Bayes Method Research Articles

UNBALANCED MULTICLASS CLASSIFICATION WITH ADAPTIVE SYNTHETIC MULTINOMIAL NAIVE BAYES APPROACH

Opinions related to rising fuel prices need to be seen and analysed. Public opinion is closely related to public policy in Indonesia in the future. Twitter is one of the media that people use to convey their opinions. This study uses sentiment analysis to look at this phenomenon. Sentiment is divided into three categories: positive, neutral, and negative. The methods used in this research are Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours, and Adaptive Synthetic Random Forest. The Adaptive Synthetic method is used to handle unbalanced data. The data used in this study are public arguments per province in Indonesia. The results obtained in this study are negative sentiments that dominate all provinces in Indonesia. There is a relationship between negative sentiment and the level of education, internet use, and the human development index. Adaptive Synthetic Multinomial Naive Bayes performed better than other methods, with an accuracy of 0.882. The highest accuracy of the Adaptive Synthetic Multinomial Naive Bayes method is 0.990 in Papua Barat Province.

Development of tomato maturity level prediction model based on portable visible spectrometer and machine learning

A tomato is classified as a fruit, which level of maturity is determined by its color. Upon distribution, tomatoes require sorting based on their ripeness level. Generally making improvements done conventionally with the human eye. This method has the disadvantage that the results are subjective. One way that can be used to measure the ripeness level of tomatoes is using a spectroscopic sensor. Spectroscopic sensors can predict the level of ripeness and its contents automatically. This study uses machine learning to create a model to classify ripeness level and predict firmness, total dissolved solids (TDS), and total acid in tomatoes. This study used tomatoes with 3 categories of maturity. Tomatoes were tested non-destructively, namely measuring firmness, total dissolved solids content, and total acid. The data obtained were processed using the Partial Least Square Regression method to predict firmness, TDS, and total acid, while the maturity level used the Naive Bayes method. The data processing results to predict the level of maturity using Naive Bayes obtained a success rate of 100%. While for the predictions of firmness, TDS, and total acid had R2 training and R2 testing, namely 0.685 and 0.678, 0.534 and 0.521, and 0.352 and 0.349, respectively.

Multi-class classification using a new Bayesian method

This paper proposes a new classification model using the Bayes method. This model not only determines the prior probability based on the k-means algorithm, builds the method for estimating the probability density function via the kernel function, but also classifies the objects to the known populations. The proposed model is described via the experiment of image classifying. In this example, we first use the Gray level co-occurrence matrix to extract the features of images, and next classify this data set based on the improved Bayesian method. In another application, we also build the classification problem for the Algerian Forest Fires data set. The outstanding advantages of this method are the adaptive ability of the kernel function, the classification for multi-class, and the reduction of computational costs. In addition, the experimental results also show the potential of the developed model.

Classification Interest Customer XL Internet Card Using Naive Bayes Algorithm At PT XML Tronik

In the world of trade, competition within a company has become commonplace and makes business people always think about breakthroughs that can ensure the continuity of their business . Basically companies have valuable assets but are rarely used as a reference, these assets are in the form of databases with large numbers. PT.XML Tronik is a company that has been in the technology sector for a long time, one of which is selling GSM Card Providers in Indonesia. The classification method used is the Naïve Bayes Algorithm, where Naïve Bayes is used to classify statistical and probability methods . Method This research uses several stages starting from data collection , analysis sales , data processing using naïve Bayes method and proceed with measurements level accuracy use WEKA application . From the results of data mining using the Naïve Bayes Algorithm, this researcher found patterns of interest in buying XL Card customers at PT.XML Tronik . Based on the results of the analysis carried out, using 20% of the test data with negative and positive labels. So that it can produce a sign ending with an accuracy level of 84.6667% with a precision of 97% and a recall of 83%.

