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The COVID-19 pandemic has profoundly impacted every aspect of our lives, from economic to the social facets of contemporary society. While the new COVID-19 waves may not be anticipated to be as severe as previous ones, it would be unreasonable to assume that they will cease any time soon. Consequently, forecasting the number of future infections, recovered patients, and death cases remains a very much logical step in trying to fight against further waves, in conjunction with ongoing vaccination efforts. In this paper, we investigate the efficiency of three intelligent machine learning algorithms, namely GMDH, Bi-LSTM, and GA + NN, for COVID-19 forecasting, with an application to Iran and the United Kingdom. The experimental results show that the algorithms can be used to forecast the next six months of COVID-19 in terms of confirmed, recovered, and death cases, which gives a more ample timeframe for using the results to make better practical yet strategic decisions and take appropriate actions or measures to deploy resources effectively to contain or curb the spread of the coronavirus. Despite the distinct dynamics observed in the data, our analysis proves the robustness of the employed models. [ABSTRACT FROM AUTHOR]

The study investigated the spatiotemporal relationship between surface hydrological variables and groundwater quality/quantity using geostatistical and AI tools. AI models were developed to estimate groundwater quality from ground-based measurements and remote sensing images, reducing reliance on laboratory testing. Different Kriging techniques were employed to map ground-based measurements and fill data gaps. The methodology was applied to analyze the Maragheh aquifer in northwest Iran, revealing declining groundwater quality due to industrial. discharges and over-extraction. Spatiotemporal analysis indicated a relationship between groundwater depth/quality, precipitation, and temperature. The Root Mean Square Scaled Error (RMSSE) values for all variables ranged from 0.8508 to 1.1688, indicating acceptable performance of the semivariogram models in predicting the variables. Three AI models, namely Feed-Forward Neural Networks (FFNNs), Support Vector Regression (SVR), and Adaptive Neural Fuzzy Inference System (ANFIS), predicted groundwater quality for wet (June) and dry (October) months using input variables such as groundwater depth, temperature, precipitation, Normalized Difference Vegetation Index (NDVI), and Digital Elevation Model (DEM), with Groundwater Quality Index (GWQI) as the target variable. Ensemble methods were employed to combine the outputs of these models, enhancing performance. Results showed strong predictive capabilities, with coefficient of determination values of 0.88 and 0.84 for wet and dry seasons. Ensemble models improved performance by up to 6% and 12% for wet and dry seasons, respectively, potentially advancing groundwater quality modeling in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: Nurses' innovative behaviours play a crucial role in addressing the challenges including adapting to emerging technologies, resource limitations and social realities such as population ageing that are intricately tied to today's healthcare landscape. Innovative behaviours improve healthcare quality, patient safety and satisfaction. Organisational factors and individual attributes influence nurses' inclination to innovate. With the rise of artificial intelligence and novel technology, healthcare institutions are actively engaged in the pursuit of identifying nurses who demonstrate innovative qualities. Developing a comprehensive protocol to elucidate the various dimensions of nurses' innovative behaviours and constructing a valid measuring instrument, rooted in this protocol represents a significant step in operationalising this concept., Methods and Analysis: The study encompasses two phases: a qualitative study combined with a literature review, followed by the design and psychometric evaluation of the instrument. To ensure diversity, a maximum variation purposive sampling method will be used during the qualitative phase to select clinical nurses. In-depth semistructured interviews will be conducted and analysed using conventional content analysis. Additionally, a comprehensive literature review will supplement any missing features not captured in the qualitative phase, ensuring their inclusion in the primary tool. The subsequent quantitative phase will focus on evaluating the questionnaire's psychometric properties, including face, content and construct validity through exploratory factor analyses (including at least 300 samples) and confirmatory factor analyses (including at least 200 samples). Internal consistency (Cronbach's alpha), reliability (test-retest), responsiveness, interpretability and scoring will also be assessed., Ethics and Dissemination: This study originates from a doctoral dissertation in nursing. Permission and ethical approval from Semnan University of Medical Sciences has been obtained with reference code IR.SEMUMS.1401.226. The study's findings will ultimately be submitted as a research paper to a peer-reviewed journal., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The growing trend of IT technologies has continued at a faster pace in recent decades. The efforts of experts in this field have always been reducing the problems and challenges existing in urban communities and improving the quality of citizens' lives. They have concentrated on using the artificial intelligence and Internet of Things in a way which leads to maximize self-actualization of citizens. Among these technologies, IoT has played a significant role. Since it can improve the urban infrastructures, and also can be implemented in the scale of home facilities and equipment, can improve the quality of life in smart homes. However, it seems that the implementation of smart residential buildings in the context of smart cities is not simple as been defined in theory and may be accompanied by challenges. In this paper, an attempt has been made to evaluate the environmental factors affecting this field by SWOT analysis tool in a case study in cities of Mazandaran Province in Iran. As a result, strategy and tactics to emphasize strengths, to make the most of existing opportunities, to eliminate the weaknesses, and to reduce the risk of perceived threats, have been identified and been introduced. Based on the results, OS strategy should be held to obtain the most valuable goals of a smart city; since the points of opportunities and strengths are relatively more than others. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

