Elbow Method Research Articles

Beam tracking in phased array antenna based on the trajectory classification

AbstractOne of the major new concepts in 5G cellular communications is a shift in the way services are delivered to users. In the new 5G protocol, individual users are recognized and tracked through beams, which are targeted and specific to individual users. The millimeter‐wave (mmWave) communications impose a directionality which results in a significant challenge in serving mobile terminals and unmanned aerial vehicles. This challenge can be relieved in mmWave systems using analog beamforming. Based on the identification of the patterns in the reference signal received power (RSRP) measurements, some classifications are employed. Some trajectories are defined for different users in Hallways. Therefore, the angle of arrival (AoA) would be the same for the users following the same trajectory. The users based on the received RSRP values are clustered by K‐means and I‐Kmeans algorithms. To this end, our algorithms to find the beamforming coefficient are employed for only one user in each cluster and the complexity of the algorithms can be lower. The proposed algorithms are spatial frequency‐based beam tracking and angular domain beam tracking algorithms. Simulation results show that tracking the sine function of AoA achieves better performance compared to tracking the AoA. Moreover, the optimal number of clusters is obtained by using the Elbow method.

Toward a smart health: big data analytics and IoT for real-time miscarriage prediction

BackgroundWe are living in an age where data is everywhere and grows up in a very speedy way. Thanks to sensors, mobile phones and social networks, we can gather a hug amount of information to understand human behavior as well as his individual life. In healthcare system, big data analytics and machine learning algorithms prove their effectiveness and efficiency in saving lives and predicting new diseases. This triggered the idea of taking advantages of those tools and algorithms to create systems that involve both doctors and patients in the treatment of disease, predict outcomes and use real-time risk factors from sensors and mobile phones.MethodsWe distinguish three types of data: data from sensors, data from mobile phones and data registered or updated by the patient in a mobile app we created. We take advantages from IoT systems such as Raspberry Pi to collect and process data coming from sensors. All data collected is sent to a NoSql Server to be then analyzed and processed in Databricks Spark. K-means centroid clustering algorithms is used to build the predictive model, create partitions and make predictions. To validate results in term of efficiency and effectiveness, we used clustering validations techniques: Random K, Silhouette and Elbow methods.ResultsThe main contribution of our work is the implementation of a new system that has the capability to be applied in several prediction disease researches using Big Data Analytics and IoT. Also, comparing to other studies in literature that use only medical or maternal risk factors from echography; our work had the advantage to use real-time risk factors (maternal and medical) gathered from sensors, react in advance and track diseases. As a case study, we create an e-monitoring real-time miscarriage prediction system to save baby’s lives and help pregnant women. In fact, doctors receive the results of clustering and track theirs patient through our mobile app to react in term of miscarriage to avoid non-suitable outcomes. While pregnant women receive only advices based on their behaviors. The system uses 15 real-time risk factors and our dataset contains more than 1,000,000 JSON files. Elbow method affirm three as the optimal number of clusters and we reach 0.99 as a value of Silhouette method, which is a good sign that clusters are well separated and matched.

A topographical analysis of encephalocele locations: generation of a standardised atlas and cluster analysis.

Encephaloceles are considered to result from defects in the developing skull through which meninges, and potentially brain tissue, herniate. The pathological mechanism underlying this process is incompletely understood. We aimed to describe the location of encephaloceles through the generation of a group atlas to determine whether they occur at random sites or clusters within distinct anatomical regions. Patients diagnosed with cranial encephaloceles or meningoceles were identified from a prospectively maintained database between 1984 and 2021. Images were transformed to atlas space using non-linear registration. The bone defect, encephalocele and herniated brain contents were manually segmented allowing for a 3-dimensional heat map of encephalocele locations to be generated. The centroids of the bone defects were clustered utilising a K-mean clustering machine learning algorithm in which the elbow method was used to identify the optimal number of clusters. Of the 124 patients identified, 55 had volumetric imaging in the form of MRI (48/55) or CT (7/55) that could be used for atlas generation. Median encephalocele volume was 14,704 (IQR 3655-86,746) mm3 and the median surface area of the skull defect was 679 (IQR 374-765) mm2. Brain herniation into the encephalocele was found in 45% (25/55) with a median volume of 7433 (IQR 3123-14,237) mm3. Application of the elbow method revealed 3 discrete clusters: (1) anterior skull base (22%; 12/55), (2) parieto-occipital junction (45%; 25/55) and (3) peri-torcular (33%; 18/55). Cluster analysis revealed no correlation between the location of the encephalocele with gender (χ2 (2, n = 91) = 3.86, p = 0.15). Compared to expected population frequencies, encephaloceles were relatively more common in Black, Asian and Other compared to White ethnicities. A falcine sinus was identified in 51% (28/55) of cases. Falcine sinuses were more common (χ2 (2, n = 55) = 6.09, p = 0.05) whilst brain herniation was less common (χ2 (2, n = 55) = .16.24, p < 0.0003) in the parieto-occipital location. This analysis revealed three predominant clusters for the location of encephaloceles, with the parieto-occipital junction being the most common. The stereotypic location of encephaloceles into anatomically distinct clusters and the coexistence of distinct venous malformations at certain sites suggests that their location is not random and raises the possibility of distinct pathogenic mechanisms unique to each of these regions.

