Development of Control System for Opening and Closing Electrical Equipment with Thai Voice Command Using by K-Nearest Neighbor Technical

A walking-assistant robot guided by the intention and power-driven is presented, its purpose is to provide physical support and walking assistance for the elderly to meet their needs of walking autonomy, friendliness, and maintaining the ability of walking and taking care of themselves. Tactile and slip sensor is selected as the human interface to perceive the users walking intent, and the sensor is also used to detect the user's slip trend. And the paper researches the feature representation and extraction method of tactile and slip signal for driving control pattern recognition. An improved classification and identification method combining K-means in clustering and K-nearest neighbor algorithm in classification is proposed. The paper introduces the overall design schemes of tactile and slip drive control system of walking-assistant robot, perception system, motion control system. Finally the feasibility and effectiveness of the entire system are verified by experiment.

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

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.

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PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ◽

10.33751/komputasi.v17i1.1735 ◽

2020 ◽

Vol 17 (1) ◽

pp. 319-328

Author(s):

Ade Muchlis Maulana Anwar ◽

Prihastuti Harsani ◽

Aries Maesya

Keyword(s):

Nearest Neighbor ◽

Information Gain ◽

Birth Certificate ◽

Population Data ◽

Community Services ◽

Birth Certificates ◽

Similar Data ◽

K Nearest Neighbor ◽

Civil Registration ◽

The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

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A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) ◽

10.23919/mipro48935.2020.9245195 ◽

2020 ◽

Author(s):

A. Sagdic ◽

C. Tekinbas ◽

E. Arslan ◽

T. Kucukyilmaz

Keyword(s):

Recommendation System ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm

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Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i9.102 ◽

2018 ◽

Vol 6 (9) ◽

pp. 152

Author(s):

S. Vijaya Rani ◽

G. N. K. Suresh Babu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intrusion Detection ◽

Error Rate ◽

Learning Process ◽

Nearest Neighbor ◽

Detection System ◽

Support Vector ◽

K Nearest Neighbor ◽

Artificial Neural

The illegal hackers penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

2015 ◽

Vol 1 (4) ◽

pp. 270

Author(s):

Muhammad Syukri Mustafa ◽

I. Wayan Simpen

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Test Results ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Sample Data ◽

New Students ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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