The competitive environment in the global market makes most countries look for better ways to solve their problems. Food waste is the largest concern facing the food security of the world. Not paying attention to process of pomegranate wastes, such as separating the peel from the other parts and ignoring the cost of using artificial intelligence for pest control in gardens and the cost of maintaining the processed products are the gaps of previous researches. To cope with this challenge, recent studies have presented sustainable closed-loop supply chains (SCLSCs) as a strategic approach and a competitive advantage. The present study distinguishes itself from other studies by using the artificial intelligence technology in a supply chain along with the reverse logistics section, i.e., waste recycling. This paper proposes a design for a CLSC pomegranates. The corresponding logistics network is designed for several periods and covers manufacturers, distribution centers, customers, factories, recycling centers (compost centers), and compost end user (compost markets). Using reverse logistics, the wasted pomegranates are also converted into recycled products including ethanol, as an automotive fuel and a renewable energy, and a type of compost processed as an organic fertilizer. The goal of proposed model is to minimize the costs of supply chains, reduce the supply risks involved, and increase the profits for gardeners and investors in the public and non-profit agriculture sectors in Iran. The first pareto solution is 1,869,908.962, 2172.638 and 65.926, and the CPU time is 412 Ms. The results show a rise in the maximum supply risk occurs in the total cost and risk but a reduction in the accountability of the network and also an increase in the disruption period findings in increased total cost and risk of the network, while it first increases and then decreases the accountability. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to investigate the relationship among technology readiness, the implementation of artificial intelligence-based customer relationship management, and the promotion of the corporate social responsibility of B2B companies active in Iran's auto parts supply industry. This study has an applicative nature and is based on the descriptive survey research method. The statistical population sample for this study included 247 people, all of them managers and senior experts at the analysed companies with at least 7 years of work experience. A questionnaire was applied for collecting research data, and its validity and reliability were confirmed through the combined reliability and Cronbach's alpha indices. After collecting the necessary data in order to test four research hypotheses, the structural equation modelling method was employed by using the SmartPLS software. The results for testing the first research hypothesis showed that technology readiness has a positive effect on corporate social responsibility and that it directly predicts 0.810 (81%) of the changes related to corporate social responsibility in the context of the analysed companies. Also, the analysis of the results obtained for testing the fourth research hypothesis showed that technology readiness through artificial intelligence-based customer relationship management has a positive effect on corporate social responsibility and that technology readiness indirectly predicts 0.493 (49.3%) of the changes related to corporate social responsibility. Based on this, it can be acknowledged that the analysed companies can take steps towards improving corporate social responsibility by increasing technology readiness, strengthening their communication technology infrastructure and also by improving customer relationship management capabilities through artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

Flyrock is known as one of the main problems in open pit mining operations. This phenomenon can threaten the safety of mine personnel, equipment and buildings around the mine area. One way to reduce the risk of accidents due to flyrock is to accurately predict that the safe area can be identified and also with proper design of the explosion pattern, the amount of flyrock can be greatly reduced. For this purpose, 14 effective parameters on flyrock have been selected in this paper i.e. burden, blasthole diameter, sub-drilling, number of blastholes, spacing, total length, amount of explosives and a number of other effective parameters, predicting the amount of flyrock in a case study, Songun mine, using linear multivariate regression (LMR) and artificial intelligence algorithms such as Gray Wolf Optimization algorithm (GWO), Moth-Flame Optimization algorithm (MFO), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO) and Multi-Verse Optimizer (MVO). Results showed that intelligent algorithms have better capabilities than linear regression method and finally method MVO showed the best performance for predicting flyrock. Moreover, the results of the sensitivity analysis show that the burden, ANFO, total rock blasted, total length and blast hole diameter are the most significant factors to determine flyrock, respectively, while dynamite has the lowest impact on flyrock generation. [ABSTRACT FROM AUTHOR]

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The concept of leveraging the predictive capacity of predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores the predictive capacity of different approaches to LS modelling using artificial intelligence. The key objective of this study is to estimate a LS map for the Taleghan-Alamut basin of Iran using Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost and CDT-SubSpace) hybrid machine learning approaches, which are state-of-the-art soft computing approaches that are hardly ever utilized in the assessment of LS. In this study, we used eighteen landslide predisposing factors (LPFs) that we considered to be the most important local morphological and geo-environmental factors influencing the occurrence of landslides. We calculated the significance of each of the LPFs in the landslide susceptibility assessment using the Random Forest Method. We also employed the Receiver Operating Characteristic curve, precision, performance, map robustness measurement and selection of the best-fitting models. The results shows that, compared to the other models, the CDT-Multiboost is the excellent model in this perspective with an average area under curve (AUC) of 0.993 based on a 4-fold cross-validation. We, therefore, consider the CDT-Multiboost models to be an effective method for improving spatial prediction of LS where landslide scarps or bodies are not clearly identified during the preparation of landslide inventory maps. Therefore, it will be helpful for preparing future landslide inventory maps and mitigate landslide damages. [ABSTRACT FROM AUTHOR]

By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve level of services in Water Distribution Networks (WDNs). In recent years, artificial intelligence techniques have been used vastly in pipe burst prediction. These techniques need big input dataset for deep learning but big dataset might not be available in old WDNs. This paper aimed to compare the performance of multivariate adaptive regression splines (MARS) and regressive convolution neural network with least square support vector regression (RCNN-LSSVR) for predicting of Pipe Burst Rate (PBR) with limited dataset. Pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure are considered as effective parameters of PBR. Data collected from real-case study in Iran include 158 cases for polyethylene and 124 cases for asbestos cement pipes during 2012–2019. The results indicate that RCNN-LSSVR model has a great performance of PBR prediction on limited dataset. [ABSTRACT FROM AUTHOR]

