scholarly journals Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Neneng Nur Amalina ◽  
Kurniawan Nur Ramadhani ◽  
Febryanti Sthevanie
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Performance Comparison ◽  
Cellular Heterogeneity ◽  
Experimental Result ◽  
Cell Type ◽  
K Nearest Neighbor ◽  
Nuclei Detection ◽  
High Degree

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.

scholarly journals Algorithms for Classification of Signals Derived From Human Brain

2021 ◽  
Vol 15 ◽  
pp. 1521-1526
Author(s):  
Georgi P. Dimitrov ◽  
Galina Panayotova ◽  
Boyan Jekov ◽  
Pavel Petrov ◽  
Iva Kostadinova ◽  
...  
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Signal Acquisition ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Non Invasive ◽  
Basic Parameters ◽  
Linear Svm

Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.

scholarly journals Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

2020 ◽  
Vol 8 (4) ◽  
pp. 270-275
Author(s):  
Deden Istiawan
Keyword(s):  
Information System ◽  
Random Forest ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Random Forest Algorithm ◽  
K Nearest Neighbor

Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.

scholarly journals Classification of batik patterns using K-Nearest neighbor and support vector machine

2020 ◽  
Vol 9 (3) ◽  
pp. 1260-1267
Author(s):  
Agus Eko Minarno ◽  
Fauzi Dwi Setiawan Sumadi ◽  
Hardianto Wibowo ◽  
Yuda Munarko
Keyword(s):  
Support Vector Machine ◽  
Data Sharing ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
Support Vector ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Average Accuracy

This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.

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.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

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