Farhat Iqbal
@uob.edu.pk
6
Scopus Publications
125
Scholar Citations
2
Scholar h-index
2
Scholar i10-index
Scopus Publications
- Novel Hybrid Approach for River Inflow Modeling: Case Study of the Indus River Basin, Pakistan
Maha Shabbir, Sohail Chand, Farhat Iqbal, and Ozgur Kisi
American Society of Civil Engineers (ASCE) - A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Springer Science and Business Media LLC - Enhanced Foreign Exchange Volatility Forecasting using CEEMDAN with Optuna-Optimized Ensemble Deep Learning Model
Rehan Kausar, Farhat Iqbal, Abdul Raziq, Naveed Sheikh, and Abdul Rehman
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
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. - A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed, and Lulwah M. Al-Essa
MDPI AG
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, and Ozgur Kisi
Springer Science and Business Media LLC - Novel hybrid and weighted ensemble models to predict river discharge series with outliers
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Elsevier BV - Study of ferromagnetism, and thermoelectric behavior of double perovskites K<inf>2</inf>Z(Cl/Br)<inf>6</inf> (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,et al.
Elsevier BV - A new ridge estimator for linear regression model with some challenging behavior of error term
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Informa UK Limited
Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter (Formula presented.) has an important role in the bias-variance tradeoff. In this article, we introduce a new approach to select the ridge parameter to deal with the multicollinearity problem with different behavior of the error term. The proposed ridge estimator is a function of the number of predictors and the standard error of the regression model. An extensive simulation study is conducted to assess the performance of the estimators for the linear regression model with different error terms, which include normally distributed, non-normal and heteroscedastic or autocorrelated errors. Based upon the criterion of mean square error (MSE), it is found that the new proposed estimator outperforms OLS, commonly used and closely related estimators. Further, the application of the proposed estimator is provided on the COVID-19 data of India. © 2023 Taylor & Francis Group, LLC. - Bagging-based ridge estimators for a linear regression model with non-normal and heteroscedastic errors
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Informa UK Limited - AN ENSEMBLE MACHINE LEARNING APPROACH FOR THE PREDICTION OF BODY WEIGHT OF CHICKENS FROM BODY MEASUREMENT
Pakistan Agricultural Scientists Forum - Cryptocurrency Trading and Downside Risk
Farhat Iqbal, Mamoona Zahid, and Dimitrios Koutmos
MDPI AG
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies. - 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, and Muhammad Yaqoob
Springer Science and Business Media LLC - A Hybrid Approach for Accurate Forecasting of Exchange Rate Prices using VMD-CEEMDAN-GRU-ATCN Model
Rehan Kausar, Farhat Iqbal, Abdul Raziq, and Naveed Sheikh
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
The foreign exchange (Forex) market has greatly influenced the global financial market. While Forex trading offers investors substantial yield prospects, some risks are also involved. It is challenging to accurately model financial time series due to their nonlinear, non-stationary and noisy properties with an uncertain and hidden relationship. Thus, developing extremely precise forecasting techniques is crucial for investors and decision-makers. This study introduces a novel hybrid forecasting model, VMD-CEEMDAN-GRU-ATCN, designed to improve Forex price prediction accuracy. To begin with, our proposed model utilizes the variational model decomposition (VMD) technique for breaking down raw prices into multiple sub-components and residual terms. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is utilized to extract features from the residual terms, which involves further decomposition and analysis of these complex information-containing terms. These sub-components are then predicted by the gated recurrent unit (GRU) model. To enhance the effectiveness of our hybrid model, we include the open, high, low, and close prices and seven Forex market technical indicators. Finally, an attention-based temporal convolutional network (ATCN) model is used to obtain the Forex price forecasts. For both one-step and multi-step ahead forecasting, our proposed VMD-CEEMDAN-GRU-ATCN model has demonstrated superior and consistent performance in predicting USD/PKR exchange rate price series. - Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning
Mamoona Zahid, Farhat Iqbal, and Dimitrios Koutmos
MDPI AG
The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility. - 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, and Naveed Sheikh
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment. - 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, and Asim Faraz
ResearchersLinks Ltd
Farhat Iqbal1, Abdul Waheed2*, Zil-e-Huma3 and Asim Faraz2 1Department of Statistics, University of Balochistan, Quetta, Pakistan 2Departmentof Livestock and Poultry Production, Faculty of Veterinary Sciences, Bahauddin Zakariya University, Multan, Pakistan 3Department of Zoology, Sardar Bahadur Khan Women’s University, Quetta, Pakistan Article Information Received 03 October 2019 Revised 13 January 2020 Accepted 24 January 2020 Available online 28 January 2021 - A Novel Hybrid Method for River Discharge Prediction
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Springer Science and Business Media LLC - 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, and Harun Sevgenler
Springer Science and Business Media LLC - Feedlot performance and serum profile of buffalo (Bubalus bubalis) calves under high input feeding systems
- 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, and Muhammad ALI
The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS - Bayesian inference of multivariate rotated GARCH models with skew returns
