scholarly journals Penentuan Klaster Koridor TransJakarta dengan Metode Majority Voting pada Algoritma Data Mining

2021 ◽  
Vol 5 (3) ◽  
pp. 565-575
Author(s):  
Arief Wibowo ◽  
Moh Makruf ◽  
Inge Virdyna ◽  
Farah Chikita Venna
Keyword(s):  
Large Scale ◽  
Majority Voting ◽  
Optimum Number ◽  
Microsoft Excel ◽  
Number Of Clusters ◽  
Calculation Results ◽  
Mode Of Transportation ◽  
Final Cluster ◽  
Index Value ◽  
Python Programming

The Covid-19 pandemic has made many changes in the patterns of community activity. Large-Scale Social Restrictions were implemented to reduce the number of transmission of the virus. This clearly affects the mode of transportation. The mode of transportation makes new regulations to reduce the number of passenger capacities in each fleet, for example, TransJakarta services. This study will categorize the TransJakarta corridors before and during the Covid-19 pandemic. The clustering method of K-Means and K-Medoids is used to obtain accurate calculation results. The calculations are performed using Microsoft Excel, Rapid Miner, and Python programming language. The clustering results obtained that using K-Means algorithm before Covid-19 pandemic, an optimum number of clusters is 3 clusters with DBI (Davies Bouldin Index) value is 0.184, and during Covid-19 pandemic, the optimum number of clusters is 2 clusters with DBI value is 0.188. Meanwhile, when using the K-Medoids algorithm before the Covid-19 pandemic, an optimum number of clusters is 3 clusters with the DBI value is 0.200, and during the Covid-19 pandemic, an optimum number of clusters is 4 clusters with the DBI value is 0.190. The final cluster is determined using the majority voting approach from all the tools used.  

scholarly journals A new approach for improving clustering algorithms performance

2020 ◽  
Vol 20 (3) ◽  
pp. 1569
Author(s):  
Anfal F. N. Alrammahi ◽  
Kadhim B. S. Aljanabi
Keyword(s):  
Clustering Algorithms ◽  
The Internet ◽  
Optimum Number ◽  
Cluster Centroid ◽  
Microsoft Excel ◽  
Algorithm Performance ◽  
Number Of Clusters ◽  
New Approach ◽  
Good Improvement ◽  
And Performance

<p class="Abstract">Clustering represents one of the most popular and used Data Mining techniques due to its usefulness and the wide variations of the applications in real world. Defining the number of the clusters required is an application oriented context, this means that the number of clusters k is an input to the whole clustering process. The proposed approach represents a solution for estimating the optimum number of clusters. It is based on the use of iterative K-means clustering under three different criteria; centroids convergence, total distance between the objects  and the cluster centroid and the number of migrated objects which can be used effectively to ensure better clustering accuracy and performance. A total of 20000 records available on the internet were used in the proposed approach to test the approach. The results obtained from the approach showed good improvement on clustering accuracy and algorithm performance over the other techniques where centroids convergence represents a major clustering criteria. C# and Microsoft Excel were the software used in the approach.</p>

Sputtering of Graphite by Hydrogen Isotopes in the Fusion Environment: A Molecular Dynamics Simulation Study

2018 ◽  
Vol 4 (4) ◽  
Author(s):  
Qiang Zhao ◽  
Yang Li ◽  
Zheng Zhang ◽  
Xiaoping Ouyang
Keyword(s):  
Molecular Dynamics ◽  
Incident Energy ◽  
Large Scale ◽  
Incident Angle ◽  
Hydrogen Isotopes ◽  
Dynamics Simulation ◽  
Calculation Results ◽  
Sputtering Yield ◽  
Increasing Temperature ◽  
Sputtering Yields

The sputtering of graphite due to the bombardment of hydrogen isotopes is crucial to successfully using graphite in the fusion environment. In this work, we use molecular dynamics to simulate the sputtering using the large-scale atomic/molecular massively parallel simulator (lammps). The calculation results show that the peak values of the sputtering yield are between 25 eV and 50 eV. When the incident energy is greater than the energy corresponding to the peak value, a lower carbon sputtering yield is obtained. The temperature that is most likely to sputter is approximately 800 K for hydrogen, deuterium, and tritium. Below the 800 K, the sputtering yields increase with temperature. By contrast, above the 800 K, the yields decrease with increasing temperature. Under the same temperature and incident energy, the sputtering rate of tritium is greater than that of deuterium, which in turn is greater than that of hydrogen. When the incident energy is 25 eV, the sputtering yield at 300 K increases below an incident angle at 30 deg and remains steady after that.

