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I contribute to collaborative research and cross-border integration. I am data scientist, recipient of the Green-Talent Award, UNESCO Young Scientist Award, Alain Bensoussan Fellowship, Endeavour-Australia Leadership Award, Talented Young Scientist Award, and Slovak National Research Award.
Mechanical properties and data analysis for the prediction of different mechanical properties of geopolymer concrete (GPC) were investigated. A relatively large amount of test data from 126 past works was collected, analyzed, and correlation between different mechanical properties and compressive strength was investigated. Equations were proposed for the properties of splitting tensile strength, flexural strength, modulus of elasticity, Poisson’s ratio, and strain corresponding to peak compressive strength. The proposed equations were found accurate and can be used to prepare a state-of-art report on GPC. Based on data analysis, it was found that there is a chance to apply some past proposed equations for predicting different mechanical properties. CEB-FIP equations for the prediction of splitting tensile strength and strain corresponding to peak compressive stress were found to be accurate, while ACI 318 equations for splitting tensile and elastic modulus overestimates test data for GPC of low compressive strength.
Azad A. Mohammed; Hemn Unis Ahmed; Amir Mosavi. Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis. Materials 2021, 14, 4690 .
AMA StyleAzad A. Mohammed, Hemn Unis Ahmed, Amir Mosavi. Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis. Materials. 2021; 14 (16):4690.
Chicago/Turabian StyleAzad A. Mohammed; Hemn Unis Ahmed; Amir Mosavi. 2021. "Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis." Materials 14, no. 16: 4690.
The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using several machine learning techniques: conditional inference random forest (CIRF), the gradient boosting model (GBM), extreme gradient boosting (XGB) and their ensembles. To investigate the future (year 2050) effects of changing climates and changing land use on future flood risk, a general circulation model (GCM) with representative concentration pathways (RCPs) of the 2.6 and 8.5 scenarios by 2050 was tested for impacts on 8 precipitation variables. In addition, future land uses in 2050 was prepared using a CA-Markov model. The performances of the flood risk models were validated with Receiver Operating Characteristic-Area Under Curve (ROC-AUC) and other statistical analyses. The AUC value of the ROC curve indicates that the ensemble model had the highest predictive power (AUC = 0.83) and was followed by GBM (AUC = 0.80), XGB (AUC = 0.79), and CIRF (AUC = 0.78). The results of climate and land use changes on future flood-prone areas showed that the areas classified as having moderate to very high flood risk will increase by 2050. Due to the changes occurring with land uses and in climates, the area classified as moderate to very high risk increased in the predictions from all four models. The areal proportion classes of the risk zones in 2050 under the RCP 2.6 scenario using the ensemble model have changed of the following proportions from the current distribution Very Low = −12.04 %, Low = −8.56 %, Moderate = +1.56 %, High = +11.55 %, and Very High = +7.49 %. The RCP 8.5 scenario has caused the following changes from the present percentages: Very Low = −14.48 %, Low = −6.35 %, Moderate = +4.54 %, High = +10.61 %, and Very High = +5.67 %. The results of current and future flood risk mapping can aid planners and flood hazard managers in their efforts to mitigate impacts.
Saeid Janizadeh; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Kourosh Ahmadi; Sajjad Mirzaei; Amir Hossein Mosavi; John P. Tiefenbacher. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management 2021, 298, 113551 .
AMA StyleSaeid Janizadeh, Subodh Chandra Pal, Asish Saha, Indrajit Chowdhuri, Kourosh Ahmadi, Sajjad Mirzaei, Amir Hossein Mosavi, John P. Tiefenbacher. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management. 2021; 298 ():113551.
Chicago/Turabian StyleSaeid Janizadeh; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Kourosh Ahmadi; Sajjad Mirzaei; Amir Hossein Mosavi; John P. Tiefenbacher. 2021. "Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future." Journal of Environmental Management 298, no. : 113551.
