scholarly journals Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

2017 ◽  
Vol 7 (1) ◽  
pp. 486
Author(s):  
Made Sudarma ◽  
I Gede Harsemadi
Keyword(s):  
Feature Extraction ◽  
Processing Time ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
Performance Comparison ◽  
K Nearest Neighbor ◽  
K Value ◽  
Id3 Algorithm ◽  
Analysis System ◽  
Music Information

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music.  This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm.  In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process.  For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data.  The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious.  Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021.  Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Yohannes Yohannes ◽  
Yulya Puspita Sari ◽  
Indah Feristyani
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Best Value ◽  
Animal Faces

Mammal is a type of animal that has many diverse characteristics, such as vertebrates and breastfeeding. In this study, the HOG feature and the k-NN method were proposed to classify 15 species of mammals. This study uses the LHI-Animal-Faces dataset which has fifteen species of mammals, where each type of mammal has 50 images measuring 100x100 pixels. The image will be conducted the process by the HOG feature extraction process and continued into the classification process using k-Nearest Neighbor. The performance of the HOG and k-NN features that get the best value is in deer and monkey, the best results for precision, recall, and accuracy are at k=3 where HOG feature extraction provides good vector features to be used in the classification process using the k-NN method.

scholarly journals Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data

2020 ◽  
Vol 9 (2) ◽  
pp. 6-12
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann

Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from there future challenges and concluding remarks can be done.

scholarly journals Deteksi Aktivitas Mencurigakan Peserta Computer Based Test Menggunakan IP Camera

2019 ◽  
Vol 1 (3) ◽  
pp. 236-243
Author(s):  
Muhammad Ihsan Zul ◽  
Dzaky Kurniawan ◽  
Rahmat Suhatman
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Ratio Data ◽  
Ip Camera ◽  
Computer Based ◽  
Illegal Activities ◽  
Pixel Mapping

Common surveillance device that used to monitor an area is known as CCTV. The CCTV will provide results in the form of video recordings, which can then be accessed by wireless communication. In its use, CCTV needs humans to monitor the real condition of the area/place. Then the use of CCTV becomes less efficient when used to oversee a place where the room rarely has movement. Because CCTV cannot detect or identify suspicious actions automatically. This research aim to develop a method that can be used to identify the activity (irregular movements) automatically. In this case, the change to be determined was the activities towards the Politeknik Caltex Riau Computer Based Test (CBT) participants. The CBT room has been employed by the IP Camera to identify participant activities. The IP camera captures the image and the image is then processed by the feature extraction method. Proposed feature exctraction method are background subtraction and pixel mapping. Pixel mapping is a method that maps objects based on specified ratio data. There are 18 ratio data generated by this feature extraction process. The determination of the illegal activities done by using the k-Nearest Neighbor. The Algorithm detects the illegal movement by using 502 datasets, and the accuracy obtained was between 98% - 98.4% with an average accuracy of 98.2% for the value of neighborliness = 3. The result can conclude that the method can identify the illegal activities of a CBT participant in the CBT room

scholarly journals Implementation of Zoning and K-Nearest Neighbor in Character Recognition of Wrésastra Script

2019 ◽  
pp. 9 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Extraction Process ◽  
Training Data ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Technological Advances ◽  
Different Types

Balinese script is an important aspect that packs the Balinese culture from time to time which continues to experience development along with technological advances. Balinese script consists of three types (1) Wrésastra, (2) Swalalita and (3) Modre which have different types of characters. The Wrésastra and Swalalita script are Balinese scripts which grouped into the script criteria that are used to write in the field of everyday life. In this research, the zoning method will be implemented in the feature extraction process to produce special features owned by Balinese script. The results of the feature extraction process will produce special features owned by Balinese script which will be used in the classification process to recognize the character of Balinese script. Special features are produced using the zoning method, it will divide the image characters area of ??Balinese scripts into several regions, to enrich the features of each Balinese script. The result of feature extractions is stored as training data that will be used in the classification process. K-Nearest Neighbors is implemented in the special feature classification process that is owned by the character of Balinese script. Based on the results of the test, the highest level of accuracy was obtained using the value K=3 and reference=10 with the accuracy of Balinese script recognition 97.5%.