On the prediction of stock price crash risk using textual sentiment of management statement

PurposeSince stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.Design/methodology/approachSpecific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.FindingsThe experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).Research limitations/implicationsIt should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.Originality/valueThe authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Comparison of SVM and Naïve Bayes Algorithms with InNER enriched to Predict Hate Speech

Hate speech is one of the negative sides of social media abuse. Hate speech can be classified into insults, defamation, unpleasant acts, provoking, inciting, and spreading fake news (hoax). The purpose of this study is to compare the SVM and Naïve Bayes methods with feature extraction in the form of Indonesian NER (InNER) for detecting hate speech. To obtain the best model, this study applies five steps: a) data collection; b) data preprocessing; c) feature engineering; d) model development; and e) evaluating and comparing models. In this study, we have collected 7100 tweets as an initial dataset. After manual annotation, this study produced 1681 tweets: 548 insult tweets, 288 blasphemy tweets, 272 provocative tweets, and 573 neutral tweets. This study use two Python libraries that accommodate NER in Indonesian, namely the NLTK library and the Polyglot library. Based on the results of the evaluation of the proposed model, model 5, which develops the SVM algorithm with the NLTK library, is the best model proposed. This model shows an accuracy score of 92.88% with a precision of 0.93, a recall of 0.93, and an F-1 score of 0.92.

Sentiment Analysis of Telemedicine Applications on Twitter Using Lexicon-Based and Naive Bayes Classifier Methods

Since the onset of the COVID-19 pandemic in Indonesia, many people have turned to telemedicine programs as an alternative to minimize social interactions, opting for consultations from the safety of their homes using smartphones and internet connectivity. Given the necessity for physical distancing and avoiding crowded places, these applications have become indispensable substitutes for in-person medical consultations. Numerous apps facilitating access to healthcare services have been introduced in Indonesia, ranging from business startups to initiatives by the Ministry of Health. Telemedicine can potentially revolutionize healthcare in Indonesia, addressing critical health challenges. A significant issue within Indonesia's healthcare system is the scarcity of doctors and their uneven distribution. With only four doctors per 10,000 people, this figure falls far below the WHO guideline of 10 doctors per 1,000. Sentiment analysis of these applications was conducted to evaluate how telemedicine applications meet public needs and offer an alternative solution. Lexicon-based and naive Bayes methods were employed to classify tweet data into positive, neutral, and negative sentiments. The results revealed 908 positive tweets, 172 negative tweets, and 168 neutral tweets, indicating predominantly positive public perceptions of telemedicine applications. The naive Bayes classifier exhibited a 74% accuracy rate, with a precision of 98% and a recall of 86%. These findings underscore the positive impact and acceptance of telemedicine applications among the Indonesian populace, emphasizing their significance in augmenting the nation's healthcare landscape.

Long-term trends of local bird populations based on monitoring schemes: are they suitable for justifying management measures?

Local biodiversity monitoring is important to assess the effects of global change, but also to evaluate the performance of landscape and wildlife protection, since large-scale assessments may buffer local fluctuations, rare species tend to be underrepresented, and management actions are usually implemented on local scales. We estimated population trends of 58 bird species using open-population N-mixture models based on count data in two localities in southeastern Spain, which have been collected according to a citizen science monitoring program (SACRE, Monitoring Common Breeding Birds in Spain) over 21 and 15 years, respectively. We performed different abundance models for each species and study area, accounting for imperfect detection of individuals in replicated counts. After selecting the best models for each species and study area, empirical Bayes methods were used for estimating abundances, which allowed us to calculate population growth rates (λ) and finally population trends. We also compared the two local population trends and related them with national and European trends, and species functional traits (phenological status, dietary, and habitat specialization characteristics). Our results showed increasing trends for most species, but a weak correlation between populations of the same species from both study areas. In general, local population trends were consistent with the trends observed at national and continental scales, although contrasting patterns exist for several species, mainly with increasing local trends and decreasing Spanish and European trends. Moreover, we found no evidence of a relationship between population trends and species traits. We conclude that using open-population N-mixture models is an appropriate method to estimate population trends, and that citizen science-based monitoring schemes can be a source of data for such analyses. This modeling approach can help managers to assess the effectiveness of their actions at the local level in the context of global change.

