List of Articles جنگل تصادفی

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  1 - High Performance Computing via Improvement of Random Forest Algorithm Using Compression and Parallelization Techniques
  Naeimeh Mohammad Karimi Mohammad Ghasemzadeh Mahdi  Yazdian Dehkordi Amin Nezarat
  This research seeks to promote one of the widely being used algorithms in machine learning, known as the random forest algorithm. For this purpose, we use compression and parallelization techniques. The main challenge we address in this research is about application of More

  This research seeks to promote one of the widely being used algorithms in machine learning, known as the random forest algorithm. For this purpose, we use compression and parallelization techniques. The main challenge we address in this research is about application of the random forest algorithm in processing and analyzing big data. In such cases, this algorithm does not show the usual and required performance, due to the needed large number of memory access. This research demonstrates how we can achieve the desired goal by using an innovative compression method, along with parallelization techniques. In this regard, the same components of the trees in the random forest are combined and shared. Also, a vectorization-based parallelization approach, along with a shared-memory-based parallelization method, are used in the processing phase. In order to evaluate its performance, we run it on the Kaggle benchmarks, which are being used widely in machine learning competitions. The experimental results show that contribution of the proposed compression method, could reduce 61% of the required processing time; meanwhile, application of the compression along with the named parallelization methods could lead to about 95% of improvement. Overall, this research implies that the proposed solution can provide an effective step toward high performance computing. Manuscript profile
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  2 - A suitable algorithm for identifying changes in micro-landforms using UAV images. Case study: Barg-e- Jahan area in Jajrud region (2015-2016)
  M.H. Tavakol M. Ghahroudi H. Sadough Kh. Alinoori
  One of the main and most important topics of geomorphology is the identification and evaluation of microlandform changes. Their recognition and spatial distribution in order to understand and evaluate changes, stability studies and regional planning is one of the basic More

  One of the main and most important topics of geomorphology is the identification and evaluation of microlandform changes. Their recognition and spatial distribution in order to understand and evaluate changes, stability studies and regional planning is one of the basic needs of applied geomorphology. Barg-e- Jahan area is located in Jajroud catchment area affected by many environmental changes. In this study, based on micro-scale geomorphological approach, using UAV images along with field survey in the Barg-e- Jahan area, microlandforms changes were investigated. UAV images with a spatial resolution of 2.5 cm were obtained from the Ministry of Energy between 2015 and 2016. These images were corrected using ENVI 5.1 and Arc Map 10.3 software, and then the desired algorithms were implemented via coding in Python. Changes were investigated with machine learning algorithms and random forest models, SVM with RBF kernel, random forest with features extracted from CNN networks, and SVM with linear kernel with features extracted from deep neural networks. Results showed that the SVM-RBF model is less accurate than other models with 88% accuracy, so the separation between the classes was limited. In the random forest, 92% of the classes were distinguishable with linear boundaries. The near-ideal model in the random forest algorithm with deep learning was observed with an accuracy of 96%. Investigations showed that most of the changes in microlandforms in this model were related to the change of vegetation cover to soil by 45.03%, and in the next place, the change of sheet wash erosion by 22.05%. According to the obtained results and field observations in 2017, it was determined that the flood of 2017 in Barg-e-Jahan area has caused major changes in the area. Its greatest impact was on the vegetation and the diagram shows at the highest degree of disturbance. In this period, the surface flow and gully formation in the area increased and it shows the high level of erosion and great changes of microlandforms in the study area. Manuscript profile
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  3 - Predicting Generalized Anxiety Disorder Among Female Students Using Random Forest Approach
  Zahra Gholami Habibeh Zare
  <p>Mental health is considered one of the major challenges for the generations. Generalized anxiety disorder (GAD) is one of many mental health complications. However, individuals with the disorder experience hyperbolic concerns and tensions regarding daily events. Furt More

  <p>Mental health is considered one of the major challenges for the generations. Generalized anxiety disorder (GAD) is one of many mental health complications. However, individuals with the disorder experience hyperbolic concerns and tensions regarding daily events. Furthermore, it is reported that approximately 5% of the population of developed countries suffer from GAD. Additionally, women are affected by this disease twice as often as men, and it is an increasing disorder among women, particularly female students. This paper aims to predict generalized anxiety disorder among female students using the random decision forest algorithm. The data mining method was utilized for prediction. Female students of Shiraz Azad University developed the research community. Therefore, 150 female students were selected by simple random method and tested with a DSM-IV questionnaire. Accordingly, a random forest algorithm is proposed to generate a prediction model. Moreover, NetBeans IDE was applied for operationalization. Java was the programming language to code the prototype, and the WEKA library was involved in the operation. However, the results showed that the prediction accuracy with the random forest algorithm exceeds 0.9, which indicates that the algorithm is likely to predict GAD accurately. The random decision forest algorithm consistently predicts an individual not suffering from GAD. The results are relatively consistent compared to the baseline employed in the R. However, the random decision forest algorithm produces high predictive performance and may display significant relationships between the proposed and dependent parameters.</p> Manuscript profile