Scopus Publications
- A comprehensive explainable machine learning framework for predicting sheep body weight
Farhat Iqbal
Small Ruminant Research, 2026 - Study of room temperature ferromagnetism, mechanical, thermodynamic, and thermoelectric aspects of BePr2X4 (X = S, Se, Te) for spintronic and energy devices
Hanan A. Althobaiti, Basma A. El-Badry, Farhat Iqbal, Abdullah Almohammedi, Hala Siddiq, Noura Dawas Alkhaldi, Naglaa AbdelAll, Q. Mahmood
Journal of Physics and Chemistry of Solids, 2026 - Agrometeorological drought early warning as a climate service: SPI projections using SARIMA models for seasonal risk management
Muhammad Ashraf, Adnan Arshad, Farhat Iqbal, Shabnam Pourshirazi, Muhammad Usman Azhar, Urooba Farman Tanoli, Tofeeq Ahmad, Alaa Ahmed, Rashid Bilal
Climate Services, 2025
Extreme weather events, such as frequent droughts, pose a significant threat to agriculture and livelihoods in countries such as Pakistan, where agriculture, which employs 62 % of the workforce, is heavily dependent on rainfall. In the current study, a climate service has been developed to develop early warnings for agrometeorological drought by applying Seasonal Autoregressive Integrated Moving Average (SARIMA) models to forecast the Standardized Precipitation Index (SPI) across 6- and 12-month intervals. This innovative approach aims to enhance the capacity for anticipating drought conditions, facilitate more effective agricultural management and decision-making in response to potential water scarcity. By using monthly precipitation data collected from 20 sites between 1991 and 2024, a comprehensive assessment of historical drought occurrences and projected seasonal conditions for the agricultural period from 2025 to 2030 and long-term 2–25 to 2050 are conducted. The best-fit SARIMA models demonstrated high accuracy (validation R2 values: 0.86–0.94; RMSE values: 0.31–0.49) across meteorological stations. From 2010 to 2024, the Quetta region experienced 17 months of extreme drought (SPI ≤ − 2.0), indicating that severe droughts were a recurrent phenomenon. Projections for 2025–2030 and 2025–2050, based on historical trends, predict prolonged mild drought conditions (SPI: −1.3 to − 1.7) during the Rabi season in Punjab and Sindh. Balochistan is expected to face severe arid conditions, with the SPI reaching − 2.1 by 2028. The SARIMA model showed high forecasting ability, with Nash-Sutcliffe Efficiency values > 0.81 across all stations, offering useful insights for irrigation planning and crop management. Our research will enable policymakers to forecast yield reductions of 25 %–35 % in drought-prone agrometeorological zones and prioritize resource allocation, providing a vital tool for seasonal risk assessment and serving as an early warning system to help plan climate-smart management practices, promote drought-tolerant crop varieties, and implement high-efficiency irrigation systems, thereby improving the resilience of rain-fed agricultural systems. - Predicting Ischemic Heart Disease and Determining Its Risk Factors: A Comparison of Various Classification Methods in Machine Learning
Thailand Statistician, 2025 - Novel Hybrid Approach for River Inflow Modeling: Case Study of the Indus River Basin, Pakistan
Maha Shabbir, Sohail Chand, Farhat Iqbal, Ozgur Kisi
Journal of Hydrologic Engineering, 2025
This study introduces a novel hybrid model for predicting daily river inflow, combining the Hampel filter (HF) for outlier correction, local mean decomposition (LMD) for initial signal decomposition, and ensemble empirical mode decomposition (EEMD) for further decomposition into intrinsic mode functions (IMFs) and residue. The innovative aspect of this model lies in its dual decomposition strategy (LMD-EEMD) followed by prediction using the K-nearest neighbor (KNN) algorithm, resulting in the HF-LMD-EEMD-KNN (HLEK) approach. This combination aims to enhance the accuracy and reliability of inflow predictions. The model’s performance was evaluated using river inflow data from