List of Articles Random forest

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  1 - Implementation of Machine Learning Algorithms for Customer Churn Prediction
  Manal Loukili Fayçal Messaoudi Raouya El Youbi
  10.61186/jist.34208.11.43.196
  Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, w More

  Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, we propose the following process which consists of six stages. The first phase consists of data pre-processing, followed by feature analysis. In the third phase, the selection of features. Then the data was divided into two parts: the training set and the test set. In the prediction process, the most popular predictive models were adopted, namely random forest, k-nearest neighbor, and support vector machine. In addition, we used cross-validation on the training set for hyperparameter tuning and to avoid model overfitting. Then, the results obtained on the test set were evaluated using the confusion matrix and the AUC curve. Finally, we found that the models used gave high accuracy values (over 79%). The highest AUC score, 84%, is achieved by the SVM and bagging classifiers as an ensemble method which surpasses them. Manuscript profile
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  2 - An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest
  Md. Nahid  Hasan Tanvir  Ahmed Md.  Ashik Md. Jahid  Hasan Tahaziba  Azmin Jia Uddin
  10.52547/jist.34246.11.42.163
  The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, w More

  The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively). Manuscript profile
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  3 - 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