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Background: Insurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology. Methods: A secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020. Results: The analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories. Conclusions: Restorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance. [ABSTRACT FROM AUTHOR]

The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants' mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70–30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants. [ABSTRACT FROM AUTHOR]

Purpose: The cation exchange capacity (CEC) is a pivotal soil attribute that influences soil chemistry, fertility, and productivity. Nevertheless, the conventional techniques employed for CEC measurements present challenges in terms of complexity, cost, and laboriousness. Hence, there is a demand for expedited, cost-effective, streamlined alternative methodologies that can yield accurate outcomes. The objective of this study was to employ and compare various techniques, including Pedotransfer Functions (PTFs) based on fundamental soil properties, support vector regression (SVR), sequential orthogonalized partial least squares (SOPLS) as a multiblock data analysis method, and Spectrotransfer Function (SPTF) utilizing visible-near infrared (VNIR) and mid infrared (MIR) diagnostic wavelengths to estimate the CEC of calcareous soils with diverse land uses in the semi-arid region of Fars province, Iran. Materials and methods: A total of 130 samples were gathered from the soils of the study region, CEC was measured using sodium acetate, and the spectral reflectance in the VNIR and transmission in the MIR regions were measured, and prediction models were created using linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), partial least squares regression (PLSR), and multiblock data analysis algorithms, after different spectral preprocessing methods. Results and discussion: The results generally indicated that spectroscopy models performed better than PTFs in predicting CEC with the multiblock SOPLS showing the best results (R2 = 0.92, RMSE = 1.67 cmol(+) kg−1, and RPIQ = 4.34). The performance of the models followed the order: SOPLS > SPTF > L-SVR > RBF-SVR. Conclusion: Our findings indicate that spectroscopy coupled with SOPLS analysis can be a robust, viable, fast, cheap, and efficient alternative assessment method with acceptable accuracy for estimating soil CEC in calcareous soils, instead of the difficult, costly, and cumbersome conventional measurement approaches or other estimation methods. [ABSTRACT FROM AUTHOR]

Karst resources are sensitive to environmental changes, especially climate change. In this research, monthly data (years 1994–2020) were collected for five karstic springs, namely Famaseb (Sp1), Faresban (Sp2), Ghalebaroodab (Sp3), Giyan (Sp4), and Gonbadkabood (Sp5), located in the Nahavand plain, west of Iran. Data mining models such as KNN, SVM, and M5tree were used to simulate the discharge of springs. The results obtained, based on two statistical indices, correlation coefficient (r) and normalized root mean square error (nRMSE), the M5tree model was more accurate than the other models and was selected to simulate the discharge of the springs. The r-value was equal to 0.736, and nRMSE was 0.113. In the later stage, discharge of the springs was projected for four time periods: 2021–2040, 2041–2060, 2061–2080, and 2081–2100 under two scenarios, RCP8.5 and RCP4.5. The results showed that in the monthly survey, the ratio of the variation in the discharge of the springs compared to the historical period in the Sp1 and Sp2 springs had the most variations. It was found that in November, there was an average increase of + 250% in the investigated periods. Also, the highest decrease compared to the historical period of these two springs was observed during May, which were about − 37 and − 70%, respectively. In springs Sp3, Sp4, and Sp5, like the previous two springs, the most fluctuations occurred in November and May, respectively. [ABSTRACT FROM AUTHOR]

Background: Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD. Methods: The research data were obtained from a multi-center study, namely the Iran-premature coronary artery disease (I-PAD). The current study used the medical records of 415 patients with CAD hospitalized in Razi Hospital, Birjand, Iran, between May 2016 and June 2019. A total of 43 variables that affect CAD were selected, and the relevant data was extracted. Once the data were cleaned and normalized, they were imported into SPSS (V26) for analysis. The present study used the ANN technique. Results: The study revealed that 48% of the study population had a history of CAD, including 9.4% with premature CAD and 38.8% with CAD. The variables of age, sex, occupation, smoking, opium use, pesticide exposure, anxiety, sexual activity, and high fasting blood sugar were found to be significantly different among the three groups of CAD, premature CAD, and non-CAD individuals. The neural network achieved success with five hidden fitted layers and an accuracy of 81% in non-CAD diagnosis, 79% in premature diagnosis, and 78% in CAD diagnosis. Anxiety, acceptance, eduction and gender were the four most important factors in the ANN model. Conclusions: The current study shows that anxiety is a high-prevalence risk factor for CAD in the hospitalized population. There is a need to implement measures to increase awareness about the psychological factors that can be managed in individuals at high risk for future CAD. [ABSTRACT FROM AUTHOR]

In most arid and semi-arid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes influencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these fluctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the fluctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indices in a 6-month time window for a long-term statistical period (1989–2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immediately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coefficient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indices, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the significance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future. [ABSTRACT FROM AUTHOR]

Purpose: The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials. Design/methodology/approach: This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021. Findings: The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation. Originality/value: This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity. [ABSTRACT FROM AUTHOR]

Objective: A more equal allocation of healthcare funds for patients who must pay high costs of care ensures the welfare of society. This study aimed to estimate the optimal co-insurance for outpatient drug costs for health insurance. Setting: The research population includes outpatient prescription claims made by the Health Insurance Organization that outpatient prescriptions in a timely manner in 2016, 2017, 2018, and 2019 were utilized to calculate the optimal co-insurance. The study population was representative of the research sample. Design: At the secondary level of care, 11 features of outpatient claims were studied cross-sectionally and retrospectively using data mining. Optimal co-insurance was estimated using Westerhut and Folmer's utility model. Participants: One hundred ninety-three thousand five hundred fifty-two individuals were created from 21 776 350 outpatient claims of health insurance. Because of cost-sharing, insured individuals in a low-income subsidy plan and those with refractory diseases were excluded. Results: Insureds were divided into three classes of low, middle, and high risk based on IQR and were separated to three clusters using the silhouette coefficient. For the first, second, and third clusters of the low-risk class, the optimal co-insurance estimates are 0.81, 0.76, and 0.84, respectively. It was equal to one for all middle-class clusters and 0.38, 0.45, and 0.42, respectively, for the high-risk class. The insurer's expenses were altered by $3,130,463, $3,451,194, and $ 1,069,859 profit for the first, second, and third clusters, respectively, when the optimal co-insurance strategy is used for the low-risk class. For middle risks, it was US$29,239,815, US$13,863,810, and US$ 14,573,432 while for high risks, US$4,722,099, US$ 6,339,317, and US$19,627,062, respectively. Conclusions: These findings can improve vulnerable populations' access to costly medications, reduce resource waste, and help insurers distribute funds more efficiently. [ABSTRACT FROM AUTHOR]