The preliminary responses on stability assessment are very effective in discontinuous rock slope stabilisations, which geo-mechanical empirical classifications/approaches can be evaluated. Although there are several geomechanical classifications for rock mass characteristics, they still have some complexities and shortcomings. In this paper, correlations based on two flexible classification systems, namely slope mass rating (SMR) and Barton's Qslope, has been discussed as a connection between failure (i.e., safety factor, reliability) and stability conditions (i.e., failure mechanism, support system) which were performed in 300 road/railway slope cases from 12 provinces in Iran. The empirical SMR-Qslope relationship has been used for sedimentary rocks defined lithologically as limestone, marlstone, sandstone, and claystone. The method was used to provide the empirical link to the primary design for discontinuous rock slopes. To this end, after field investigations and the necessary geo-engineering data gathered on the studied slopes. The information used to provide the SMR and Qslope retirements prepare SMR-Qslope relation for Iranian data. The artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, decision tree, random forest, multilayer perception, and Naïve-Bayes classifiers have been used for detailed classifications. The accurate correlations provided have been implemented and revised in the Python programming language. According to the learning model results, SMR-Qslope equation for Iranian data has been developed as SMR = 6.699 ln (Qslope) + 58.99 / R2 = 0.62 with 0.95 (95%) accuracy. The evaluated results were verified and controlled by Jorda-Bordehore et al. (Stability assessment of rock slopes using empirical approaches: comparison between slope mass rating and Q-slope, 2018) and Maion (Proposta de correlação entre os índices SMR e Q-slope , 2019) empirical relationships as a basic table. By comparing these studies' results, it can be stated that the present study has improved the accuracy of SMR-Qslope relationship. [ABSTRACT FROM AUTHOR]

This paper develops a new method of onset detection for the Tar, a traditional Iranian musical instrument. The proposed method is based on both types of pitch and energy features. Therefore, it can be utilized to detect either soft or hard onsets. Through this combination, we obtained a more precise separation between two adjacent notes. This ability is especially useful to detect the reaz, repeatedly played notes with the same frequency and short durations. For the evaluation of the method, a data set with predetermined onsets was produced and the results were compared with an energy-based method explained in terms of F-measure. [ABSTRACT FROM AUTHOR]

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter. [ABSTRACT FROM AUTHOR]

Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS–BA, ANFIS–WA, MFNN–BA, and MFNN–WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash–Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS–BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS–BA had more reliable performance compared to other models. Thus, the ANFIS–BA model has high potential for predicting SSL. [ABSTRACT FROM AUTHOR]

This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved. [ABSTRACT FROM AUTHOR]

Introduction: Most heart diseases show symptoms on ECG, but diagnosing heart disease with ECG requires the knowledge and experience of medical specialized. Because these specialists may not always be available, it is necessary to design tools to diagnose heart disease in these situations. Objective: In this paper, a two-stage approach based on artificial neural networks is designed to diagnose heart disease using ECG information. Method: To design the proposed approach, first ECG information for 861 refers to a number of medical centers in Arak city is collected and and data consulted is proccesed by specialists. Then 154 features from ECG as input variables in proposed approach has been specified. In the first stage of approach, an artificial neural network is designed to detect the status of the ECG in two situation as usable and unusable. Then, in the second stage, using the usable ECG information, an artificial neural network is designed to diagnose the presence or absence of heart disease. Finally, the performance of the two-stage approach is evaluated and its accuracy and precision in determining the status of the ECG as well as the disease status is determined. Results In the proposed approach, the neural network for the determining of ECG status has an precision of 97.1% and an accuracy of 97.3%, and also the neural network for the diagnosis of heart disease has an precision of 95.8% and an accuracy of 95.4%. Conclusion: Considering the high effeciency of the proposed approach in the determining of ECG status and also diagnosing heart disease, it is possible to use this approach as a reliable assistant to assist the treatment staff. [ABSTRACT FROM AUTHOR]

This paper describes the application of multi-layer perceptron (MLP), radial basis network and adaptive neuro-fuzzy inference system (ANFIS) models for computing dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) levels in the Karoon River (Iran). Nine input water quality variables including EC, PH, Ca, Mg, Na, Turbidity, PO, NO and NO, which were measured in the river water, were employed for the models. The performance of these models was assessed by the coefficient of determination R, root mean square error and mean absolute error. The results showed that the computed values of DO, BOD and COD using both the artificial neural network and ANFIS models were in close agreement with their respective measured values in the river water. MLP was also better than other models in predicting water quality variables. Finally, the sensitive analysis was done to determine the relative importance and contribution of the input variables. The results showed that the phosphate was the most effective parameters on DO, BOD and COD. [ABSTRACT FROM AUTHOR]

Groundwater over-abstraction may cause land subsidence (LS), and the LS mapping suffers the subjectivity associated with expert judgment. The paper seeks to reduce the subjectivity associated with the hazard, vulnerability, and risk mapping by formulating an inclusive multiple modeling (IMM), which combines two common approaches of multi-criteria decision-making (MCDM) at Level 1 and artificial intelligence (AI) at Level 2. Fuzzy catastrophe scheme (FCS) is used as MCDM, and support vector machine (SVM) is employed as AI. The developed methodology is applied in Iran's Tasuj plain, which has experienced groundwater depletion. The result highlights hotspots within the study area in terms of hazard, vulnerability, and risk. According to the receiver operating characteristic and the area under curve (AUC), significant signals are identified at both levels; however, IMM increases the modeling performance from Level 1 to Level 2, as a result of its multiple modeling capabilities. In addition, the AUC values indicate that LS in the study area is caused by intrinsic vulnerability rather than man-made hazards. Still, the hazard plays the triggering role in the risk realization. [ABSTRACT FROM AUTHOR]

