Bayes Technique Research Articles

Leveraging Naive Bayes for Enhanced Survival Analysis in Breast Cancer

The study aims to predict breast cancer survival using Naïve Bayes techniques by comparing different machine learning models on a comprehensive dataset of patient records. The main classification groups were survival and non-survival. The objective was to assess the performance of the Naïve Bayes classifier in the field of data mining and to achieve significant results in survival classification, aligning with current academic research. The Naive Bayes classifier attained an average accuracy of 91.08%, indicating consistent performance, though with some variability across different folds. Conversely, Logistic Regression achieved a higher accuracy of 94.84%, demonstrating proficiency in recognizing instances of class 1, yet encountering challenges with class 0.The Decision Tree model, with an accuracy of 93.42%, exhibited similar performance patterns. With an accuracy of 95.68%, Random Forest surpassed the Decision Tree. Nonetheless, all models encountered challenges in accurately classifying instances of class 0. The Naive Bayes algorithm was juxtaposed with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Future research aims to enhance prediction models with novel methods and tackle the challenge of accurately identifying instances of class 0.

Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

The creating of novel models essentially stems from the requirement to appropriate describe survival cases. In this study, a novel lifetime model with two parameters is proposed and studied for modeling more types of data used in different study cases, including symmetric, asymmetric, skewed, and complex datasets. The proposed model is obtained by compounding the exponential and XLindley distributions, and it is regarded as a strong competitor for the widely applied symmetrical and non-symmetrical models. Several characteristics and statistical properties are investigated. The unknown parameters of the recommended model for the complete sample are estimated using two estimation methods; notably, maximum likelihood estimation and Bayes techniques based on several loss functions as well as an approximate tool are used to construct the confidence intervals for the unknown parameters of the suggested model. The estimation procedures are compared using a Monte Carlo simulation experiment to demonstrate their effectiveness. In the end, the applicability and flexibility of the recommended model are conducted using two real lifetime datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing distributions.

An Artificial Intelligence Approach to Monitor and Predict Student Academic Performance

Recent progress in technology has altered the learning behaviours of students. It can easily be said that the improvements in technologies especially from the area of artificial intelligence and data mining can empower students to learn more efficiently, effectively, and contentedly. The purpose of this paper is aimed at developing a prediction model that gives a guide to the stakeholders likes university, student’s sponsor and parents that can monitor and interpret the on-going student’s performance. The proposed system will help the user to identify students who have a high risk or low risk to ending the semester with unsatisfactory results through discovering the essential features that can influence student’s academic performance. The methodology used were mixed method approach which is qualitative and quantitative. For the part of quantitative technique, the data set for this research will be collected from the academic records section. Meanwhile, the part of qualitative will go through a few main stages which are focused on developmental phase. The prediction model used in this research are RepTree, k-NN and Naïve Bayes. The finding states that RepTree had the highest accuracy compared to k-NN and Naïve Bayes techniques. It also suggests that the RepTree algorithm would be the best prediction model which has focused the research in term of developing the system of monitoring students’ academic performance at UPTM.

Particle Swarm Optimization for Optimizing Public Service Satisfaction Level Classification

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.

Advancing Sentiment Analysis during the Era of Data-Driven Exploration via the Implementation of Machine Learning Principles

Information technology has seamlessly woven into the fabric of our daily existence, making it nearly inconceivable to envision life without the influence of social media platforms. Communication networks, encompassing mediums like television and radio broadcasts, have transcended their role as mere sources of entertainment, evolving into contemporary vehicles for disseminating significant information, viewpoints, and concepts among users. Certain subsets of this data hold pivotal importance, serving as valuable reservoirs for analysis and subsequent extraction of crucial insights, destined to inform future decision-making processes. Within the scope of this undertaking, we delve into the intricacies of sentiment analysis, leveraging the power of machine learning to prognosticate and dissect data derived from external origins. A prime focal point of this endeavor revolves around the implementation of the Naive Bayes technique, a supervised approach that imparts knowledge to the system, enabling it to forecast the emotional undercurrents of forthcoming input data. Empirical findings stemming from this venture substantiate the prowess of the Naive Bayes method, positioning it as a formidable and highly efficient tool in the arsenal of sentiment analysis methodologies. Its remarkable accuracy in discerning the positive and negative polarity of data reinforces its merit. Furthermore, this approach expedites the generation of high-caliber results within an abbreviated timeframe, setting it apart from alternative techniques and processes inherent in the realm of machine learning.

Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients

Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.

Applying Naive Bayes Techniques for Accurate Sentiment Analysis in Movie Reviews

This study examines the effectiveness of Naive Bayes and Logistic Regression classifiers in analyzing the sentiment of movie reviews. Two feature extraction approaches, namely Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), are utilized. We employed a dataset comprising 50,000 IMDB reviews that underwent preprocessing techniques such as denoising, stop word removal, and stemming. The reviews were transformed into vectors using Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TFIDF) approaches. Our investigation demonstrates that Logistic Regression surpasses Naive Bayes in terms of accuracy. Logistic Regression achieves 89.52% accuracy for Bag-of-Words (BoW) and 89.23% accuracy for Term FrequencyInverse Document Frequency (TF-IDF), while Naive Bayes achieves 85.01% accuracy for BoW and 85.74% accuracy for TF-IDF. Naive Bayes has consistent performance with a minimum disparity between training and testing accuracies, indicating strong generalization skills despite its slightly lower accuracy. The results suggest that Logistic Regression outperforms Naive Bayes in terms of accuracy. However, Naive Bayes remains a strong contender because to its simplicity and consistent performance across various feature extraction methods. This comparison offers significant insights for choosing suitable classifiers and feature extraction techniques for text classification problems in sentiment analysis.

Multi-Hypothesis Marginal Multi-Target Bayes Filter for a Heavy-Tailed Observation Noise

A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student’s t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters.

Development of an Extended Medical Diagnostic System for Typhoid and Malaria Fever

In developing countries like Nigeria, malaria and typhoid fever are major health challenges in society today. The symptoms vary and can lead to other illnesses in the body which include prolonged fever, fatigue, nausea, headaches, and the risk of contracting infection occurring concurrently if not properly diagnosed and treated. There is a strong need for cost-effective technologies to manage disease processes and reduce morbidity and mortality in developing countries. Some of the challenging issues confronting healthcare are lack of proper processing of data and delay in the dissemination of health information, which often causes delays in the provision of results and poor quality of service delivery. This paper addressed the weaknesses of the existing system through the development of an Artificial Intelligence (AI) driven extended diagnostic system (EDS). The dataset was obtained from patients’ historical records from the Lagos University Teaching Hospital (LUTH) and contained two-hundred and fifty (250) records with five (5) attributes such as risk level, gender, symptom 1, symptom 2, and ailment type. The malaria and typhoid dataset was pre-processed and cleansed to remove unwanted data and information. The EDS was developed using the Naive Bayes technique and implemented using software development tools. The performance of the system was evaluated using the following known metrics: accuracies of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The performance of the EDS was substantially significant for both malaria and typhoid fevers.

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.

Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.

A new approach to develop biometric fingerprint using human right thumb fingernail

In this article, we analyzed the structure of human nails to develop a modern biometric authentication system based on the nail bed and finger lunula. The results of the studies on the collected images proved that each fingernail has distinctive characteristics in terms of the length and width of the nail and the lunula, even identical twins. We focused on the fingernail of the right thumb because of its large size and the accuracy and clarity of the nail. The mediation of the indicative points on the nail bed and on the lunula was used to form the pentagonal structure and use it as a region of interest. Then an ensemble independent component analysis, principal component analysis, haar wavelet, and scale invariant feature transformation, were used. Later we classified these algorithms using support vector machine and Naive Bayes techniques, the performance of each algorithm was analyzed by feature extraction with two classifiers. This study was conducted on 100 participants and showed that this new method could be used as a biometric identification system for humans. There was no similarity in results for all verified samples.

A Review Article On Enhancing Email Spam Filter’s Accuracy Using Machine Learning

In today’s era, almost everyone is using emails on their daily basis. In our proposed research, we suggest a machine learning-based strategy for enhancing email spam filters' accuracy. Traditional rule-based filters have grown less effective as spam emails have multiplied exponentially. Models can be trained to identify emails as spam or not using machine learning algorithms, particularly supervised learning. We need to create a simple and straightforward machine learning model in order to reach more accurate results while categorizing email spam. We picked the Naive Bayes technique for our model since it is quicker and more accurate than other algorithms. The suggested method can have incorporated into current email systems to enhance spam filtering functionality. This review paper provides an overview of the machine learning model we have suggested.

Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes

In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.

Intepretable Deep Gaussian Naive Bayes Algorithm (Idgnba) Based Task Offloading Framework for Edge-Cloud Computing

When it comes to Internet of Things (IoT) applications and machine learning based computing, resource-restricted edge devices are inadequate due to the exponential growth of mobile information and the massive need for processing power. An edge offload, the migration of complex tasks from IoT devices to edge cloud servers, is a distributed computing paradigm that has the potential to overcome the IoT device resource limits, lessen the computational load, and increase the effectiveness with which activities are processed. However, due to the NP-hard nature of the optimum offloading decision-making issue, an efficient solution using traditional optimization techniques is difficult. Current deep learning algorithms still have a lot of problems, such as their slow pace of learning and limited ability to adapt to new environments. We provide a unique interpretable deep Gaussian naive Bayes technique (IDGNBA) for extremely fine offloading choices to address these issues. Through several simulation studies, we assess the efficacy of IDGNBA and find that it performs better in terms of offloading than traditional techniques. The model has strong mobility and can quickly adjust to a fresh MEC working atmosphere while taking offloading decisions in real-time.

Heart Failure Prediction using Gaussian Naïve Bayes Algorithm

Heart failure affects a minimum of 26 million individuals and its occurrence has been increasing day-by-day. Heart failure occurs when the heart cannot pump enough blood to meet the body's needs. This is caused due to various reasons such as coronary heart disease, heart valve malfunctioning, diabetes, anaemia etc. So, it is important to predict heart failure in its early stage to reduce the mortality rate. Cardiovascular disease is the major contributing factor for the prediction of heart failure. This research uses Gaussian Naïve Bayes technique which comes under supervised learning algorithm to predict heart failure. Gaussian Naïve Bayes algorithm treats each feature to be independent and each feature has equal importance in predicting heart failure. For the purpose of predicting heart failure, the mean and standard deviation of each column in a dataset is used. Different datasets are used for the implementation of heart failure prediction, and performance metrics are identified for each dataset. The findings of this study suggest that the Gaussian Naive Bayes algorithm can be a useful algorithm in predicting heart failure, and it has the potential to improve patient outcomes by facilitating early detection of heart failure. There are various metrics used for the performance analysis of the system such as, accuracy, recall, precision, confusion matrix and ROC curve. Finally, this study concludes by presenting the implementation of heart failure using Gaussian Naïve Bayes.

Analysis and Classification of Breast Cancer Disease Via Different Datasets and Classifier Models

Nowadays, Tumour is one of the important reasons of human death worldwide, producing about 9.6 million people in 2018. BC (breast cancer) is the common reason for cancer deaths in females. BC is a type of cancer that can be treated when detected early. The main motive of this analysis is to detect cancer early in life using ML (machine learning) techniques. The features of the people included in the WDBC (Wisconsin diagnostic breast cancer) and Coimbra BC datasets were classified by SVOF-KNN, KNN, and Naïve Bayes techniques. The pre-processing data phase was applied to the datasets before classification. After the data pre-processing steps, three classification methods were applied to the data. Specificity and Sensitivity rates were used to calculate the success of the techniques. As an outcome of the BC diagnosis classification, the SVOF-KNN technique was found with a 91 percent specificity rate and 90 percent sensitivity rate. When the outcomes attained from feature extraction and selection are calculated. It is seen that feature extraction, selection, and data pre-processing techniques improve the specificity and sensitivity rate of the detection system.

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques-Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors-are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs.

Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia

One of the tools web users use to access, share, and discuss subjects of interest is social media. One social networking site, Twitter, is often used in real time to communicate this. Due to its significant negative impacts on both health and the economy, smoking is still a topic of regular debate and debate in Indonesia. This research was conducted to assess sentiment towards smokers and differentiate between positive and negative emotions. The data used in this study were obtained by crawling the Twitter social media network. Three Bayes techniques (NB), Support Vector Machine (SVM), and Logistic Regression are used in this study. In this study, 40.25% of Twitter users agreed with the existence of smokers in Indonesia, while 59.74% disagreed. The Naive Bayes method was used in this study, giving the highest accuracy value = 62.1% using 60% training data and 40% test data.