Identification System for Different Punjabi Dialects Using Random Forest Technique

2018 ◽  
Vol 6 (12) ◽  
pp. 254-259
Author(s):  
Ravinder Singh ◽  
Anand Sharma
Keyword(s):  
Random Forest ◽  
Identification System

scholarly journals A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data

2021 ◽  
Vol 11 (21) ◽  
pp. 10336
Author(s):  
Yitao Wang ◽  
Lei Yang ◽  
Xin Song ◽  
Quan Chen ◽  
Zhenguo Yan
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Ensemble Learning ◽  
Classification Model ◽  
Gradient Boosting ◽  
Identification System ◽  
Dynamic Feature ◽  
Passenger Ships ◽  
Extreme Gradient Boosting ◽  
Ship Classification

AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision.

scholarly journals Indian Handwritten Script Identification System based on Random Forest Tree Ensembles

2019 ◽  
Vol 8 (2) ◽  
pp. 2097-2103
Keyword(s):  
Random Forest ◽  
Processing System ◽  
Forest Tree ◽  
Structural Features ◽  
Identification System ◽  
Document Processing ◽  
Script Language ◽  
Handwritten Documents ◽  
Script Identification ◽  
Random Forest Tree

The work proposal addresses to introduce a methodology for Indian unconstrained handwritten script identification by practicing distinct features and classifiers. By utilizing classifiers like RF, SVM, k-NN, and LDA for Indian script identification using statistical, geometric, and structural features. To preserve all the information present on handwritten documents such as historical, medieval, inscription, financial administration, public records, government archives, letters, land councils, various agreements, etc. in digitalize form needs textual document processing system (e.g. OCR). To build a precise and productive multi-script/language textual document processing system must have script identification. For this study use, total 1288 (line wise) samples of ten scripts use in India are collected from different persons of different gender, age, education and region (rural or urban). After successful training and testing, 81.8% and 0.252 accuracies and the OOB error rate are achieved by Random Forest respectively. And 77.8%, 73.5%, and 65.5% accuracy is achieved in SVM, k-NN and LDA classifiers respectively

scholarly journals Ship classification based on random forest using static information from AIS data

2021 ◽  
Vol 2113 (1) ◽  
pp. 012072
Author(s):  
Yitao Wang ◽  
Lei Yang ◽  
Xin Song ◽  
Xuan Li
Keyword(s):  
Random Forest ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Transportation ◽  
Automatic Identification System ◽  
Related Data ◽  
Static Information ◽  
Ship Classification ◽  
Type Information

Abstract With the wide use of automatic identification system (AIS), a large amount of ship-related data has been provided for marine transportation analysis. Generally, AIS reports the type information of ships, but there are still many ships with type unknown in AIS data. It is necessary to develop algorithms which can identify ship type from AIS data. In this paper, we employ random forest to classify ships according to the static information from AIS messages. Moreover, the importance of static features is discussed, which explains the reason why some classes of ships are misclassified. The method of this paper is proved to be effective in ship classification using static information.

scholarly journals Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

2020 ◽  
Vol 6 (2) ◽  
pp. 20-26
Author(s):  
Amreen Khan ◽  
Dr. Abhishek Bhatt
Keyword(s):  
Random Forest ◽  
High Performance ◽  
Research Work ◽  
Personal Data ◽  
Recognition System ◽  
Identification System ◽  
Optimum Approach ◽  
Vein Recognition ◽  
Effective Models ◽  
Result Analysis

In recent years, the need for security of personal data is becoming progressively important. A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. With respect to this concern, the identification system based on the fusion of multibiometric values is the most recommended in order to significantly improve and obtain high performance accuracy. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Retinex Algorithm, Stacked Deep Auto Encoder and Random forest (RF) classifier based on multi-biometric fingerprint as well as finger-vein recognition system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. In order to gain above mentioned objectives, fingerprint and fingervein dataset is taken for training and testing. The result analysis shows approx. 97% accuracy, 92% precision rate as well as 0.04 EER that shows enhancement over existing work.

scholarly journals Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li-Jun Liu ◽  
Wei-Kang Shen ◽  
Jia-Ming Zhu
Keyword(s):  
Random Forest ◽  
Stock Market ◽  
Factor Model ◽  
Risk Identification ◽  
Order Moment ◽  
Identification System ◽  
Random Forest Algorithm ◽  
High Order Moment ◽  
Portfolio Change ◽  
Identification Tool

With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.

scholarly journals V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4286
Author(s):  
Camelia Skiribou ◽  
Fouzia Elbahhar
Keyword(s):  
Random Forest ◽  
Noise Level ◽  
Spectrum Sharing ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Random Forest Classifier ◽  
Identification System ◽  
Signal Identification ◽  
Communication Signals ◽  
Time Frequency

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time–frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.

scholarly journals Speaker Recognition using Random Forest

2021 ◽  
Vol 37 ◽  
pp. 01022
Author(s):  
K Khadar Nawas ◽  
Manish Kumar Barik ◽  
A Nayeemulla Khan
Keyword(s):  
Random Forest ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Promising Result ◽  
Identification System ◽  
Biometric Authentication ◽  
Human Machine Interaction ◽  
Accuracy Level ◽  
Authentication Security ◽  
Machine Interaction

Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample. A person’s speech note contains many features that can be used to discriminate his/her identity. A model that can identify a speaker has wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements a speaker identification system based on Random Forest as a classifier to identify the various speakers using MFCC and RPS as feature extraction techniques. The output obtained from the Random Forest classifier shows promising result. It is observed that the accuracy level is significantly higher in MFCC as compared to the RPS technique on the data taken from the well-known TIMIT corpus dataset.

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