machine learning algorithms

Research impact is a key concern for stakeholders, as it reflects the beneficial and profitable applications of research across multiple dimensions, including society, economy, environment, culture, and health. This study aims to analyze the interrelationships among variables influencing scientific outputs and to identify the most effective machine learning algorithms for predicting their scientific, social, and economic impacts.

The current research is applied in purpose and descriptive in method, utilizing a scientometric approach. This study aims to explore the relationship between the quantity of scientific publications and scientific cooperation, as well as the scientific, social, and economic impact of Iran's scientific contributions in the field of biotechnology. Additionally, it seeks to determine which machine learning algorithms are more effective in predicting the impact of scientific outputs across various dimensions. The research focuses on Iran's biotechnology scientific outputs indexed in the Scopus database from 2003 to 2024. The data, extracted on 21 January 2024, were analyzed using the SciVal analytical database. In this research, Pearson's correlation coefficient and the R software package were used to examine the relationships between the studied indicators. Machine learning algorithms, including multiple linear regression, nearest neighbors, decision trees, random forests, and gradient boosting, were applied and evaluated as predictive models. The Python programming language was employed to conduct tests and implement these algorithms.

The findings of this study showed that Iran's scientific outputs in this field in the period from 2003 to 2023 had increased 36 times, which is considered extremely high progress. A positive and significant relationship was observed between international collaboration and various impact indicators, including citation counts, Field-Weighted Citation Impact, Output in Top 10% Citation Percentiles, Patent-Citations Count, Patent-Citations per Scholarly Output, Scholarly Output cited by Patents, Patents Count, Views Count, Output in Top 10% Views Percentiles, Views per Publication, and Field-Weighted Views Impact. The index of academic collaboration also shows a positive and significant relationship with several indicators, including citation counts, Field-Weighted Citation Impact, Output in the Top 10% Citation Percentiles, Publications in the Top 10% Journal Percentiles (by Cite Score Percentile), Patents Count, Scholarly Output cited by Patents, Views Count, and Field-Weighted Views Impact. Academic-government collaboration  is also positively and significantly correlated with three indicators citations per publication, Patent-Citations Count, and Patent-Citations per Scholarly Output. In the case of the impact of the studied scientific outputs, citation counts are positively and significantly associated with several indicators, including Scholarly Output cited by Patents, Patents Count, Views Count, Views per Publication, and the Field-Weighted Views Impact of the field of biotechnology. In terms of economic impact, the result indicated that the number of patent citations is a key representative indicator. This indicator showed positive and significant relationships with other metrics, including academic Collaboration, international Collaboration, citation counts, citations per Publications, Field-Weighted Citation Impact, Views Count positive and significant relationship in Output in Top 10% Views Percentiles, Views per Publication and Field-Weighted Views Impact in the field of biotechnology. Regarding social impact, the analysis revealed that Views Count is positively and significantly correlated with several indicators, including citation counts, Field-Weighted Citation Impact, the number of patent citations, Patent-Citations per Scholarly Output, Scholarly Output cited by Patents and Patents Count in biotechnology. Based on the results, multivariate linear regression demonstrated higher accuracy and a lower standard deviation, making it a more effective model for predicting the scientific, technological, and social impact of Iran's scientific outputs in biotechnology.

International cooperation is the most significant factor influencing the quality of articles, including metrics such as citations, views, and applicability. Therefore, it is crucial to explore and implement strategies to enhance such collaborations. To better assess the effectiveness of research, it is recommended to employ a diverse set of indicators during both quantitative and qualitative evaluations of articles. For policymakers in science and technology, the primary focus should be on value creation and generating added value, particularly in the economic sector. This is essential despite the observed quantitative and qualitative growth in Iran's scientific outputs.

