Background Noise Identification System Based on Random Forest for Speech

2017 ◽  
pp. 323-332 ◽  
Author(s):  
Shambhu Shankar Bharti ◽  
Manish Gupta ◽  
Suneeta Agarwal
Keyword(s):  
Random Forest ◽  
Background Noise ◽  
Identification System ◽  
Noise Identification

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 Effect of Natural Background Noise and Man-Made Noise on Automated Frog Calls Identification System

2021 ◽  
Vol 3 (1) ◽  
pp. 208-213
Author(s):  
Haryati Jaafar ◽  
Dzati Athiar Ramli
Keyword(s):  
Background Noise ◽  
Experimental Studies ◽  
Outdoor Environment ◽  
Biological Research ◽  
Identification System ◽  
Natural Background ◽  
K Nearest Neighbor ◽  
Zero Crossing ◽  
Short Time ◽  
Natural Background Noise

Frog identification based on their calls becomes important for biological research and environmental monitoring. However, identifying particular frog calls becomes challenging particularly when the frog calls are interrupted with noises either in natural background noise or man-made noise. Hence, an automatic identification frog call system that robust in noisy environment has been proposed in this paper. Experimental studies of 675 audio obtained from 15 species of frogs in the Malaysian forest and recorded in an outdoor environment are used in this study. These audio data are then corrupted by 10dB and 5dB noise. A syllable segmentation technique i.e. short time energy (STE) and Short Time Average Zero Crossing Rate (STAZCR) and feature extraction, Mel-Frequency Cepstrum Coefficients (MFCC) are employed to segment the desired syllables and extract the segmented signal. Subsequently, the Local Mean k-Nearest Neighbor with Fuzzy Distance Weighting (LMkNN-FDW) are employed as a classifier in order to evaluate the performance of the identification system. The experimental results show both of natural background noise and man-made noise outperform by 95.2% and 88.27% in clean SNR, respectively.

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