Bayes Method Research Articles

Implementation Of The Naïve Bayes Method With Approach Holistic In Predicting Student Achievement

Within the realm of education, students frequently encounter many obstacles, including defiance, absence of drive, and other intricate factors that can impact the caliber of their learning. This issue can result in many students abstaining from attending class, withdrawing from school, or prematurely leaving before completing their education. This highlights the need for proactive and preventive measures taken by educational institutions to assist children in overcoming learning barriers from a young age. Professional educational and training institutions should promptly identify potential obstacles to students' learning and take proactive measures to assist them in overcoming these difficulties. These endeavors encompass vigilant supervision, effective communication among educators, parents, and students, as well as impactful mentoring and counseling initiatives. By adopting this methodology, educational establishments can establish an all-encompassing and nurturing atmosphere that prioritizes the academic achievement of students. In this study, we employ the Naive Bayes algorithm and the Holistic Approach, two powerful tools in the field of data analysis, to forecast student academic performance at Otista Bandung Vocational School. The Naive Bayes algorithm allows us to make predictions based on the probability of an event occurring given certain conditions, while the Holistic Approach ensures that we consider all relevant factors in our analysis. This research aims to offer early forecasts for students who are susceptible to low academic performance, enabling schools to proactively intervene and avert a fall in student success. By implementing this approach, schools may offer targeted support to individuals requiring more help, while also fostering an inclusive environment that promotes academic success for all students, regardless of their circumstances.

Employee Performance Assessment Model in Survey Service Companies Using Analytical Hierarchy Process and Naive Bayes

Assessment of the performance of employees of a survey service company produces a final score that is not in accordance with the company's transformation strategy in responding to external challenges and demands. The variable assessment process still uses 180 Degree Feedback which is one-way from superiors to employees. This makes the company's learning and growth activities, especially in the human resources department, not occur. Therefore, companies should improve the assessment model to become a tool for acquiring highly qualified performing employees. This study contributes to obtain a more comprehensive employee performance assessment model proposal and evaluate the model so that it can be implemented. The research limitation lies in the use of data, namely contract employees with the position of Project Manager. This study uses the Analytical Hierarchy Process and Naïve Bayes methods to obtain variable weights. Based on the results of the analysis, it was found that SOE (State Owned Enterprises) AKHLAK was a new variable. The classification of the assessment variables is divided into Performance and Behavioral aspects based on the Minister of SOE Regulation. Changes that occur include formulas, mechanisms, and rating scales. The results of the verification and validation show that the model is in accordance with the design so that it can be implemented. Model trials with 360 DF show that PM's final score has a good chance of being assessed as a full-time employee. This valuation model can change along with company policies in responding to internal and external changes.

Identifying Sustainability Efforts in Company’s Reports Using Text Mining and Machine Learning

This study delves into the utilization of text mining to scrutinize social and environmental reports of companies, showcasing its effectiveness in evaluation. It explores various text mining techniques and practically applies decision tree, k-nearest neighbors, and naïve Bayes methods. The paper offers guidance on extracting pertinent terms related to four CSR dimensions: Environment, Employee, Social responsibility, and Human rights. Results demonstrate the successful differentiation of text based on these dimensions, leveraging a CSR-relevant dictionary by Pencel and Malascue. Employing document classification techniques, the study constructs four models using distinct text mining approaches for comparative analysis. Through this research, the valuable role of text mining in assessing social and environmental disclosures is underscored, providing insights into optimizing these techniques for evaluations and emphasizing their potential to enhance understanding and decision-making in corporate social responsibility assessments. Keywords: sustainability, text mining, machine learning, Corporate Social Responsibility - CSR, environmental reports

Klasifikasi Cyberbullying pada Komentar Instagram dengan Menggunakan Supervised Learning

Cyberbullying is an action related to the use of digital technology to intentionally hurt, humiliate or bully other people online. This research focuses on the classification of cyberbullying comments on social media, especially Instagram comments, where many parties who then become a group of people who don't like something will come together to provide negative opinions and comments, which can cause lowered self-confidence and other bad impacts for other users and account owner. Therefore, a classification of Instagram comments regarding cyberbullying was carried out as an effort to prevent this action. The data used in this research is 2000 data, where this data will go through various processes so that it can be executed. In this research, the Naïve Bayes method was used by dividing two classes, namely Bully and Not Bully. Based on the results of the tests that have been carried out, the results obtained are an accuracy value of 84%, a precision value of 84%, a recall of 84%, and an f1-score of 84%.

