Implementation of K-Means Clustering Algorithm in Determining Classification of the Spread of the COVID-19 Virus in Bali

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Putu Mas Anggita Putra ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Infectious Disease ◽  
Respiratory System ◽  
Clustering Algorithm ◽  
Task Force ◽  
Clustering Algorithms ◽  
Partition System ◽  
Green Zone ◽  
Using Data ◽  
Human Respiratory System

The COVID-19 virus or also known as SARS-Cov-2 is an infectious disease caused by the Coronavirus which attacks the human respiratory system. The COVID-19 case has affected all provinces in Indonesia, including Bali. There is a total of 7481 cases in Bali and this is due to the lack of understanding of the community towards the COVID-19 prone areas in Bali. Therefore, it is necessary to group the areas prone to COVID-19 in Bali. One of the clustering algorithms is K-Means, this algorithm uses several groups for the placement of some data with a partition system. The grouping will be carried out using data from the Bali COVID-19 Task Force website on September 18, 2020, using RapidMiner application. The results obtained divided Bali into 3 clusters with Denpasar as the center of the highest spread of COVID-19 in Bali as the red zone, then Badung, Buleleng, Bangli, Gianyar, and Karangasem in the yellow zone, and other districts in the green zone.

scholarly journals Algoritma Clustering Untuk Membentuk Cluster Zona Penyebaran Covid-19

2021 ◽  
Vol 12 (1) ◽  
pp. 59-67
Author(s):  
Efori Bu'ulolo ◽  
Bister Purba
Keyword(s):  
Respiratory System ◽  
Clustering Algorithm ◽  
Task Force ◽  
Green Zone ◽  
North Sumatra ◽  
Cluster 2 ◽  
Human Respiratory System ◽  
At Home

Covid-19 yaitu suatu penyakit yang menyerang sistem pernapasan manusia dan dapat menular dengan mudah. Sumatera Utara salah satu daerah yang dilanda pandemi Covid-19. Melalui Gugus Tugas Percepatan dan Penanganan Covid-19 provinsi Sumatera Utara telah melakukan berbagai upaya untuk pencegahan penyebaran Covid-19 seperti belajar dan ibadah dirumah, himbauan pakai masker dan lain sebagainya. Untuk mempermudah  identifikasi penyebaran Covid-19 Tim Gugus membagi zona penyebaran Covid-19 berdasarkan jumlah kasus positif. pembagian zona dengan menggunakan satu variabel yaitu positif menyebabkan penanganan Covid-19 tidak maksimal karena hanya terkonsentrasi pada zona dengan kasus positif yang terbanyak sedangkan potensi penyebaran bukan hanya dari kasus positif. Oleh karene itu, dibutuhkan teknik yang lain dapat mengelompokkan / cluster zona penyebaran Covid-19. Salah satu teknik yang sesuai untuk pengelompokkan / cluster yaitu algoritma clustering K-Medoids. Hasil dari implementasi algoritma Algoritma K-Medoids yaitu cluster zona penyebaran Covid-19 di Sumatera Utara dibagi dalam 3(tiga) Cluster yaitu cluster 1, cluster 2 dan cluster 3. Cluster 1 identik dengan zona merah, Cluster 2 identik dengan zona kuning dan cluster 3 identik dengan zona hijau. Abstract Covid-19 is a disease that attacks the human respiratory system and can be transmitted easily. North Sumatra is one of the areas hit by the Covid-19 pandemic. Through the Task Force for the Acceleration and Handling of Covid-19, the province of North Sumatra has made various efforts to prevent the spread of Covid-19, such as studying and worship at home, appealing to wear masks and so on. To make it easier to identify the spread of Covid-19, the Cluster Team divides the Covid-19 spread zones based on the number of positive cases. zoning by using one variable, namely positive, causes the handling of Covid-19 to be not optimal because it is only concentrated in the zone with the most positive cases, while the potential for spread is not only from positive cases. Therefore, another technique is needed to group / cluster the Covid-19 spread zones. One technique that is suitable for grouping / clustering is the K-Medoids clustering algorithm. The results of the implementation of the K-Medoids Algorithm algorithm, namely the Covid-19 spread zone cluster in North Sumatra is divided into 3 (three) clusters, namely cluster 1, cluster 2 and cluster 3. Cluster 1 is identical to the red zone, Cluster 2 is identical to the yellow zone and cluster 3 is identical to the green zone

scholarly journals Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

2021 ◽  
Vol 18 (20) ◽  
pp. 10769
Author(s):  
Oscar Jossa Jossa Bastidas ◽  
Sofia Zahia ◽  
Andrea Fuente-Vidal ◽  
Néstor Sánchez Sánchez Férez ◽  
Oriol Roda Roda Noguera ◽  
...  
Keyword(s):  
Regression Models ◽  
Clustering Algorithm ◽  
Short Term Memory ◽  
Exercise Adherence ◽  
Attrition Rates ◽  
User Training ◽  
Active Lifestyles ◽  
Using Data ◽  
First 90 Days