Pemanfaatan Teknologi Computer Vision Untuk Implementasi Deteksi Masker Menggunakan Metode Supervised Learning

The use of masks is still very strict in public places, especially in hospitals, this is solely done to prevent the spread of the corona virus again. The purpose of this research is to assist examination workers or health protocol workers in supervising the use of masks in public places. Mask detection is a solution to this problem, by utilizing computer vision technology and applying supervised learning algorithms. For this mask detection classification method, this system uses the Naive Bayes method. The output of this mask detection system is planned to distinguish people wearing masks and not wearing masks, by giving red labels to people who are not wearing masks and green labeling to people wearing masks. The distance aspect is used in testing this mask detection system, the system is able to work well by getting an error rate presentation below 2% and getting the highest accuracy of 100% with an average percentage value of 98%. On the other hand, there are still weaknesses in this system, the use of brown masks that are in harmony with skin color can worsen the results of the classification system

Flexible Variational Bayes Based on a Copula of a Mixture

Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimization. Variational approximation is useful when exact inference is intractable or very costly. Our article develops a flexible variational approximation based on a copula of a mixture, which is implemented by combining boosting, natural gradient, and a variance reduction method. The efficacy of the approach is illustrated by using simulated and real datasets to approximate multimodal, skewed and heavy-tailed posterior distributions, including an application to Bayesian deep feedforward neural network regression models. Supplementary materials, including appendices and computer code for this article, are available online.

Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.

Wood Type Identification System using Naive Bayes Classification

Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.

Research on decision-level fusion method based on structural causal model in system-level fault detection and diagnosis

At present, system-level fault detection and diagnosis (FDD) research often uses correlation-based machine learning methods combined with multiple heterogeneous diagnosis methods to improve the fault detection rate (FDR), that is, decision-level fusion. Since it does not take into account the causal direction of the decision relationship, it will affect the realization of the fusion objectives, and lead to the reduction of the fusion range and the decrease of the global decision on FDR. In this regard, the structural causal model (SCM), a commonly used causal model in causal science, can use the causal graph to ensure causal direction of fusion, and the structural equation can be used to achieve fusion objectives to increase FDR, which can improve this problem. In this paper, we propose seven fusion objectives according to the diagnostic advantage interval of each preliminary method, and use SCM to construct causal graph and structural equation to achieve decision-level fusion according to the proposed seven fusion objectives, thereby improving FDR. The proposed method is validated through the simulation platform Tennessee Eastman process. We choose to combine the prediction results of Linear Discriminant Analysis method and Gaussian Naive Bayes method to achieve decision-level fusion. The results show that compared with the single method and the Bayesian network decision-level fusion method, the proposed method can achieve the best results in the FDR of each single system state and average FDR, and the above indicators are significantly improved.

Penerapan Metode Naïve Bayes Dalam Mendiagnosa Kerusakan Printer

Printers are devices that are utilized as print media. Despite the fact that a lot of printers have issues, little is known about printer damage at this time. while damage necessitates careful handling. The Naive Bayes method is one of many methods that can be used in expert systems. The point of this examination is to figure out how to carry out the utilization of diagnosing printer harm utilizing the Guileless Bayes strategy at the TR PrintComp organization and to figure out what the test aftereffects of the application are in diagnosing printer harm utilizing the Gullible Bayes technique at the TR PrintComp organization. The exploration strategy involved is writing concentrate on in this examination bringing about an application for diagnosing harm to printers utilizing the Credulous Bayes technique. Black box testing and confusion matrix methods were used for testing. The consequences of this exploration show that scientists have prevailed with regards to making a specialist framework for diagnosing printer harm utilizing the Gullible Bayes technique utilizing the PHP and MySQL programming dialects as a data set. Based on expert system testing, the Naive Bayes method was used to accurately diagnose printer damage with a 72.4% success rate. It is anticipated that this expert system application will offer a different approach to dealing with printer damage issues. The result from this master framework is as data or data in regards to the sort of printer harm and is joined by arrangements and ways of defeating them. The author's implementation of the Naive Bayes method for diagnosing printer damage is able to quickly identify the kind of damage.

The Implementation of Data Mining for Predicting XAU/USD Price Trends in the Forex Market on MetaTrader 5 using Naïve Bayes Method

The one of the trading instruments is forex or foreign exchange. The forex market provides various commodities, one of which is XAU/USD. XAU/USD dataset with daily, h4, h1 time frames obtained from the MetaTrader 5 application via the FBS broker. Predicting forex prices is difficult because there are various factors that influence it, so data mining methods are needed to predict increases or decreases. Naïve Bayes is a method and logic that can be applied in making predictions. So the research objective of this final project is to apply naïve Bayes methods and logic in predicting the price of XAU/USD on the daily, h4, h1 time frames. The application of the Naïve Bayes method uses several libraries to support research, namely pandas, jcopml, and sklearn. In naïve Bayesian logic research, this is called from the sklearn library using gaussianNB. In this study, the performance reference uses an f1 score matrix because the number of false positives and false negatives is not tight (symmetrical). This study produces values for each time frame obtained from the confusion matrix formula with f-scores of 49.99% (daily), 53.52% (h4), 55.44% (h1).