Perbandingan Algoritma K-Means dan K-Medoids Untuk Pemetaan Daerah Penanganan Diare Pada Balita di Kabupaten Kuningan

Diarrhea is an endemic disease that contributes to the high mortality rate in Indonesia, especially among children under five. The Kuningan District Health Office had difficulties in monitoring and supervising the spread of diarrheal diseases. This study aims to produce a mapping scheme of priority areas in handling the prevention and control of the spread of diarrheal disease in children under five in Kuningan Regency. The method used is the Data Mining Clustering method by comparing two algorithms, namely the K-Means algorithm and the K-Medoids algorithm. Determination of the optimum number of clusters using the Elbow and Silhouette Coefficient methods. With this method, the result is that in the K-Means algorithm the optimum number of clusters is 3 clusters while the K-Medoids algorithm is 2 clusters. The best cluster evaluation uses the Davies-Bouldin Index (DBI) method and the results show that the K-Means DBI value is always smaller than the K-Medoids algorithm in either 2 clusters or 3 clusters, this shows that the K-Means algorithm is better than the K-Medoids algorithm. Based on these results, it is recommended to map priority areas for handling diarrheal diseases using the K-Means algorithm with 3 clusters, namely medium priority areas consisting of 9 regions, high priority areas consisting of 3 regions and low priority areas consisting of 25 regions. The results of the mapping can be used as input for the Kuningan District Health Office to develop strategies for preventing and preventing diarrheal diseases in children under five.

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

Construction of glass composition analysis and identification model based on K-means algorithm optimized by genetic algorithm

Ancient glass is highly susceptible to weathering by the environment in which it is buried. During the weathering process the proportion of its composition changes, thus affecting the correct judgment of its category. In this paper, the two categories of high potassium and lead-barium glass were subclassified separately, and firstly, the elbow method was used to combine the SC contour coefficients to obtain their optimal number of clusters as 3,7 respectively; after that, the initial point selection was set as the decision variable, the interclass distance and the minimum as the fitness function, and the initial population size was set as 50, the clustering distance as Euclidean distance and other parameters, and the genetic algorithm was used to optimize the K-means clustering to achieve the subclassification delineation. Taking lead barium as an example, after optimization, CHI is increased by 4, DBI is decreased by 0.3, contour coefficient is increased by 0.2, and the clustering effect becomes better. Finally, the Kappa consistency test was performed, and the Kappa coefficient was 0.822 indicating the reasonableness of the clustering results; by changing the selected feature vector dimensions for sensitivity analysis, the Kappa coefficients were all above 0.8, which were not sensitive to the dimensionality of the feature vectors. The model effectively implements the problem of subclassifying different classes of glass for subclassification.

K-Means Algorithm and Clustering Technique for A Recommender System

Most of netizens are fond of traveling. Though they are interested to travel, majority of them are confused about where to go. Whenever the netizens planed for a travel, they spent hours searching for an interesting place that matches their point of interest. Therefore, there is a need for a recommender system which can recommend several interesting travel venues based on their preferences. Hence, information regarding different users prefers different travel locations are required. In this paper, Google review is used as a reference to divide the users into clusters of similar interests. The data has been divided into six scenarios which consist of the original data, scaled data, Principal Component Analysis (PCA) data, PCA scaled data, MinMax scaled data and Robust scaled data. The distance metric technique is performed in all these scenarios. Minkowski distance metric is used to measure the distance between the data points in the dataset and the elbow method to these scenarios to know the inertia values. This has revealed that the PCA with scaled data has lower distance and inertia value. The K-Means clustering is performed on this PCA-scaled data which divided the data into four different clusters. The results revealed are imperative to build any recommender system based on the users' preference.

