Lisp Detection and Correction Based on Feature Extraction and Random Forest Classifier

2018 ◽  
pp. 55-64
Author(s):  
Anirudh Itagi ◽  
Cyril Joe Baby ◽  
Subham Rout ◽  
K. P. Bharath ◽  
R. Karthik ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Random Forest Classifier

scholarly journals Smart System for Thermal Comfort Prediction on Residential Buildings Using Data-Driven Model with Random Forest Classifier

2021 ◽  
Vol 5 (4) ◽  
pp. 40-45
Author(s):  
O. E. Taylor ◽  
P. S. Ezekiel ◽  
V. T. Emma
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Thermal Comfort ◽  
Residential Buildings ◽  
Random Forest Classifier ◽  
Data Driven ◽  
Smart System ◽  
Building Area ◽  
Using Data ◽  
Testing Set

Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.

Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

2021 ◽  
pp. 234-259
Author(s):  
Nitika Kapoor ◽  
Parminder Singh
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Forest Classification ◽  
Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

scholarly journals Heart Disease Prediction Method using Hybrid Classifier

2019 ◽  
Vol 8 (9S4) ◽  
pp. 57-61
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Research Work ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Tree Classifier

The data mining is the approach which can extract useful information from the data. The following research work that has been described is related to the heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. For the heart disease prediction the classifier that is designed in this research work is hybrid classifier. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps which are data pre-processing, feature extraction and classification. In this paper, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. We have proposed a hybrid model that has been implemented in python. Moreover, the results are compared with Support Vector Machine (SVM) and K-Nearest Neighbor classifier (KNN).

scholarly journals Perbandingan Identifikasi Wajah Dengan Ekstraksi Fitur Haralick Dan CNN

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Fani Nurona Cahya
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Random Forest Classifier

Kecanggihan teknologi telah berdampak pada setiap segi kehidupan. Salah satunya pengolahan citra yang menjadikan objek wajah sebagai karakter utama. Deteksi wajah digunakan untuk mengetahui ada atau tidaknya wajah pada suatu gambar sehingga keberadaannya sangat vital. penelitian ini adalah untuk mendapatkan algoritma pengenalan yang kuat dengan akurasi tinggi. Struktur umum proses pengenalan wajah dalam penelitian ini mencoba dua eksperimen yakni dengan menggunakan Feature Extraction Haralic dengan Random Forest, dan klasifikasi dengan menggunakan CNN. Penelitian feature ectraction dengan haralic ini terdiri dari tiga tahap. Ini dimulai dengan tahap pra-pemrosesan: konversi ruang warna dan pengubahan ukuran gambar, dilanjutkan dengan ekstraksi fitur wajah, dan kemudian set fitur yang diekstraksi diklasifikasikan. Dalam sistem ini, Random Forest Classifier dan CNN akan menajadi acuan novelty untuk merealisasikan tahap terakhir berdasarkan fitur wajah. Hasil akurasi dari eksperimen ini masih kurang sehinggap perlu mencoba eksperimen lain.

An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

2021 ◽  
pp. 1063293X2110105
Author(s):  
Meenal Thayumanavan ◽  
Asokan Ramasamy
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Brain Images ◽  
Skull Stripping ◽  
The Brain

Nowadays, the most demanding and time consuming task in medical image processing is Brain tumor segmentation and detection. Magnetic Resonance Imaging (MRI) is employed for creating a picture of any part in a body. MRI provides a competent quick manner for analyzing tumor in the brain. This proposed framework contains different stages for classifying tumor like Preprocessing, Feature extraction, Classification, and Segmentation. Initially, T1-weighted magnetic resonance brain images are considered as an input for computational purpose. Median filter is proposed to optimize the skull stripping in MRI images. Abnormal brain tissues are extracted in low contrast, in addition to meticulous location of edges of affected tissue can be detected. Then, Discrete Wavelet Transform (DWT) and Histogram of Oriented Gradients (HOG) are performing feature extraction process. HOG is used for extracting the features like texture and shape. Then, Classification is performed through Machine learning categorization techniques via Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). These classifiers classify the brain image as either normal or abnormal and the performance is analyzed by various parameters such as sensitivity, specificity and accuracy.

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