Introduction Wegener granulomatosis (WG) appears with clinical symptoms, including recurrent respiratory infection, renal manifestations, and nonspecific systemic symptoms. Objective To study the clinical manifestations of WG in Iranian ethnicities, and data on 164 patients were recorded from 2013 to 2018. Methods The data included demographics, symptoms, and the Birmingham Vasculitis Activity Score (BVAS). The symptoms involved the following sites: the nose, sinus, glottis, ears, lungs, kidneys, eyes, central nervous system, mucous membranes, skin, heart, stomach, intestine, as well as general symptoms. The clinical manifestations of nine ethnicities were analyzed. Results In total, 48% of the patients were male and 51% were female, with a median age of 51 years. The BVAS was of 15.4, the sites most involved were the sinus (n = 155), nose (n = 126), lungs (n = 125), and ears (n = 107). Gastrointestinal (n = 14) and cardiac (n = 7) involvement were less common. Among the patients, 48.17% were Persian, 13.41% were Azari, 11.17% were Gilaki, 11.17% were Kurd, and 10.9% were Lor. Conclusion Our findings indicated that the sinus, nose, lungs, and ears were the sites most involved, and gastrointestinal and cardiac involvement were less common. In the present study, involvement of the upper and lower respiratory tract was higher than that reported in Western and Asian case series. Moreover, we report for the first time that, in all patients with ear involvement, the left ear was the first to be affected. The clinical manifestations among Iranian ethnicities were not different, and the Gilaki ethnicity had the highest BVAS, mostly because the weather was humid; therefore, in Iran, in areas with humidity, the rate of the disease was higher. [ABSTRACT FROM AUTHOR]

Bank customer validation is carried out with the aim of providing a series of services to users of a bank and financial institutions. It is necessary to perform various analytical methods for user's accounts due to the high volume of banking data. This research works in the field of money laundering detection from real bank data. Banking data analysis is a complex process that involves information gathered from various sources, mainly in terms of personality, such as bills or bank account transactions which have qualitative characteristics such as the testimony of eyewitnesses. Operational or research activities can be greatly improved if supported by proprietary techniques and tools, due to the vast nature of this information. The application of data mining operations with the aim of discovering new knowledge of banking data with an intelligent approach is considered in this research. The approach of this research is to use the spiking neural network (SNN) with a group of sparks to detect money laundering, but due to the weakness in accurately identifying the characteristics of money laundering, the maximum entropy principle (MEP) method is also used. This approach will have a mapping from clustering and feature extraction to classification for accurate detection. Based on the analysis and simulation, it is observed that the proposed approach SNN-MFP has 87% accuracy and is 84.71% more functional than the classical method of using only the SNN. In this analysis, it is observed that in real banking data from Mellat Bank, Iran, in its third and fourth data, with a comprehensive analysis and reaching different outputs, there have been two money laundering cases. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a leading cause of cancer-related deaths, emphasizing the importance of timely diagnosis for effective treatment. Machine learning models have shown promise in assisting with GC diagnosis. Objectives: This study aimed at comparing the performance of various feature selection methods in identifying influential factors related to GC based on lifestyle using machine learning models. The ultimate goal was to enhance early detection and treatment of the disease. Methods: The data of patients from Shahid Ayatollah Modarres Hospital and Shohadaye Tajrish Hospital between 2013 and 2021 were utilized. Three feature selection methods (filter, wrapper, and filter-wrapper) were employed. The k-fold method validated each model. Four classifiers k Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) compared their outputs based on feature selection methods. Results: The filter-wrapper method outperformed others, achieving an area under the ROC curve and Fl score of 95.8% and 94.7%, respectively. GBDT also performed well. The wrapper and RF classifiers achieved an area under the ROC curve and Fl scores of 95.7% and 93.6%, respectively, after the filter-wrapper method. Without feature selection methods, the RF classifier had an area under the ROC curve and F1 scores of 95.6% and 91.7%, respectively, surpassing other classifiers. Conclusions: This study suggests that appropriate feature selection methods for identifying influential factors related to GC based on lifestyle can facilitate early diagnosis and treatment. The filter-wrapper method demonstrated the best performance in this regard. [ABSTRACT FROM AUTHOR]

Mazandaran province in northern Iran is one of the fire-prone areas in the country in which a wide area of its natural resources have been destroyed by fire in recent years. This research aimed to detect the spatiotemporal relationships between climatic variables and fire regimes in Mazandaran province in recent decades. The fire variables (dependent variables) were the number and area of fires. The climatic variables (independent variables) were seasonal temperature mean, seasonal maximum temperature mean, seasonal absolute maximum temperature, seasonal precipitation mean, seasonal relative humidity mean, seasonal wind speed mean, and seasonal maximum wind speed mean for 26 years (1996–2021). Pearson's correlation coefficient and regression models were used to investigate the temporal relationship between fire and climatic variables during study period. Data mining models were used to detect the spatial relationship between fire ignition and climatic parameters and to produce the fire danger maps. The fire occurrence map was obtained from Mazandaran Natural Resources and Watershed Administration and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The climatic maps were obtained by interpolation methods in GIS. The weight of climatic parameters in fire ignition was determined using MDG and MDA statistics from random forest (RF) algorithm. Then different data mining models (logistic regression, random forest, support vector machine, and SVM-RF ensemble model) and 70% of actual fires were used for modeling fire danger in R software. The area under the curve and 30% of actual fires were applied for accuracy assessment of the models. Results of temporal relationships indicated that there are significant relationships among the number of fires and seasonal absolute maximum temperature, seasonal precipitation mean, and seasonal relative humidity mean. On the other hand, a significant relationship was observed between the area of fires and seasonal temperature mean. The results of spatial relationship demonstrated that seasonal temperature mean, seasonal precipitation mean, and seasonal relative humidity mean had the greatest spatial importance in fire ignition. The results of accuracy assessment of fire danger models indicated that SVM-RF and RF models were the best models for fire danger mapping. Therefore, using the maps obtained from these models, it is possible to predict the climate-caused fires in natural ecosystems of Mazandaran province. [ABSTRACT FROM AUTHOR]

Background: Diagnosing patient deterioration and preventing unexpected deaths in the emergency department is a complex task that relies on the expertise and comprehensive understanding of emergency physicians concerning extensive clinical data. Objectives: Our study aimed to predict emergency department mortality and compare different models. Methods: During a one-month period, demographic information and records were collected from 1,000 patients admitted to the emergency department of a selected hospital in Tehran. We rigorously followed The Cross Industry Standard Process for data mining and methodically progressed through its sequential steps. We employed Cat Boost and Random Forest models for prediction purposes. To prevent overfitting, Random Forest feature selection was employed. Expert judgment was utilized to eliminate features with an importance score below 0.0095. To achieve a more thorough and dependable assessment, weimplemented a K-fold cross-validation method with a value of 5. Results: The Cat Boost model outperformed Random Forest significantly, showcasing an impressive mean accuracy of 0.94 (standard deviation: 0.03). Ejection fraction, urea (body waste materials), and diabetes had the greatest impact on prediction. Conclusions: This study sheds light on the exceptional accuracy and efficiency of machine learning in predicting emergency department mortality, surpassing the performance of traditional models. Implementing such models can result in significant improvements in early diagnosis and intervention. This, in turn, allows for optimal resource allocation in the emergency department, preventing the excessive consumption of resources and ultimately saving lives while enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