A study on the composition analysis and identification of ancient glass products

The study of ancient glass, as an important branch of scientific and technological archaeology, is an important physical material for exploring the economic, technological and cultural exchanges between China and foreign countries on the Silk Road. It is very important for studying the development of ancient society and cultural exchanges between China and foreign countries. In this paper, we focus on the composition analysis and identification of ancient glass products, based on data mining in the annexes and with the help of software analysis, we give the weathering pattern of glass surface, predict the chemical composition content before weathering, as well as cluster analysis and correlation analysis of different types of glass and their chemical compositions. In this paper, a sample of glass types and the correlation of their chemical compositions is established for analysis. Based on this, a decision tree classification model was built to classify the artifacts in the sample and determine the magnitude of the chemical composition of the classification criteria. The Elbow method (elbow rule) is used to determine the optimal number of aggregated classes k, and K-Means clustering analysis is performed, and then the decision tree classification model is used to quasi-determine and classify the subclass classification criteria for the clustered results. Finally, the XGBoost model is trained to achieve the prediction of the given sample types.

Yield Estimation of Dolichos Bean (Lablab purpureus L.) Using Different Clustering Techniques

Dolichos bean (Lablab purpureus L.) is an important leguminous vegetable crop. Diversity of genes to a high extent in the gene pool provides better opportunities to develop appropriate plant varieties. Hence, finding advantageous traits or their combinations is the key step. Genetic diversity is important for parents to be chosen for potential breeding programmes. In general, genetically divergent parents are used in segregating generations to produce the optimal recombinants. Green seed yield of the crop is influenced by number of yield contributing characters such as, green pod yield, pods per plant, days to 50 percent flowering, number of primary branches, raceme per plant, nodes per raceme, raceme length and 100 fresh seed weight. The association between the characters and yield provides basis for further breeding programme. Cluster analysis was used to classify the germplasm accessions into different clusters based on the morphological traits of the accessions. The data on germplasm accessions (64) was collected from the Department of Genetics and Plant Breeding, UAS, GKVK, Bengaluru, Karnataka. Euclidean distance metric was used to measure the difference between germplasms. Elbow method indicated three optimum genotype clusters. Three clustering methods were used viz., single, complete and average linkage. Dunn and Silhouette indices were used to validate the clusters. It was found that Average linkage method was best among the three. Further, in the third cluster by average linkage method with 12 genotypes was found to have highest green seed yield and green pod yield. Hence these 12 genotypes can be used for further breeding programmes.

OPTIMIZATION OF THE K-NEAREST NEIGHBORS ALGORITHM USING THE ELBOW METHOD ON STROKE PREDICTION

Stroke is the second most deadly disease in the world according to WHO. The sufferer has an injury to the nervous system. Because of this, health experts, especially in the field of nursing, need special attention. Technological advances continue to change over time so that information needs are needed in life. Currently the data on stroke sufferers is extensive enough so that adequate information presentation techniques are needed so that the information received is very accurate and in accordance with user needs. Therefore, it is necessary to process data mining on stroke patient data to obtain useful information for users. This study aims to prove the performance of the Elbow Method to produce the optimum k value in the stroke prediction data using the K-Nearest Neighbors (KNN) algorithm. The optimum k value is generated from the Elbow Method which is executed with the Google Collaboratory using the Python programming language. The test results show that the Elbow Method produces the optimum k value at k = 7. The KNN model that uses the optimum k value from the Elbow Method can increase the accuracy and precision values ​​reaching 6% and 0.12, respectively.

Genetic Diversity Analysis in Foxtail Millet [Setaria italica (L.) P. Beauv.] Genotypes