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

An Optimizing Method of Competitive Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 894-899
Author(s):  
Hong Men ◽  
Hai Yan Liu ◽  
Lei Wang ◽  
Yun Peng Pan
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Clustering Analysis ◽  
Learning Rate ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Competitive Neural Network ◽  
Output Neurons ◽  
Simulation Results ◽  
Strong Ability

This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.

scholarly journals COVID-19 Authorized Entry with Face Mask Detection using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 1862-1867
Author(s):  
Anju Ajay
Keyword(s):  
Large Scale ◽  
Facial Recognition ◽  
Face Mask ◽  
Raspberry Pi ◽  
Wireless Internet ◽  
Great Demand ◽  
Embedded Device ◽  
Internet Connection ◽  
Battery Power ◽  
Python Programming

There are no effective face mask detection applications in the current COVID-19 scenario, which is in great demand for transportation, densely populated places, residential districts, large-scale manufacturers, and other organizations to ensure safety. In addition, the lack of big datasets of photographs with mask has made this task more difficult. With the use of Python programming, the Open CV library, Keras, and tensor flow, this project presents a way for recognizing persons without wearing a face mask using the facial recognition methodology. This is a self-contained embedded device that was created with the Raspberry Pi Electronic Development Board and runs on battery power. We make use of a wireless internet connection using USB modem. In comparison to other existing systems, our proposed method is more effective, reliable, and consumes significantly less data and electricity

scholarly journals Herpetofauna Distribution and Ecology in East Kutai District, East Kutai, East Kalimantan

2020 ◽  
Vol 5 (8) ◽  
pp. 279-284
Author(s):  
Sus Trimurti ◽  
Andi Sarina ◽  
Lariman .
Keyword(s):  
Diversity Index ◽  
Environmental Parameters ◽  
Microsoft Excel ◽  
Field Orientation ◽  
The Public ◽  
East Kalimantan ◽  
Recorded Data ◽  
Distribution And Ecology ◽  
Index Value ◽  
Number Of Individuals

Herpetofauna consisting of reptiles and amphibians is a group of fauna whose biodiversity potential is rarely known and is not well known by the public. The purpose of this study was to determine the distribution, ecology and diversity of herpetofauna in Mesangat Wetlands. The method used in this research is the Visual Encounter Survey (VES) search method by searching directly for the type of herpetofauna around the predetermined pathway. Field orientation is carried out to determine the location of the observation, the location of the observation is divided into 3 locations (Mesangat Hilir, Tengah and Hulu) in one observation location there are 5 stations (lanes) for herpetofauna observation. Observations were made in the morning starting at 09.00 - 12.00 WITA and at night starting at 20.00 - 23.00 WITA. Recorded data related to the number of individuals, activities, time found, environmental parameters and measured SVL if possible. Identified species were found using the identification key book Field Frogs of Borneo Inger and Stuebing (2014) and A Field Guide to the Snakes of Borneo Stuebing, Inger and Lardner (2014). The data were analyzed using Microsoft Excel 2010. The results showed that the distribution of herpetofauna in Mesangat Wetlands was fairly evenly distributed and the ecology of Mesangat Wetlands supported for herpetofauna life and the diversity index value obtained was 1.53 which was classified as medium category.

scholarly journals Clustering Fasilitas Kesehatan Berdasarkan Kecamatan Di Karawang Dengan Algoritma K-Means

2021 ◽  
Vol 8 (1) ◽  
pp. 83
Author(s):  
Bagus Muhammad Islami ◽  
Cepy Sukmayadi ◽  
Tesa Nur Padilah
Keyword(s):  
Data Mining ◽  
Developing Countries ◽  
Data Processing ◽  
Health Problems ◽  
Health Facilities ◽  
Microsoft Excel ◽  
Two Factors ◽  
Clustering Data ◽  
Index Value ◽  
Cluster 2