four rivers in the Indus River Basin, with key metrics including root relative squared error (RRSE). In the training phase, the HLEK model achieved MAE values of 7.072, 5.859, 2.308, and 3.709 for the Indus, Kabul, Jhelum, and Chenab rivers, respectively, significantly outperforming traditional models. The study concludes that the HLEK hybrid model significantly improves prediction accuracy over simpler models, providing a robust tool for forecasting river inflows. This enhanced accuracy is crucial for water resource management and planning in the Indus River Basin and potentially other regions. - A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables
Maha Shabbir, Sohail Chand, Farhat Iqbal
Environmental and Ecological Statistics, 2024 - A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed, Lulwah M. Al-Essa
Risks, 2024
The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging. - Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models
Maha Shabbir, Sohail Chand, Farhat Iqbal, Ozgur Kisi
Water Resources Management, 2024 - Enhanced Foreign Exchange Volatility Forecasting using CEEMDAN with Optuna-Optimized Ensemble Deep Learning Model
Rehan Kausar, Farhat Iqbal, Abdul Raziq, Naveed Sheikh, Abdul Rehman
Sains Malaysiana, 2024
Foreign Exchange (FX) is the largest financial market in the world, with a daily trading volume that significantly exceeds that of stock and futures markets. The prediction of FX volatility is a critical financial concern that has garnered significant attention from researchers and practitioners due to its far-reaching implications in the financial markets. This paper presents a novel hybrid ensemble forecasting model integrating a decomposition strategy and three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination addresses individual models' limitations and further improves the accuracy and stability of FX volatility forecasting. The proposed approach utilizes the CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to form the input dataset for the DL models. In addition, we employed an attention mechanism to improve the effectiveness of the DL techniques. Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. The proposed approach is evaluated against various benchmark models using evaluation measures such as MSE, MAE, HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The results demonstrate that our proposed approach provides accurate and reliable forecasts of FX volatility under different forecasting regimes, making it a valuable tool for financial practitioners and researchers. - Novel hybrid and weighted ensemble models to predict river discharge series with outliers
Maha Shabbir, Sohail Chand, Farhat Iqbal
Kuwait Journal of Science, 2024
In this study, a novel hybrid framework named HVK/HVA-HEM was designed to predict river discharge with outliers. Firstly, the Hampel filter (HF) identifies and corrects outliers in the discharge series. Next, this series was denoised and decomposed using ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) respectively. The HF-VMD components were employed to K-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models, while the HF-EEMD series was applied to the multilayer perceptron (MLP) model to obtain the predictions of the proposed HVK(HF-VMD-KNN), HVA(HF-VMD-ARIMA), and HEM(HF-EEMD-MLP) hybrid models. Lastly, using the mean absolute error (MAE) weights of HVK , HVA and HEM predictions, the HVK-HEM and HVA-HEM models were formulated. The application of the new hybrid framework was displayed using the discharge of four rivers in Pakistan. In terms of the RMSE of Kabul River, the HEM hybrid model had better performance than MLP (175.2053 m3/s), HF-MLP (156.1853 m3/s), EEMD-MLP (133.4049 m3/s) and VMD-MLP (170.1337 m3/s). Similarly, the proposed HVK and HEM hybrid models are more efficient than their respective single, HF, EEMD, and VMD-based models. Overall, the proposed HVA-HEM hybrid model outperformed all competing and proposed models. - Study of ferromagnetism, and thermoelectric behavior of double perovskites K2Z(Cl/Br)6 (Z = Ta, W, Re) for spintronic, and energy application
