Implementation of the Advanced Traffic Management System using k-Nearest Neighbor Algorithm

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%.

A Temperature Identification Method Based on Chromaticity Statistical Features of Raw Format Visible Image and K-nearest Neighbor Algorithm

2020 IEEE 1st China International Youth Conference on Electrical Engineering (CIYCEE) ◽

10.1109/ciycee49808.2020.9332599 ◽

2020 ◽

Author(s):

Wenmao Li ◽

Qizheng Ye ◽

Zhe Yuan ◽

Yang He

Keyword(s):

Nearest Neighbor ◽

Statistical Features ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Visible Image ◽

Identification Method ◽

K Nearest Neighbor Algorithm

Identification and Classification of Technical Lignins by means of Principle Component Analysis and k‐Nearest Neighbor Algorithm

Chemistry–Methods ◽

10.1002/cmtd.202100065 ◽

2021 ◽

Vol 1 (8) ◽

pp. 352-353

Author(s):

Friedrich Fink ◽

Franziska Emmerling ◽

Jana Falkenhagen

Keyword(s):

Principle Component Analysis ◽

Nearest Neighbor ◽

Component Analysis ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Principle Component ◽

K Nearest Neighbor Algorithm ◽

Technical Lignins

Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

Applied Sciences ◽

10.3390/app11157132 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7132

Author(s):

Jianfeng Xi ◽

Shiqing Wang ◽

Tongqiang Ding ◽

Jian Tian ◽

Hui Shao ◽

...

Keyword(s):

Traffic Accidents ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Operating Data ◽

Detection Model ◽

Driving Behaviors ◽

K Nearest Neighbor Algorithm ◽

Fatigue Driving ◽

Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

Application of improved k-means k-nearest neighbor algorithm in the movie recommendation system

2020 13th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid51228.2020.00076 ◽

2020 ◽

Author(s):

Chang Cai ◽

Li Wang

Keyword(s):

Recommendation System ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm ◽

Movie Recommendation

A novel approach for precipitation forecast via improved K-nearest neighbor algorithm

Advanced Engineering Informatics ◽

10.1016/j.aei.2017.05.003 ◽

2017 ◽

Vol 33 ◽

pp. 89-95 ◽

Cited By ~ 13

Author(s):

Mingming Huang ◽

Runsheng Lin ◽

Shuai Huang ◽

Tengfei Xing

Keyword(s):

Nearest Neighbor ◽

Precipitation Forecast ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Novel Approach ◽

K Nearest Neighbor Algorithm

Detect functionally equivalent code fragments via k-nearest neighbor algorithm

2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci.2012.6463128 ◽

2012 ◽

Cited By ~ 1

Author(s):

Dandan Kong ◽

Xiaohong Su ◽

Shitang Wu ◽

Tiantian Wang ◽

Peijun Ma

Keyword(s):

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm

Classification of News Articles for Learning Using the K-Nearest Neighbor Algorithm

10.1109/icet53279.2021.9575101 ◽

2021 ◽

Author(s):

Utomo Pujianto ◽

Harits ar Rosyid ◽

Muhammad Khoirul Anam

Keyword(s):

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm

Implementation of the K-Nearest Neighbor Algorithm for Identification of Orchid Flower Image

JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer ◽

10.32503/jtecs.v1i2.1750 ◽

2021 ◽

Vol 1 (2) ◽

pp. 177

Author(s):

Nofita Sari ◽

Resty Wulanningrum

Keyword(s):

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm ◽

Orchid Flower

Phalaenopsis adalah bahasa latin dari bunga anggrek yang merupakan salah satu bunga yang banyak digemari masyarakat untuk menghiasi rumah mereka. Bunga anggrek memiliki banyak jenis yang mungkin banyak masyarakat hanya mengetahui jenisnya dari warnanya saja. Banyak yang kurang mengamati tentang bunga anggrek itu sendiri. Terkadang 1 warna terdiri dari beberapa jenis.yang sangat menonjol untuk membedakannya adalah dilihat dari kelopak bunga anggrek. Penelitian ini mengambil sampel 3 jenis bunga angrek, yaitu jenis Phalaenopsis Amabilis, Dendrobium Phalaenopsis, dan Phalaenopsis Violacea. Tahapan implementasi yang dilakukan adalah dengan melakukan preprocessing yang meliputi grayscale dan deteksi tepi Kirsch, selanjutnya proses identifikasinya dengan menerapkan algoritma K-Nearest Neighbor. Hasil yang dari penelitian ini antara lain telah dihasilkan sebuah aplikasi untuk deteksi bunga anggrek berdasarkan kelopak bunga dan didapatkan akurasi sebesar 86,7%. Besarnya akurasi yang didapatkan berpengaruh dari banyaknya data training dan data testing yang digunakan saat ujicoba.