Smart technologies such as artificial intelligence (AI), the Internet of Things (IOT), machine learning (ML), and big data analytics play a key role in optimizing light vehicle repair and maintenance. These technologies, by predicting failures, reducing repair costs, and increasing operational efficiency, are of special importance in military environments, as optimal vehicle performance directly impacts mission success. In this research, an intelligent native model of light vehicle repair and maintenance is presented for the logistic and support of a military-enforcement organization. This model is a combination of the random forest algorithm, time series data, logistic regression, and Markov chain and is developed based on real repair data of four types of vehicles widely used in the organization over two months and industrial Diag parameters. Also, a dual-objective productivity model based on data envelopment analysis has been added to optimize management decisions. The model reduced repair costs by 47% and increased the accuracy of failure prediction by 0.46%. Also, the optimal management of the spare parts supply chain played a key role in reducing unexpected failures. A specialized questionnaire identified five main criteria for model implementation: Data reliability, implementation cost, prediction accuracy, response speed, and ease of use. The results showed that repair costs were reduced by between 8% and 15%, and unplanned failures were reduced by up to 42%. Intelligence in this military-enforcement organization does not require the use of all intelligence criteria and the use of various sensors, and the proposed model provides a comprehensive solution for optimizing the maintenance and repair of military-enforcement vehicles, which the organization can benefit from in all its dimensions by generalizing and changing the data type of this model.

This research aims to conduct a comprehensive review and meta-analysis of previous studies on the use of machine learning to improve educational quality. The study seeks to identify gaps in existing research and provide new insights for optimizing educational methods through the application of machine learning. The research method involves a meta-analysis of prior studies in this field, revealing that most research has focused on the benefits of machine learning, but its challenges and limitations have not been thoroughly examined. The findings of this research indicate that machine learning can significantly contribute to enhancing educational quality. However, challenges such as data privacy issues, the need for advanced technological infrastructure, and potential resistance to adopting new technologies have not been fully explored. Additionally, the cultural and social impacts of using machine learning in educational settings, particularly in military academies, have received less attention. This lack of focus could have significant implications for the success or failure of implementing these technologies. The conclusion of this research focuses on providing practical recommendations and strategies for optimizing the use of machine learning in the education system. These recommendations can assist decision-makers and policymakers in developing and implementing more effective programs and strategies to enhance educational quality. By identifying and addressing existing gaps, this study paves the way for a more effective utilization of machine learning in improving educational quality.

Improving educational quality requires attention to factors such as educational content, teaching methods, the quality of instructors, and the educational environment. Previous research has shown that interactive teaching methods, up-to-date and relevant content, and a suitable educational environment can have a significant impact on the quality of education. Machine learning can analyze educational data to identify student learning patterns and provide personalized recommendations that improve their performance and motivation. Numerous studies in this field have examined the role of machine learning in enhancing educational quality, but there has not been a comprehensive review of the literature in this area. This study aims to review previous research in the field under review and demonstrates that most of these studies focus on the benefits of machine learning without thoroughly examining its challenges and limitations. Among the challenges in this area, particularly with regard to improving educational quality in military academies, are issues related to data privacy, the need for advanced technological infrastructure, and the potential resistance of instructors and students to adopting new technologies. Additionally, the cultural and social impacts of using machine learning in military academies have received less attention. The main objective of this research is to identify and fill these research gaps and provide solutions for the optimal use of machine learning in improving educational quality. By offering practical recommendations and solutions, this research can assist decision-makers and policymakers in developing more effective programs and strategies to enhance educational quality.

This article examines and designs a prediction model for analyzing customer behavior using artificial intelligence algorithms and neural networks. The main goal of this research is to simulate and predict customer purchasing behaviors in dynamic and changing markets with the help of machine learning models. In this regard, first, the theoretical foundations related to consumer behavior prediction, applications of artificial intelligence in data analysis and neural network algorithms are examined. Then, different prediction models are introduced and their application in customer data analysis and business decision-making is described. Also, the challenges and opportunities of using artificial intelligence algorithms in predicting customer behavior in different industries are examined. The research findings show that the use of neural networks and machine learning algorithms can significantly improve the accurate prediction of customer behavior and the optimization of marketing and sales strategies. Finally, the results of modeling and predicting customer behavior for organizations and businesses can be used as an efficient tool in strategic decision-making and resource management.