Q. Mahmood, Farhat Iqbal, Tahani H. Flemban, Eman Algrafy, Hind Althib, M.G.B. Ashiq, Murefah mana AL-Anazy, Hamid Ullah, Amani Rached, Tahani Alqahtani, El Sayed Yousef, T. Ghrib
Journal of Physics and Chemistry of Solids, 2024 - Bagging-based ridge estimators for a linear regression model with non-normal and heteroscedastic errors
Maha Shabbir, Sohail Chand, Farhat Iqbal
Communications in Statistics Simulation and Computation, 2024 - A new ridge estimator for linear regression model with some challenging behavior of error term
Maha Shabbir, Sohail Chand, Farhat Iqbal
Communications in Statistics Simulation and Computation, 2024 - AN ENSEMBLE MACHINE LEARNING APPROACH FOR THE PREDICTION OF BODY WEIGHT OF CHICKENS FROM BODY MEASUREMENT
Mohd Urooj, Farkhund Iqbal, Zil-E- Huma
Journal of Animal and Plant Sciences, 2023 - Cryptocurrency Trading and Downside Risk
Farhat Iqbal, Mamoona Zahid, Dimitrios Koutmos
Risks, 2023 - Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels
Farhat Iqbal, Abdul Raziq, Zil-E-Huma, Cem Tirink, Abdul Fatih, Muhammad Yaqoob
Tropical Animal Health and Production, 2023 - A Hybrid Approach for Accurate Forecasting of Exchange Rate Prices using VMD-CEEMDAN-GRU-ATCN Model
Rehan Kausar, Farhat Iqbal, Abdul Raziq, Naveed Sheikh
Sains Malaysiana, 2023 - Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning
Mamoona Zahid, Farhat Iqbal, Dimitrios Koutmos
Risks, 2022 - Modeling and Forecasting the Realized Volatility of Bitcoin using Realized HAR-GARCH-type Models with Jumps and Inverse Leverage Effect
Mamoona Zahid, Farhat Iqbal, Abdul Raziq Abdul Raziq, Naveed Sheikh
Sains Malaysiana, 2022 - Comparing the Predictive Ability of Machine Learning Methods in Predicting the Live Body Weight of Beetal Goats of Pakistan
Farhat Iqbal, Abdul Waheed, Zil-e Huma, Asim Faraz
Pakistan Journal of Zoology, 2022 - A Novel Hybrid Method for River Discharge Prediction
Maha Shabbir, Sohail Chand, Farhat Iqbal
Water Resources Management, 2022 - Modeling and predicting the growth of indigenous Harnai sheep in Pakistan: non-linear functions and MARS algorithm
Farhat Iqbal, Ecevit Eyduran, Abdul Raziq, Muhammad Ali, Zil-e-Huma, Cem Tirink, Harun Sevgenler
Tropical Animal Health and Production, 2021 - Feedlot performance and serum profile of buffalo (Bubalus bubalis) calves under high input feeding systems
Buffalo Bulletin, 2021 - Bayesian inference of multivariate rotated GARCH models with skew returns
Farhat Iqbal, Kostas Triantafyllopoulos
Communications in Statistics Simulation and Computation, 2021 - An application of least square support vector machine model with parameters optimization for predicting body weight of Harnai sheep breed
Farhat IQBAL, Abdul RAZIQ, Zil E HUMA, Muhammad ALI
Turkish Journal of Veterinary and Animal Sciences, 2021 - Modeling the volatility of cryptocurrencies: An empirical application of stochastic volatility models
Mamoona Zahid, Farhat Iqbal
Sains Malaysiana, 2020 - Predicting egg production in chukar partridges using nonlinear models and multivariate adaptive regression splines (MARS) algorithm
T. Sengul, S. Celik, E. Eyduran, F. Iqbal
European Poultry Science, 2020 - Predicting live body weight of harnai sheep through penalized regression models
Journal of Animal and Plant Sciences, 2019 - Frequency of anemia in pregnant women of different age groups at Quetta: A hospital-based cross sectional study
Akhtar Bibi
Pesquisa Agropecuaria Brasileira, 2019 - Predicting the body weight of Balochi sheep using a machine learning approach
Zil E HUMA, Farhat IQBAL
Turkish Journal of Veterinary and Animal Sciences, 2019 - Nonlinear Growth Functions for Body Weight of Thalli Sheep using Bayesian Inference
Pakistan Journal of Zoology, 2019 - A bayesian approach for describing the growth of Chukar partridges
F. Iqbal, E. Eyduran, N. Mikail, V. Sarıyel, Z.E. Huma, A. Aygün, İ. Keskin