In this paper, the monthly long-term precipitation of the city of Ardabil from 1976 to 2020 is simulated by a modern hybrid learning machine. To this end, the Wavelet and Outlier Robust Extreme Learning Machine (ORELM) models are integrated to produce a hybrid model called "Wavelet-Outlier Robust Extreme Learning Machine (WORELM)". First, the observed data are normalized, and the best normalization coefficients for the study are acquired. About 70% of the observed data are employed to train the artificial intelligence models and the remaining (i.e., 30%) to test them. After that, the optimal mother wavelet and the best decomposition level of the wavelet model are computed. Then, the optimal number of the hidden layer neurons and the best activation function of the ORELM model is chosen by performing a trial and error procedure. In this study, the regularization parameter of the ORELM model is also optimized. Moreover, using the autocorrelation function (ACF), the most influencing lags of the time-series data are detected, and 14 WORELM models are developed. By analyzing the WORELM models, the best model and the effective lags of the time-series data are introduced. The outcomes of the hybrid WORELM model are finally compared with other machine learning (ML) algorithms to prove the superiority of the WORELM model. [ABSTRACT FROM AUTHOR]

A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relationships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH. [ABSTRACT FROM AUTHOR]

Background: Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis. Methods: This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19. Results: Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy. Conclusion: This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system). [ABSTRACT FROM AUTHOR]

The key problem in oil exploration and engineering is the lack of accurate and reliable data about the reservoir parameters of a field. Having a precise assessment of petrophysical properties can provide the ability to make decisions with a high degree of confidence about planning for production, exploitation, and further field development scenario. In this research, an artificial intelligence (AI)-based approach was developed to improve the estimation of reservoir parameters including porosity and volume of shale, which has a significant role in different stages of hydrocarbon exploration, in the Kashafrud Gas Reservoir in the northeast of Iran. For this purpose, we measured the petrophysical properties of 27 samples of the Kashafrud Formation. To increase the amount of data for employing a multilayer perceptron (MLP) artificial neural network (ANN), a geostatistical algorithm was used to increase the amount of laboratory measured data of porosity and volume of shale to 686 and 702, respectively. In addition, 2263 well-logging data from the same well were provided. The optimal MLP network with the topology of 6-7-1, and 6-8-1 was selected to estimate the porosity and shale volume with mean squared error (MSE) of 2.78731E−4, and 1.28701E−9, respectively. The training process was performed using two different sets of input data. In the first approach, all available well-logging data were used as input, ending up in high MSE. In the second approach, some selected well logs were used based on the results of sensitivity analysis which clearly improved the estimations. The ability of MLP networks made great improvements in the estimation of the both parameters up to 99.9%. The presence of valuable core data in this study significantly improved the process of comparison and conclusion. The final results prove that AI is a trusted method, also the potential of the ANN method for the reservoir characterization and evaluation associated problems should be taken into consideration. Due to the unavailability of core data along the whole wells, the application of intelligent methods, such as machine learning (ML) can be used to estimate the parameters in other oil or gas fields and wells. [ABSTRACT FROM AUTHOR]

Water resource management and crop growth control require the calculation of reference evapotranspiration (ET0), but meteorological data can often be incomplete, necessitating models with minimal inputs. This study was conducted in Iran's arid synoptic stations of Sirjan and Kerman, where data scarcity is severe. Penman–Monteith FAO-56 was selected as the target data for modeling artificial neural network (ANN), fuzzy neural adaptive inference system (ANFIS), and ANN-gray wolf optimization (ANN-GWO). The performance of these models was evaluated using an input dataset consisting of the current station's minimum and maximum temperatures, ET0, and the wind speed of the nearby station (external data) in three different combinations. The models' accuracy was assessed using two widely used criteria: root mean square error (RMSE) and coefficient of determination (R2), as well as the empirical Hargreaves equation. In the absence of climatic data, the ANFIS, ANN, and ANN-GWO methods using minimum and maximum temperatures, which are relatively easier to estimate, outperformed the empirical Hargreaves equation method in both stations. The results demonstrate that the ANFIS method performed better than ANN and ANN-GWO in all three input combinations. All three methods showed improvement when external data (wind speed and ET0 of the adjacent station) were used. Ultimately, the ANFIS method using minimum and maximum temperatures and the adjacent station's ET0 in Kerman and Sirjan yielded the best results, with an RMSE of 0.33 and 0.36, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation. [ABSTRACT FROM AUTHOR]

Flight network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the air transport network is analyzed with a multi-objective genetic algorithm to reduce the number of airways and to aggregate the passengers and also to reduce route changes and the travel time fortravelers. The proposed topology model of this study is based on the combination of two topologies – point-to-point and hub-and-spoke – with multiple goals for decreasing in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. Four state-of-the-art Multi-objective Genetic Algorithms (MOGAs) are considered for comparison studies and are tested and assessed in data of the Iran airline industry in 2018, as an experiment to real-world applications. Using the combination of point-to-point and hub-and-spoke topologies can improve the performance of the MOGA to solve a network-wide flight trajectory planning. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 km) and up to 18%, respectively. The proposed model also suggests that the current air routes of Iran can be decreased to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 km) and 5%, respectively. The simulation results show the potential benefits of the proposed model and the advantages of it. Optimizing the structure of the flight network can significantly reduce operational cost while ensuring the operation safety. According to the results, the multi-objective optimization model is successfully able to precisely design the efficiently optimized airline topologies. [ABSTRACT FROM AUTHOR]

