Porous Media in the Simulation of Greenhouse Crops Using the Naïves Bayes EM Algorithm

2019 ◽  
Vol 10 ◽  
pp. 1873-1885
Author(s):  
Guillermo Alfonso De la Torre Gea
Keyword(s):  
Porous Media ◽  
Em Algorithm ◽  
Natural Ventilation ◽  
Naive Bayes ◽  
Equations Of Motion ◽  
Naïve Bayes ◽  
Training Data ◽  
Data Set ◽  
Climate Conditions ◽  
Porous Media Model

The porous media approach has become more popular thus, it solves the equations of motion and energy numerically and therefore obtains detailed distributions of temperature and airspeed. However, those models are not allowed to forecast the relationships between the porosity of the volume of the crop with respect to the variables that comprise the climate in natural ventilation greenhouses at the same time in terms of probability. A porous media model of the crop and its approximations were developed and analyzed through non-supervised Bayesian Networks clustering, with the aim of determining the influence of porous media in function to the density crop, over the climate conditions in a natural ventilation greenhouse. Also, a naïve Bayes model unsupervised by the EM algorithm, initialized with random parameters was developed. The resulting model maximized the likelihood of the training data set. The relationships between the pressure drops in the flow limits at the crop were established. Porosity is directly influenced by humidity, temperature and slowly to CO2 concentration. Solar radiation, speed air and slowly the height are inversely influenced with the porosity. Naïve Bayes EM application to a CFD model has been providing a greater understanding of the interactions between the variables.

scholarly journals Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 1623-1630
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Naive Bayes ◽  
Early Stage ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Wide Range

As huge amount of data accumulating currently, Challenges to draw out the required amount of data from available information is needed. Machine learning contributes to various fields. The fast-growing population caused the evolution of a wide range of diseases. This intern resulted in the need for the machine learning model that uses the patient's datasets. From different sources of datasets analysis, cancer is the most hazardous disease, it may cause the death of the forbearer. The outcome of the conducted surveys states cancer can be nearly cured in the initial stages and it may also cause the death of an affected person in later stages. One of the major types of cancer is lung cancer. It highly depends on the past data which requires detection in early stages. The recommended work is based on the machine learning algorithm for grouping the individual details into categories to predict whether they are going to expose to cancer in the early stage itself. Random forest algorithm is implemented, it results in more efficiency of 97% compare to KNN and Naive Bayes. Further, the KNN algorithm doesn't learn anything from training data but uses it for classification. Naive Bayes results in the inaccuracy of prediction. The proposed system is for predicting the chances of lung cancer by displaying three levels namely low, medium, and high. Thus, mortality rates can be reduced significantly.

scholarly journals Optimasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Anomaly dengan Univariate Fitur Selection

2020 ◽  
Vol 4 (2) ◽  
pp. 40-49
Author(s):  
Harianto Harianto ◽  
◽  
Andi Sunyoto ◽  
Sudarmawan Sudarmawan ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Data Set ◽  
System Data ◽  
And Training

System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.

IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PEMESANAN DRIVER GO-JEK ONLINE DENGAN MENGGUNAKAN METODE NAIVE BAYES (STUDI KASUS: PT. GO-JEK INDONESIA)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Delisman Laia ◽  
Efori Buulolo ◽  
Matias Julyus Fika Sirait
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Transportation Industry ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Taxi Service ◽  
Bayes Algorithm ◽  
Using Data

PT. Go-Jek Indonesia is a service company. Go-jek online is a technology-based motorcycle taxi service that leads the transportation industry revolution. Predictions on ordering go-jek drivers using data mining algorithms are used to solve problems faced by the company PT. Go-Jek Indonesia to predict the level of ordering of online go-to drivers. In determining the crowded and lonely time. The proposed method is Naive Bayes. Naive Bayes algorithm aims to classify data in certain classes. The purpose of this study is to look at the prediction patterns of each of the attributes contained in the data set by using the naive algorithm and testing the training data on testing data to see whether the data pattern is good or not. what will be predicted is to collect the data of the previous driver ordering, which is based on the day, time for one month. The Naive Bayes algorithm is used to predict the ordering of online go-to-go drivers that will be experienced every day by seeing each order such as morning, afternoon and evening. The results of this study are to make it easier for the company to analyze the data of each go-jek driver booking in taking policies to ensure that both drivers and consumers or customers.Keywords: Go-jek Driver, Data Mining, Naive Bayes

Machine Learning increases efficiency and timeliness of National Procedural Clinical Standards KPI reporting (Preprint)

2021 ◽  
Author(s):  
Graeme Hart ◽  
Michael Woodburn ◽  
Nada Marhoon ◽  
Alan Pritchard ◽  
Jeff Feldman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Free Text ◽  
Data Set ◽  
Quality Reporting ◽  
Bayes Model ◽  
Data Pipeline ◽  
Naïve Bayes Model