Farhat Iqbal and Kostas Triantafyllopoulos
Informa UK Limited
Abstract Bayesian inference is proposed for volatility models, targeting financial returns, which exhibit high kurtosis and slight skewness. Rotated GARCH models are considered which can accommodate the multivariate standard normal, Student t, generalized error distributions and their skewed versions. Inference on the model parameters and prediction of future volatilities and cross-correlations are addressed by Markov chain Monte Carlo inference. Bivariate simulated data is used to assess the performance of the method, while two sets of real data are used for illustration: the first is a trivariate data set of financial stock indices and the second is a higher dimensional data set for which a portfolio allocation is performed. - Modeling the volatility of cryptocurrencies: An empirical application of stochastic volatility models
Mamoona Zahid and Farhat Iqbal
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies. - Predicting egg production in chukar partridges using nonlinear models and multivariate adaptive regression splines (MARS) algorithm
T. Sengul, S. Celik, E. Eyduran, and F. Iqbal
Verlag Eugen Ulmer - Predicting live body weight of harnai sheep through penalized regression models
- Frequency of anemia in pregnant women of different age groups at Quetta: A hospital-based cross sectional study
Akhtar Bibi
Bolan Society for Pure and Applied Biology (BSPAB)
The present study was conducted with the aimed to determine the frequency of anemia and lack of hemoglobin in human pregnancy at Bolan Medical College Hospital Quetta during 2017. As many as 625 pregnant women patients were examined, of these 350 were found anemic. Blood samples of randomly selected three hundred and fifty pregnant women of age group 17-44 years were analyzed, and was classified under first trimester (21), second trimester (58) and third trimester (271). The data was analyzed to estimate the frequency of anemia during pregnancy ranging from mild: 288 (46.1%) to moderate: 54 (8.6%) and severe: 8 (1.3%). Findings revealed that anemia was found to be prevalent in third trimester (271) as compared to the second (58) and first (21) trimester of pregnancy. Chi-square test was applied at 5% level of significance to check the association between age groups, parity and gestation age (G age). Significant association was found with anemic level at p-value <0.001 in age group and G age, whereas parity was not found associated with anemic level at 5% level of significance. The present study revealed that overall prevalence rate of anemia was 56% which indicated high prevalence of this disease in the region. The study concluded that pregnant women are at high hazard of blood anemia. Fresh balanced diets rich in iron, folic acid and vitamin B12 are recommended for their health and fine growth of developing baby. Keywords: Anemia; Frequency; Gestation-age; Hemoglobin; Parity; Quetta http://dx.doi.org/10.19045/bspab.2019.80045
RECENT SCHOLAR PUBLICATIONS
- 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 - 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 - 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 - Enhanced Foreign Exchange Volatility Forecasting using CEEMDAN with Optuna-Optimized-O ptimized Ensemble Deep Learning Model
R Kausar, F Iqbal, A Raziq, N Sheikh, A Rehman
SAINS MALAYSIANA 53 (9), 3229-3239 2024 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - Cryptocurrency trading and downside risk
F Iqbal, M Zahid, D Koutmos
Risks 11 (7), 122 2023 - 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 - 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 - Forecasting Bitcoin volatility using hybrid GARCH models with machine learning
M Zahid, F Iqbal, D Koutmos
Risks 10 (12), 237 2022 - Modeling and forecasting the realized volatility of Bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect
M Zahid, F Iqbal, A Raziq, N Sheikh
Sains Malaysiana 51 (3), 929-942 2022 - 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 - A novel hybrid method for river discharge prediction
M Shabbir, S Chand, F Iqbal
Water Resources Management 36 (1), 253-272 2022
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
Citations: 418 - 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
Citations: 93 - 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
Citations: 63 - 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, E Eyduran, MA Bajwa, A Waheed, F Iqbal, Y Javed
International Journal of Agriculture & Biology 14 (4) 2012
Citations: 57 - 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
Citations: 49 - 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) 2017
Citations: 41 - M-estimators of some GARCH-type models; computation and application
F Iqbal, K Mukherjee
Statistics and Computing 20, 435-445 2010
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
Citations: 21 - 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, 1-12 2021
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
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
Citations: 14 - 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
Citations: 13 - A novel hybrid method for river discharge prediction
M Shabbir, S Chand, F Iqbal
Water Resources Management 36 (1), 253-272 2022
Citations: 13 - 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
Citations: 12 - 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
Citations: 11 - Forecasting Bitcoin volatility using hybrid GARCH models with machine learning
M Zahid, F Iqbal, D Koutmos
Risks 10 (12), 237 2022
Citations: 10 - 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
Citations: 10 - Fitting Nonlinear Growth Models on Weight in Mengali Sheep Through Bayesian Inference
F Iqbal, ...
Pakistan Journal of Zoology 51 (2), 459 – 466 2018
Citations: 10 - 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
Citations: 9 - Prevalence of refractive errors in school going children in district Faisalabad, Pakistan
F Iqbal, I Khalil, M Zahid
Adv Ophthalmol Vis Syst 10 (1), 4-6 2020
Citations: 9