Implementation of Naïve Bayes Method Diagnosing Diseases Nile Tilapia

The Nile tilapia, also known as Oreochromis niloticus, was a freshwater fish species first produced in East Africa in 1969. It became a popular aquaculture fish in freshwater ponds across Indonesia. Besides its delicious taste, the Nile tilapia is rich in nutrients essential for human health. However, cultivating Nile tilapia was challenging due to frequent bacterial diseases. These diseases often led to mass fish deaths, causing financial losses, especially for new fish farmers. The rapid spread of diseases emphasized the need for prompt intervention to prevent further losses. Farmers needed adequate knowledge about Nile tilapia diseases, but often struggled to absorb information provided by the government. Hence, the presence of experts or veterinarians was crucial in assisting farmers to address these issues. Farmers of Nile tilapia sought assistance from experts or veterinarians, but this was not easy. It involved substantial costs and time, while quick intervention was necessary to mitigate losses. The solution proposed was the development of an expert system for diagnosing and treating Nile tilapia diseases. Thus, an expert system was built to assist fish farmers in identifying fish diseases and their treatments by implementing the naïve Bayes method. The expert system transferred human knowledge to computers, enabling them to solve problems like experts, thereby making expert knowledge accessible to non-experts. Naïve Bayes was implemented to determine the highest probability based on input symptoms. This research used five test data samples to apply the naïve Bayes method to diagnose Nile tilapia diseases, resulting in an accuracy rate of 80%. Therefore, the implementation of naïve Bayes in diagnosing Nile tilapia diseases is considered reasonably effective.

ANALISIS PEMELIHARAAN PREDIKTIF KENDARAAN OPERASIONAL MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN DECISION TREE

Utilizing the team to carry out operational vehicle maintenance activities is crucial to maintaining smooth mobility. Well-scheduled maintenance can prevent unexpected problems and minimize disruption to vehicle operations. The problem is that the implementation of operational vehicle maintenance policies is not yet optimal. The aim of this research is to focus on operational vehicles so that use is not disrupted and mobility runs smoothly, maintenance scheduling is needed. Completion of this research method will use the Naïve Bayes and Decision Tree data mining applications. This research produces a comparison of the two data mining applications to determine maintenance performance with an accuracy level of the Naïve Bayes method of 33.33% and a Decision Tree at 75.00%. The results of the best algorithm performance analysis are used as a reference for implementing vehicle maintenance scheduling.

Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.

Delirium is the most common neuropsychological complication among older adults admitted to the intensive care unit (ICU) and is often associated with a poor prognosis. This study aimed to construct and validate an interpretable machine learning (ML) for early delirium prediction in older ICU patients. This was a retrospective observational cohort study and patient data were extracted from the Medical Information Mart for Intensive Care-IV database. Feature variables associated with delirium, including predisposing factors, disease-related factors, and iatrogenic and environmental factors, were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, decision trees, support vector machines, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, F1 score, calibration plot, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to improve the interpretability of the final model. Nine thousand seven hundred forty-eight adults aged 65 years or older were included for analysis. Twenty-six features were selected to construct ML prediction models. Among the models compared, the XGBoost model demonstrated the best performance including the highest AUC (0.836), accuracy (0.765), sensitivity (0.713), recall (0.713), and F1 score (0.725) in the training set. It also exhibited excellent discrimination with AUC of 0.810, good calibration, and had the highest net benefit in the validation cohort. The SHAP summary analysis showed that Glasgow Coma Scale, mechanical ventilation, and sedation were the top three risk features for outcome prediction. The SHAP dependency plot and SHAP force analysis interpreted the model at both the factor level and individual level, respectively. ML is a reliable tool for predicting the risk of critical delirium in elderly patients. By combining XGBoost and SHAP, it can provide clear explanations for personalized risk prediction and more intuitive understanding of the effect of key features in the model. The establishment of such a model would facilitate the early risk assessment and prompt intervention for delirium.