BACKGROUND: The data mining of construction accidents based on a robust modeling process can be used as a practical technique for reducing the frequency of construction accidents. OBJECTIVE: This study was designed to data-mine construction accidents. METHODS: This study was conducted in 2020 on construction accidents in Iran for ten years (2009–2018). The instruments to collect the required data were the checklists and descriptive reports of the accidents. The dependent variables of the study included reactive safety indicators related to construction accidents (lost working days (LWD) and total accident costs (TAC)). The independent variables consisted of four latent factors: personal variables, organizational variables, unsafe working conditions, and unsafe acts. The data were collected based on the conceptual model designed for data mining. The data mining process was carried out based on the structural equation modeling by IBM AMOS V. 23.0. RESULTS: A total of 5742 construction accidents occurring in 10 years were analyzed. The means of TAC and LWD indicators were estimated to be 248.20±52.60 days and 1893.10±152.22 $. These two indicators directly correlated with the two latent factors of unsafe conditions and unsafe acts and their related variables and were indirectly influenced by latent personal and organizational factors. The relationship between unsafe conditions and unsafe acts was significantly positive. The relationship between latent personal and organizational factors and the two construction accident indicators was significantly negative (p <0.05). CONCLUSION: The model results showed that personal and organizational variables could, directly and indirectly, affect reactive safety indicators in construction projects. Thus, these findings can be used to design and improve safety strategies to prevent and decrease construction accidents and incidents. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of this study is to evaluate the impact of Persian Scientific Papers (PSPs) on Persian Wikipedia by studying Wikipedia's citations to these papers. Design/methodology/approach: The present study is applied research, which has been performed by the web-mining method, such as downloading web pages, extracting information (references), identifying papers, detecting peer-review journals and calculating the frequency rates. The statistical population included 10,000 Persian Wikipedia Pages (PWPs) that were analyzed in two rounds with a six-month interval. Findings: The number of pages containing the Persian references section was 3,994 and 4,063 out of the 10,000 pages extracted in the first and second rounds. The ratio of pages that cited scientific sources (58 and 67 pages) to the pages extracted from the PWP was equal to 0.58 and 0.67%. The ratio of pages that cited scientific sources to pages with Persian references in each round was equal to 1.45 and 1.64%. The number of references extracted from the PWP in each round equaled 30,441 and 35,891. Eight titles from reputable Persian journals had received at least three citations from Wikipedia. Originality/value: The present study has determined the extent of interaction between science and society (knowledge flow) in the form of citations from Wikipedia articles to articles in peer-reviewed journals. The study of this issue in Persian Wikipedia in more than 2000 Persian peer-reviewed journals shows the originality of the present paper. Studying citation reliability in a collaborative and openly editable platform is another originality of the work. [ABSTRACT FROM AUTHOR]

Background: Allogenic hematopoietic stem cell transplantation is considered as an effective treatment for patients with acute myeloid leukemia. However, complications of transplantation, like aGVHD, affect the efficiency of allogenic hematopoietic stem cell transplantation. The present study aimed to implement different models of data mining (DM) (single and ensemble) for prediction of allogenic hematopoietic stem cell transplantation in patients with acute myeloid leukemia (transplantation against host disease). Method: We conducted this developmental study on 94 patients with 34 attributes in Taleghani Hospital, Tehran, Iran, during 2009--2017. In this practical study, data were analyzed via decision tree (DT) algorithms, including decision tree, random forest and gradient boosting (ensemble learning), artificial neural network (Single Learning), and support vector machine. Some criteria, like specificity, accuracy, Fmeasure, AUC (area under curve), and sensitivity, were reported in order to evaluate DT algorithms. Results: There were 34 transplantation-related variables; some predictors, such as liver, hemoglobin, and donor blood group, were found to be the most important ones. To predict aGVHD, the two selected algorithms included the most appropriate DM models, artificial neural network and support vector machine classifiers, with ROC of 100. Conclusion: This study indicated that DT algorithms could be successfully used for approving the efficiency of the models predicting allogenic hematopoietic stem cell transplantation. [ABSTRACT FROM AUTHOR]

Background: Trauma registries collect and document data about the acute injury care in hospitals. The goal of trauma care systems is to reduce injury occurrence and enhance trauma patient survival rates. Objectives: In this article, the Kashan trauma registry was used to predict trauma patient discharge status using machine learning. Methods: This study employed 3930 Kashan Trauma Centre Registry entries after preprocessing. The study experimented with decision trees of varying complexity, using three separate metrics - information gain, Gini index, and gain ratio - to build and evaluate the trees. Finally, bagging, boosting and stacking ensemble learning techniques were implemented to evaluate their predictive performance. Ensemble learning models were developed based on decision trees of varying depths that utilized different learning measures/metrics. The predictive performance of the algorithms was evaluated using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC). This study aimed to compare ensemble-learning techniques like bagging, boosting and stacking to decision trees configured with various parameter settings, to assess their ability to predict trauma patients' discharge status outcomes. Results: The stacking technique, which used decision tree algorithms (depth=5) that integrated parameters like information gain, gain ratio and Gini index at the base level along with KNN (k=12) using Euclidean distance, and then incorporated logistic regression as the meta-classifier, demonstrated superior predictive performance compared to using individual decision trees, bagging or boosting approaches alone. Conclusion: However, while decision trees are straightforward algorithms and ensemble methods are more time-consuming and computationally complex, this study indicates that stacking learning is superior to single decision tree methods with a variety of parameters, bagging, and boosting. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to identify the impact of demographical and organizational variables such as age, gender, experiences use of knowledge management system (KMS), education and job level on knowledge sharing (KS) performance of knowledge workers in knowledge activities of a KMS. Specifically, it seeks to explore that is there any relationship between the KS behavior patterns of high KS performance knowledge workers with their performance. Furthermore, this study using its conceptual attitude model aims to show that whether knowledge workers' behavior patterns in sharing information and knowledge throughout a KMS have any specific effect or not. Design/methodology/approach: This paper proposed a framework to mine knowledge workers' raw data using data mining techniques such as clustering and association rules mining. Also, this research uses a case-based approach to a knowledge-intensive company in Iran that works in the field of information technology with 730 numbers of workers. Findings: Findings suggest that demographical and organizational variables such as age, education and experience use of KMS have positive effects on knowledge worker's KS behavior in KMSs. In fact, people who have lower age, higher education degrees and more experience use of KMS, have more participation in KS in KMS. Also, results depict that the experienced use of KMS has the most impact on the intention of KS in this KMS. Findings emphasize on the importance of the influence of the behavioral, organizational environments and psychological factors such as reward system, top management support, openness and trust, on KS performance of knowledge workers in the KMS. In fact, according to data, the KMS reward system caused to increasing participation of the users in KS, also in each knowledge activity that top managers participate in, the scores were higher. Practical implications: This research helps top managers in designing policies and strategies to improve the participation of knowledge workers in KS and helps human resource managers to improve their membership policies. Also, assist Information Technology (IT) managers to enhance KMSs' design to leverage with organization strategies in the field of improving KS and encourage people to participate in KMS. Originality/value: This research has two key values. First, this paper applies a data mining framework to mining and analyzing data and this paper uses actual data of a KMS in a specialist company in Iran, with about 27,740 real data points. Second, this paper investigates the impact of demographical and organizational attributes on KS behavior, which little is empirically known about the impact of demographical variables on KS intention. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is still an ongoing requirement for variety of applications such as land use management plans. The central objective of the present research was to investigate the effect of using ensemble machine learning methods for developing accurate landslide prediction. We aimed to explore and compare three techniques, namely the random forests, support vector machine and multiple-layer neural networks with an adaptive neuro-fuzzy inference system, which incorporates three metaheuristic methods including grey wolf optimization, particle swarm optimization, and shuffled frog leaping algorithm for landslide susceptibility assessment in the East Azerbaijan of Iran. Also, two ensemble ways (voting and stacking) were used in final decision stage. A sum of 766 locations with landslide inventory was recognized in the context of the study. Then the all models were trained using tenfold cross-validation technique. Lastly, the receiver operating characteristic and statistical procedures were employed to validate and contrast the predictive capability of the models. The findings of the study show the ANFIS–PSO model had high performance on the validation dataset (AUC = 0.89). Besides, the study revealed that using stacking ensemble technique could increase the predicting capability of all models (AUC = 0.911). [ABSTRACT FROM AUTHOR]