The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition.

scholarly journals Hierarchical kt jet clustering for parallel architectures

2017 ◽  
Vol 9 (2) ◽  
pp. 195-213
Author(s):  
Richárd Forster ◽  
Ágnes Fülöp
Keyword(s):  
Hierarchical Clustering ◽  
Particle Physics ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Energy ◽  
Theoretical Physics ◽  
High Energy Particle ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods ◽  
Using Data

AbstractThe reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately. More granular parallelization of the kt cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The kt method allows to know the development of particles due to the collision of high-energy nucleus-nucleus. The hierarchical clustering algorithms works on graphs, so the particle information used by the standard kt algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit's standard longitudinal invariant kt implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be 1:6 times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a 12 times faster computing performance, also being scalable enough to efficiently run on GPUs.

scholarly journals UNSUPERVISED TEXTURE CLASSIFICATION OF ENTROPY BASED LOCAL DESCRIPTOR USING K-MEANS CLUSTERING ALGORITHM

2011 ◽  
pp. 133-140 ◽  
Author(s):  
S. S. Sreeja Mole ◽  
L. Ganesan
Keyword(s):  
Texture Analysis ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Texture Classification ◽  
Clustering Algorithms ◽  
Texture Feature ◽  
Texture Features ◽  
Local Descriptor ◽  
Descriptor Approach

This paper presents an efficient approach for unsupervised Texture Segmentation and Classification, based on features extracted from entropy based local descriptor using K-means clustering with spatial information. The K- means clustering algorithm is commonly used in computer vision as a form of image segmentation. Texture analysis refers to a class of mathematical procedures and models that characterizes the spatial variations within imagery as a means of extracting information. Texture analysis may require the solution of two different problems first is Segmentation and Classification of a given image according to the different texture and second was for of a given texture with respect to a set of known textures. Based on the proposed concept, this paper describes the entropy based local descriptor using K-Means with spatial information approach. Experimental results show that the proposed framework performs very well compared to other clustering algorithms in all measured criteria. Spatial information has been effectively used for unsupervised texture classification for Brodatz of texture images. The model is not specifically confined to a particular texture feature. We tested this algorithm using other texture features. The proposed entropy based local descriptor approach gives good accuracy when compared with other methods.

scholarly journals Review of Pathogenesis of COVID-19: Considerations

2020 ◽  
pp. 30-34
Author(s):  
C. N. Stanley ◽  
O. A. Ayodeji ◽  
P. C. Stanley
Keyword(s):  
Infectious Disease ◽  
Severe Acute Respiratory Syndrome ◽  
Respiratory System ◽  
Primary Target ◽  
Global Pandemic ◽  
Human Respiratory System

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a primary target on the human respiratory system. Coronavirus disease was first discovered in Wuhan, China in December 2019 and has currently become a global pandemic. A lot is still unknown about COVID -19 pathogenesis. Prompt assessment, adequate follow up, test and retest of recovered cases to corroborate immune related considerations will go a long way to understand the pathogenesis.

APPLYING ROBUST DIRECTIONAL SIMILARITY BASED CLUSTERING APPROACH RDSC TO CLASSIFICATION OF GENE EXPRESSION DATA

2006 ◽  
Vol 04 (03) ◽  
pp. 745-768
Author(s):  
H. X LI ◽  
SHITONG WANG ◽  
YU XIU
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Expression Data ◽  
Number Of Clusters ◽  
Robust Clustering ◽  
New Approach ◽  
Clustering Approach

Despite the fact that the classification of gene expression data from a cDNA microarrays has been extensively studied, nowadays a robust clustering method, which can estimate an appropriate number of clusters and be insensitive to its initialization has not yet been developed. In this work, a novel Robust Clustering approach, RDSC, based on the new Directional Similarity measure is presented. This new approach RDSC, which integrates the Directional Similarity based Clustering Algorithm, DSC, with the Agglomerative Hierarchical Clustering Algorithm, AHC, exhibits its robustness to initialization and its capability to determine the appropriate number of clusters reasonably. RDSC has been successfully employed to both artificial and benchmarking gene expression datasets. Our experimental results demonstrate its distinctive superiority over the conventional method Kmeans and the two typical directional clustering algorithms SPKmeans and moVMF.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

Air Pollution's Effects on the Human Respiratory System

2016 ◽  
Vol 44 (5) ◽  
pp. 383-395 ◽  
Author(s):  
Nehaarika Kantipudi ◽  
Vivek Patel ◽  
Graham Jones ◽  
Markad V. Kamath ◽  
Adrian R. M. Upton
Keyword(s):  
Respiratory System ◽  
Human Respiratory System
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