From conventional turbines to cutting-edge bladeless turbines, energy harvesting from wind has been well explored by researchers for more than a century. The vortex bladeless wind turbine (VBT) is considered an advanced design that alternatively harvests energy from oscillation. This research investigates enhancing the output electrical power of VBT through simulation of the fluid–solid interactions (FSI), leading to a comprehensive dataset for predicting procedure and optimal design. Hence, the long short-term memory (LSTM) method, due to its time-series prediction accuracy, is proposed to model the power of VBT from the collected data. To find the relationship between the parameters and the variables used in this research, a correlation matrix is further presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and their predictions indicates that the LSTM method is suitable for modeling. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of two and a half hours to two minutes. In addition, one of the most important achievements of this study is to suggest a mathematical relation of output power, which helps to extend it in different sizes of VBT with a high range of parameter variations.
Mahsa Dehghan Manshadi; Majid Ghassemi; Seyed Mousavi; Amir Mosavi; Levente Kovacs. Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory. Energies 2021, 14, 4867 .
AMA StyleMahsa Dehghan Manshadi, Majid Ghassemi, Seyed Mousavi, Amir Mosavi, Levente Kovacs. Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory. Energies. 2021; 14 (16):4867.
Chicago/Turabian StyleMahsa Dehghan Manshadi; Majid Ghassemi; Seyed Mousavi; Amir Mosavi; Levente Kovacs. 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory." Energies 14, no. 16: 4867.
With advancements in the automated industry, electromagnetic inferences (EMI) have been increasing over time, causing major distress among the end-users and affecting electronic appliances. The issue is not new and major work has been done, but unfortunately, the issue has not been fully eliminated. Therefore, this review intends to evaluate the previous carried-out studies on electromagnetic shielding materials with the combination of [email protected], [email protected], [email protected] and [email protected]@Polymer composites in X-band frequency range and above to deal with EMI. VOSviewer was also used to perform the keyword analysis which shows how the studies are interconnected. Based on the carried-out review it was observed that the most preferable materials to deal with EMI are polymer-based composites which showed remarkable results. It is because the polymers are flexible and provide better bonding with other materials. Polydimethylsiloxane (PDMS), polyaniline (PANI), polymethyl methacrylate (PMMA) and polyvinylidene fluoride (PVDF) are effective in the X-band frequency range, and PDMS, epoxy, PVDF and PANI provide good shielding effectiveness above the X-band frequency range. However, still, many new combinations need to be examined as mostly the shielding effectiveness was achieved within the X-band frequency range where much work is required in the higher frequency range.
Saba Ayub; Beh Guan; Faiz Ahmad; Yusuff Oluwatobi; Zaib Nisa; Muhammad Javed; Amir Mosavi. Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review. Polymers 2021, 13, 2580 .
AMA StyleSaba Ayub, Beh Guan, Faiz Ahmad, Yusuff Oluwatobi, Zaib Nisa, Muhammad Javed, Amir Mosavi. Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review. Polymers. 2021; 13 (15):2580.
Chicago/Turabian StyleSaba Ayub; Beh Guan; Faiz Ahmad; Yusuff Oluwatobi; Zaib Nisa; Muhammad Javed; Amir Mosavi. 2021. "Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review." Polymers 13, no. 15: 2580.
During the past decades, the relationship between various psychological parameters had been studied in detail. However, the dependency structure of correlated parameters was rarely investigated. Knowing the dependence structure helps in finding the probability matrix of the interaction between the parameters. In this research, a novel approach was introduced in psychological analysis using copula functions. For this purpose, the self-esteem and anxiety of 141 university students in Iran were extracted using the Coopersmith Self-esteem Inventory and the Zang Anxiety Scale. Then the dependence structure of self-esteem and anxiety were established using copula functions. The Frank copula achieved the best fit for the joint variables of self-esteem and anxiety. Finally, the probability matrix of different classes of anxiety, taking into account self-esteem classes, was extracted. The results indicated that poor self-esteem leads to severe or very severe anxiety, with more than 98% probability, while strong self-esteem may lead to normal and mild anxiety, with about 80% probability. It can be concluded that the method was promising, and that copula functions can open a window to the dependence structure analysis of psychological parameters.