Introduction: Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist. Aim: Investigating the association between CVDs and anthropometric measurements among Iranian adults. Methods: For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models. Results: During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%). Conclusion: BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction. [ABSTRACT FROM AUTHOR]

Badlands are landforms related to runoff, with dissected V-shaped valleys, short steep slopes, and high drainage density, and results from a very important type of erosion that develops due to a complex interaction of conditioning factors, including climatic, hydrologic, geologic and soil properties, topographic characteristics, and land use. The main goals of this study were (1) create badland susceptibility maps of the Firozkuh watershed and five machine learning algorithms (models)—functional discriminant analysis (FDA), generalized linear model (GLM), mixture discriminant analysis (MDA), multivariate adaptive regression spline (MARS), and support vector machine (SVM); and (2) compare the accuracy of these models. Sixteen conditioning factors were chosen to model and classify badland susceptibility based on a literature review, data availability, and field surveys. Model accuracy was assessed using ROC curve and AUC analyses. The analyses showed that SVM was "excellent," MARS was "very good," MDA was "good," and GLM and FDA were "moderate" in classification accuracy. The land area of the very high and high classes ranged from 31 to 51% of the Firozkuh watershed for the GLM and SVM models, respectively. This indicates that badland erosion is a very important problem in the study area. Climatic, hydrologic, geologic, topographic, and soil conditions as well as land use changes render the Firozkuh watershed prone to badland formation and soil erosion which results in substantial socioeconomic losses. Badland susceptibility mapping is an important tool that can be used to improve managing future badland erosion in the Firozkuh watershed and other areas affected by badland erosion. [ABSTRACT FROM AUTHOR]

Research information databases and search engines are one of the main resources used by researchers every day. To accurately retrieve information from these databases, data need to be stored correctly. Manual controlling of data quality is costly and time consuming. Here we suggest data mining methods for controlling the quality of a research database. To this end, common errors that are seen in a database should be collected. Metadata of every record in addition to its error codes is saved in a dataset. Statistics and data mining methods are applied to this dataset and patterns of errors and their relationships are discovered. Here we considered Iran’s scientific information database (Ganj) as a case study. Experts defined 59 errors. Intimate features of every record, such as its subject, authors’ names and name of the university, with its error codes were saved in a dataset. The dataset containing 41021 records was formed. Statistics methods and association rules were applied to the dataset and the relationship between errors and their pattern of repetition were discovered. Based on our results, in average by considering 25% of errors in every subject, up to 80% of errors of all the records in a subject are covered. All the records were also clustered using K-means clustering. Although there was some similarity between records of different subjects, there was not seen any evident relationship between the pattern of repetition of the errors and the subject of records. [ABSTRACT FROM AUTHOR]

Distresses are integral parts of pavement that occur during the life of the road. Bitumen distress is known as one of the most important problems of Iran's roads, especially in tropical areas and transit routes with heavy axes; so, identifying the effective factors in creating the bleeding phenomenon is very necessary and important. Therefore, this study was conducted to investigate the parameters of the mixing design in creation of bleeding phenomenon and its severity. The collected data were then analyzed and grouped using Design Expert and SPSS software. The results show that all five parameters of optimal bitumen percent, bitumen percent in asphalt mixture, void percent of Marshall Sample, percent void and filler to bitumen ratio are effective on bitumen and its intensity. Among the mentioned parameters, two parameters of percent of bitumen compared to asphalt mixture and the void percent in the Marshall sample have a greater effect on the severity of the bleeding phenomenon. [ABSTRACT FROM AUTHOR]

Background: Diagnostic tests are important in clinical medicine. To determine the test performance indices — test sensitivity, specificity, likelihood ratio, predictive values, etc. — the test results should be compared against a gold-standard test. Herein, a technique is presented through which the aforementioned indices can be computed merely based on the shape of the probability distribution of the test results, presuming an educated guess. Methods: We present the application of the technique to the probability distribution of hepatitis B surface antigen measured in a group of people in Shiraz, southern Iran. We assumed that the distribution had two latent subpopulations — one for those without the disease, and another for those with the disease. We used a nonlinear curve fitting technique to figure out the parameters of these two latent populations based on which we calculated the performance indices. Results: The model could explain > 99% of the variance observed. The results were in good agreement with those obtained from other studies. Conclusion: We concluded that if we have an appropriate educated guess about the distributions of test results in the population with and without the disease, we may harvest the test performance indices merely based on the probability distribution of the test value without need for a gold standard. The method is particularly suitable for conditions where there is no gold standard or the gold standard is not readily available. [ABSTRACT FROM AUTHOR]

