compbiomed.2024.109593},}
Comput. Biol. Chem.
Synergistic Modeling of Hemorrhagic Dengue Fever: Passive Immunity Dynamics and Time-Delay Neural Network Analysis
Hassan Raza, Muhammad Junaid Ali Asif Raja*, Rikza Mubeen, Zaheer Masood, and Muhammad Asif Zahoor Raja
This research establishes a mathematical framework to describe secondary immunity dynamics in infants against dengue hemorrhagic fever acquired through maternal antibodies. Researchers analyzed passive immunity via dengue immunoglobulin across multiple scenarios using Adams numerical methods and time-delay exogenous neural networks with Levenberg-Marquardt optimization, demonstrating extremely low MSE results of the order 10-9 to 10-11 with absolute prediction errors between 10-3 to 10-5.
@article{raza2025dengue,title={Synergistic Modeling of Hemorrhagic Dengue Fever: Passive Immunity Dynamics and Time-Delay Neural Network Analysis},author={Raza, Hassan and Raja, Muhammad Junaid Ali Asif and Mubeen, Rikza and Masood, Zaheer and Raja, Muhammad Asif Zahoor},journal={Computational Biology and Chemistry},year={2025},doi={10.1016/j.compbiolchem.2025.108365},}
Process Saf.
Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems Under Environmental and Climatic Disruptions
Adil Sultan, Muhammad Junaid Ali Asif Raja*, Chuan-Yu Chang, Chi-Min Shu, Muhammad Shoaib, Adiqa Kausar Kiani, and Muhammad Asif Zahoor Raja
Intelligent predictive networks for nonlinear oxygen-phytoplankton-zooplankton coupled marine ecosystems under climate disruptions.
@article{sultan2025oxygen,title={Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems Under Environmental and Climatic Disruptions},author={Sultan, Adil and Raja, Muhammad Junaid Ali Asif and Chang, Chuan-Yu and Shu, Chi-Min and Shoaib, Muhammad and Kiani, Adiqa Kausar and Raja, Muhammad Asif Zahoor},journal={Process Safety and Environmental Protection},year={2025},doi={10.1016/j.psep.2024.11.092},}
Comput. Biol. Med.
Novel Design of Fractional Cholesterol Dynamics and Drug Concentrations Model with Analysis on Machine Predictive Networks
Muhammad Junaid Ali Asif Raja*, Shahzaib Ahmed Hassan, Chuan-Yu Chang, Hassan Raza, Rikza Mubeen, Zaheer Masood, and Muhammad Asif Zahoor Raja
Within the intricate fabric of human physiology, cholesterol, a lipid present in cell membranes exerts a discernible effect on the concentration of the drug in human body. This study presents a fractional-order model examining cholesterol-drug interactions employing NARX neural networks to predict temporal dynamics using Grunwald-Letnikov fractional solver for synthetic training data generation, demonstrating minimal error (MSE 10-12) between NARX predictions and fractal techniques.
@article{raja2025cholesterol,title={Novel Design of Fractional Cholesterol Dynamics and Drug Concentrations Model with Analysis on Machine Predictive Networks},author={Raja, Muhammad Junaid Ali Asif and Hassan, Shahzaib Ahmed and Chang, Chuan-Yu and Raza, Hassan and Mubeen, Rikza and Masood, Zaheer and Raja, Muhammad Asif Zahoor},journal={Computers in Biology and Medicine},year={2025},doi={10.1016/j.compbiomed.2024.109423},}
2024
Chaos Solit.
Nonlinear Chaotic Lorenz-Lü-Chen Fractional-Order Dynamics: A Novel Machine Learning Expedition with Deep Autoregressive Exogenous Neural Networks
Shahzaib Ahmed Hassan, Muhammad Junaid Ali Asif Raja*, Chuan-Yu Chang, Chi-Min Shu, Muhammad Shoaib, Adiqa Kausar Kiani, and Muhammad Asif Zahoor Raja
Machine learning expedition with deep autoregressive exogenous neural networks for nonlinear chaotic Lorenz-Lu-Chen fractional-order dynamics.
@article{hassan2024lorenz,title={Nonlinear Chaotic Lorenz-L{\"u}-Chen Fractional-Order Dynamics: A Novel Machine Learning Expedition with Deep Autoregressive Exogenous Neural Networks},author={Hassan, Shahzaib Ahmed and Raja, Muhammad Junaid Ali Asif and Chang, Chuan-Yu and Shu, Chi-Min and Shoaib, Muhammad and Kiani, Adiqa Kausar and Raja, Muhammad Asif Zahoor},journal={Chaos, Solitons \& Fractals},year={2024},doi={10.1016/j.chaos.2024.115620},}
Heliyon
Fractional Gradient-Optimized Explainable Convolutional Neural Network for Alzheimer’s Disease Diagnosis
Zeshan Aslam Khan, Muhammad Waqar, Naveed Ishtiaq Chaudhary, Muhammad Junaid Ali Asif Raja*, Saadia Khan, Farrukh Aslam Khan, Iqra Ishtiaq Chaudhary, and Muhammad Asif Zahoor Raja
Alzheimer is one of the brain syndromes that steadily affects brain memory. This paper presents an interpretable deep learning model with generalized fractional order-based CNN classifier incorporating explainable artificial intelligence capabilities designed to improve accuracy while providing transparency in diagnostic predictions. The model achieves an improved accuracy of 99 percent on the standard ADNI dataset while remaining simpler than complex benchmark architectures.
@article{khan2024alzheimer,title={Fractional Gradient-Optimized Explainable Convolutional Neural Network for Alzheimer's Disease Diagnosis},author={Khan, Zeshan Aslam and Waqar, Muhammad and Chaudhary, Naveed Ishtiaq and Raja, Muhammad Junaid Ali Asif and Khan, Saadia and Khan, Farrukh Aslam and Chaudhary, Iqra Ishtiaq and Raja, Muhammad Asif Zahoor},journal={Heliyon},year={2024},doi={10.1016/j.heliyon.2024.e39037},}
Nano
Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks
Muhammad Junaid Ali Asif Raja*, Zaheer Masood, Ijaz Hussain, Aneela Zameer, Ammara Mehmood, and Muhammad Asif Zahoor Raja
Nonlinear delay differential system for analyzing vulnerabilities in nanoscale hardware implants using intelligent computing networks.
@article{raja2024nano,title={Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks},author={Raja, Muhammad Junaid Ali Asif and Masood, Zaheer and Hussain, Ijaz and Zameer, Aneela and Mehmood, Ammara and Raja, Muhammad Asif Zahoor},journal={Nano},year={2024},doi={10.1142/S1793292024501303},}
