random forest algorithm

The development of metasurface absorbers offers a potential solution to achieve low weight, thin thickness, favorable absorption rate, and acceptable terahertz absorption characteristics. In order to optimize the absorption properties of metasurfaces, the absorption spectrum is usually used as an important evaluation criterion, which can show many important characteristics such as the rate of absorption at different frequencies. However, the analysis of absorption spectra related to a large number of variable structural parameters is required when designing the structure, which consumes a lot of resources and time, because electromagnetic wave absorption involves the processes of complex impedance matching and electric field excitation. To address this issue, this study proposes a machine learning approach based on a random forest algorithm to predict absorption rates based on structural parameters, reducing the need for numerical simulation and spectrum analysis time. With the random forest model, the absorption rate is predicted with the R2 score of more than 0.99. In addition, the proposed absorber design has the advantages of being thin, insensitive to polarization and with a relatively stable incident angle, due to the symmetry of the structure. This study presents a practical and effective approach for the design of complex systems related to absorbing, reflecting and transmitting electromagnetic wave propagation.

In this research, a new approach for image segmentation based on genetic algorithm and random forest is presented. Image segmentation can be done using supervised learning. In this learning, there are a number of images from data set with their labels. In image segmentation, different parts of the image separate from each other. In this process, all the pixels in the image are given a label, so that the pixels with the same label have common characteristics with each other. To provide a model that can perform image segmentation, it is necessary to extract features from input images and perform segmentation using a suitable classifier and these features. Image feature extraction is done using image filters. In this research, a hybrid combination of 4 Gabor filter banks and Sobel, Prewitt, Canny edge, Scharr, Gaussian, median, and Roberts filters are used for effective feature extraction. One of the most important of these filters, which also has a degree of freedom, is the Gabor filter. This filter has a number of   hyperparameters that change the efficiency of the classifier by changing these hyperparameters. In this research, an attempt has been made to adjust these hyperparameters using genetic algorithm. The fitness function proposed in this research is f1-score. random forest classifier is utilized for image segmentation and classification. The results of the experiments show that the hyperparameters found by the genetic algorithm have been able to perform a satisfactory segmentation on data set.

Despite all the efforts of security experts to detect SQL injection attacks, according to OWASP  report’s, SQL injection attack is still used as the most important cyber attack by attackers. In order to detect attacks, two methods are used: signature-based and behavior-based. Signature-based methods are used for known attacks, and behavior-based methods are suitable for detecting unknown attacks. Behavior-based intrusion detection systems are more useful because attacks are implemented in different ways. Behavior can be analyzed by methods such as classification, clustering, etc. One of the most important classification algorithms is the random forest algorithm which has high accuracy and on the other hand the implementation and interpretation of the results can be done easily using this algorithm. According to the studies, the accuracy of the random forest algorithm is highly dependent on its input parameters. These parameters include 9 items, including the number of trees, their depth, voting method, information gain, and so on. Optimal determination of these parameters is an optimization problem with large state space. In this research, a method based on genetic algorithm to determine the optimal values of these parameters is presented. Due to the optimal determination of the parameters, the obtained results show an improvement in the detection accuracy compared to the default state of the algorithm and other researches. The evaluation results indicate that the intrusion detection accuracy in the proposed method was %98, which is about %11 higher than the random forest algorithm with default parameters and %08 higher than previous studies.

Due to increasing population and decreasing energy sources, this research studies the consumption of domestic energy. The purpose of this study is to predict the factors affecting household energy consumption. To do this, we use 3 algorithms, M5Rules, K-nearest neighbor and random forest, available in Weka software. In this study, the feature correlation algorithm is used to select the most important factors affecting energy consumption and their impact. The results show that lights and fixtures, temperature of the living room, outside temperature, temperature outside of Chievres Station, wind speed, humidity in the kitchen and the temperature in the laundry area have the most impact on household energy consumption. Among the methods, random forest algorithm presented the best results.

This paper presents a novel method based on machine learning strategies for fault locating in high voltage direct current (HVDC) transmission lines. In the proposed fault-location method، only post-fault voltage signals measured at one terminal are used for feature extraction. In this paper، due to high dimension of input feature vectors، two different estimators including the generalized regression neural network (GRNN) and the random forest (RF) algorithm are examined to find the relation between the features and the fault location. The results of evaluation using training and test patterns obtained by simulating various fault types in a long overhead transmission line with different fault locations، fault resistance and pre-fault current values have indicated the efficiency and the acceptable accuracy of the proposed approach.