Bayes Algorithm Research Articles

Android Malware Detection using Permission Based Static Analysis

The increase of mobile device enhancement grows. With this development, mobile phones are supporting many programs, and everyone takes advantage of them. Nevertheless, malware applications are increasing more and more so that people can come across lots of problems. Android is a mobile operating system that is the most used on smart mobile phones. Because it is the most used and open source, it has been the target of attackers. Android security is related to the permissions allowed by users to the applications. There have been many studies on permission-based Android malware detection. In this study, a permission-based Android malware system is analyzed. Unlike other studies, we propose a permission weight approach. Each of the permissions is given a different score using this approach. Then, K-nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms are applied, and the proposed method is compared with the previous studies and the expected experimental results of the proposed approach will be higher.

Abstract 13918: Improvement of Pulmonary Arterial Hypertension (PAH) Risk Assessment Model Using Cardiac Magnetic Resonance Imaging Variables

Introduction: PAH is a deadly disease without cure. Formalized risk stratification allows therapeutic adjustments that optimize drug utilization. Risk scores, like REVEAL 2.0 recommended by PAH guidelines only offer good discrimination. Our goal was to create risk models with excellent discrimination (C-Index over 0.8), using modern statistical techniques and expanded variable pools including imaging and genomics. The AIM of this study was to demonstrate the improved performance with the addition of cardiac MRI variables. Methods: PAH patients from the ASPIRE cardiac MRI database were analyzed. Imaging variable (IMV) selection was performed using three machine learning methods: logistic regression, Lasso, and Random Forest. Rankings of the IMVs from these sources were aggregated to arrive at a consensus list. The selected IMVs were added to the set of variables for deriving the REVEAL 2.0 composite score. Bayesian networks (BN) were then built to predict 1-year survival based on the Tree-Augmented naïve Bayes (TAN) algorithm. Five-fold cross-validation was performed to assess the improvement in survival prediction from adding the selected imaging variables. Results: A total of 343 PAH previously diagnosed subjects were included in this analysis. The rank aggregation algorithm identified several IMV, including LVSVI, RVCO, LVEDVI, and RVESVI, that were predictive of survival but not the REVEAL 2.0 composite score. Adding these IMV to the REVEAL 2.0 variables, we built a BN model that depicts the non-linear relationships among the predictors and one-year survival (Fig 1). We obtained an average AUC of 0.83 over the five cross-validation test sets, an improvement over the AUC of 0.78 using only the REVEAL 2.0 variables. A Mann-Whitney non-parametric test shows the improvement is statistically significant at the 0.1 level. Conclusion: Advanced statistical models that include cardiac IMVs improve performance of PAH risk assessment models.

Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation.

Backpropagation has rapidly become the workhorse credit assignment algorithm for modern deep learning methods. Recently, modified forms of predictive coding (PC), an algorithm with origins in computational neuroscience, have been shown to result in approximately or exactly equal parameter updates to those under backpropagation. Due to this connection, it has been suggested that PC can act as an alternative to backpropagation with desirable properties that may facilitate implementation in neuromorphic systems. Here, we explore these claims using the different contemporary PC variants proposed in the literature. We obtain time complexity bounds for these PC variants, which we show are lower bounded by backpropagation. We also present key properties of these variants that have implications for neurobiological plausibility and their interpretations, particularly from the perspective of standard PC as a variational Bayes algorithm for latent probabilistic models. Our findings shed new light on the connection between the two learning frameworks and suggest that in its current forms, PC may have more limited potential as a direct replacement of backpropagation than previously envisioned.

CARDIAC DISEASE PREDICTION USING RANDOM FOREST WITH LINEAR MODEL

Making forecasts and diagnosing ailments has never been simple for medical professionals when it comes to heart conditions. Due to this, people can take the necessary action to treat heart disease before it gets worse if it is discovered in its early stages anywhere in the world. The three main causes of heart disease-drinking alcohol, smoking cigarettes, and not exercising-have become serious issues in recent years. The health care industry has produced a substantial amount of data over time, which has made machine learning capable of providing effective outcomes in prediction and decision-making. Only male patients risk level for heart disease is predicted by the Heart Disease Prediction (HDP) in the current system, which is created using the Naive Bayes and Decision Tree algorithms. For prediction, the algorithm makes use of medical parameters such age, sex, smoking status, BMI, and physical health, etc. A patients likelihood of developing heart disease is predicted by the HDP. Fishers Discriminant Ratio is one of the methods for feature selection based on random forest (RF), which is recommended to identify the best features for heart disease prediction and enhance the precision of RF-based classification. Our goal in this study is to identify the best factors that can improve the prediction accuracy of heart disease and finding the most effective variables to raise the accuracy of heart disease prediction. Evaluation criteria, namely accuracy, specificity, sensitivity, and area under the ROC curve, are employed to verify the efficacy of the proposed approach on a public dataset comprising patients of both genders. The primary benefits of applying machine learning for heart disease prediction are that it reduces the complexity of the doctors time, is patient- and cost-friendly, and manages the largest (enormous) amount of data through feature selection and the random forest algorithm. Early diagnosis of cardiovascular disease can help with lifestyle modifications for high-risk patients, which can lower complications and be a significant medical milestone. KEYWORDS: Random Forest, Decision Tree, Naive Bayes, Feature Selection, and Evaluation Metrics.

