Channel Coverage Identification Conditions for Massive MIMO Millimeter Wave at 28 and 39 GHz Using Fine K-Nearest Neighbor Machine Learning Algorithm

2021 ◽  
pp. 143-163
Author(s):  
Vankayala Chethan Prakash ◽  
G. Nagarajan ◽  
N. Priyavarthan
Keyword(s):  
Machine Learning ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor

scholarly journals Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China

2021 ◽  
Vol 8 ◽  
Author(s):  
Xueyuan Huang ◽  
Yongjun Wang ◽  
Bingyu Chen ◽  
Yuanshuai Huang ◽  
Xinhua Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Random Forest ◽  
Blood Cells ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Kappa Coefficient ◽  
Gradient Boosting ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor

Background: Predicting the perioperative requirement for red blood cells (RBCs) transfusion in patients with the pelvic fracture may be challenging. In this study, we constructed a perioperative RBCs transfusion predictive model (ternary classifications) based on a machine learning algorithm.Materials and Methods: This study included perioperative adult patients with pelvic trauma hospitalized across six Chinese centers between September 2012 and June 2019. An extreme gradient boosting (XGBoost) algorithm was used to predict the need for perioperative RBCs transfusion, with data being split into training test (80%), which was subjected to 5-fold cross-validation, and test set (20%). The ability of the predictive transfusion model was compared with blood preparation based on surgeons' experience and other predictive models, including random forest, gradient boosting decision tree, K-nearest neighbor, logistic regression, and Gaussian naïve Bayes classifier models. Data of 33 patients from one of the hospitals were prospectively collected for model validation.Results: Among 510 patients, 192 (37.65%) have not received any perioperative RBCs transfusion, 127 (24.90%) received less-transfusion (RBCs < 4U), and 191 (37.45%) received more-transfusion (RBCs ≥ 4U). Machine learning-based transfusion predictive model produced the best performance with the accuracy of 83.34%, and Kappa coefficient of 0.7967 compared with other methods (blood preparation based on surgeons' experience with the accuracy of 65.94%, and Kappa coefficient of 0.5704; the random forest method with an accuracy of 82.35%, and Kappa coefficient of 0.7858; the gradient boosting decision tree with an accuracy of 79.41%, and Kappa coefficient of 0.7742; the K-nearest neighbor with an accuracy of 53.92%, and Kappa coefficient of 0.3341). In the prospective dataset, it also had a food performance with accuracy 81.82%.Conclusion: This multicenter retrospective cohort study described the construction of an accurate model that could predict perioperative RBCs transfusion in patients with pelvic fractures.

Sentiment Analysis for Iraqis Dialect in Social Media

2018 ◽  
Vol 1 (2) ◽  
pp. 24-32
Author(s):  
Lamiaa Abd Habeeb
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Multinomial Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

In this paper, we designed a system that extract citizens opinion about Iraqis government and Iraqis politicians through analyze their comments from Facebook (social media network). Since the data is random and contains noise, we cleaned the text and builds a stemmer to stem the words as much as possible, cleaning and stemming reduced the number of vocabulary from 28968 to 17083, these reductions caused reduction in memory size from 382858 bytes to 197102 bytes. Generally, there are two approaches to extract users opinion; namely, lexicon-based approach and machine learning approach. In our work, machine learning approach is applied with three machine learning algorithm which are; Naïve base, K-Nearest neighbor and AdaBoost ensemble machine learning algorithm. For Naïve base, we apply two models; Bernoulli and Multinomial models. We found that, Naïve base with Multinomial models give highest accuracy.

scholarly journals Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias

2020 ◽  
Vol 5 (2) ◽  
pp. 57
Author(s):  
Novia Hasdyna ◽  
Rozzi Kesuma Dinata
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Ornamental Fish ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Matthew Correlation Coefficient

K-Nearest Neighbor (K-NN) is a machine learning algorithm that functions to classify data. This study aims to measure the performance of K-NN algorithm by using Matthew Correlation Coefficient (MCC). The data that used in this study are the ornamental fish which consisting of 3 classes named Premium, Medium, and Low. The analysis results of the Matthew Correlation Coefficient on K-NN using Euclidean Distance obtained the highest MCC value in Medium class which is 0.786542. The second highest MCC value is in Premium class which is 0.567434. The lowest MCC value is in Low class which is 0.435269. Overall, the MCC values is statistically which is 0,596415.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1797
Author(s):  
Ján Vachálek ◽  
Dana Šišmišová ◽  
Pavol Vašek ◽  
Jan Rybář ◽  
Juraj Slovák ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control Charts ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Conveyor Belt ◽  
Standard Uncertainty ◽  
K Nearest Neighbor ◽  
Industrial Products ◽  
Dynamic Identification ◽  
Identification Technique

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

scholarly journals Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

2021 ◽  
Author(s):  
Gothai E ◽  
Usha Moorthy ◽  
Sathishkumar V E ◽  
Abeer Ali Alnuaim ◽  
Wesam Atef Hatamleh ◽  
...  
Keyword(s):  
Big Data ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Squared Error ◽  
Big Data Applications ◽  
Distance Weighted ◽  
Classification Tasks

Abstract With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3–4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

scholarly journals A machine-learning approach to predict postprandial hypoglycemia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wonju Seo ◽  
You-Bin Lee ◽  
Seunghyun Lee ◽  
Sang-Man Jin ◽  
Sung-Min Park
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Artificial Pancreas ◽  
Individual Performance ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Support Vector ◽  
K Nearest Neighbor

Abstract Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

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