Foxtail millet (Setaria italica) is one of the least studied crops in terms of genetic resource characterization and genetic enhancement while compare to cereals. Hundred foxtail millet lines representing the national collection were evaluated at Medziphema, Nagaland University in Summer-2022 for polygenic trait evaluation and data subjected to genetic diversity analyses. Hundred foxtail millet genetic resources were divided into ten clusters by non-hierarchical cluster analysis (k-mean) was used and Elbow method was used to find out the optimum number of clusters. Genotypes were grouped into different cluster viz. Cluster I (6), Cluster II (9), Cluster III (12), Cluster IV (4), Cluster V (9), Cluster VI (8), Cluster VII (14), Cluster VIII (17), Cluster IX (17) and Cluster X (4). Cluster X had four accessions which are characterized by desired mean values and the genotypes belongs to these clusters can be utilized for the future breeding programs. The first five PCA components generated up to 82.43 percent of the diversity across genotypes tested for various agro-morphological variables. The first principal component explained 37.50 percent while, the second, third, fourth and five principal component exhibited 16.53 percent, 10.91 percent, 9.53 percent and 7.49 percent variability respectively, among the germplasm lines for the traits under study. Genotypes G2, G5, G10, G27 have low values for days to 50% flowering, days to maturity, plant height and high values for panicle length, flag leaf length and peduncle length, biological yield, fodder yield and grain yield per plant so that these genotypes can be used for general cultivation under Nagaland conditions and these materials could be used in further breeding programme

Research on Water Level Changes in Lake Mead

Lake Mead, the largest reservoir in the U.S., assumes a pivotal role in the water supply. However, in recent years, the reservoir's water volume has gradually decreased, and many states have cut back on water supply. Therefore, it is crucial to explore the pattern of water level changes in Lake Mead and thereby find ways to alleviate water shortage. We first discussed the pattern of development of historical water levels in Lake Mead. We classified the annual minimum and maximum water level data of Lake Mead using hierarchical clustering, and determined the group of clusters using the Elbow Method to perform the analysis, obtaining the best result. We then classified the historical water levels into early drought periods, non-drought periods, and recent drought periods based on the clustering results. Two models were also built to predict future Lake Mead water level changes. To facilitate the overall analysis, we drew scatterplots of the maximum and minimum water levels concerning time and found that the data of the first model is more consistent and regular, so we predicted the data using Linear Fitting-based time series analysis. However, the data needed since 2005 in model 2 showed a very obvious segmentation in roughly 2011, as seen from the scatterplot. We built a breakpoint regression model to make the prediction.

Clustering the residential noise in apartment house based on spectral and temporal analysis

This study aimed to investigate how the residential noises cluster based on spectral and temporal characteristics of the noises in apartment houses. The type of noise was floor impact, air-borne, plumbing, transmitted outdoor unit of air-conditioner, traffic (road, rail, and air-craft noise), and construction noise. Duration of sound was edited to 5 s and sound pressure level was adjusted to be equal to be 50 dBA. K-means clustering analysis was performed to classify various sound sources, and the elbow method was used to confirm the number of clusters. As a result, residential noise sources were classified into twelves clusters according to spectral and temporal characteristics and difference in psychoacoustic parameters between clusters was also found. In addition, perceptual aspect of each cluster was investigated through sematic differential test.

PENERAPAN DATA MINING UNTUK PENGELOMPOKKAN HASIL DIAGNOSA PENYAKIT PESERTA JKN-KIS BPJS KESEHATAN KANTOR CABANG PRABUMULIH

BPJS Kesehatan has collaborated with several health facilities in Indonesia to provide health services. So far, the use of grouping the results of disease diagnosis of JKN-KIS participants used by BPJS Health Partners and also the Health Office only uses a general description of the patient data. Optimal utilization of disease diagnosis data from JKN-KIS patients has not been done. Therefore, Clustering is also a method of grouping several data objects into a certain group of information with a high degree of similarity. Testing the best value with the Elbow method produces a K value that is close to The Shape of a 90 degree angle that is in the 4th cluster. So therefore, the medical record data grouping system produces the best cluster by using 8 clusters.

K-MEANS AND ELBOW METHOD FOR CLUSTER ANALYSIS OF ELEMENTARY SCHOOL DATA

This research was conducted to find the groups of elementary schools in the Special Capital Region of Jakarta, also known as DKI Jakarta. Elementary school data were selected because it is the first stage of formal education in Indonesia. This research used K-means clustering with the elbow method to determine optimal cluster numbers. The optimal cluster number is three with Cluster 2 having the most members, followed by Cluster 1 and Cluster 0. The data distribution of Cluster 2 shows that the second-most student body and public schools located in East and West Jakarta have an adequate student-to-teacher ratio based on Article 17 of Government Regulation 74, 2008.

