NMR-based metabolomics of Indonesian jamu for diabetes: chemical composition, toxicity, and zebrafish behavioral impact - Muhammad Safwan Ahamad Bustamam; Wimbuh Tri Widodo; Mohd Zuwairi Saiman; Siti Munirah Mohd. Faudzi; Siok Geok Chong; Nur Diana Safura Mohd Ezmir; Emelia Asmara Md Daud; Sonny Kristianto; Intan Safinar Ismail (Natural Product Research, 2026)

Integrating a hybrid statistical Downscaling-based HMM-RF model for enhanced rainfall prediction in Selangor - Noor Hamizah Mohamad Sani; Shazlyn Milleana Shaharudin; Muhammad Safwan Ibrahim; Upmanu Lall; Mou Leong Tan (Stochastic Environmental Research and Risk Assessment, 2025)
Accurate rainfall prediction is crucial for effective weather forecasting and climate modeling. This study aims to assess the effectiveness of a novel hybrid Statistical Downscaling-based Hidden Markov Model-Random Forest Model (SD-based HMM-RF) for rainfall prediction in Selangor, Malaysia. It also examines the best imputation methods for handling missing data, selects predictors for statistical downscaling by reducing dimensionality, and addresses uncertainties in zero-bounded rainfall data. The study utilized observed data (predictand) from 33 rainfall stations and atmospheric data (predictor), covering the period from 2008 to 2018. Seven imputation methods were tested: Mean Imputation (MeI), Median Imputation (MI), Expectation-Maximization (EM) Algorithm, Markov Chain Monte Carlo (MCMC), k-Nearest Neighbor (kNN), Non-iterative Partial Least Square (NIPALS), and Random Forest (RF). Principal Component Analysis (PCA) was used to manage high-dimensional data and select predictors, while HMM was applied to address uncertainties in zero-bounded rainfall data. Five hybrid models: Random Forest (SD-based HMM-RF), Support Vector Machine (SD-based HMM-SVM), Decision Tree (SD-based HMM-DT), k-Nearest Neighbors (SD-based HMM-KNN), and Artificial Neural Networks (SD-based HMM-ANN) were evaluated. Performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Forecast Error (MFE), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Rank Correlation Coefficient (ρ), were used to identify the most accurate rainfall prediction model. MI emerged as the best-performing method, achieving the lowest RMSE (0.146) and MAE (0.021), along with the highest NSE (1.000) and Pearson correlation coefficient, r (1.000) for all stations. PCA identified five principal components (PC1–PC5) with a cumulative variance of up to 93.254%. HMM analysis determined that three hidden states (K = 3) at iteration 2000 were optimal, with the lowest Bayesian Information Criterion (BIC) of 260,018.30. Among the models, SD-based HMM-RF consistently outperformed others, showing superior accuracy and reliability in rainfall prediction, as indicated by its RMSE (2.298), NSE (0.752), MAE (1.900), near-zero MFE (-0.049), KGE (0.526), and ρ (0.898). By improving rainfall prediction accuracy, the novel hybrid model can enhance early warning systems, inform infrastructure planning, and reduce the economic impact of future flooding events, thereby contributing to more resilient urban development in the region.

Debris flow susceptibility and propagation modeling: a deep learning and flow-R framework - Muhammad Khalid; Muhammad Zulkarnain bin Abd Rahman; Jabir Hussain Syed; Nafees Ali; Hamza Daud; Muhammad Afaq Hussain; Muhammad Safwan Ruslan; Omar Farouk Fauzi (Bulletin of Engineering Geology and the Environment, 2025)

Physiological responses of hybrid grouper (Epinephelus fuscoguttatus X Epinephelus lanceolatus) to temperature fluctuation stress revealed by LCMS/MS-based mucus and sera metabolomics - Ellia Kartini Mujar; Saleema Matusin; Nur Syahirah Yahya; Muhammad Safwan Ahamad Bustamam; Annie Christianus; Khozirah Shaari; Nur Kartinee Kassim; Faridah Abas; Jen Kit Tan; Intan Safinar Ismail (Fish Physiology and Biochemistry, 2025)

Gingerol nanoparticles attenuate complete Freund adjuvant-induced arthritis in rats via targeting the RANKL/OPG signaling pathway - Rida Siddique; Faqir Muhammad; Muhammad Naeem Faisal; Bushra Akhtar; Ammara Saleem; Shaneel Kousar; Ali Sharif; Muhammad Saeed; Safwan Muhammad (Inflammopharmacology, 2024)

Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) - Safwan Mohammed; Sana Arshad; Firas Alsilibe; Muhammad Farhan Ul Moazzam; Bashar Bashir; Foyez Ahmed Prodhan; Abdullah Alsalman; Attila Vad; Tamás Ratonyi; Endre Harsányi (Journal of Hydrology, 2024)

Effects of prenatal dichromatic light exposure on hatching results and post-hatch performance of Japanese quail - Muhammad Safwan; Shahid Mehmood; Shahid Sherzada; Muhammad Usman; Syed Ghulam Mohayud Din Hashmi; Shaheryar Ali; Abd ur Rehman; Muhammad Faisal Riaz; Usman Elahi; Murrawat Hussain; Hafiz Rao Abdul Latif; Kinza Saleem; et al. (Tropical Animal Health and Production, 2023)

Solvent-Dependent Photoluminescence Emission and Colloidal Stability of Carbon Quantum dots from Watermelon Peels - Muhammad Safwan Zaini; Josephine Ying Chyi Liew; Suriati Paiman; Tan Sin Tee; Mazliana Ahmad Kamarudin (Journal of Fluorescence, 2023)

Prophylactic effects of dietary Isochrysis galbana on immersion-challenged red hybrid tilapia (Oreochromis spp.) with Streptococcus agalactiae by spleen-NMR-metabolomics - Muhammad Safwan Ahamad Bustamam; Khozirah Shaari; Chong Chou Min; Md Shirajum Monir; Hamza Ahmed Pantami; Intan Safinar Ismail (Aquaculture International, 2023)

Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia - Sana Arshad; Jamil Hasan Kazmi; Muhammad Gohar Javed; Safwan Mohammed (European Journal of Agronomy, 2023)

Immunomodulatory effects of Isochrysis galbana incorporated diet on Oreochromis sp. (red hybrid tilapia) via Sera-1H NMR metabolomics study - Muhammad Safwan Ahamad Bustamam; Hamza Ahmed Pantami; Khozirah Shaari; Chong Chou Min; Ahmed Mediani; Intan Safinar Ismail (Fish & Shellfish Immunology, 2023)

Characterization of nanocellulose recovery from Elaeis guineensis frond for sustainable development - Muhammad Safwan Mohaiyiddin; Ong Hui Lin; Wei Tieng Owi; Chi Hoong Chan; Chin Hua Chia; Sarani Zakaria; Al Rey Villagracia; Hazizan Md Akil (Clean Technologies and Environmental Policy, 2016)