Flood zoning maps provide valuable information about the nature of flood and its effect on floodplain lands. A set of effective factors must be defined to map flood susceptibility or, in general, to develop a model for assessing natural disaster risk. The factors affecting flood were used in eastern catchment area of lake Urmia which include altitude, slope, distance from the river, topographic moisture index, topographic position index, roughness index, curvature level, topographic curvature section, total curvature, NDVI index, land use, lithology and rainfall according to the experiences of experts and researchers reported in previous studies. After preparing the effective layers on the flood and the point layer of the flood points, as well as performing the linear test, five methods including Multiple Linear Regression, Partial Least Squares Model, Quantile Regression, Ridge Regression and Robust Regression were used for modeling and predictions. Then the ROC curve was used to validate the results. The results of this validation showed that the partial least squares (PLS) and multiple linear regression (MLR) models with the maximum area under the curve (AUC) (0.983 and 0.997, respectively) and the lowest standard deviation (0.015 and 0.018, respectively) ) have performed better. Among these two models, PLS has slightly better results than MLR. Finally, a random forest model was used to determine the importance of the input factors, and it was found that the factors of height, distance from the waterway and slope percentage are the most influential factors on floods in the study area.

Rice is an important crop and the main food of more than half of the world’s population, which needs water and heat to grow. Thus, mapping and monitoring rice fields with efficient means such as remote sensing technology is necessary for food security and the lack of water sources. The phenology extracted from the time series of vegetation indices is used for monitoring and mapping the area under rice cultivation. In addition to the phenological curve, the LST time series map, which is calculated from Landsat 8 images and is related to the phenomenon of evaporation and transpiration of irrigated crops, can cause the separation of rice cultivation from rainfed crops, summer crops, water, etc. Therefore, in this study, the effect of the LST time series map is investigated map for improving the accuracy of rice field identification.

Since the planting to harvest period of rice is from May to October, in this study, the time-series maps of LST and NDVI for the 3rd of April, 21st of May, 6th of John, 22nd of John, 8th of July, 24th of July, 9th of August, 12th of October, and 28th of October have been calculated after download the Landsat-8 time-series in 2020 The ground truth map of the study area has been obtained from the US Department of Agriculture. To identify rice fields and calculate the LST and NDVI using the Landsat-8 images, initial pre-processing including radiometric and geometric corrections has been applied to these images first. After initial corrections and the calculation of NDVI and LST maps, to identify rice fields in the study area, machine learning algorithms such as Support Vector Machine, K-Nearest Neighborhood, Multilayer Perspective, Logistic Regression, and Decision Tree, have been proposed.

The results of the proposed method at the state of California showed that using the time series map of  Land surface temperature (LST) with the time-series map of  Normalized Difference vegetation Index, improved the results of identifying rice fields (the average Overall Accuracy= + 3/572% and the average kappa coefficient= +7/112%). Visual results showed that some cultivation such as tomato, corn, cucumber, fallow, and water were removed from the rice final map when using the LST time-series map with the NDVI time-series map. According to the numerical results, the Support Vector Machine algorithm (Overall Accuracy 94/28 and Kappa Coefficient 88/29), the Multilayer Perceptron algorithm  (Overall Accuracy 94/26 and Kappa Coefficient 88/21), and the K-Nearest Neighborhood algorithm (Overall Accuracy 93/71 and Kappa Coefficient 87/08) showed the highest Overall Accuracy and Kappa Coefficient compared to the Logistic Regression algorithm (overall accuracy 91/96 and kappa coefficient 83/54) and the Decision Tree algorithm (Overall Accuracy 91/34 and Kappa Coefficient 81/97), respectively.

Although, many methods have been proposed to identify rice fields from satellite images. But, the similarity of rice class with other classes is one of the main challenges related to rice identification. In this research, the effect of LST time series maps to improve the identification accuracy of rice fields in Landsat-8 time-series images was investigated. In this study, the effect of the time series map of land surface temperature index extracted from Landsat-8 images on improving the accuracy of identifying rice fields from other rice fields due to the evapotranspiration process using machine learning algorithms was investigated. The results showed the effectiveness of the proposed index in improving the identification accuracy of rice fields. One of the reasons for improving the accuracy of identifying rice fields is to extract the characteristics of the thermal growing season from the Earth's surface temperature time series (LST) maps along with the rice phenology curve. The results showed that due to the flooding of rice fields when using the NDVI time series map, water class and fields summer crops were identified as rice class. But, water and summer crops classes were removed from the rice final map using a land surface temperature time-series map with the extraction of thermal growth season characteristics. Therefore, the results showed that there was a direct relationship between LST time-series maps and rice cultivation.

