scholarly journals Classification of The NTEV Problems on The Commercial Building

2018 ◽  
Vol 9 (2) ◽  
pp. 380 ◽  
Author(s):  
Mohd Abdul Talib Mat Yusoh ◽  
Saidatul Habsah Asman ◽  
Zuhaila Mat Yasin ◽  
Ahmad Farid Abidin
Keyword(s):  
Linear Regression ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Commercial Building ◽  
K Nearest Neighbor ◽  
City Block ◽  
S Transform ◽  
Processing Signal ◽  
Block Variables

Neutral to Earth Voltage (NTEV) is one of power quality (PQ) problems in the commercial building that need to be resolved.  The classification of the NTEV problems is a method to identify the source types of disturbance in alleviating the problems.  This paper presents the classification of NTEV source in the commercial building which is known as the harmonic, loose termination, and lightning.  The Euclidean, City block, and Chebyshev variables for K-Nearest Neighbor (K-NN) classifying are being utilized in order to identify the best performance for classifying the NTEV problems.  Then, S-Transform (ST) is applied as a pre-processing signal to extract the desired features of NTEV problem for classifier input.  Furthermore, the performance of K-NN variables is validated by using the confusion matrix and linear regression.  The classification results show that all the K-NN variables capable to identify the NTEV problems. While the K-NN results show that the Euclidean and City block variables are well performed rather than the Chebyshev variable.  However, the Chebyshev variable is still reliable as the confusion matrix shows minor misclassification. Then, the linear regression outperformed the percentage close to a perfect value which is hundred percent.

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.

The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
Noor Azuan Abu Osman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Olympic Games ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
Machine Learning Approach ◽  
Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of  three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

scholarly journals Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongyan Wang
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Test Score ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Classification Efficiency

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

scholarly journals Klasifikasi Kematangan Jeruk Lemon Menggunakan Metode K-Nearest Neighboard

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Indrawati Indrawati
Keyword(s):  
Moisture Content ◽  
Water Content ◽  
Nearest Neighbor ◽  
Training Image ◽  
K Nearest Neighbor ◽  
Oil Glands ◽  
Shortest Distance ◽  
Approximate Distance ◽  
Dark Green

Abstrak— Klasifikasi jeruk lemon adalah disiplin bidang ilmu yang menggambarkan identifikasi jeruk berdasarkan sifatnya. Beberapa sifat dari jeruk lemon, diantaranya kulit terluar lemon kaya akan kelenjar minyak, kematangan ditandai dengan warna kulit kuning terang. Jeruk lemon yang berwarna hijau gelap, menandakan jeruk lemon tersebut belum matang dan kandungan air di dalamnya akan lebih sedikit. Pada penelitian ini kematangan diklasifikasikan menggunakan metode K-Nearest Neighbor. Hasilnya adalah klasifikasi kematangan dengan kadar air 90% jarak terdekat rata-rata sebesar 10,86 dengan akurasi 85%, sedangkan pada pengujian jeruk lemon dengan kematangan 80% diperoleh jarak terdekat 7,3 dengan akurasi 81%. Pada pengujian dengan kematngan dengan kadar air 70 persen diperoleh jarak rata-rata terdekat 19,4 dan akurasi 86,11%. Untuk jeruk lemon dengan kategori tidak matang dengan kadar air 50% diperoleh jarak terdekat sebesar 19,46 dan akurasi 88,9 % , sedangkan pada pengujian jeruk lemon mentah dengan kadar air 40% diperoleh jarak terdekat 16,19 dan akurasi 88,73 dan untuk pengujian jeruk lemon tidak matang dengan kadar air 30% diperoleh klasifikasi dengan jarak terdekat rata-rata sebesar 1,85 dan akuras 84,13%. Hal ini menunjukkan bahwa sistem klasifikasi dengan menggunakan metode K-NN cukup baik, indikatornya adalah jarak terdekat rata-rata yang dihasilkan antara citra uji dan citra training bernilai antara 1,85 sampai 19,46 dan akurasinya antara 81% sampai88,89 %.Kata kunci— Akurasi, Jeruk lemon, Klasifikasi, kedekatan, tetangga, uji.Abstract— Classification of lemon is the discipline of science that describes the identification of citrus by its character. Some characterof lemon, lemon outer shell is rich in oil glands, maturity is marked by bright yellowskin color, lemon which is dark green, indicates the immature lemon and water content in it will be less. In this study maturity are classified using K-Nearest Neighbor method. The result is a classification of maturity with 90% moisture content has shortest distance average of 10.86 with an accuracy of 85%, while in the testing of lemon with a maturity of 80% obtained the nearest distance of 7.3 with an accuracy of 81%. In maturity testing with a water content of 70 percent derived average approximate distance of 19.4 and 86.11% accuracy. For the lemon with the category of immature by moisture content of 50% obtained the nearest distance at 19.46 and accuracy of 88.9%, while in the testing of raw lemon with a moisture content of 40% obtained the nearest distance 16.19 and accuracy of 88.73 and for testing of immature lemon with a water content of 30% obtained classifications with the average nearest distance of 1.85 and accuracy of 84.13%. This indicates that the classification system using K-NN was very good, the indicator is the average nearest distance between the tested images and training image between 1.85 to 19.46 valuable and accuracy between 81% to 88.89%.Keywords— Accuracy, Lemon, classification,nearets, neighbors, test.

scholarly journals A High Performace of Local Binary Pattern on Classify Javanese Character Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Ajib Susanto ◽  
Daurat Sinaga ◽  
Christy Atika Sari ◽  
Eko Hari Rachmawanto ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Characteristic Extraction ◽  
Research Objects ◽  
Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

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