Air route network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the flight network in air transport is analyzed with a multi-objective genetic algorithm regarding Geographic Information System (GIS) which is used to optimize this Iran airlines topology to reduce the number of airways and the aggregation of passengers in aviation industries organization and also to reduce changes in airways and the travel time for travelers. The proposed model of this study is based on the combination of two topologies - point-to-point and Hub-and-spoke - with multiple goals for causing a decrease in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. The proposed Multi-objective Genetic Algorithm (MOGA) is tested and assessed in data of the Iran airlines industry in 2018, as an example to real-world applications, to design Iran airline topology. MOGA is proven to be effective in general to solve a network-wide flight trajectory planning. Using the combination of point-to-point and Hub-and-spoke topologies can improve the performance of the MOGA algorithm. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 kilometers) and up to 18%, respectively. The proposed algorithm also suggests that the current air routes of Iran can be decreased up to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 kilometers) and 5%, respectively. Two intermediate airports were supposed for these experiments. The computational results show the potential benefits of the proposed model and the advantage of the algorithm. The structure of the flight network in air transport can significantly reduce operational cost while ensuring the operation safety. According to the results, this intelligent multi-object optimization model would be able to be successfully used for a precise design and efficient optimization of existing and new airline topologies. [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]

Ilam province is located in the southwest of Iran, and the primary motivation for this research is to study different dimensions of rainfall fluctuations in the Ilam province. This study is of great importance for the management of the environment in terms of the application of rainfall distribution in different areas. After collecting the data, first, the average number of rainfall months for each of the studied stations for a period was obtained. Then the data were arranged numerically in the order of Gregorian months. Ultra-innovative artificial intelligence methods were used to perform spatial–temporal analysis. The results show that in autumn and winter all three factors were influential on rainfall in the region. The equation method of regression line trend was used to express the changes in rainfall in the study period, and it was concluded that during this period the rainfall trend in March, June, and December in all stations was decreasing. In May, all stations had an upward trend except for Harsin station. In other months, there are decreasing and increasing trends among the stations. The general trend for rainfall during the study period is also one of decreasing. Regarding the results, the standard deviation for the simulation is equal to 10.22 for autumn and 12.35% for winter. This value is about 17.97% and 7.19%, respectively, for the observed rainfall. The results show there are no significant differences between the model and measured data, so the artificial network is applicable for the simulated monthly precipitation. [ABSTRACT FROM AUTHOR]

Point predictions of hydroclimatic processes through nonlinear modeling tools are associated with uncertainty. The main goal of this research was to construct prediction intervals (PIs) for nonlinear artificial neural network (ANN)-based models of evaporation and the standardized precipitation index (SPI). These are two critical indicators for climate for four stations in Iran (i.e., Tabriz, Urmia, Ardabil, and Ahvaz) to qualify their predicted uncertainty values (UVs). We used classical techniques of bootstrap (BS), mean variance estimation (MVE), and Delta, as well as an optimization-based method of lower upper bound estimation (LUBE), to construct and compare the PIs. The wavelet-based denoising method was also adopted to denoise input data, enhancing the modeling performance. The obtained results indicate the ability of the BS and LUBE methods to estimate the uncertainty bound. The Delta method mostly failed to find the desired coverage due to its narrow PIs. On the other hand, the MVE method, due to its wide bound, did not convey valuable information about uncertainty. According to the obtained results, denoising the input vector could enhance the PI quality in the modeling of the SPI by up to 76%. It was more prominent than reducing the UV for evaporation models, which was observed the most at the Ardabil station, up to 30%. The inherently more random nature of drought than the evaporation process was interpreted as the cause of this reaction. From the results, Urmia station seems the riskiest regarding drought ventures. Significance Statement: The point predictions of evaporation and precipitation (in the form of SPI) are subject to uncertainty. The best way is to provide an area with the highest contingency as a prediction interval. The reduction in the width of such an interval leads to increased confidence in explaining and predicting these processes. We investigated different methods and found that by utilizing the optimization-based method for denoised inputs, uncertainty values of the output were conveyed better. Additionally, we concluded that the more random the process, the greater its uncertainty. A primary sense of the drought risk was made from the uncertainty perspective. [ABSTRACT FROM AUTHOR]

Due to the population growth and continuous migration of people from rural areas to urban areas, it is important to identify the suitable locations for future development in order to find suitable sites for various kinds of facilities such as schools, hospital and fire stations for new and existing urban areas. Site suitability modelling is a complex process involving various kinds of objectives and issues. Such a complex process includes spatial analysis, use of several decision support tools such as high-spatial resolution remotely sensed data, geographical information system (GIS) and multi criteria analysis (MCA) such as analytical hierarchy process (AHP), and in some cases, prediction techniques like cellular automata (CA) or artificial neural networks (ANN). This paper presents a comparison between the results of AHP and the ordinary least square (OLS) evaluation model, based on various criteria, to select suitable sites for new hospitals in Qazvin city, Iran. Based on the obtained results, proximity to populated areas (0.3) and distance to air polluted areas (0.23–0.26) were the two highest important criteria with high weight value. The results show that these two techniques not only have similarity in size (in m2) for each suitability class but they also have similarity in spatial distribution of each class in the entire study area. Based on calculations of both techniques, 1–2%, 25%, 40–43%, 16–20% and 14% of study areas are assigned as ‘not suitable', ‘less suitable', ‘moderately suitable', ‘suitable' and ‘most suitable' areas for construction of new hospitals. Results revealed that a 75% similarity was found in the distribution of suitability classes in Qazvin city using both techniques. Nineteen per cent (19%) of the study area are assigned as ‘suitable' and ‘most suitable' by both methods, so these areas can be considered as safe or secure areas for clinical purposes. Moreover, almost all (99.8%) suitable areas are located in district 3, because of its higher population, less numbers of existing hospitals and large numbers of barren land plots of acceptable size. [ABSTRACT FROM AUTHOR]