Elham Dehghani; Somayeh Ranjbar; Moharram Atashafrooz; Hossein Negarestani; Amir Mosavi; Levente Kovacs. Introducing Copula as a Novel Statistical Method in Psychological Analysis. International Journal of Environmental Research and Public Health 2021, 18, 7972 .
AMA StyleElham Dehghani, Somayeh Ranjbar, Moharram Atashafrooz, Hossein Negarestani, Amir Mosavi, Levente Kovacs. Introducing Copula as a Novel Statistical Method in Psychological Analysis. International Journal of Environmental Research and Public Health. 2021; 18 (15):7972.
Chicago/Turabian StyleElham Dehghani; Somayeh Ranjbar; Moharram Atashafrooz; Hossein Negarestani; Amir Mosavi; Levente Kovacs. 2021. "Introducing Copula as a Novel Statistical Method in Psychological Analysis." International Journal of Environmental Research and Public Health 18, no. 15: 7972.
Entropy models have been recently adopted in many studies to evaluate the shear stress distribution in open-channel flows. Although the uncertainty of Shannon and Tsallis entropy models were analyzed separately in previous studies, the uncertainty of other entropy models and comparisons of their reliability remain an open question. In this study, a new method is presented to evaluate the uncertainty of four entropy models, Shannon, Shannon-Power Law (PL), Tsallis, and Renyi, in shear stress prediction of the circular channels. In the previous method, the model with the largest value of the percentage of observed data within the confidence bound (Nin) and the smallest value of Forecasting Range of Error Estimation (FREE) is the most reliable. Based on the new method, using the effect of Optimized Forecasting Range of Error Estimation (FREEopt) and Optimized Confidence Bound (OCB), a new statistic index called FREEopt-based OCB (FOCB) is introduced. The lower the value of FOCB, the more certain the model. Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, and had the highest certainty, followed by ρgRs and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy, with the value of FOCB equal to 57.726, had less certainty.
Amin Kazemian-Kale-Kale; Azadeh Gholami; Mohammad Rezaie-Balf; Amir Mosavi; Ahmed Sattar; Amir Azimi; Bahram Gharabaghi; Hossein Bonakdari. Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method. Geosciences 2021, 11, 308 .
AMA StyleAmin Kazemian-Kale-Kale, Azadeh Gholami, Mohammad Rezaie-Balf, Amir Mosavi, Ahmed Sattar, Amir Azimi, Bahram Gharabaghi, Hossein Bonakdari. Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method. Geosciences. 2021; 11 (8):308.
Chicago/Turabian StyleAmin Kazemian-Kale-Kale; Azadeh Gholami; Mohammad Rezaie-Balf; Amir Mosavi; Ahmed Sattar; Amir Azimi; Bahram Gharabaghi; Hossein Bonakdari. 2021. "Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method." Geosciences 11, no. 8: 308.
Concrete, as one of the essential construction materials, is responsible for a vast amount of emissions. Using recycled materials and gray water can considerably contribute to the sustainability aspect of concrete production. Thus, finding a proper replacement for fresh water in the production of concrete is significant. The usage of industrial wastewater instead of water in concrete is considered in this paper. In this study, 450 concrete samples are produced with different amounts of wastewater. The mechanical parameters, such as slump, compressive strength, water absorption, tensile strength, electrical resistivity, rapid freezing, half-cell potential and appearance, are investigated, and a specific concentration and impurities of wastewater that cause a 10% compressive strength reduction were found. The results showed that the usage of industrial wastewater does not significantly change the main characteristics of concrete. Although increasing the concentration of wastewater can decrease the durability and strength features of concrete nonlinearly, the negative effects on durability tests are more conspicuous, as utilizing concentrated wastewaters disrupt the formation of appropriate air voids, pore connectivity and pore-size distribution in the concrete.
