scholarly journals Identification of Violent Response with Stretch Sensor Data from a Smart-Jacket using Naïve Bayes Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 5265-5270
Keyword(s):  
Supervised Learning ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Pressure Sensors ◽  
Naïve Bayes ◽  
Sensor Data ◽  
Body Movements ◽  
Bayes Algorithm ◽  
Do So

In this paper, a smart-jacket using stretch sensors, pressure sensors was built for purpose of generating body-movements data and in order to record different kinds of signals and the distribution of the same on the jacket. Every degree of motion, when exercised, generates voltage changes in the stretch sensors as it is its property to do so. This data is collected in a flora chip set, which is Arduino based. The collected data is processed, pruned and filtered for outliers. This paper concerns with a supervised learning algorithm called Naive Bayes, which is applied over independent datasets, meaning one set of observation has no direct relations to each other. The placement of sensor are on the shoulders and elbows and the responses from each are independent of each other. Using Naive Bayes, the date has been classified for the violent response and the normal action.

scholarly journals Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm

2019 ◽  
Vol 9 (2S) ◽  
pp. 53-56
Keyword(s):  
Naive Bayes ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Confusion Matrix ◽  
Daily Basis ◽  
Naïve Bayes ◽  
Human Beings ◽  
Waste Generation ◽  
Future Prediction ◽  
Bayes Algorithm

Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

scholarly journals A Machine Learning Framework for Improving Classification Performance on Credit Approval

2021 ◽  
Vol 10 (1) ◽  
pp. 47-52
Author(s):  
Pulung Hendro Prastyo ◽  
Septian Eko Prasetyo ◽  
Shindy Arti
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Information Gain ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Credit Scoring ◽  
Research Work ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Bayes Algorithm

Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.

scholarly journals Predicting & Visulaizing the Clusters Assignments in Health Care Dataset for Disease Prediction

2019 ◽  
Vol 9 (2) ◽  
pp. 3124-3129
Keyword(s):  
Naive Bayes ◽  
Final Analysis ◽  
Naïve Bayes ◽  
Disease Prediction ◽  
Usable Information ◽  
Diabetes Prediction ◽  
Hidden Patterns ◽  
Bayes Algorithm ◽  
Effective Analysis ◽  
Do So

DM is the process which is used for the analyzing hidden patterns of data. This analyzing completed according to the several perspectives for categorization into usable information. Here, DM is referred as the Data Mining It is composed and assembled in same regions, like data warehouses, for effective analysis, DM algorithms. In paper we will use these records and will find the major attribute which plays an important role in disease prediction. To do so, first we implemented Naive bayes’ algorithm where every pair of features being classified is independent of each other. Once we get the Naive Bayes’ Result then we apply the Clustering technique on the same dataset. Simple K-Means Clustering is used to get the clusters of the data results. We can visualize the Cluster assignments for each attribute against the Resultant or prediction attribute. We can have the better understanding through these visualizations about the dependencies of attributes on the prediction variable. K-means algorithm is an iterative algorithm that tries to partition the dataset into K predefined distinct nonoverlapping subgroups (clusters) where each data point belongs to only one group. And after final analysis of the result of both techniques we found two attributes which are having maximum weight as compare to others. These two attributes Glucose and Insulin must consider in the diabetes prediction.

scholarly journals A Machine Learning-Based Intelligent System for Predicting Diabetes

2019 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Nabila Shahnaz Khan ◽  
Mehedi Hasan Muaz ◽  
Anusha Kabir ◽  
Muhammad Nazrul Islam
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Personal Health ◽  
Diabetic Patients ◽  
Technological Growth ◽  
Bayes Algorithm ◽  
The Comparative Study

In this era of technological growth, the diagnosis of diseases and finding cures, personal health parameter management and predicting the possibility of susceptibility to some diseases have become accessible and easy. Although all over the world millions of people are falling victim to diabetes, in most of the cases they are not even aware of their situation due to the silent nature of diabetes. Therefore, the objective of this research is to propose an intelligent system based on a machine learning algorithm to improve the accuracy of predicting diabetes. To attain this objective, an algorithm was proposed based on Naïve Bayes with prior clustering. Second, the performance of the proposed algorithm was evaluated using 532 data related to diabetic patients. Finally, the performance of the existing Naïve Bayes algorithm was compared with the proposed algorithm. The results of the comparative study showed that the improvement in the accuracy has been made apparent for the proposed algorithm.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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