Background: As a common disease among people of almost any age, allergic rhinitis has many adverse effects such as lowering the quality of life and efficiency at work or school. Considering these conditions and the collection of large amounts of data, the present research was conducted on allergic rhinitis and asthma patients' data to extract the common symptoms of these diseases using cluster analysis and the k-means algorithm. Materials and Methods: The present cross-sectional research was conducted in Mashhad city. The inclusion criteria were affliction with one or two respiratory allergy diseases diagnosed by an allergy specialist through clinical history taking and physical examination. A researcher-made checklist was used in the present study for data collection. Then, the K-means algorithm's cluster analysis model was conducted to extract clusters (WEKA software (3, 6, 9)). Results: Overall, 1,231 patients met the inclusion criteria. The result of the Cluster analysis consisted of 1: Cluster 1 in allergic rhinitis consisted of 702 patients, and cluster 2 consisted of 382 patients. 2: 46 asthma patients were assigned to cluster 1 and 23 to cluster 2. 3: Also, 60 asthma and allergic rhinitis patients were assigned to cluster 1 and 19 to cluster 2. The most common symptoms in all patients were rhinorrhea, sneezing, nasal congestion, and itchy nose. Conclusion: Overall, Salsola kali was the most common allergen in allergic rhinitis and asthma patients. Also, the most common symptoms in patients are rhinorrhea, sneezing, itchy nose, and nasal congestion. This study can help physicians diagnose allergic rhinitis and asthma in geographical areas with a high prevalence of Salsola kali. [ABSTRACT FROM AUTHOR]

Background & Aims: In December 2019, the corona virus in China's Wuhan city, the center of Hubei province, and spread very quickly and completely involved society members and government bodies in all countries. On the other hand, today, with the existence of a new interface called social media, most of the natural conversations and expressions of people with each other and in small and large groups are recorded, and therefore, with the help of data mining and text mining tools, it is possible to analyze big data. Very close to the real estimates of what is going on in the public mind. The use of these emerging methods of computational analysis can provide an objective picture of what is happening in society in front of the eyes of the managers, and in this way, increase the quality of the analysis of managers and decision-makers lead to better policies and planning, and so on. These models can provide a basis for evaluating the effectiveness of adopted policies and programs. But in general, it can be said that at a time when there is still no clear understanding of the help that intelligent data mining can have by using cognitive approaches, among society members and managers of the country, the existence of such research can fulfill this understanding, expectation a, and demand. It created among society and its managers that, in principle, perceptions, and decisions should be made using methods that rely on objective big data. From this point of view, the importance of the current research is doubled in terms of the impact it will have in clarifying and introducing such methods and its macro decision makers. It is obvious that the error in perception is caused by unconscious perceptual biases, generalizing parts of society to the whole society, not being up-to-date with the changes and developments of social beliefs and ruling based on what was perceived in the past, and many other perceptual errors of this kind., leads to decisions, criticisms, and, wrong behaviors that both lose resources and opportunities and waste the capital and public trust of the society finally, leads to decisions and policies that are not only ineffective but sometimes leave destructive and harmful effects on society and solve the crisis. Theoretically, this type of analysis is new in the space of media analysis, and the aspects that this research deals with the natural crisis of Corona are new and, of course, very practical. The combined use of artificial intelligence and cognitive science in analysis has a lot of room for growth and this research can encourage interdisciplinary research and theoretical activities in this field. From a practical point of view, the results of this research will be very useful and practical for all those who are involved in the field of macro policies, media people, also related business owners. The presentation of the results and achievements of this research can lead to the demands of the stakeholders, but during the time of the research and when its achievements were revealed, based on the request of the National Corona Headquarters and relevant decision-making institutions in the country, consultations were given based on the achievements. It was their request. Therefore, the key issue in the current research is to find a model to explain the dynamics of changing feelings and attitudes of Athenian society in the natural crisis of Corona. This research is conducted on Persian-speaking social media users and for Iranian society. Methods: Since in order to achieve this goal, it is necessary to carry out a lot of research and examine the problem from various dimensions, the method of Meta synthesis was chosen, and for this purpose, a call was made in the last months of 2018 and with the peak of the Corona wave. And is affected by various aspects of people's lives, was held at the headquarters of the development of cognitive sciences and technologies. For this purpose, more than 25 million Persian language posts from social media were collected, preprocessed, and then made available to interested teams between February 1, 1998, and June 11, 1999. Then, the results of these plans have been extracted in the form of 148 propositions. Results: The results of the analysis of propositions, during qualitative research, led to the identification of 11 main categories, which were placed in 6 different dimensions of a comprehensive model. In this way, various aspects of the cognitive approach of Iranian social media users in this natural crisis led us to a comprehensive model that we called "Samee". Conclusion: In general, it can be stated that this model specifies that the influencers are effective on the mentalities, emotions, and perceptual attitudes and behavior of users in cyberspace, and again, the behaviors of users in cyberspace are effective on their mentalities, emotions, and attitudes. It is impressive. When the data of this research was extracted and analyzed, it was related to the first months of the corona outbreak. Obviously, similar research will be needed to carry out this study until the end of the Corona era and even months after that and to examine the next events and the cognitive approaches of social network users. In addition, other types of crises occur in societies, which are not necessarily natural crises, but this method of analysis can be done on those crises as well, and its results can be examined and applied in society. Using the methods introduced in this research can provide very valuable achievements for the managers and policymakers of the country. The research that this research is based on, points to the limitations of any computational work in the social and cultural field. We will eat them. One of the very key tasks to pave the way for such analyzes is to conduct preliminary research, which will generally be either in the direction of creating the necessary infrastructure for computational social sciences or in the direction of the necessary infrastructure for linguistic analysis and natural language processing in Persian. Since the theories and algorithms used in natural language processing for the Persian language are generally derived from the same theories and algorithms of natural language processing, researchers that deal with natural language processing with a cognitive approach can be very innovative and solve many problems that in these fields, it should be resolved. [ABSTRACT FROM AUTHOR]

The present study is focused on the method of designing historical structures through data analysis and soft programming. The current research aimed to find the design method of the old columns of the structure and compare it with modern design methods. The case study was Goharshad Mosque (1400 AC) located in Iran, Mashhad. A correlation matrix was created to find the relationship between structure parameters by data mining in Python. The results indicate that the modern design method was more reliable than the old method due to the safety factor, but some parameters such as loading calculation in the historical method and the modern method were the same with more than 70% similarity. But the results of the coefficient of determination show that the loading of the R² results was more than 0.44 and the area of the columns was more than -0.5. The modern and old design has a big engineering gap. Finally, the current study shows the old structure design method and compares it with the new design method. [ABSTRACT FROM AUTHOR]