Analisis Segmentasi Recency dan Customer Value Pada AVANA Indonesia Dengan Algoritma K-Means dan Model RFM (Recency, Frequency and Monetary)

Avana Indonesia is a social commerce startup headquartered in Malaysia. Wanting to expand their business and enter the Indonesian market, they still don't have the best marketing strategy in place, so a service sales deal is not enough. That's why we need a marketing strategy that focuses on customers with customer relationship management, one of which is customer segmentation. Customer segmentation can be done by implementing a data mining process which is carried out using the K-Means clustering algorithm based on the RFM (Recency, Frequency, Monetary) model. The number of clusters in the clustering process is determined using the elbow method. Cluster analysis based on customer value using the recency clustering method reveals active, warm, cold, and inactive customers. Then the two from recency frequency (customer value) segmentation produce common, ultra-high, low, and high clusters.

Clustering Courses Based On Student Grades Using K-Means Algorithm With Elbow Method For Centroid Determination

Students who have taken courses will receive grades from a performance index with a weight of 0 to 4. The amount of historical student data, particularly on course grades, has the potential to discover new insights. Still, course grades are closed data and are only for academic and management purposes. The research aims to a grouping of courses with high average grades. In this research, the clustering of courses using the k-means clustering algorithm using the elbow method to determine the centroid. Based on the Sum of Squares calculation, the optimal number of clusters with k=2 was obtained. The clustering results produced cluster 1 with a centroid value of 2.686 and 15 members and cluster 2 with a centroid value of 3.245 and 40 members. It can be concluded from this research that the members of cluster 2 are a group of courses with high average grades.&#x0D;

The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective

In this article, we investigate the impact of “Renewable Electricity Output” on the green economy in the context of the circular economy for 193 countries in the period 2011–2020. We use data from the World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Last Squares-WLS, and Pooled Ordinary Least Squares-OLS. Our results show that Renewable Electricity Output is positively associated, among others, with “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, with “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. In adjunct, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries. Furthermore, we improve the machine learning analysis with a Deep Learning approach using Convolutional Neural Network-CNN but the algorithm is not appropriate for the analyzed dataset. Less complex machine learning algorithms show better statistical results.

Clusterization of Agroforestry Farmers using K-Means Cluster Algorithm and Elbow Method

Proper policy is crucial to support the development of forest areas. Therefore, good planning based on supporting data is crucial. All information considering farmers’ conditions and interests in Talang Mulya Village, situated around Wan Abdul Rachman Forest Park, is urgently needed. So far, policies and programs launched have only been general and inappropriate to implement for the whole farmers. The main objective of this research was to perform fast agroforestry farmers clustering with differences in the main characters to each other using the K-means clustering algorithm and Elbow method based on 10 variables of social and land cultivation conditions. Results showed that agroforestry farmers in Talang Mulya Village could be best divided into 4 clusters with the proportion of 30%, 40%, 23.3%, and 6,67% for clusters 1, 2, 3, and 4, respectively. Agroforestry farmers were dominated by farmers with the specific characteristics of the lowest number of families working on the land and the cultivated main plant species with a sufficient level of formal education, family dependents, farming experience, household members that help in the farm, size of land area, expenditure and income from land cultivation, and maintenance activities. The research results could be used as a consideration for determining specific and targeted activity programs to increase the cultivation capabilities and welfare of farmers in Talang Mulya Village. Keywords: Agroforestry, Elbow Method, K-means Clustering, Tahura Wan Abdul Rachman, Talang Mulya Village

Clustering Analysis of Tweets About COVID-19 Using the K-Means Algorithm

One of the trending topics in 2020 to 2022 is tweets about Coronavirus Disease 2019 (COVID-19). A large number of tweets regarding COVID-19 that have appeared have been mixed and not grouped properly, making it difficult for Twitter users to read and sort them based on the information they want. One solution that can be applied to overcome the problems described is through clustering of tweets information about COVID-19. In this study, researchers used quantitative research with the K-Means method, which is one of the clustering methods used in grouping data. The data used in this study is a dataset taken from Kaggle, namely Omicron-Covid-19 Variant Tweets, and also taken through a scraping process with Bright Data with a total of 4,103 datasets. The results showed that determining the best cluster using the Elbow method on the dataset produced empirical evidence that the best cluster was k = 5. The results of grouping tweets regarding COVID-19 using the K-Means Clustering method with k = 5 resulted in the largest number of cluster members being cluster 4 with 1,185 tweets, the second largest was cluster 1 with 1,047 tweets, the third largest was cluster 2 with 757 tweets, the fourth largest was cluster 3 as many as 744 tweets, and the smallest number of cluster members is cluster 5 as many as 370 tweets.