Ehsan Nasseralshariati; Danial Mohammadzadeh; Nader Karballaeezadeh; Amir Mosavi; Uwe Reuter; Murat Saatcioglu. The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of Concrete. Materials 2021, 14, 4088 .
AMA StyleEhsan Nasseralshariati, Danial Mohammadzadeh, Nader Karballaeezadeh, Amir Mosavi, Uwe Reuter, Murat Saatcioglu. The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of Concrete. Materials. 2021; 14 (15):4088.
Chicago/Turabian StyleEhsan Nasseralshariati; Danial Mohammadzadeh; Nader Karballaeezadeh; Amir Mosavi; Uwe Reuter; Murat Saatcioglu. 2021. "The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of Concrete." Materials 14, no. 15: 4088.
Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in graphene- and metal-based composites, the utilization of alternating deposition method provides the highest shielding effectiveness. However, in polymer-based composite, the utilization of chemical vapor deposition method showed the highest shielding effectiveness. Furthermore, this review reveals that there is a gap in the literature in terms of the application of artificial intelligence and machine learning methods. The results further reveal that within the past half-decade machine learning methods, including artificial neural networks, have brought significant improvement for modelling EMI materials. We identified a research trend in the direction of using advanced forms of machine learning for comparative analysis, research and development employing hybrid and ensemble machine learning methods to deliver higher performance.
Saba Ayub; Beh Guan; Faiz Ahmad; Muhammad Javed; Amir Mosavi; Imre Felde. Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods. Metals 2021, 11, 1164 .
AMA StyleSaba Ayub, Beh Guan, Faiz Ahmad, Muhammad Javed, Amir Mosavi, Imre Felde. Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods. Metals. 2021; 11 (8):1164.
Chicago/Turabian StyleSaba Ayub; Beh Guan; Faiz Ahmad; Muhammad Javed; Amir Mosavi; Imre Felde. 2021. "Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods." Metals 11, no. 8: 1164.
A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme.
Yan Cao; Amir Raise; Ardashir Mohammadzadeh; Sakthivel Rathinasamy; Shahab S. Band; Amirhosein Mosavi. Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction. Energy Reports 2021, 1 .
AMA StyleYan Cao, Amir Raise, Ardashir Mohammadzadeh, Sakthivel Rathinasamy, Shahab S. Band, Amirhosein Mosavi. Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction. Energy Reports. 2021; ():1.
Chicago/Turabian StyleYan Cao; Amir Raise; Ardashir Mohammadzadeh; Sakthivel Rathinasamy; Shahab S. Band; Amirhosein Mosavi. 2021. "Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction." Energy Reports , no. : 1.
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
Nooshin Ayoobi; Danial Sharifrazi; Roohallah Alizadehsani; Afshin Shoeibi; Juan M. Gorriz; Hossein Moosaei; Abbas Khosravi; Saeid Nahavandi; Abdoulmohammad Gholamzadeh Chofreh; Feybi Ariani Goni; Jiří Jaromír Klemeš; Amir Mosavi. Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods. Results in Physics 2021, 27, 104495 -104495.
AMA StyleNooshin Ayoobi, Danial Sharifrazi, Roohallah Alizadehsani, Afshin Shoeibi, Juan M. Gorriz, Hossein Moosaei, Abbas Khosravi, Saeid Nahavandi, Abdoulmohammad Gholamzadeh Chofreh, Feybi Ariani Goni, Jiří Jaromír Klemeš, Amir Mosavi. Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods. Results in Physics. 2021; 27 ():104495-104495.