scholarly journals Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

2021 ◽  
Vol 4 (2) ◽  
pp. 95
Author(s):  
Yuita Arum Sari ◽  
Anggi Gustiningsih Hapsani ◽  
Sigit Adinugroho ◽  
Lukman Hakim ◽  
Siti Mutrofin
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Normal Skin ◽  
Feature Selection Method ◽  
K Nearest Neighbor ◽  
Color Feature ◽  
K Value ◽  
Linear Discriminant

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

scholarly journals Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 167-176
Author(s):  
Wahyu Hidayat ◽  
◽  
Ema Utami ◽  
Ahmad Fikri Iskandar ◽  
Anggit Dwi Hartanto ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Performance Comparison ◽  
Test Results ◽  
K Nearest Neighbor ◽  
K Value ◽  
Jaccard Distance ◽  
Comparison Results ◽  
Class 1 ◽  
Correct Data

During Covid-19 pandemic, there was various hoax news about Covid-19. There are truth-clarification platforms for hoax news about Covid-19 such as Jala Hoax and Saber Hoax which categorize into misinformation and disinformation. Classification of supervised learning methods is applied to carry out learning from fact labels. Dataset is taken from Jala Hoax and Saber Hoax as many as 559 data which are made into Class 1 (Misleading Content, Satire/Parody, False Connection), Class 2 (False Context, Imposter Content), Class 3 (Fabricated and Manipulated Content). K-Nearest Neighbor (K-NN) is used to classify categories of misinformation and disinformation. Dissimilarity measure Jaccard Distance is compared with Euclidean, Manhattan, and Minkowski and uses k-value variance in the K-NN to determine the performance comparison results for each test. Results of Jaccard Distance at the value of k = 4 get a higher value than other model with an accuracy 0.696, precision 0.710, recall 0.572, and F1-Score. Maximum results tend to be on label of the most data class in Class 1 (Misleading Content, Satire or Parody, False Connection) with a total of 58 correct data from 61 test data.

An improved Laplacian Eigenmaps method for machine nonlinear fault feature extraction

2017 ◽  
Vol 232 (21) ◽  
pp. 3833-3842 ◽  
Author(s):  
Quansheng Jiang ◽  
Qixing Zhu ◽  
Wei Liu ◽  
Bangfu Wang ◽  
Fengyu Xu
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Extraction Process ◽  
New Method ◽  
K Nearest Neighbor ◽  
Laplacian Eigenmaps ◽  
Local Linear Embedding ◽  
Fault Feature Extraction

In the feature extraction of mechanical fault detection field, manifold learning is one of the effective nonlinear techniques. In this paper, aiming for the situations of noise sensitivity to manifold learning algorithms, an improved Laplacian Eigenmap (I-LapEig) algorithm is proposed and applied to the process of fault feature extraction. The new method takes advantage of local principal component analysis to eliminate the influence of noise points by reconstructing the neighborhood relation amongst the samples, and maintain the global intrinsic manifold structure, which enhances the performance of the feature extraction. To determine the parameters of I-LapEig algorithm, an adaptive neighborhood choose approach is presented. The K-nearest neighbor classifier is also adopted to implement feature classification and recognition. The experimental results on S-curve, rotor bed data, and compressor fault data show that the new method can effectively improve the performance of noise reduction in the feature extraction process when compared with the conventional local linear embedding and Laplacian Eigenmaps.

scholarly journals A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1274
Author(s):  
Daniel Bonet-Solà ◽  
Rosa Ma Alsina-Pagès
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Multimedia Retrieval ◽  
Natural Environments ◽  
K Nearest Neighbor ◽  
Acoustic Environments

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.

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