The most crucial concern of hydrologists in analyzing water resources for planning and managing sustainable water resources is reliable and accurate streamflow prediction. In this regard, the application of data mining algorithms has grown significantly in recent decades. In the present study, seven standalone decision tree models, namely Random Forest (RF), Random Tree (RT), REP Tree (REPT), instance-based learning (IBK), KStar, M5P, and Bagging, and six hybrid models, namely, Additive Regression -M5P (AR-M5P), CVParameter Selection-M5P (CVP-M5P), Random SubSpace-M5P (RS-M5P), Random Committee-REP Tree (RC-REPT), and Random Committee-Random Tree (RC-RT), were used to predict streamflow in the Kurkursar River, Iran. A time series from 1989 to 2019 was used for training (80%) and testing (20%) phases. The RMSE, PSR, MAE, PBAIAS, and NSE statistical criteria were employed to evaluate the performance and accuracy of the models, and dimensional diagrams were drawn to assess them visually. The results showed that among all the standalone and hybrid models, BAGGING had the best performance (RMSE = 0.51615 m 3 s , MAE = 0.09201 m 3 s , NSE = 0.7467, PBIAS = -10.511%, and PSR = 0.50319) and the REP Tree (REPT) model the weakest performance (RMSE = 1.36649, MAE = 0.2451607, NSE = 0.3327, PBIAS -11.274, and PSR = 0.81685). The final result shows that all models provided good predictions and performance. Also, hybrid models were able to increase the accuracy of the model. [ABSTRACT FROM AUTHOR]

Background: The rapid prevalence of coronavirus disease 2019 (COVID‐19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID‐19 patients using data mining techniques. Methods: In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results: Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion: Data mining methods have the potential to be used for predicting outcomes of COVID‐19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID‐19 patients. [ABSTRACT FROM AUTHOR]

The purpose of current study is to predict Snow Water Equivalent (SWE) in Sohrevard watershed, Iran, using different machine learning algorithms such as Bayesian Artificial Neural Network (BANN), Support Vector Machine (SVM), Cubist and Random Forest (RF) with Latin Hypercube Sampling (LHS). In this regard, nine geo-environmental variables—altitude, slope, eastness, profile curvature, plan curvature, solar radiation, Topographic Position Index (TPI), Topographic Wetness Index (TWI) and wind exposition index—were used as SWE influencing factors. Based on the results obtained from the error metrics, the RF algorithm (train and testing stages, r = 0.96 and 0.76; Root Mean Square Error (RMSE) = 2.54 and 5.46 cm; Mean Absolute Error (MAE) = 1.74 and 4.05 cm; Percent BIAS (PBIAS) = 0.4 and 2.3 respectively) was selected as the best model. Based on our findings, the highest amount of SWE was concentrated in the eastern part of the watershed. SWE modeling is a useful tool for optimal and integrated management of water resources. [ABSTRACT FROM AUTHOR]

The data employed in urban gain modeling classes are often imbalanced, negatively affecting the accuracy of traditional and standard data mining and machine learning models. This study presents a new framework on the basis of clustering-based modeling and ensemble models to deal with the class imbalance problem in urban gain modeling. The random forest (RF), artificial neural network (ANN) and support vector machine (SVM) models served as the base models for the generation and evaluation of the results within this framework. The changes in urban land-use pattern of Isfahan in Iran in two time intervals of 1994-2004 and 2004-2014 were considered for the modeling. The findings showed that the proposed sampling strategy yields higher Hits and Correct Rejections rates than the strategies applied in previous studies in all three models. In the second part of the proposed framework (ensemble models), there was no substantial difference in the confusion matrix entries. [ABSTRACT FROM AUTHOR]

Medical data mining is considered as a new solution to the analysis of medical data and further expansion of the relative knowledge. Medical data mining has a high potential to discover the hidden patterns. Recently, some studies have been conducted on the relationship between the individuals' environmental surroundings and their exposure to diseases, thus clearly confirming the considerable impact of the quality of environmental indicators such as environmental pollutants on diseases. In this respect, the current study investigated the effect of the environmental conditions on the chance of diabetes based on medical data mining technique. Given that diabetes is a global threat to the human health, an Ensemble Classifier based on Genetic Algorithm (ECGA) method was designed to study the environmental conditions in diabetes. Decision tree, random forest, K-nearest neighbor, and naive were included in the designed ensemble classifier. It was found that ECGA was more accurate than the base classifier algorithms. For study purposes, three datasets were collected from different regions in Iran with different climatic conditions and it was found that environmental conditions had a significant impact on diabetes. [ABSTRACT FROM AUTHOR]

In Streamflow prediction the most important triggering/controlling variables are related to climate, physiography, and landscape patterns. This study investigated the effect of different landscape metrics to relate spatial patterns to surface runoff processes and predict monthly streamflow using climatic and physiographic variables for the 42 sub-basins of the Urmia Lake Basin in Iran. We developed an innovative data-driven framework and considered two different modelling approaches i.e., modelling in homogenous clusters (local approach) and modelling in the entire area as an entity (global approach). The results of basin LULC monitoring from the 20-year experimental period display drastic changes in the land use of the basin such as reduction in lake area (48.3%) due to increasing irrigated areas (22.5%), increasing residential areas (14.2%), and decrease in rangeland (6.0%). Streamflow prediction results in the global experiment showed Group Method of Data Handling (GMDH) and Random Forest (RF) with NSE of 0.76 and NRMSE of 6.44% have similar results and outperformed Partial Least Squares regression (PLS), but in clustering experiment GMDH with NSE of 0.88 and NRMSE of 5% shows the highest accuracy and outperformed both RF and PLS. The results confirmed that modelling in homogenous clusters (local prediction) significantly enhanced the performance of prediction. [ABSTRACT FROM AUTHOR]

BACKGROUND: Many occupational accidents annually occur worldwide. The construction industry injury is greater than the average injury to other industries. The severity of occupational accidents and the resulting injuries in these industries is very high and severe and several factors are involved in their occurrence. OBJECTIVE: Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm. METHODS: In this study, occupational accidents were analyzed and modeled during five years at construction sites of 5 major projects affiliated with a gas turbine manufacturing company based on census sampling. 712 accidents with all the studied variables were selected for the study. The process was implemented in MATLAB software version 2018a using combined artificial neural network and genetic algorithm. Additional information was also collected through checklists and interviews. RESULTS: Mean and standard deviation of accident severity rate (ASR) were obtained 283.08±102.55 days. The structure of the model is 21, 42, 42, 2, indicating that the model consists of 21 inputs (selected feature), 42 neurons in the first hidden layer, 42 neurons in the second hidden layer, and 2 output neurons. The two methods of genetic algorithm and artificial neural network showed that the severity rate of accidents and occupational injuries in this industry follows a systemic flow and has different causes. CONCLUSION: The model created based on the selected parameters is able to predict the accident occurrence based on working conditions, which can help decision makers in developing preventive strategies. [ABSTRACT FROM AUTHOR]