Chicago/Turabian StyleNooshin Ayoobi; Danial Sharifrazi; Roohallah Alizadehsani; Afshin Shoeibi; Juan M. Gorriz; Hossein Moosaei; Abbas Khosravi; Saeid Nahavandi; Abdoulmohammad Gholamzadeh Chofreh; Feybi Ariani Goni; Jiří Jaromír Klemeš; Amir Mosavi. 2021. "Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods." Results in Physics 27, no. : 104495-104495.
Gully erosion is one of the advanced forms of water erosion. Identifying the effective factors and gully erosion predicting is one of the important tools to control and manage such phenomenon. The main purpose of this study is to evaluate the effect of four different resampling algorithms including cross-validation (5-fold and 10-fold) and bootstrapping (Bootstrap and Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), and random forest (RF) models in spatial modeling and evaluation of head-cut gully erosion in Konduran watershed. For this purpose, based on an extensive field survey, the points of the head-cut of the gully erosion were identified first, and a map of the distribution of head-cut gully erosion in the study area was prepared. Then 18 variable identify and prepare as factors affecting the occurrence of head-cut gully erosion. To assess the efficiency of the models, receiver operating characteristics (ROC) and area under the curve (AUC) were used. The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms.
Fengjie Wang; Mehebub Sahana; Bahareh Pahlevanzadeh; Subodh Chandra Pal; Pravat Kumar Shit; Jalil Piran; Saeid Janizadeh; Shahab S. Band; Amir Mosavi. Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alexandria Engineering Journal 2021, 60, 5813 -5829.
AMA StyleFengjie Wang, Mehebub Sahana, Bahareh Pahlevanzadeh, Subodh Chandra Pal, Pravat Kumar Shit, Jalil Piran, Saeid Janizadeh, Shahab S. Band, Amir Mosavi. Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alexandria Engineering Journal. 2021; 60 (6):5813-5829.
Chicago/Turabian StyleFengjie Wang; Mehebub Sahana; Bahareh Pahlevanzadeh; Subodh Chandra Pal; Pravat Kumar Shit; Jalil Piran; Saeid Janizadeh; Shahab S. Band; Amir Mosavi. 2021. "Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility." Alexandria Engineering Journal 60, no. 6: 5813-5829.
Oscillating water column (OWC) is an advanced form of wave energy converter (WEC). This study aims at improving the efficiency of an amended OWC through a novel methodology for simulating several vertical plates within the chamber. This paper provides a numerical investigation considering one, two, three, and four vertical plates. The open field operation and manipulation (OpenFOAM) solver is verified based on the Reynolds-Averaged Navier–Stokes (RANS) equation. Results show the number and the position of plates where the convertor’s efficiency improves. The work undertaken here also revealed a reduction in the net force imposed on the convertor’s structure, especially the front wall. Consequently, adding plates acquires more efficiency with lower force on the system.
Mobin Masoomi; Mahdi Yousefifard; Amir Mosavi. Efficiency Assessment of an Amended Oscillating Water Column Using OpenFOAM. Sustainability 2021, 13, 5633 .
AMA StyleMobin Masoomi, Mahdi Yousefifard, Amir Mosavi. Efficiency Assessment of an Amended Oscillating Water Column Using OpenFOAM. Sustainability. 2021; 13 (10):5633.
Chicago/Turabian StyleMobin Masoomi; Mahdi Yousefifard; Amir Mosavi. 2021. "Efficiency Assessment of an Amended Oscillating Water Column Using OpenFOAM." Sustainability 13, no. 10: 5633.
Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies water during the drought period. The present research study focused on the delineation of potential groundwater zones in Saveh City, the northern part of the Markazi Province in Iran. The groundwater potential mapping was prepared using hybrid deep learning and machine learning algorithm of the boosted tree (BT), artificial neural network (ANN), deep learning neural network (DLNN), deep learning tree (DLT), and deep boosting (DB). This study was carried out by using fourteen groundwater potential conditioning factors and 349 each for springs and non-springs points. The performance of each model was validated through statistical analysis of sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and receiver operating characteristic (ROC)-area under curve (AUC) analysis. The validation result showed that the success rate of AUC is very good for the DB model (0.87–0.99) and other models are also good i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), and DLT (0.83–0.91). Among the several factors used in this study altitude, rainfall, distance to fault and soil types are the more important conditioning factors for groundwater potential modeling. Finally, all the models in this study had high efficiency in groundwater potential mapping, but it is recommended to use the Deep Boost model due to the better results in future studies. The result of this work will be useful to planners for optimal use and future planning of groundwater.
