Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and k-Nearest Neighbor

2021 ◽  
pp. 235-241
Author(s):  
Md Junayed Hasan ◽  
Jaeyoung Kim ◽  
Jong-Myon Kim
Keyword(s):  
Nearest Neighbor ◽  
Health State ◽  
K Nearest Neighbor ◽  
Bag Of Features ◽  
State Classification ◽  
Spherical Tank ◽  
Health State Classification

scholarly journals Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

2020 ◽  
Vol 10 (7) ◽  
pp. 2525 ◽  
Author(s):  
Md Junayed Hasan ◽  
Jaeyoung Kim ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Nearest Neighbor ◽  
Selection Process ◽  
Health State ◽  
K Nearest Neighbor ◽  
Bag Of Features ◽  
State Classification ◽  
Spherical Tank ◽  
Art Works ◽  
Health State Classification

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

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 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.

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