Abstrak: Masalah kesehatan yang ada di dalam masyarakat terutama di negara- negara berkembang seperti Indonesia dipengaruhi oleh dua faktor yaitu aspek fisik dan aspek non fisik. Berdasarkan data yang diperoleh dari karawangkab.bps.go.id data dibagi menjadi 3 cluster yaitu sedikit, sedang dan terbanyak. Algoritma yang digunakan adalah K-Means cluster yang diimplementsikan menggunakan Microsoft Excel dan Rapidminer Studio. Hasil pengolahan data fasilitas kesehatan di karawang menghasilkan 3 cluster dengan cluster 1 yang mempunyai fasilitas kesehatan sedikit sebanyak 23 kecamatan, cluster 2 yang mempunyai fasilitas kesehatan sedang sebanyak 5 kecamatan dan cluster 3 yang mempunyai fasilitas kesehatan terbanyak terdapat 2 kecamatan. Kinerja yang dihasilkan dari algoritma K-means menghasilkan nilai Davies Boildin Index sebesar 0,109.   Kata kunci: clustering, data mining, fasilitas kesehatan, K-Means.   Abstract: Health problems that exist in society, especially in developing countries like Indonesia, are built by two factors, namely physical and non-physical aspects. Based on data obtained from karawangkab.bps.go.id the data is divided into 3 clusters, namely the least, medium and the most. The algorithm used is the K-Means cluster which is implemented using Microsoft Excel and Rapidminer Studio. The results of data processing of health facilities in Karawang produce 3 clusters with cluster 1 which has 23 sub-districts of health facilities, cluster 2 which has medium health facilities as many as 5 districts and cluster 3 which has the most health facilities in 2 districts. The performance resulting from the K-means algorithm results in a Davies Boildin Index value of 0.109.   Keywords: clustering, data mining, health facilities, K-Means.

Calculation of Contact Forces of Large-Scale Heavy Forging Manipulator Grippers

2009 ◽  
Vol 419-420 ◽  
pp. 645-648 ◽  
Author(s):  
Qun Ming Li ◽  
Dan Gao ◽  
Hua Deng
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Contact Forces ◽  
Robotic Hand ◽  
Closure Condition ◽  
Contact Points ◽  
Robot Hands ◽  
Calculation Results ◽  
Forging Manipulator ◽  
Gripping Force

Different from dexterous robotic hands, the gripper of heavy forging manipulator is an underconstrained mechanism whose tongs are free in a small wiggling range. However, for both a dexterous robotic hand and a heavy gripper, the force closure condition: the force and the torque equilibrium, must be satisfied without exception to maintain the grasping/gripping stability. This paper presents a gripping model for the heavy forging gripper with equivalent friction points, which is similar to a grasp model of multifingered robot hands including four contact points. A gripping force optimization method is proposed for the calculation of contact forces between gripper tongs and forged object. The comparison between the calculation results and the experimental results demonstrates the effectiveness of the proposed calculation method.

Automated Detection of Paleoenvironmental Proxy, Eucampia Index, in a Microscopic Slide Using a Convolutional Neural Network System

2020 ◽  
Author(s):  
Saki Ishino ◽  
Takuya Itaki
Keyword(s):  
Southern Ocean ◽  
Large Scale ◽  
Classification Performance ◽  
Automated Detection ◽  
Model Verification ◽  
Training Dataset ◽  
Test Dataset ◽  
Counting Error ◽  
Index Value ◽  
Particle Images

Abstract The Eucampia Index, which is calculated from valve ratio of Antarctic diatom Eucampia ainarctica varieties, has been expected to be a useful indicator of sea ice coverage or/and sea surface temperature variation in the Southern Ocean. To verify the relationship between the index value and the environmental factors, considerable effort is needed to classify and count valves of E. antarctica in a very large number of samples. In this study, to realize automated detection of the Eucampia Index, we constructed a deep-learning (one of the learning methods of artificial intelligence) based models for identifying Eucampia valves from various particles in a diatom slide. The microfossil Classification and Rapid Accumulation Device (miCRAD) system, which can be used for scanning a slide and cropping images of particles automatically, was employed to collect images in training dataset for the model and test dataset for model verification. As a result of classifying particle images in the test dataset by the initial model "Eant_1000px_200616", accuracy was 78.8%. The Eucampia Index value prepared in the test dataset was 0.80, and the value predicted using the developed model from the same dataset was 0.76. The predicted value was in the range of the manual counting error. These results suggest that the classification performance of the model is similar to that of a human expert. This study revealed that a model capable of detecting the ratio of two diatom species can be constructed using the miCRAD system for the first time. The miCRAD system connected with the developed model in this study is capable of automatically classifying particle images at the same time of capturing images so that the system can be applied to a large-scale analysis of the Eucampia index in the Southern Ocean. Depending on the setting of the classification category, similar method is relevant to investigators who have to process a large number of diatom samples such as for detecting specific species for biostratigraphic and paleoenvironmental studies.

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