BACKGROUND Background: Quality Assurance activities are frequently dependent on manual assessment of text-based records. Increasingly, these records have digital structures that may be amenable to computer analysis. We used the Australian Commission for Safety and Quality in Healthcare (ACSQHC) National Clinical Care Colonoscopy standard reporting requirement as a proof of concept for an analytics process to streamline and reduce manual reporting overheads. The endoscopy unit performs approximately 4,500 colonoscopies (mainly outpatient) per year. Quarterly reporting of colonoscopy outcomes requires approximately 30 hours of manual data abstraction, collation and combination from a variety of electronic databases. The most time consuming is manual retrieval and abstraction of histopathology records from the EMR. OBJECTIVE 1. To reduce the manual overheads of quarterly National Standards KPI reporting for colonoscopy compliance using an automated data pipeline and Artificial Intelligence tools. 2. The service also wished to minimise the risk of failure to follow up in new cancer diagnoses for outpatient colonoscopies. 3. To develop a data and analytic pipeline that would be easily re-purposed for additional standards, audit and research projects. METHODS A data pipeline and analysis environment were established in the hospitals’ secure Microsoft Azure databricks resource. A Training data set of 1000 colonoscopies was extracted using from the procedural Provation database using the the ProvationMD ® reporting tool and linked to relevant histopathology reports provided from the Clinical Research Data Warehouse (CRDW). The Machine Learning (ML) training data set was created when histopathological reports were manually coded by Gastroenterology Registrars & nurses into the following categories: Adenoma Clinically Significant Sessile Serrated Adenoma Cancer Adequate Bowel Preparation Complete examination A variety of Natural Language Processing (NLP) & ML models were assessed and refined to minimize error rate. Sensitivity was prioritised for the diagnosis of Cancer to minimize missed cases. Reporting to clinicians and quality co-ordinators was established using Microsoft Power BI. RESULTS The Naïve Bayes model for multinomial data resulted in high accuracy, but impacted recall. Sensitivity improved using a virtual ensemble approach, layering models within the processing pipeline and maximised using Microsoft’s ® Text Analytics – Healthcare NLP model with our custom Naïve Bayes model. F1 scores between 0.89 and 0.93 were achieved. The algorithm checks daily for new data and performs the analysis. Quarterly analysis and reporting time decreased from 30 hours to less than 5 minutes and reports can now be continuously updated in the Microsoft Power BI reporting portal. CONCLUSIONS Advanced analytic techniques can be deployed for mandatory quality reporting in a secure, cloud based, hospital data domain. The cost was far less than the manual processes it replaces. Reporting is more timely as it is automated. The potential for training such algorithms for other QA reporting is high. Text based research and audit within the free text domain of the EMR clinical documentation also becomes possible. CLINICALTRIAL Not applicable

scholarly journals Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection

2020 ◽  
Vol 1 (3) ◽  
pp. 123-134
Author(s):  
Budiman Budiman ◽  
Reni Nursyanti ◽  
R Yadi Rakhman Alamsyah ◽  
Imannudin Akbar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Study Program ◽  
Data Set ◽  
Lower Accuracy ◽  
Accuracy Result ◽  
Bayes Algorithm

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.

scholarly journals Structure Extension of Tree-Augmented Naive Bayes

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 721 ◽  
Author(s):  
YuGuang Long ◽  
LiMin Wang ◽  
MingHui Sun
Keyword(s):  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Training Data ◽  
Conditional Probability Distribution ◽  
Independence Assumption ◽  
Bayesian Network Classifiers ◽  
Leibler Divergence ◽  
The Difference ◽  
Structure Extension

Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler divergence, the difference between NB and its variations lies in different orders of conditional mutual information represented by these augmenting edges in the tree-shaped network structure. In this paper, we propose to relax the independence assumption by further generalizing tree-augmented naive Bayes (TAN) from 1-dependence Bayesian network classifiers (BNC) to arbitrary k-dependence. Sub-models of TAN that are built to respectively represent specific conditional dependence relationships may “best match” the conditional probability distribution over the training data. Extensive experimental results reveal that the proposed algorithm achieves bias-variance trade-off and substantially better generalization performance than state-of-the-art classifiers such as logistic regression.

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

scholarly journals COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY

2020 ◽  
Vol 17 (1) ◽  
pp. 37-42
Author(s):  
Yuris Alkhalifi ◽  
Ainun Zumarniansyah ◽  
Rian Ardianto ◽  
Nila Hardi ◽  
Annisa Elfina Augustia
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Total Sample ◽  
Naïve Bayes ◽  
Food Assistance ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.

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