Yunzhi Chen; Wei Chen; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Behzad Adeli; Saeid Janizadeh; Adrienn A. Dineva; Xiaojing Wang; Amirhosein Mosavi. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International 2021, 1 -21.
AMA StyleYunzhi Chen, Wei Chen, Subodh Chandra Pal, Asish Saha, Indrajit Chowdhuri, Behzad Adeli, Saeid Janizadeh, Adrienn A. Dineva, Xiaojing Wang, Amirhosein Mosavi. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International. 2021; ():1-21.
Chicago/Turabian StyleYunzhi Chen; Wei Chen; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Behzad Adeli; Saeid Janizadeh; Adrienn A. Dineva; Xiaojing Wang; Amirhosein Mosavi. 2021. "Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential." Geocarto International , no. : 1-21.
The present research aims at evaluating the mechanical performance of untreated and treated crumb rubber concrete (CRC). The study was also conducted to reduce the loss in mechanical properties of CRC. In this study, sand was replaced with crumb rubber (CR) with 0%, 5%, 10%, 15%, and 20% by volume. CR was treated with NaOH, lime, and common detergent for 24 h. Furthermore, water treatment was also carried out. All these treatments were done to enhance the mechanical properties of concrete that are affected by adding CR. The properties that were evaluated are compressive strength, indirect tensile strength, unit weight, ultrasonic pulse velocity, and water absorption. Compressive strength was assessed after 7 and 28 days of curing. The mechanical properties were decreased by increasing the percentage of the CR. The properties were improved after the treatment of CR. Lime treatment was found to be the best treatment of all four treatments followed by NaOH treatment and water treatment. Detergent treatment was found to be the worse treatment of all four methods of treatment. Despite increasing the strength it contributed to strength loss.
Hamad Awan; Muhammad Javed; Adnan Yousaf; Fahid Aslam; Hisham Alabduljabbar; Amir Mosavi. Experimental Evaluation of Untreated and Pretreated Crumb Rubber Used in Concrete. Crystals 2021, 11, 558 .
AMA StyleHamad Awan, Muhammad Javed, Adnan Yousaf, Fahid Aslam, Hisham Alabduljabbar, Amir Mosavi. Experimental Evaluation of Untreated and Pretreated Crumb Rubber Used in Concrete. Crystals. 2021; 11 (5):558.
Chicago/Turabian StyleHamad Awan; Muhammad Javed; Adnan Yousaf; Fahid Aslam; Hisham Alabduljabbar; Amir Mosavi. 2021. "Experimental Evaluation of Untreated and Pretreated Crumb Rubber Used in Concrete." Crystals 11, no. 5: 558.
This study investigates the effect of art on promoting the meaning of the urban space. After considering the semantic dimension of the urban space and the mechanism of transferring the meanings of art through the views of experts, a model is presented for examining the art’s cooperation in promoting urban space meaning. In the first stage, the categories of space meanings influenced by art were extracted using the qualitative method of interpretative phenomenological analysis, and by examining 61 in-depth interviews in 6 urban spaces eligible for urban art in Tehran. In the second stage, these categories were surveyed in these spaces through 600 questionnaires after converting to the questionnaire items. Based on the results, “experience and perception capability”, “social participation”, and “relationship with context” were the main themes of the semantic relationships between art and urban space. Further, the lower scores related to the theme of “social participation” in the quantitative investigations indicate that this theme was weaker than the other themes in promoting the meaning of the urban space through the art in the selected urban spaces.