The application of naive bayes method for final project topic selection within the project-based learning framework in the data mining course

<p>This study aims to explore the potential application of the Naive Bayes Method in predicting the determination of final project topics for students within the context of project-based learning (PBL) in the Data Mining course. Adopting an observational quantitative approach, the research involved 34 students from the Educational Technology Informatics Study Program at Padang State University between 2013-2015 as research subjects. The sampling technique employed was stratified random sampling, aligning with the research findings. The study's results indicate that the Naive Bayes Method is proficient in providing accurate predictions concerning the determination of final project topics, with significant accuracy, precision, and recall values. Moreover, the integration with project-based learning (PjBL) approach effectively enhances the authenticity and relevance of the learning experience for students, further substantiating the effectiveness of Project-Based Learning (PjBL).</p>

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

Classification System for Soil Types Suitable for Food Crops using Naïve Bayes Method

Classification System for Soil Types Suitable for Food Crops using Naïve Bayes Method

Penerapan Data Mining untuk Menentukan Produk Terlaris pada PT Joynare Mitra Teknologi menggunakan Metode Naive Bayes

The research applies the Naive Bayes method to determine the best-selling products at PT Joynare Mitra Teknologi. The contribution of this research is to provide a reference for other companies interested in using similar techniques to analyze their product data. Testing was carried out by processing data using Microsoft Excel and RapidMiner, with training and testing data. The test results show that the Naive Bayes method can classify best-selling products with a high level of accuracy, reaching 87.00%. Apart from that, the resulting precision reached 72.41%, and recall was 80.77%. The best-selling products in hardware sales such as motherboards, CPUs, RAM, hard disks, SSD, and printers can be identified through data analysis. The conclusion from the research is that the Naive Bayes method is very appropriate to use to optimize sales strategies for hardware products based on customer preferences.

Adaptive designs in public health: Vaccine and cluster randomized trials go Bayesian.

Clinical trials in public health-particularly those conducted in low- and middle-income countries-often involve communicable and non-communicable diseases with high disease burden and unmet needs. Trials conducted in these regions often are faced with resource limitations, so improving the efficiencies of these trials is critical. Adaptive trial designs have the potential to save trial time and resources and reduce the number of patients receiving ineffective interventions. In this paper, we provide a detailed account of the implementation of vaccine and cluster randomized trials within the framework of Bayesian adaptive trials, with emphasis on computational efficiency and flexibility with regard to stopping rules and allocation ratios. We offer an educated approach to selecting prior distributions and a data-driven empirical Bayes method for plug-in estimates for nuisance parameters.

Empirical Bayes Poisson matrix completion

An empirical Bayes method for the Poisson matrix denoising and completion problems is proposed, and a corresponding algorithm called EBPM (Empirical Bayes Poisson Matrix) is developed. This approach is motivated by the non-central singular value shrinkage prior, which was used for the estimation of the mean matrix parameter of a matrix-variate normal distribution. Numerical experiments show that the EBPM algorithm outperforms the common nuclear norm penalized method in both matrix denoising and completion. The EBPM algorithm is highly efficient and does not require heuristic parameter tuning, as opposed to the nuclear norm penalized method, in which the regularization parameter should be selected. The EBPM algorithm also performs better than others in real-data applications.

Perbandingan Klasifikasi antara Naives Bayes dan Decision Tree dalam Prediksi Penyakit Diabetes Tahap Awal

Diabetes is a health condition characterized by an elevated blood glucose level. There are two types, namely diabetes type 1 and diabetes type 2. Diabetes type 1 is caused by a lack of insulin production by the pancreas. Symptoms of diabetes include excessive thirst, frequent urination, and constant hunger. Classification is a process that helps us group data or information into categories based on similar characteristics. In the context of diabetes, classification methods can be used to group individuals based on their risk levels of developing diabetes. By using classification methods, doctors can determine an individual's risk of diabetes and design an appropriate treatment plan. This study involves a comparison between the Naïve Bayes and Decision Tree methods. The results of this research indicate that the algorithm generated is the best among the two algorithms in identifying diabetes patients. An accuracy of 66.67% was obtained for Naïve Bayes, while an accuracy of 91.67% was obtained for Decision Tree. In this study, it was found that the Decision Tree method has a higher accuracy rate than the Naïve Bayes method in the case study and data testing.

