scholarly journals A cans waste classification system based on RGB images using different distances of k-means clustering

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Yulia Resti, F Nasution, I Yani, A.S. Mohruni, F A Almahdini
Keyword(s):  
Euclidean Distance ◽  
Conveyor Belt ◽  
Manhattan Distance ◽  
Halogen Lamp ◽  
Minkowski Metric ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Image Capturing ◽  
Metric Distance ◽  
Incandescent Lamps

Abstract. This study aims to build a classify the cans waste based on the pixel of captured Red, Green, and Blue (RGB) image by implement different metric 3 distances of k-means clustering; Manhattan, Euclidean, and Minkowski metric distance. The image capturing is designed using combinations of two the conveyor belt speeds of 0.181 m/sec and 0.086 m/sec, two the lightings of halogen and incandescent lamps, and four lighting angles of 300, 450, 600, and 900. The classification results note that the implementation of Manhattan distance on the k-means clustering method for classifying the cans waste into three can types has the highest level of accuracy in the majority of data. The highest accuracy level of classification is obtained from data of captured image on the conveyor belt speeds of 0.181 m/sec, the lightings of halogen lamp, and the lighting angles of 450 by implementing the Euclidean distance, while the lowest accuracy level of classification is obtained from data of captured image on the lighting angles of 300 with the same speeds and the lamp by implementing the Manhattan distance. The highest average accuracy is obtained by implementing the Euclidean distance, that derived from the average accuracy at lighting angle of 450.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Linkage analysis using geographical proximity: a test of the efficacy of distance measures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shumpei Haginoya ◽  
Aiko Hanayama ◽  
Tamae Koike
Keyword(s):  
Environmental Factors ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Geographical Proximity ◽  
Discrimination Accuracy ◽  
Content Type ◽  
Shortest Route ◽  
The Impact

Purpose The purpose of this paper was to compare the accuracy of linking crimes using geographical proximity between three distance measures: Euclidean (distance measured by the length of a straight line between two locations), Manhattan (distance obtained by summing north-south distance and east-west distance) and the shortest route distances. Design/methodology/approach A total of 194 cases committed by 97 serial residential burglars in Aomori Prefecture in Japan between 2004 and 2015 were used in the present study. The Mann–Whitney U test was used to compare linked (two offenses committed by the same offender) and unlinked (two offenses committed by different offenders) pairs for each distance measure. Discrimination accuracy between linked and unlinked crime pairs was evaluated using area under the receiver operating characteristic curve (AUC). Findings The Mann–Whitney U test showed that the distances of the linked pairs were significantly shorter than those of the unlinked pairs for all distance measures. Comparison of the AUCs showed that the shortest route distance achieved significantly higher accuracy compared with the Euclidean distance, whereas there was no significant difference between the Euclidean and the Manhattan distance or between the Manhattan and the shortest route distance. These findings give partial support to the idea that distance measures taking the impact of environmental factors into consideration might be able to identify a crime series more accurately than Euclidean distances. Research limitations/implications Although the results suggested a difference between the Euclidean and the shortest route distance, it was small, and all distance measures resulted in outstanding AUC values, probably because of the ceiling effects. Further investigation that makes the same comparison in a narrower area is needed to avoid this potential inflation of discrimination accuracy. Practical implications The shortest route distance might contribute to improving the accuracy of crime linkage based on geographical proximity. However, further investigation is needed to recommend using the shortest route distance in practice. Given that the targeted area in the present study was relatively large, the findings may contribute especially to improve the accuracy of proactive comparative case analysis for estimating the whole picture of the distribution of serial crimes in the region by selecting more effective distance measure. Social implications Implications to improve the accuracy in linking crimes may contribute to assisting crime investigations and the earlier arrest of offenders. Originality/value The results of the present study provide an initial indication of the efficacy of using distance measures taking environmental factors into account.

scholarly journals A Scheme of Selecting Vehicles to Assist Download Based on WebGIS for VANET

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qinggang Su ◽  
Peng Xiong
Keyword(s):  
Euclidean Distance ◽  
Global Optimum ◽  
Manhattan Distance ◽  
Web Browser ◽  
Problem Of Time ◽  
Time Space ◽  
Blind Area ◽  
Markov Decision ◽  
Platform Independence ◽  
Client Side

The assisted download is an effective method solving the problem that the coverage range is insufficient when Wi-Fi access is used in VANET. For the low utilization of time-space resource within blind area and unbalanced download services in VANET, this paper proposes an approximate global optimum scheme to select vehicle based on WebGIS for assistance download. For WebGIS, this scheme uses a two-dimensional matrix to respectively define the time-space resource and the vehicle selecting behavior, and uses Markov Decision Process to solve the problem of time-space resource allocation within blind area, and utilizes the communication features of VANET to simplify the behavior space of vehicle selection so as to reduce the computing complexity. At the same time, Euclidean Distance(Metric) and Manhattan Distance are used as the basis of vehicle selection by the proposed scheme so that, in the case of possessing the balanced assisted download services, the target vehicles can increase effectively the total amount of user downloads. Experimental results show that because of the wider access range and platform independence of WebGIS, when user is in the case of relatively balanced download services, the total amount of downloads is increased by more than 20%. Moreover, WebGIS usually only needs to use Web browser (sometimes add some plug-ins) on the client side, so the system cost is greatly reduced.

scholarly journals HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM

2021 ◽  
Vol 25 (01) ◽  
pp. 80-91
Author(s):  
Saba K. Naji ◽  
◽  
Muthana H. Hamd ◽  
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Recognition System ◽  
Manhattan Distance ◽  
Distance Measurements ◽  
Local Ternary Pattern ◽  
Face Recognition System ◽  
Cosine Distance

Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

scholarly journals Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

2020 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani
Keyword(s):  
Classification System ◽  
Naive Bayes ◽  
Conveyor Belt ◽  
Original Model ◽  
Model Based ◽  
Accuracy Level ◽  
Conveyor Belts ◽  
Sorting Process ◽  
Input Variables

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values ​​of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

Ear biometric verification approach based on morphological and geometric invariants

2020 ◽  
Vol 20 (3) ◽  
pp. 1479
Author(s):  
Wurood A. Jbara
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Main Idea ◽  
High Accuracy ◽  
Geometric Invariants ◽  
Biometric Verification ◽  
Accuracy Level ◽  
Image Capturing ◽  
Verification Process ◽  
Biometric Model

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

A Modified Version of the K-Means Algorithm Based on the Shape Similarity Distance

2013 ◽  
Vol 457-458 ◽  
pp. 1064-1068
Author(s):  
Dan Li ◽  
Xin Bao Li
Keyword(s):  
Cluster Analysis ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Shape Similarity ◽  
Popular Method ◽  
Similarity Estimation ◽  
Metric Distance ◽  
Similarity Distance ◽  
Shape Data ◽  
Better Than

K-means Algorithm is a popular method in cluster analysis, and it is most based on the Euclidean distance. In this paper, a modified version of the K-means algorithm based on the shape similarity distance (SSD-K-means) is presented. The shape similarity distance is one kind of non-metric distance measure for similarity estimation based on the characteristic of differences. To demonstrate the effectiveness of the method we proposed, this new algorithm has been tested on three shape data datasets. Experiment results prove that the performance of the SSD-K-means is better than those of the classical K-means algorithm based on the traditional Euclidean and Manhattan distances.

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