Mehrdad Karimimoshaver; Bahare Eris; Farshid Aram; Amir Mosavi. Art in Urban Spaces. Sustainability 2021, 13, 5597 .
AMA StyleMehrdad Karimimoshaver, Bahare Eris, Farshid Aram, Amir Mosavi. Art in Urban Spaces. Sustainability. 2021; 13 (10):5597.
Chicago/Turabian StyleMehrdad Karimimoshaver; Bahare Eris; Farshid Aram; Amir Mosavi. 2021. "Art in Urban Spaces." Sustainability 13, no. 10: 5597.
Numerous research studies have been conducted to improve the weak properties of recycled aggregate as a construction material over the last few decades. In two-stage concrete (TSC), coarse aggregates are placed in formwork, and then grout is injected with high pressure to fill up the voids between the coarse aggregates. In this experimental research, TSC was made with 100% recycled coarse aggregate (RCA). Ten percent and twenty percent bagasse ash was used as a fractional substitution of cement along with the RCA. Conventional concrete with 100% natural coarse aggregate (NCA) and 100% RCA was made to determine compressive strength only. Compressive strength reduction in the TSC was 14.36% when 100% RCA was used. Tensile strength in the TSC decreased when 100% RCA was used. The increase in compressive strength was 8.47% when 20% bagasse ash was used compared to the TSC mix that had 100% RCA. The compressive strength of the TSC at 250 °C was also determined to find the reduction in strength at high temperature. Moreover, the compressive and tensile strength of the TSC that had RCA was improved by the addition of bagasse ash.
Muhammad Javed; Afaq Durrani; Sardar Kashif Ur Rehman; Fahid Aslam; Hisham Alabduljabbar; Amir Mosavi. Effect of Recycled Coarse Aggregate and Bagasse Ash on Two-Stage Concrete. Crystals 2021, 11, 556 .
AMA StyleMuhammad Javed, Afaq Durrani, Sardar Kashif Ur Rehman, Fahid Aslam, Hisham Alabduljabbar, Amir Mosavi. Effect of Recycled Coarse Aggregate and Bagasse Ash on Two-Stage Concrete. Crystals. 2021; 11 (5):556.
Chicago/Turabian StyleMuhammad Javed; Afaq Durrani; Sardar Kashif Ur Rehman; Fahid Aslam; Hisham Alabduljabbar; Amir Mosavi. 2021. "Effect of Recycled Coarse Aggregate and Bagasse Ash on Two-Stage Concrete." Crystals 11, no. 5: 556.
In the current paper, the efficiency of three new standalone data mining algorithms [e.g., M5P, Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of Bagging, BA (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier, ASC (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from 1979-2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all the data were used (i.e., R and Q –with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, the BA-M5P outperformed the other models.
Khabat Khosravi; Ali Golkarian; Martijn J. Booij; Rahim Barzegar; Wei Sun; Zaher M. Yaseen; Amir Mosavi. Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms. Hydrological Sciences Journal 2021, 1 .
AMA StyleKhabat Khosravi, Ali Golkarian, Martijn J. Booij, Rahim Barzegar, Wei Sun, Zaher M. Yaseen, Amir Mosavi. Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms. Hydrological Sciences Journal. 2021; ():1.
Chicago/Turabian StyleKhabat Khosravi; Ali Golkarian; Martijn J. Booij; Rahim Barzegar; Wei Sun; Zaher M. Yaseen; Amir Mosavi. 2021. "Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms." Hydrological Sciences Journal , no. : 1.