Abstract: Train timetabling with minimum delays is the most important operating problem in any railway industry. This problem is considered to be one of the most interesting research topics in railway optimization problems. This paper deals with scheduling different types of trains in a single railway track. The primary focus of this paper is on the periodic aspects of produced timetables and the proposed modeling is based on the periodic event scheduling problem (PESP). To solve large-scale problems, a hybrid meta-heuristic algorithm based on simulated annealing (SA) and particle swarm optimization (PSO) is proposed and validated using some numerical examples and an Iranian case study that covers the railway line between two cities of Isfahan and Tehran. [Copyright &y& Elsevier]

Introduction: A hospital information system (HIS) is a comprehensive, integrated information system designed to manage the administrative, financial and clinical aspects of a hospital. As an area of medical informatics, the introduction of a HIS into a hospital was purported to reduce the time spent on administrative and clinical activities by electronic data processing. However, adoption has been slow, and a key concern has been that staffs will require more time to complete their work using HIS. And also Most of available HIS have lots of deficiencies in data gathering, so hospital managers decided to keep their paper records and same time use HIS. Most previous studies addressing this issue have been done with concentration on clinical staff activities. Methods: Comparative observational of the impact of a HIS on Medical Records Department time in four units: Admission Discharge and Transfer/ADT, Medical Filing and Retrieval, Medical coding/billing, and Medical Statistics. Measurements: We used a time-motion method to measure staff time spent in predefined activities. Medical Records staff monitored while using HIS and paper based system. 15 clerical medical records staff were observed treating 300 documents while still using paper-based records, and same persons were observed treating 400 patients' records with HIS. Results: Following HIS implementation, the use of the computerized system decreased the average total workload by 61.35% (from 66.41 to 23.93 m). Conclusion: Hospital Information System (HIS) reduces three nonclinical activities time noticeably which is vital to decision making and plays a crucial role in the success of the organization. [ABSTRACT FROM AUTHOR]

Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient (R), root mean square error (RMSE), coefficient of residual mass (CRM) and scatter index (SI) were used to measure the performance of models. Tthe dynamic model approach was shown to give the best results with relatively fewer errors and higher correlations. To assess the ability of GP relative to the neuro-fuzzy (NF) and artificial neural networks (ANN), several NF and ANN models were developed by using the same data set. The obtained results showed the superiority of GP to the NF and ANN approaches. The Stephen-Stewart and Christiansen methods were also considered for comparison. The results indicated that the proposed GP model performed quite well in modeling evaporation processes from the available climatic data. The results also showed that the estimated solar radiation data can be applied successfully instead of the recorded sunshine data. [ABSTRACT FROM AUTHOR]

Background: Data mining as a multidisciplinary field is rooted in the fields such as statistics, mathematics, computer science and artificial intelligence and has been gaining momentum in scientific, managerial, and executive applications in health care. Data mining can be defined as the automated extraction of valuable, practical and hidden knowledge and information from large data. Applying data mining in medical records and data is of utmost importance for health care givers and providers and brings vital and valuable outcomes. Data mining can help doctors come up with better recommendations and plans for treatment which actually in many respects have significant impact on patients' life and satisfaction In this paper we have proposed and utilized data mining methods to extract hidden information in medical records of pelvis stone patients with ureteral stone. We have tried to design a decision support system model to be applicable for selecting type of treatment for these groups of patients. Methods: We gathered needed information from Shahid Hashemi Nejad hospital. In this research we have used decision tree as a data mining tool, for selecting suitable treatment for patients with ureteral stone. This model can predict probability of success of each treatment. Results: In this research we extracted effective attributes in selecting type of treatment for patients with ureteral stone. Conclusions: By using this model we can have eight percent improvement in number of patients who have stone free output after treating. In fact, this model has a better functionality than expert system of hospitals. [ABSTRACT FROM AUTHOR]

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R 2 ) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves. • Chromium fate and transport is evaluated by experimental practices and estimated by AI. • A sensitive analysis is carried out for effective parameters in warm and cold seasons. • Machine learning computations are applied for Cr bioaccumulation in tomato. • The NN regressor and LR have the best goodness-of-fit for estimating Cr in tomato leaves. [ABSTRACT FROM AUTHOR]

• A Decision Support System for flash-flood risk management in the arid areas is designed. • An integrated GIS and Watershed Modelling System is applied for flood flow simulation. • AI and Classic Delphi are employed for smart sustainable management of flash-flood risks. • Flood risk is analysed using Failure Mode and Effects Analysis & Hazard & Operability techniques. This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions. [ABSTRACT FROM AUTHOR]