Enhancing Spam Filtering: A Machine Learning Approach to SMS and Email Fraud Classification

Abstract: Spam is an unsolicited text message or SMS that may contain malicious content and is sent on mobile devices. Fraudsters send fictitious texts in an attempt to get victims to reply to their messages, and they may also steal account numbers, passwords, and other private information. It was suggested to use a model built around algorithms for machine learning to prevent falling for scammers' tricks. The Naïve Bayes method and term frequencies-inverse document frequency vectorizer are used to implement the suggested model. acquired the dataset from the Kaggle database and used it to train the model. The PyCharm IDE can be used to access the local host webpage that makes up this model. The obtained findings indicate a 95% accuracy and 100% precision for the model.

Diagnosis Gangguan Pernapasan Pada Anak (Balita) Menggunakan Metode Forward Chaining dan Naïve Bayes

Respiratory system disorders often occur in humans and can be transmitted at any time from human to human. Respiratory disorders occur more often than disorders or infections of other body organ systems. Starting from the common cold with relatively mild to severe symptoms, coughing, fever, sore throat and shortness of breath. This disease is very dangerous, especially if it infects toddlers. To solve existing problems, researchers are trying to build an expert system to help people recognize respiratory diseases such as common cold, flu, bronchitis, pneumonia, asthma, allergies (rhinitis), sinusitis, tuberculosis, bronchopneumonia. Based on the symptoms entered into the system later, the system will use forward chaining and naïve Bayes methods as its inference engine which will produce a disease diagnosis. Based on 9 types of disease data with 30 types of symptom data for respiratory system diseases, the system has an accuracy of 87.6%. With these results, researchers hope to help the public as a medium for initial consultation in diagnosing respiratory diseases.

Classification of Hypertension Using Naïve Bayes Method with Data Discretization Approach Risk Factors

Classification of Hypertension Using Naïve Bayes Method with Data Discretization Approach Risk Factors

Bayes estimator for the inequality-constrained regression model*

This article studies the Bayesian estimation of the inequality-constrained regression model under a balanced loss function. The usual Bayes estimator is often involved in some complex integrals and has no explicit expression, which causes the corresponding properties be difficult to describe. Based on the idea of linear Bayes method suggested by Rao (1973), the present paper constructs a linear Bayes estimator for the regression parameters without specify their specific prior distributions. It is proved that the proposed linear Bayes estimator outperforms the corresponding least squares estimator, and numerical simulations show that when there is collinearity in design matrix, the linear Bayes estimator performs better than the usual Bayes estimator.

Enhanced Laplace approximation

The Laplace approximation has been proposed as a method for approximating the marginal likelihood of statistical models with latent variables. However, the approximate maximum likelihood estimators derived from the Laplace approximation are often biased for binary or temporally and/or spatially correlated data. Additionally, the corresponding Hessian matrix tends to underestimates the standard errors of these approximate maximum likelihood estimators. While higher-order approximations have been suggested, they are not applicable to complex models, such as correlated random effects models, and fail to provide consistent variance estimators. In this paper, we propose an enhanced Laplace approximation that provides the true maximum likelihood estimator and its consistent variance estimator. We study its relationship with the variational Bayes method. We also define a new restricted maximum likelihood estimator for estimating dispersion parameters and study their asymptotic properties. Enhanced Laplace approximation generally demonstrates how to obtain the true restricted maximum likelihood estimators and their variance estimators. Our numerical studies indicate that the enhanced Laplace approximation provides a satisfactory maximum likelihood estimator and restricted maximum likelihood estimator, as well as their variance estimators in the frequentist perspective. The maximum likelihood estimator and restricted maximum likelihood estimator can be also interpreted as the posterior mode and marginal posterior mode under flat priors, respectively. Furthermore, we present some comparisons with Bayesian procedures under different priors.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle.

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.