Incredible advances in artificial intelligence (AI) have affected various service sectors and social welfare policies in developed countries in recent years. As a result, AI and novel technologies (e.g., big data and machine learning algorithms) have shaped many social developments and forced policymakers to confront difficult challenges and questions. Similarly to the pre-digital era, pilot implementations of these technologies in the insurance and banking industries, health systems, and civil registration systems have resulted in widespread systemic violations of many human rights enshrined in international documents. However, domestic or international monitoring institutions have not defined the possibility of “responsibility acceptance” because these violations are automated (i.e., machine-based). It is hence impossible to activate an accountable mechanism through the current legal frameworks.

Most of the research has been done on predicting the price changes of cryptocurrencies, and the researchers used machine learning algorithms in this field. The purpose of this article is to present an approach to predicting Bitcoin price changes using machine learning algorithms along with data from people's sentiment analysis towards famous people such as Elon Musk on Twitter, Reddit, and Telegram social networks. The data collected for this research is from 2021 and 2022. The distinguishing feature of this research is the use of technical, economic indicators and sentiment analysis simultaneously to predict the price trend. The recursive feature removal method was used to select the feature and in the next step, the results were compared by testing 8 classification algorithms. In this research, the xgboost algorithm showed excellent performance with a record accuracy of 88% in predicting the trend of Bitcoin price changes.

In recent years, the application of artificial intelligence, especially machine learning, has grown significantly in the field of HRM, which is unknown to many managers and experts in the field of HR due to the newness of this field. A lot of data is being generated by users of organization in HRM domains and the related fields, which are difficult to analyze and use in HR activities. The capabilities of data science and machine learning have been able to make great contributions to the field of HRM and beyond to the management of the organization with descriptive, diagnostic, predictive and prescriptive reports and analyses. The purpose of the research is to examine the measures that have been taken so far in the field of HRM intelligence, and in this research, three main questions are answered. The first question is to identify HRM activities that can be made intelligent. In the second question, the application of various ML algorithms in HRMI has been identified. In the third question, based on the maturity levels of data analytics, the classification of "ML algorithms in intelligent HRM functions" has been made. In order to answer , a wide range of articles were extracted from reliable scientific databases and journals and analyzed based on a mixed method. In this method, qualitative and quantitative methods for data analysis were investigated at the same time. IN the quantitative part, text mining algorithms were used Python language, and in the qualitative part, thematic analysis was used MAXQDA2020 .

Twin Sentinel-2 MSI sensors are spatially like the Landsat-8 OLI super spectral instrument, aiming to additional data continuity for land surface monitoring were launched by European Space Agency. In this paper, the potential of these data was evaluated for discrimination of lithological units and alterations in the Esfordi phosphate deposit area and was compared with OLI and fused OLI data. Decorrelation stretch method was used for enhancing the lithological units of the study area, and all of the 3 datasets acceptably discriminated the rock units. Among these, MSI data could produce the lithological map with high resolution and highest level of reality owing to its high spatial resolution. For statistical comparison, Support Vector Machine and Random Forest methods were applied on datasets for classification of the lithological units and their accuracy was assessed using confusion matrices. Furthermore, the corresponding band ratios to which were defined for Landsat-5 TM, were applied on datasets for detecting the altered areas. Then the areas of each highlighted alteration zones were estimated for comparison. Furthermore, the scatterplots of band ratio images were prepared. MSI dataset revealed the highest overall accuracy and Kappa coefficient in Support Vector Machine and Random Forest classification. Also, the results of band ratioing showed that MSI and fused OLI data have the most correlation and similarities. This study demonstrated that MSI data are more optimal than OLI data for lithological and alteration mapping. Also, using fused OLI data in dates which there is no MSI data acquisition or for producing seamless geological maps in continental scale besides to MSI data, is efficient.

It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of object-oriented classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.