Globally, most industrial parks that have not gone through a proper site selection process have been neglected or underutilised and such active sites tend to burn a lot of fuels and generate greenhouse gases. Technology must be used to conduct on-site investigations of the spatial and non-spatial factors of industrial parks, especially the emerging Eco-Industrial Park (EIP). The paper seeks to appraise the global application, capability and acceptability of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) tools in the EIP sites selection and planning. Also, to identify the benefits of mixing MCDM tools when integrating with GIS. There have been some reviews on the use of GIS and MCDM methods, but so far, there is none on the role of GIS and MCDM methods in EIP site selection. Geospatial and MCDM tools can be used to carefully select EIP sites, where materials and energy can be shared to achieve cleaner production, reduce environmental pollution and promote sustainable development. To understand the usage of these tools and their role in the EIP site selection process, a critical review of twenty-two papers published from 2007 to 2020 indexed by Google Scholar, Springer, IEEE Xplore and Web of Science have been developed for the first time. The articles are classified based on country/region, journal of publication, year and citations of publication, the authors and technologies applied. The results show that most studies on EIP site selection come from Iran and China. The Journal of Environmental Monitoring and Assessment has the highest number of articles published at 28%. The highest number of articles was published in 2014 with 312 citations. GIS and Analytic Hierarchy Process (AHP) topped the technologies used, accounting for 42% and 24% respectively, and hybrid tools 34%. As categorised by criteria content, there was an overlap of criteria in most journals. Though, water bodies, utilities, infrastructure, and natural characteristics were prominent. Renewable energy resources were not given attention by most researchers, and risk assessment was absent from the site selection literature. A blend of traditional MCDM tools constitutes the state-of-the-art tools, and when these are combined with GIS in EIP site surveys, they execute to obtain actual results. Future EIP site selection that is integrated with risk assessment is envisioned to be industry 4.0 (4IR)-driven through a combination of GIS and artificial intelligence, and the recently developed MCDM hybrid modules. [Display omitted] • Poor site selection has led to industrial park being neglected or underutilised. • Abandoned/underutilised active industrial parks can burn fuels to cause GHG. • First time of critical review of role of GIS and MCDM tools in EIP site selection. • GIS and AHP currently mainly used for large-scale EIP site criteria selection. • EIP site choice risk study using GIS, AI and MCDM hybrid can sustain industry 4.0 [ABSTRACT FROM AUTHOR]

• Reviewed more than 200 papers that used machine learning in groundwater quality modeling. • Neural networks are the most used machine learning model in groundwater quality modeling. • Nitrate is the most modeled contaminants. • Suggestions for further works are proposed. Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management. [ABSTRACT FROM AUTHOR]

Background: Polypharmacy (PP) is increasingly common in Iran, and contributes to the substantial burden of drug-related morbidity, increasing the potential for drug interactions and potentially inappropriate medications. Machine learning algorithms (ML) can be employed as an alternative solution for the prediction of PP. Therefore, our study aimed to compare several ML algorithms to predict the PP using the health insurance claims data and choose the best-performing algorithm as a predictive tool for decision-making. Methods: This population-based cross-sectional study was performed between April 2021 and March 2022. After feature selection, information about 550 thousand patients were obtained from National Center for Health Insurance Research (NCHIR). Afterwards, several ML algorithms were trained to predict PP. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. Results: The study sample comprised 554 133 adults with a median (IQR) age of 51 years (40 – 62) that nested in 27 cities within the Khuzestan province of Iran. Most of the patients were female (62.5%), married (63.5%), and employed (83.2%) during the last year. The prevalence of PP in all populations was about 36.0%. After performing the feature selection, out of 23 features, the number of prescriptions, Insurance coverage for prescription drugs, and hypertension were found as the top three predictors. Experimental results showed that Random Forest (RF) performed better than other ML algorithms with recall, specificity, accuracy, precision and F1-score of 63.92%, 89.92%, 79.99%, 63.92% and 63.92% respectively. Conclusion: It was found that ML provides a reasonable level of accuracy in predicting polypharmacy. Therefore, the prediction models based on ML, especially the RF algorithm, performed better than other methods for predicting PP in Iranian people in terms of the performance criteria. [ABSTRACT FROM AUTHOR]

By determining the hydraulic flow units (HFUs) in the reservoir rock and examining the distribution of porosity and permeability variables, it is possible to identify areas with suitable reservoir quality. In conventional methods, HFUs are determined using core data. This is while considering the non-continuity of the core data along the well, there is a great uncertainty in generalizing their results to the entire depth of the reservoir. Therefore, using related wireline logs as continuous data and using artificial intelligence methods can be an acceptable alternative. In this study, first, the number of HFUs was determined using conventional methods including Winland R35, flow zone index, discrete rock type and k-means. After that, by using petrophysical logs and using machine learning algorithms including support vector machine (SVM), artificial neural network (ANN), LogitBoost (LB), random forest (RF), and logistic regression (LR), HFUs have been determined. The innovation of this article is the use of different intelligent methods in determining the HFUs and comparing these methods with each other in such a way that instead of using only two parameters of porosity and permeability, different data obtained from wireline logging are used. This increases the accuracy and speed of reaching the solution and is the main application of the methodology introduced in this study. Mentioned algorithms are compared with accuracy, and the results show that SVM, ANN, RF, LB, and LR with 90.46%, 88.12%, 91.87%, 94.84%, and 91.56% accuracy classified the HFUs respectively. [ABSTRACT FROM AUTHOR]