European Poultry Science, 2019 - Fitting nonlinear growth models on weight in Mengali sheep through Bayesian inference
Farhat Iqbal, Mohammad Masood Tariq, Ecevit Eyduran, Zil-e Huma, Abdul Waheed, Farhat Abbas, Muhammad Ali, Nadeem Rashid, Majed Rafeeq, Asadullah Asadullah, Zahid Mustafa
Pakistan Journal of Zoology, 2019 - New agricultural politics in turkey: The econometric assessment of cotton production and yield 1925 – 2015
Journal of Animal and Plant Sciences, 2017 - Robust value-at-risk forecasting of Karachi Stock Exchange
Farhat Iqbal
Afro Asian Journal of Finance and Accounting, 2017 - Effects of additive outliers on asymmetric garch models
Pakistan Journal of Statistics, 2017 - Adsorption kinetics of malachite green and methylene blue from aqueous solutions using surfactant-modified organoclays
Haseeb Ullah, Muhammad Nafees, Farhat Iqbal, Saifullah Awan, Afzal Shah, Amir Waseem
Acta Chimica Slovenica, 2017 - Risk forecasting of Karachi Stock Exchange: A comparison of classical and Bayesian GARCH models
Farhat Iqbal
Pakistan Journal of Statistics and Operation Research, 2016 - Chemical control of codling moth, cydia Pomonella L. (Lepidoptera: Tortricidae) in relation to pheromone trap catches and degree days in Upland Balochistan
Pakistan Journal of Zoology, 2015 - The pesticide exposure through fruits and meat in Pakistan
Fresenius Environmental Bulletin, 2015 - Modelling the monthly and annual temperature series of Quetta, Pakistan
Farhat Iqbal, Sohail Chand
Pakistan Journal of Statistics and Operation Research, 2014 - Pollution Status of Pakistan: A Retrospective Review on Heavy Metal Contamination of Water, Soil, and Vegetables
Amir Waseem, Jahanzaib Arshad, Farhat Iqbal, Ashif Sajjad, Zahid Mehmood, Ghulam Murtaza
Biomed Research International, 2014 - Prediction of live weight from morphological characteristics of commercial goat in Pakistan using factor and principal component scores in multiple linear regression
Journal of Animal and Plant Sciences, 2013 - Efficient bootstrap forecast intervals for return and volatility using the linear estimator of arch models
Middle East Journal of Scientific Research, 2013 - Comparison of non-linear functions to describe the growth in Mengali sheep breed of Balochistan
Pakistan Journal of Zoology, 2013 - Robust estimation of the simplified multivariate GARCH model
Farhat Iqbal
Empirical Economics, 2013 - Diagnostic checking for GARCH-type models
Farhat Iqbal
Communications in Statistics Theory and Methods, 2013 - Diagnostic test for GARCH models based on absolute residual autocorrelations
Farhat Iqbal
Pakistan Journal of Statistics and Operation Research, 2013 - Robust estimation for the orthogonal garch model
FARHAT IQBAL
Manchester School, 2013 - Prediction of body weight from testicular and morphological characteristics in indigenous mengali sheep of Pakistan using factor analysis scores in multiple linear regression analysis
International Journal of Agriculture and Biology, 2012 - A study of value-at-risk based on M-estimators of the conditional heteroscedastic models
Farhat Iqbal, Kanchan Mukherjee
Journal of Forecasting, 2012 - A weighted linear estimator of multivariate ARCH parameters
Farhat Iqbal
Communications in Statistics Simulation and Computation, 2011 - M-estimators of some GARCH-type models; computation and application
Farhat Iqbal, Kanchan Mukherjee
Statistics and Computing, 2010
RECENT SCHOLAR PUBLICATIONS
- A comprehensive explainable machine learning framework for predicting sheep body weight
F Iqbal
Small Ruminant Research, 107776 , 2026
2026 - Agrometeorological drought early warning as a climate service: SPI projections using SARIMA models for seasonal risk management
M Ashraf, A Arshad, F Iqbal, S Pourshirazi, MU Azhar, UF Tanoli, T Ahmad, ...
Climate Services 40, 100622 , 2025
2025 - Study of Room Temperature Ferromagnetism, Mechanical, Thermodynamic, and Thermoelectric Aspects of BePr2X4 (X= S, Se, Te) for Spintronic and Energy Devices
HA Althobaiti, BA El-Badry, F Iqbal, A Almohammedi, H Siddiq, ...