The research deals with the effect of skylines on citizens' pleasantness. The research method is based on the respondents' judgment of the color images of the skylines. 360 citizens were asked to complete a questionnaire to express their opinions and preferences along with the reasons. Three types of nature, traditional, and contemporary skylines were identified as the dominant skylines. The results showed that people prefer the nature and the traditional skyline over the contemporary skyline. They introduced some features as peacefulness, memorability, and distinctiveness as the reasons for their choice. The people's residence place could influence their attitudes toward the skyline, and most of those living in the areas with contemporary contexts selected the skyline of their contemporary context as the favorite skyline. They did not look for the sense of peacefulness in the skyline, but they underlined attractiveness. Variables of age and gender had no effect on the preferences; however, by an increase in education level, the tendency to select the traditional and contemporary skyline increased.
Mehrdad Karimimoshaver; Mastooreh Parsamanesh; Farshid Aram; Amir Mosavi. The impact of the city skyline on pleasantness; state of the art and a case study. Heliyon 2021, 7, e07009 .
AMA StyleMehrdad Karimimoshaver, Mastooreh Parsamanesh, Farshid Aram, Amir Mosavi. The impact of the city skyline on pleasantness; state of the art and a case study. Heliyon. 2021; 7 (5):e07009.
Chicago/Turabian StyleMehrdad Karimimoshaver; Mastooreh Parsamanesh; Farshid Aram; Amir Mosavi. 2021. "The impact of the city skyline on pleasantness; state of the art and a case study." Heliyon 7, no. 5: e07009.
The polymer solar cells also known as organic solar cells (OSCs) have drawn attention due to their cynosure in industrial manufacturing because of their promising properties such as low weight, highly flexible, and low-cost production. However, low η restricts the utilization of OSCs for potential applications such as low-cost energy harvesting devices. In this paper, OSCs structure based on a triple-junction tandem scheme is reported with three different absorber materials to enhance the absorption of photons which in turn improves the η, as well as its correlating performance parameters. The investigated structure gives the higher value of η = 14.33% with Jsc = 16.87 (mA/m2), Voc = 1.0 (V), and FF = 84.97% by utilizing a stack of three different absorber layers with different band energies. The proposed structure was tested under 1.5 (AM) with 1 sun (W/m2). The impact of the top, middle, and bottom subcells’ thickness on η was analyzed with a terse to find the optimum thickness for three subcells to extract high η. The optimized structure was then tested with different electrode combinations, and the highest η was recorded with FTO/Ag. Moreover, the effect of upsurge temperature was also demonstrated on the investigated schematic, and it was observed that the upsurge temperature affects the photovoltaic (PV) parameters of the optimized cell and η decreases from 14.33% to 11.40% when the temperature of the device rises from 300 to 400 K.
Kamran Bangash; Syed Kazmi; Waqas Farooq; Saba Ayub; Muhammad Musarat; Wesam Alaloul; Muhammad Javed; Amir Mosavi. Thickness Optimization of Thin-Film Tandem Organic Solar Cell. Micromachines 2021, 12, 518 .
AMA StyleKamran Bangash, Syed Kazmi, Waqas Farooq, Saba Ayub, Muhammad Musarat, Wesam Alaloul, Muhammad Javed, Amir Mosavi. Thickness Optimization of Thin-Film Tandem Organic Solar Cell. Micromachines. 2021; 12 (5):518.
Chicago/Turabian StyleKamran Bangash; Syed Kazmi; Waqas Farooq; Saba Ayub; Muhammad Musarat; Wesam Alaloul; Muhammad Javed; Amir Mosavi. 2021. "Thickness Optimization of Thin-Film Tandem Organic Solar Cell." Micromachines 12, no. 5: 518.
Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.
Saeed Nosratabadi; Sina Ardabili; Zoltan Lakner; Csaba Mako; Amir Mosavi. Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS. Agriculture 2021, 11, 408 .
AMA StyleSaeed Nosratabadi, Sina Ardabili, Zoltan Lakner, Csaba Mako, Amir Mosavi. Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS. Agriculture. 2021; 11 (5):408.
Chicago/Turabian StyleSaeed Nosratabadi; Sina Ardabili; Zoltan Lakner; Csaba Mako; Amir Mosavi. 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS." Agriculture 11, no. 5: 408.