Agricultural practices affect arbuscular mycorrhizal fungal (AMF) hyphae growth and glomalin production, which is a recalcitrant carbon (C) source in soil. Since the spatial distribution of glomalin is an interesting issue for agronomists in terms of carbon sequestration, digital maps are a cost-free and useful approach. For this study, a set of 120 soil samples was collected from an experimental area of 310 km2 from the Sarab region of Iran. Soil total glomalin (TG) and easily extractable glomalin (EEG) were determined via ELISA using the monoclonal antibody 32B11. Soil organic carbon (OC) was also measured. The ratios of TG/OC and EEG/OC as the glomalin–C quotes of OC were calculated. A total of 17 terrain-related attributes were also derived from the digital elevation model (DEM) and used as static environmental covariates in digital soil mapping (DSM) using three predictive models, including multiple linear regression (MLR), random forests (RF), and Cubist (CU). The major findings were as follows: (a) DSM facilitated the interpretation of recalcitrant C source variation; (b) RF outperformed MLR and CU as models in predicting and mapping the spatial distribution of glomalin using available covariates; (c) the best accuracy in predictions was for EEG, followed by EEG/OC, TG, and TG/OC. [ABSTRACT FROM AUTHOR]

Morphology and feature selection are key approaches to address several issues in fisheries science and stock management, such as the hypothesis of admixture of Caspian common carp (Cyprinus carpio) and farmed carp stocks in Iran. The present study was performed to investigate the population classification of common carp in the southern Caspian basin using data mining algorithms to find the most important characteristic(s) differing between Iranian and farmed common carp. A total of 74 individuals were collected from three locations within the southern Caspian basin and from one farm between November 2015 and April 2016. A dataset of 26 traditional morphometric (TMM) attributes and a dataset of 14 geometric landmark points were constructed and then subjected to various machine learning methods. In general, the machine learning methods had a higher prediction rate with TMM datasets. The highest decision tree accuracy of 77% was obtained by rule and decision tree parallel algorithms, and "head height on eye area" was selected as the best marker to distinguish between wild and farmed common carp. Various machine learning algorithms were evaluated, and we found that the linear discriminant was the best method, with 81.1% accuracy. The results obtained from this novel approach indicate that Darwin's domestication syndrome is observed in common carp. Moreover, they pave the way for automated detection of farmed fish, which will be most beneficial to detect escapees and improve restocking programs. [ABSTRACT FROM AUTHOR]

Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation. [ABSTRACT FROM AUTHOR]

Artificial-intelligence and machine-learning algorithms are gaining the attention of researchers in the field of groundwater modelling. This study explored and assessed a new approach based on Gini-, entropy- and ratio-based classification trees to predict spatial patterns of groundwater potential in a mountainous region of Iran. To do this, a springs inventory was undertaken, and 362 springs were identified in the study area. A set of geo-environmental and topo-hydrological factors (slope, aspect, elevation, topographic wetness index, distance from fault, distance from river, precipitation, land use, lithology, plan curvature and roughness index) were used as predictors of groundwater. Results showed that Gini (AUC = 0.865) achieved the best results, followed by entropy (AUC = 0.847) and ratio (AUC = 0.859). Lithology was determined to be the variable with the best association with groundwater in the study area. These results indicate that all three algorithms provide robust models of groundwater potential in this mountainous region. Gini, entropy and ratio were investigated for groundwater potential mapping. Eleven groundwater-affecting factors were considered. Lithology is the most important factor for groundwater potential mapping Gini based decision tree is the best, followed by entropy and ratio models [ABSTRACT FROM AUTHOR]

One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. [ABSTRACT FROM AUTHOR]

Driver drowsiness causes many road accidents, and preparing a risk map of these accidents with spatial criteria and data mining algorithms highlights accident points well. In this study, accidents risk caused by driver drowsiness in Qazvin province, Iran, was modelled using decision tree (DT), random forest (RF) and support-vector regression (SVR) algorithms in GIS environment. Seven spatial criteria including road segment length, road width, slope angle, speed limit, land use/cover, distance to service area and distance to speed camera were selected as effective criteria in modelling. The effect of criteria in modelling was applied using a fuzzy method, and three risk maps were prepared. Evaluation with ROC-AUC showed that the AUC for RF, SVR and DT models were 0.904, 0.863 and 0.805, respectively, and the RF model overall had the best performance. Examining the importance of criteria showed that the speed limit was the most important criterion for modelling. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed a surge in research interest in bio-nanocomposite-based packaging materials, but still, a lack of systematic analysis exists in this domain. Bio-based packaging materials pose a sustainable alternative to petroleum-based packaging materials. The current work employs bibliometric analysis to deliver a comprehensive outline on the role of bio nanocomposites in packaging. India, Iran, and China were revealed to be the top three nations actively engaged in this domain in total publications. Islamic Azad University in Iran and Universiti Putra Malaysia in Malaysia are among the world's best institutions in active research and publications in this field. The extensive collaboration between nations and institutions highlights the significance of a holistic approach towards bio-nanocomposite. The National Natural Science Foundation of China is the leading funding body in this field of research. Among authors, Jong whan Rhim secured the topmost citations (2234) in this domain (13 publications). Among journals, Carbohydrate Polymers secured the maximum citation count (4629) from 36 articles; the initial one was published in 2011. Bio nanocomposite is the most frequently used keyword. Researchers and policymakers focussing on sustainable packaging solutions will gain crucial insights on the current research status on packaging solutions using bio-nanocomposites from the conclusions. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to compare the trend of academic project management research in Iran and the World in five-year periods with a text mining approach and TF–IDF method. Design/methodology/approach: The research population consists of 1205 theses presented between 2000 and 2019 in Iranian universities. The central library website of the mentioned universities was used for data collection, and the text mining approach with the TF–IDF method was used for data analysis. Findings: The remarkable results of this study include: Concrete structures are the most frequent among structural systems, Risk Management is the most frequent among PMBOK Knowledge Areas, Design-build (DB) system is the most frequent among Project Delivery Systems, Engineering, procurement and construction (EPC) is the most frequent among DB Project Delivery Systems, Financial Management is the most frequent among specialized construction knowledge areas, Soft Skills is the most frequent among Global Trends, Contracting Companies is the most frequent among Project Parties, Construction Projects is the most frequent among Project Areas, Power Plant and Refinery is the most frequent among Project Subjects, Optimization is the most frequent among Problem-Solving Approaches, Fuzzy Logic is the most frequent among Novel Algorithms and Motivation is the most frequent among Soft Skills. Originality/value: The innovative aspect of this research is that for the first time, text mining has been used to analyze academic research on project and construction management, and also for the first time, academic research on construction industry in Iran has been compared with global research. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year. [ABSTRACT FROM AUTHOR]

The teleconnection modeling of hydro-climatic events is a complex problem with highly uncertain circumstances. In contrast to the classic fuzzy logic methods, by using the Z-number in addition to the constraint of information, and by evaluating the data reliability, it is possible to characterize the degree of ambiguity of data. In this regard, this study investigates the performance of the Z-number-based model (ZBM) in prediction of classified monthly precipitation (MP) events of two synoptic stations in Iran (up to five months in advance). To this end, the sea surface temperature (SST) of adjacent seas was used as a predictor. The suggested model, by using Z-number directly and applying fuzzy Hausdorff distance to determine weights of if-then rules, predicted MP events of both the stations with over 70% confidence. Analysis of the results in the test step showed that the ZBM compared to the traditional fuzzy approach improved the results by 69% for Kermanshah and 112% for Tabriz. Overall, the Z-number concept by assessing events reliability can be used in various sectors of water resources management such as decision-making and drought monitoring. [ABSTRACT FROM AUTHOR]