This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R2) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R2 value of 0.974, MSE of 4.14 × 10−6, and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches. [ABSTRACT FROM AUTHOR]

The structure of the electricity market in Iran is based on a pay-as-bid auction mechanism. In such a market, hydropower generators need to have accurate estimates of energy price in peak hours of the day-ahead market to optimally operate the reservoir and maximize the revenue. This paper aims at providing a robust model with the best predictors for forecasting the maximum daily electricity price (MDEP) in Iran's electricity market. To reach the goal, hourly electricity prices in 2020 and 2021 were used and several artificial intelligence models were employed to predict the MDEP and ADEP (average daily electricity price). A sensitivity analysis of the inputs showed that in most of the models for forecasting the day-ahead MDEP (P t max), the best predictors were P t − 1 max , P t − 7 max , and P t − 30 m a x . Also, the convolutional neural-long short-term memory network (CNN-LSTM) had the best performance for forecasting both MDEP and ADEP in Iran's energy market. Compared to the multivariate linear regression model, the CNN-LSTM dealt well with sinusoidal characteristics and fluctuation of electricity prices. Therefore, it could improve the accuracy and correlation of the MDEP forecasts by 31% and 3.5%, respectively. • The maximum daily electricity price (MDEP) is very important for hydropower plant's operation. • Nonlinearity, instability, and hourly fluctuations of electricity price make it difficult to forecast. • Several artificial intelligent models, SVM, ANN, ANFIS, ANN-GA, and CNN-LSTM, were used for forecasting MDEP. • The best predictors (P t − 1 max , P t − 7 max , and P t − 30 max) were determined in a sensitivity analysis. • The CNN-LSTM model improved the accuracy of forecasts for MDEP by 31% in comparison with the conventional MLR model. [ABSTRACT FROM AUTHOR]

When exposed to water, dispersive (D) soils are eroded and washed away by underground or surface flowing waters. Although soil dispersion is due to its chemical composition, the results of the commonly used chemical method, that is, the Sherard approach, do not match with those of the popular robust Pinhole test. Due to the deficiency of the chemical method, this study aimed to employ artificial intelligence (AI)‐based approaches for predicting the D classification of the soils. To this end, a database containing 321 records of the results of chemical and Pinhole tests on borrow soil samples was collected from various construction sites in Iran. The predictive models for soil dispersion evaluation were developed using the artificial neural network (ANN) and the support vector machine (SVM). The D classification results were presented as output classes versus target classes. Through the comparison of statistical indices, it was found that the results of the proposed models conform to those of the Pinhole test. It was also shown that the ANN model is more accurate than the SVM model for predicting the dispersion potential of the soil. Furthermore, it was indicated that the new models significantly outperform the Sherard method in determining the D classification of the soil. Highlights: Dispersive soils can be classified employing artificial intelligence (AI)‐based methods.AI‐based approaches have superior predictive ability in contrast to traditional approachs.ANN and SVM techniques can accurately identify soil dispersion potential.Higher accuracy of new methods compared to common Sherard approach was validated with Pinhole test results. [ABSTRACT FROM AUTHOR]

Due to its heterogeneous and complex nature, groundwater modeling needs great effort to quantify the aquifer, a crucial tool for policymakers and hydrogeologists to understand the variations in groundwater levels (GWL). This study proposed a set of supervised machine learning (ML) models to delineate the GWL changes in the Zarand-Saveh complex aquifer in Iran using 15-year (2005–2020) monthly dataset. The wavelet transform (WT) procedure was also used to improve the GWL prediction ability of ML models for 3-month horizons using input datasets of precipitation, evapotranspiration, temperature, and GWL. The four well-accepted standalone ML methods, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least square support vector machine (LSSVM), were implemented and compared with the hybrid wavelet conjunction models. The methods were compared based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Comparison outcomes showed that the hybrid wavelet–ML considerably improved the standalone model results. The wavelet transform-least square support vector machine (WT-LSSVM) model was superior to other standalone and hybrid wavelet–ML methods to predict GWL. The best GWL predictions were acquired from the WT-LSSVM model with input scenario 5 involving all influential variables, and this model produced RMSE, MAE, R, and NSE as 0.05, 0.04, 0.99, and 0.99 for 1 month ahead of GWL prediction, while the corresponding values were obtained as 0.18, 0.14, 0.95, and 0.90 for 3 months ahead of GWL prediction, respectively. [ABSTRACT FROM AUTHOR]

The bibliometric analysis investigated the impact of publications on trends in the literature and bioenergy research using artificial intelligence (AI) from 1991 to 2021. In this study, 1721 publications were extracted from the Web of Science, and an analysis of the countries, authorship, institutions, journals, and keywords was visualised. In the recent decades, this field has entered an outbreak phase. India was the most productive country in this area, followed by China, Iran, and the US. It also noted several notable differences between trends and subjects in developed and developing countries. The former led this field at the initial stage and later attached importance to using AI for research feedstock and impact assessment. Developing countries encouraged the advancement of this area and emphasised the feedstock usage of phase treatment and process optimisation. In addition, a co-authorship and institutes study revealed that authors and institutes in distant regions rarely collaborated. The journal analysis shows strong links between Energy, Fuel, and Energy Conversion and Management. Machine learning is by far the most common application of artificial intelligence (AI) technology in bioenergy research, with 53% of the articles using it. In these AI-related publications, the keyword artificial neural network (ANN) appeared most frequently in the articles. [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]