Journal of Physics and Chemistry of Solids, 113351 , 2025
2025
Citations: 1 - Predicting Ischemic Heart Disease and Determining Its Risk Factors: A Comparison of Various Classification Methods in Machine Learning
M Yaqoob, F Iqbal
Thailand Statistician 23 (3), 677-691 , 2025
2025 - Novel Hybrid Approach for River Inflow Modeling: Case Study of the Indus River Basin, Pakistan
M Shabbir, S Chand, F Iqbal, O Kisi
Journal of Hydrologic Engineering 30 (3), 04025006 , 2025
2025
Citations: 1 - A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables
M Shabbir, S Chand, F Iqbal
Environmental and Ecological Statistics 31 (4), 921-948 , 2024
2024 - A new ridge estimator for linear regression model with some challenging behavior of error term
M Shabbir, S Chand, F Iqbal
Communications in Statistics-Simulation and Computation 53 (11), 5442-5452 , 2024
2024
Citations: 28 - Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models …
M Shabbir, S Chand, F Iqbal, O Kisi
Water Resources Management 38 (11), 4179-4196 , 2024
2024
Citations: 6 - A novel hybrid deep learning method for accurate exchange rate prediction
F Iqbal, D Koutmos, EA Ahmed, LM Al-Essa
Risks 12 (9), 139 , 2024
2024
Citations: 12 - Bagging-based ridge estimators for a linear regression model with non-normal and heteroscedastic errors
M Shabbir, S Chand, F Iqbal
Communications in Statistics-Simulation and Computation 53 (8), 3653-3667 , 2024
2024
Citations: 22 - Novel hybrid and weighted ensemble models to predict river discharge series with outliers
M Shabbir, S Chand, F Iqbal
Kuwait Journal of Science 51 (2), 100188 , 2024
2024
Citations: 8 - Study of ferromagnetism, and thermoelectric behavior of double perovskites K2Z (Cl/Br) 6 (Z= Ta, W, Re) for spintronic, and energy application
Q Mahmood, F Iqbal, TH Flemban, E Algrafy, H Althib, MGB Ashiq, ...
Journal of Physics and Chemistry of Solids 186, 111816 , 2024
2024
Citations: 8 - Enhanced Foreign Exchange Volatility Forecasting using CEEMDAN with Optuna-Optimized Ensemble Deep Learning Model
MED Optuna, R KAUSAR, F IQBAL, A RAZIQ, N SHEIKH, A REHMAN
Sains Malaysiana 53 (9), 3229-3239 , 2024
2024
Citations: 4 - A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
R Kausar, F Iqbal, A Raziq, N Sheikh
Sains Malaysiana 52 (11), 3293-3306 , 2023
2023
Citations: 11 - AN ENSEMBLE MACHINE LEARNING APPROACH FOR THE PREDICTION OF BODY WEIGHT OF CHICKENS FROM BODY MEASUREMENT.
M Urooj, F Iqbal
JAPS: Journal of Animal & Plant Sciences 33 (4) , 2023
2023
Citations: 4 - Cryptocurrency trading and downside risk
F Iqbal, M Zahid, D Koutmos
Risks 11 (7), 122 , 2023
2023
Citations: 11 - Prediction of river inflow of the major tributaries of Indus river basin using hybrids of EEMD and LMD methods
M Shabbir, S Chand, F Iqbal
Arabian Journal of Geosciences 16 (4), 257 , 2023
2023
Citations: 11 - Cryptocurrency Trading and Downside Risk. Risks 11: 122
F Iqbal, M Zahid, D Koutmos
2023 - A novel hybrid framework to model the relationship of daily river discharge with meteorological variables
M Shabbir, S Chand, F Iqbal
Meteorology Hydrology and Water Management. Research and Operational … , 2023
2023
Citations: 6 - Forecasting Bitcoin volatility using hybrid GARCH models with machine learning
M Zahid, F Iqbal, D Koutmos
Risks 10 (12), 237 , 2022
2022
Citations: 34
MOST CITED SCHOLAR PUBLICATIONS
- Pollution status of Pakistan: a retrospective review on heavy metal contamination of water, soil, and vegetables
A Waseem, J Arshad, F Iqbal, A Sajjad, Z Mehmood, G Murtaza
BioMed research international 2014 (1), 813206 , 2014
2014.0
Citations: 469 - Comparison of non-linear functions to describe the growth in Mengali sheep breed of Balochistan
MM Tariq, F Iqbal, E Eyduran, MA Bajwa, ZE Huma, A Waheed
Pakistan Journal of Zoology 45 (3), 661-665 , 2013
2013.0
Citations: 105 - Predicting the body weight of Balochi sheep using a machine learning approach
ZE Huma, F Iqbal
Turkish Journal of Veterinary & Animal Sciences 43 (4), 500-506 , 2019
2019.0
Citations: 89 - Adsorption Kinetics of Malachite Green and Methylene Blue from Aqueous Solutions Using Surfactant-modified Organoclays.