Background: Unknown cases of pneumonia appeared in late 2019 in Wuhan, China. Following the worldwide spread of the disease, the World Health Organization declared it a pandemic on March 11, 2020. The total number of infected people worldwide as of December 16, 2020, was more than 74 million, more than one million and six hundred thousand of whom died from Coronavirus Disease 2019 (COVID-19). This study aimed to identify the risk factors for the mortality of COVID-19 in Hamadan, west of Iran. Materials and Methods: This cross-sectional study used the information of all patients with COVID-19 admitted to Shahid Beheshti and Sina hospitals in Hamadan during January 2020-November 2020. Logistic regression model, decision tree, and random forest were used to assess risk factors for death due to COVID-19. Results: This study was conducted on 1853 people with COVID-19. Blood urea nitrogen change, SPO2 at admission, the duration of hospitalization, age, neutrophil count, lymphocyte count, number of breaths, complete blood count, systolic blood pressure, hemoglobin, and sodium were effective predictors in both methods of decision tree and random forest. Conclusion: The risk factors identified in the present study may serve as surrogate indicators to identify the risk of death due to COVID-19. The proper model to predict COVID-19-related mortality is random forest based on sensitivity. [ABSTRACT FROM AUTHOR]

Background: Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injuries in fatigue and drowsiness accidents. Methods: The Classification and Regression Tree method (CART) was applied 11,392 drivers were involved in fatigue and drowsiness accidents in three provinces of Iran, over the 7 years from 2011-2018. A two-level target variable was used to increase the accuracy of the model. First, dataset in each of three provinces was classified into homogeneous clusters using a two-step clustering algorithm. Oversampling method was used for imbalanced accident severity datasets. Then, classification was improved by boosting method. Results: The classification tree reveals that the month, time of day, collision type, and vehicle type were common factors. Also, driver's age was important in female drivers cluster; the geometry of the place and seat belt/helmet usage were important in urban roads cluster; and area type, road type, road direction, and vehicle factor were important in rural roads cluster. Also, the combination of the CART algorithm with oversampling and boosting increased the accuracy of the models. Conclusion: The analysis results revealed motorcycles, lack of using a helmet or seat belt, curvy roads, roads with two-way undivided and one-way movement direction increased the injury and death of drivers. Collision with fixed object, run-off-road, overturning, falling, and defective vehicles increased the severity of accidents. Female drivers older than 44 years old have a higher probability of fatality. Identifying the factors affecting the severity of driver injuries in such accidents in each province could assist in determining engineering countermeasures and training educational programs to mitigate these crash severities. [ABSTRACT FROM AUTHOR]

Objectives: To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors. Method: Data analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers. Results: The data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54. Conclusions: In this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length. [ABSTRACT FROM AUTHOR]

Indigenous knowledge about climate change that makes adaptability necessary by coping strategies leads to the sustainability of nomadic livelihoods. This paper proposed a new adaptability approach, an association coping strategy (ACS), which is established based on the association rulesʼ recommendations. The adaptability was empirically analyzed using 12 coping strategies used by Kashkooli nomads in Sepidan plain, Fars province, Iran. Required data were gathered both in the form of time series from November 1988 to February 2020 and cross section during June–July 2019. Household perceptions of climatic and environmental variables, supported by meteorological data, revealed that climate change was negatively impacting the Kashkooli nomadic livelihood in Sepidan plain. The findings also indicated that taking simultaneous coping strategies resulted in selling livestock surplus, saving water consumption, livestock insurance, rangeland regeneration schemes, and immigration to more suitable rangelands. Therefore, Kashkooli nomadic livelihood survived climate changes using ACSs. The association rules were useful for recommending anticipatory coping strategies that were adopted based on indigenous knowledge about climate change by nomads to establish appropriate adaptability. [ABSTRACT FROM AUTHOR]

Fires have increased in northeastern Iran as its semi-arid climate landscape is desiccated by human activities. To combat fire outbreaks in any region, fire susceptibility must be mapped using accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of Golestan Province in northeastern Iran using new data-mining models. Fire effective factors, including elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance to river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined using a random-forest algorithm. Fire-susceptibility maps were produced in R 3.0.2 software using GAM, MARS, SVM algorithms, and a new ensemble of the three models: GAM-MARS-SVM. The four fire-susceptibility maps were validated using the area under the curve. The results show that the distance to the village, annual mean rainfall, and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest precision of fire-susceptibility mapping. The fire-susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province. The fire-susceptibility map produced by the ensemble model can be very useful for creating and enhancing management strategies for preventing fires, particularly in the higher-risk portions of Golestan Province. [ABSTRACT FROM AUTHOR]

A landslide is a geomorphological hazard with significant ecological and economic damages. The present study aimed to identify landslide-prone areas in Farizi watershed via the Support Vector Machine (SVM), the boosted regression trees (BRT) model, a Logistic Model Tree (LMT), and the Random Forest (RF) algorithm with high computability. The effects on landslide occurrences in this study include altitude, slope, slope direction, distance to road, lithology, distance to waterway, land use, distance to fault, slope cross-section profile, slope longitudinal profile, precipitation, topographic wetness index, and soil layers. To use the soil layer, texture, bulk density, permeability, structure, and plasticity were conducted for analyses of soil physical properties. Geomorphologists examined each parameter according to its effect size on the landslide hazards and used it as a raster as background image ror other layers for the main layers in landslide susceptibility zoning. In order to evaluate the results of the models, data analysis was based on the calculation of the total area under the ROC curve obtained from 30% of landslides. The results showed that the SVM with the AUC as 0.86 and the RF algorithm with the AUC as 0.89 had better operating characteristic in landslide susceptibility zoning of the studied watershed. Prioritization of effective factors showed that lithology, slope, slope direction, distance to fault, and land use had the highest effects on landslide occurrences in the study area. As a result, our proposed methods can improve prediction performance, and the landslide prediction system can give warnings. Landslide susceptibility assessment is a complex and multistep process that has been studied by many researchers. In this study, the SVM, BRT, LMT, and RF algorithms to assess landslide susceptibility and its performance based on various statistical measurements have been discussed. The SVM map shows a high-risk zone covering 71% of the study area. Also, there are also scattered points in the landslide zone throughout the area. In the landslide susceptibility map extracted from the BRT algorithm, a large part of the high-risk zone covers 51% of the area. In the landslide susceptibility map extracted from the LMT algorithm, the high-risk zone covers 69% of the area, and in the landslide susceptibility map extracted from the RF algorithm, the high-risk covers 61% of the area. [ABSTRACT FROM AUTHOR]