scholarly journals Finding Optimal Stations Using Euclidean Distance and Adjustable Surrounding Sphere

2021 ◽  
Vol 11 (2) ◽  
pp. 848
Author(s):  
Athita Onuean ◽  
Hanmin Jung ◽  
Krisana Chinnasarn
Keyword(s):  
Air Pollution ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Monitoring Network ◽  
K Nearest Neighbor ◽  
Rapid Urbanization ◽  
Spatial Coverage ◽  
K Nearest Neighbor Algorithm ◽  
Effective Network ◽  
Better Than

Air quality monitoring network (AQMN) plays an important role in air pollution management. However, setting up an initial network in a city often lacks necessary information such as historical pollution and geographical data, which makes it challenging to establish an effective network. Meanwhile, cities with an existing one do not adequately represent spatial coverage of air pollution issues or face rapid urbanization where additional stations are needed. To resolve the two cases, we propose four methods for finding stations and constructing a network using Euclidean distance and the k-nearest neighbor algorithm, consisting of Euclidean Distance (ED), Fixed Surrounding Sphere (FSS), Euclidean Distance + Fixed Surrounding Sphere (ED + FSS), and Euclidean Distance + Adjustable Surrounding Sphere (ED + ASS). We introduce and apply a coverage percentage and weighted coverage degree for evaluating the results from our proposed methods. Our experiment result shows that ED + ASS is better than other methods for finding stations to enhance spatial coverage. In the case of setting up the initial networks, coverage percentages are improved up to 22%, 37%, and 56% compared with the existing network, and adding a station in the existing one improved up by 34%, 130%, and 39%, in Sejong, Bonn, and Bangkok cities, respectively. Our method depicts acceptable results and will be implemented as a guide for establishing a new network and can be a tool for improving spatial coverage of the existing network for future expansions in air monitoring.

scholarly journals Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia

Petir ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 80-85
Author(s):  
Yohannes Yohannes ◽  
Muhammad Ezar Al Rivan
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Shape Feature ◽  
Shape Features ◽  
K Nearest Neighbor ◽  
Histogram Of Oriented Gradient ◽  
Global Contrast ◽  
The Face ◽  
Better Than

Mammal type can be classified based on the face. Every mammal’s face has a different shape. Histogram of Oriented Gradient (HOG) used to get shape feature from mammal’s face. Before this step, Global Contrast Saliency used to make images focused on an object. This process conducts to get better shape features. Then, classification using k-Nearest Neighbor (k-NN). Euclidean and cityblock distance with k=3,5,7 and 9 used in this study. The result shows cityblock distance with k=9 better than Euclidean distance for each k. Tiger is superior to others for all distances. Sheep is bad classified.

scholarly journals Dialogue Act Classification, Instance-Based Learning, and Higher Order Dialogue Structure

2010 ◽  
Vol 1 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Barbara Di Eugenio ◽  
Zhuli Xie ◽  
Riccardo Serafin
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Linguistic Features ◽  
Instance Based Learning ◽  
Dialogue Structure ◽  
K Nearest Neighbor Algorithm ◽  
Semantic Spaces ◽  
Better Than

In this paper, we explore instance-based learning methods for dialogue act classification on two corpora, MapTask and CallHome Spanish. We start with Latent Semantic Analysis (LSA), and extend it as Feature Latent Semantic Analysis (FLSA). FLSA adds richer linguistic features to LSA, which only uses words. In particular, we explore the extended dialogue context, both linearly (the previous dialogue act) and hierarchically (conversational games). We show how the k-Nearest Neighbor algorithm obtains its best results when applied to the reduced semantic spaces generated by FLSA. Empirically, our results are better than previously published results on these two corpora; linguistically, we confirm and extend previous observations that the hierarchical dialogue structure encoded via the notion of Game is of primary importance for dialogue act recognition.

scholarly journals An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2117
Author(s):  
Xuesheng Peng ◽  
Ruizhi Chen ◽  
Kegen Yu ◽  
Feng Ye ◽  
Weixing Xue
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Physical Distance ◽  
Spatial Distance ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

scholarly journals Result Prediction Using K-Nearest Neighbor Algorithm For Student Performance Improvement

2017 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Vijay Muralidharan ◽  
Pravien M. ◽  
Janani Balaji
Keyword(s):  
Student Performance ◽  
Performance Improvement ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Historical Data ◽  
Accurate Prediction ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
The University

Abstract—The examination results have become an integral part of every student’s life. The educational institution’s ranking is greatly influenced by the university results. This paper mainly focuses on the prediction of a student’s university result by making use of different attributes. These attributes might be of quantitative and qualitative type. The quantitative attributes used are Internal Assessments, Attendance percentage, Number of On-Duties taken and Overall Assignments completed. The qualitative attributes include Subject feedback, Faculty feedback, and whether the student is a Day Scholar/Hosteller. Here, we make use of k-Nearest Neighbor algorithm (or k-NN for short) against the historical data of students for more accurate prediction of results. In this method all the attributes considered are converted to the same scale. This algorithm makes use of the Euclidean distance formula which is used to find the nearest record. This algorithm predicts better results which help students maximize their academic output.

scholarly journals Nearby Product Recommendation System Based on Users Rating

2019 ◽  
pp. 963-968 ◽  
Author(s):  
Akanksha Jyoti ◽  
Abhijeet Roy ◽  
Suraj Singh ◽  
Nabab Shaikh ◽  
Payal Desai
Keyword(s):  
Collaborative Filtering ◽  
Euclidean Distance ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Similarity Score ◽  
The Internet ◽  
K Nearest Neighbor ◽  
Past Behavior ◽  
Rate Score ◽  
K Nearest Neighbor Algorithm

The recommendation system is very popular nowadays. Recommendation system emerged over the last decade for better findings of things over the internet. Most websites use a recommendation system for tracking and finding items by the user's behavior and preferences. Netflix, Amazon, LinkedIn, Pandora etc. platform gets 60%-70% views results from recommendation. The purpose of this paper is to introduce a recommendation system for local stores where the user gets a nearby relevant recommended item based on the rating of other local users. There are various types of recommendation systems one is User-based collaborative filtering by which the system built upon and uses user's past behavior like ratings and gives similar results made by another user. In collaborative filtering uses Euclidean distance algorithm is used to find the user's rate score to make relations with other users and Euclidean distance similarity score distinguish similarity between users. K-nearest neighbor algorithm is used to implement and find the number of users like new user where K is several similar users. Integrate with map interface to find shortest distances among stores whose product are recommended. The dataset of JSON is used to parse through the algorithm. The result shows a better approach towards the recommendation of products among local stores within a region.

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

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%.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

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%.

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