PENERAPAN METODE NAÏVE BAYES PADA KASUS GANGGUAN PSIKOLOGIS PADA MASA PANDEMI COVID-19

The COVID-19 pandemic is a type of deadly virus that has become a new cause of stress. Physical and psychological health is the hope of every individual. But not all individuals can enjoy this health because of contracting a disease. Stress experienced by individuals can affect physiological functions, namely there are 4 diseases in psychological disorders including stress, depression, anxiety disorders and social phobia. Therefore we need a web-based system using the nave Bayes method by paying attention to the symptoms experienced which will help psychological disorders in recognizing mental health against Covid-19 transmission during the pandemic. In this study, the data in question is psychological data due to the COVID-19 pandemic. There are many modern algorithms created to diagnose symptoms due to this pandemic, but here the researcher wants to prove that Naïve Bayes can also perform the diagnosis quite well. Nave Bayes has a high accuracy value so that it can be used for various purposes, including for document classification, spam detection and other classification problems. The use of the Naïve Bayes method in this study can be rated as a material for research evaluation. With the Naive Bayes method in the rating, it can be seen from the disease. The results of this study are a desktop program for the diagnosis of stress, depression, anxiety disorders, and social phobias during the covid-19 pandemic.

CGAN-EB: A non-parametric empirical Bayes method for crash frequency modeling using conditional generative adversarial networks as safety performance functions

In this paper, a new non-parametric empirical Bayes approach called CGAN-EB is proposed for approximating empirical Bayes (EB) estimates in traffic locations (e.g., road segments) which benefits from the modeling advantages of deep neural networks, and its performance is compared in a simulation study with the traditional approach based on negative binomial model (NB-EB). The NB-EB uses negative binomial model in order to model the crash data and is the most common approach in practice. To model the crash data in the proposed CGAN-EB, conditional generative adversarial network is used, which is a powerful deep neural network based method that can model any types of distributions. A number of simulation experiments are designed and conducted to evaluate the CGAN-EB performance in different conditions and compare it with the NB-EB. The results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model (i.e. data conform to the assumptions of the NB model) and outperforms NB-EB in experiments reflecting conditions frequently encountered in practice, specifically low sample means, and when crash frequency does not follow a log-linear relationship with covariates.

THE USE OF NAÏVE BAYES ALGORITHM IN FORECASTING THE FURTHER STUDY OF HIGH SCHOOL STUDENT

Schools with a large number of students who continue their studies to college will be the main choice as secondary schools from the elementary level. Therefore, increasing the number of students continuing their studies is very important to meet the competition. To get around this, schools can predict the continuation of high school/vocational high school students' studies to college. The goal is that the percentage of prediction results can be used as a reference for improving the quality of education services in schools. In making this prediction, the Naïve Bayes method or algorithm is used. In this case, the Naïve Bayes algorithm is a classification method with a probability and statistical approach that is suitable for predicting the continuation of high school/vocational high school students' studies to college. The prediction result of continuing study to university using Naïve Bayes on test data has an accuracy of 0.740.

The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%.

A semi-supervised adaptive discriminative discretization method improving discrimination power of regularized naive Bayes

Recently, many improved naive Bayes methods have been developed with enhanced discrimination capabilities. Among them, regularized naive Bayes (RNB) produces excellent performance by balancing the discrimination power and generalization capability. Data discretization is important in naive Bayes. By grouping similar values into one interval, the data distribution could be better estimated. However, existing methods including RNB often discretize the data into too few intervals, which may result in a significant information loss. To address this problem, we propose a semi-supervised adaptive discriminative discretization framework for naive Bayes, which could better estimate the data distribution by utilizing both labeled data and unlabeled data through pseudo-labeling techniques. The proposed method also significantly reduces the information loss during discretization by utilizing an adaptive discriminative discretization scheme, and hence greatly improves the discrimination power of classifiers. The proposed RNB+, i.e., regularized naive Bayes utilizing the proposed discretization framework, is systematically evaluated on a wide range of machine-learning datasets. It significantly and consistently outperforms state-of-the-art NB classifiers.