H Ullah, M Nafees, F Iqbal, MS Awan, A Shah, A Waseem
Acta Chimica Slovenica 64 (2), 449 , 2017
2017.0
Citations: 56 - Prediction of body weight from testicular and morphological characteristics in indigenous mengali sheep of Pakistan using factor analysis scores in multiple linear regression …
MM Tariq, EE Ecevit Eyduran, MA Bajwa, AW Abdul Waheed, ...
2012.0
Citations: 56 - PREDICTION OF LIVE WEIGHT FROM MORPHOLOGICAL CHARACTERISTICS OF COMMERCIAL GOAT IN PAKISTAN USING FACTOR AND PRINCIPAL COMPONENT SCORES IN MULTIPLE LINEAR REGRESSION
E Eyduran, A Waheed, MM Tariq, F Iqbal, S Ahmad
The Journal of Animal and Plant Sciences 23 (6), 1532-1540 , 0
Citations: 56 - Forecasting Bitcoin volatility using hybrid GARCH models with machine learning
M Zahid, F Iqbal, D Koutmos
Risks 10 (12), 237 , 2022
2022.0
Citations: 34 - A new ridge estimator for linear regression model with some challenging behavior of error term
M Shabbir, S Chand, F Iqbal
Communications in Statistics-Simulation and Computation 53 (11), 5442-5452 , 2024
2024.0
Citations: 28 - Modeling and predicting the growth of indigenous Harnai sheep in Pakistan: non-linear functions and MARS algorithm
F Iqbal, E Eyduran, A Raziq, M Ali, C Tirink, H Sevgenler
Tropical Animal Health and Production 53 (2), 248 , 2021
2021.0
Citations: 23 - Predicting the live weight of Harnai sheep through penalized regression models
F Iqbal, M Ali, ZE Huma, A Raziq
The Journal of Animal & Plant Sciences 29 (6), 1541-1548 , 2019
2019.0
Citations: 23 - M-estimators of some GARCH-type models; computation and application
F Iqbal, K Mukherjee
Statistics and Computing 20 (4), 435-445 , 2010
2010.0
Citations: 23 - Bagging-based ridge estimators for a linear regression model with non-normal and heteroscedastic errors
M Shabbir, S Chand, F Iqbal
Communications in Statistics-Simulation and Computation 53 (8), 3653-3667 , 2024
2024.0
Citations: 22 - Comparing the predictive ability of machine learning methods in predicting the live body weight of beetal goats of Pakistan.
F Iqbal, A Waheed, A Faraz
Pakistan Journal of Zoology 54 (1) , 2022
2022.0
Citations: 20 - A novel hybrid method for river discharge prediction
M Shabbir, S Chand, F Iqbal
Water Resources Management 36 (1), 253-272 , 2022
2022.0
Citations: 17 - Nonlinear Growth Functions for Body Weight of Thalli Sheep using Bayesian Inference.
F Iqbal, A Waheed, A Faraz
Pakistan journal of zoology 51 (4) , 2019
2019.0
Citations: 17 - The pesticide exposure through fruits and meat in Pakistan
N Faheem, A Sajjad, Z Mehmood, F Iqbal, Q Mahmood, S Munsif, ...
Fresenius Environmental Bulletin 24 (12), 4555-4566 , 2015
2015.0
Citations: 17 - Fitting Nonlinear Growth Models on Weight in Mengali Sheep Through Bayesian Inference
F Iqbal, ...
Pakistan Journal of Zoology 51 (2), 459 – 466 , 2018
2018.0
Citations: 14 - A novel hybrid deep learning method for accurate exchange rate prediction
F Iqbal, D Koutmos, EA Ahmed, LM Al-Essa
Risks 12 (9), 139 , 2024
2024.0
Citations: 12 - A Study of Value‐at‐Risk Based on M‐Estimators of the Conditional Heteroscedastic Models
F Iqbal, K Mukherjee
Journal of Forecasting 31 (5), 377-390 , 2012
2012.0
Citations: 12 - A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
R Kausar, F Iqbal, A Raziq, N Sheikh
Sains Malaysiana 52 (11), 3293-3306 , 2023
2023.0
Citations: 11