A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping

Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

Mental Depression Deduction Using Modified Regression Model to Prevent Suicidal Attempt

This study explores a novel approach for predicting depression using association-based multilevel linear regression. The suggested approach, known as association-based multilevel linear regression, uses data on mental depression to predict the prevalence of depression. Several statistical techniques can be used to forecast depression. Several statistical methods, including Linear Regression (LR), Multilevel Linear Regression (MLR), Naïve Bayes algorithm and Decision Tree (DT) were used in this investigation. Because these algorithms are able to predict mental depression based on certain characteristics such as precision and efficiency, their performance reduces. The results of these algorithms' predictions vary significantly, especially in terms of accuracy. The mental health data is fed into a developed model that has been trained to make predictions in order to address the aforementioned problem. Depression is the subject of conversation. A great degree of accuracy is shown by the association-based multilevel linear regression technique and the evaluation of prediction of accuracy in relation to other statistical methods. This study used association-based multilevel linear regression technique. When compared to traditional methods, the method exhibits a substantially greater level of accuracy, almost 99%.

Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology.

Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.

Sentiment Analysis for Indonesian Salt Policy uses Naïve Bayes and Information Gain Methods

Salt production is one of the concerns of the Indonesian government. Several government policies such as salt imports have had a major impact on local salt farmers. Other factors are due to the increased demand for salt, decreased domestic salt production which is unfavorable due to weather factors, and the price of imported salt is lower than that of local salt. Many people express their opinions regarding the salt import policy, via Twitter social media. Sentiment analysis can be applied to analyze tweets or writings by the public regarding salt import policies and classify the data. This study uses the naïve Bayes classifier algorithm model as a sentiment classification algorithm on Twitter social media tweets. The classification process uses the Naïve Bayes algorithm. The feature extraction and weighting method is the TF-IDF method. Not all of the features resulting from the TF-IDF process are used, so feature selection is carried out using the information gain method. Model testing was carried out 5 times with 500 data, using feature selection and without feature selection. Without feature selection, the highest accuracy result is 84% at K=4, while without feature selection it produces an accuracy of 71% at K=3, so there is an increase of 13%.

Heart Disease Prediction using Machine Learning Algorithms

Presently, health conditions are on the rise primarily due to lifestyle factors and genetic predispositions. Among these, heart disease has notably surged, posing a significant risk to people's lives. Each person possesses unique benchmarks for vital health indicators like blood pressure, cholesterol levels, and pulse rates. However, according to established medical standards, normal values typically fall within certain ranges: Blood pressure ideally registers at 120/90, cholesterol between 100-129 mg/dL, pulse rate around 72, fasting blood sugar level at 100 mg/dL, heart rate ranging from 60-100 beats per minute (bpm), normal ECG readings, and major vessel widths spanning from 25 mm (1 inch) in the aorta to a mere 8 μm in capillaries. This study explores various classification techniques employed to predict the risk levels of individuals based on parameters such as age, gender, blood pressure, cholesterol levels, and pulse rate. The "Disease Prediction" system relies on predictive modeling to anticipate a user's disease risk by analyzing symptoms provided as input. By evaluating user-input symptoms, the system computes the probability of specific diseases as an output. Disease prediction involves the implementation of five techniques: Naïve Bayes, KNN, Decision Tree, Linear Regression, and Random Forest Algorithms. These methods gauge the likelihood of an individual developing a particular ailment. Consequently, the collective average prediction accuracy probability stands at an impressive 83%. Keywords: Naïve Bayes, KNN, Decision Tree, Linear Regression & Random Forest.

SMS Spam Detection using Machine Learning

SMS spam detection using Naive Bayes algorithm is a widely used technique in the field of text classification. The main aim of this approach is to classify the incoming messages into spam or ham categories. The Naive Bayes algorithm works by calculating the probability of a message belonging to a particular class, based on the occurrence of different words in the message. In this paper, we present an efficient and accurate approach for SMS spam detection using the Naive Bayes algorithm. The proposed approach utilizes a pre- processing step for feature extraction, which includes tokenization, stop-word removal, and stemming. The Naive Bayes algorithm is then trained on a dataset of labeled messages to learn the probability distributions of different words in spam and ham messages. Finally, the trained model is used to classify incoming messages into spam or ham categories. The results of our experiments show that the proposed approach achieves high accuracy in detecting SMS spam messages. Keywords: Naive Bayes Algorithm, Text Classification, Probability, Feature Extraction, Tokenization, StopWord Removal, Stemming, Labeled Dataset, Probability Distribution, Accuracy

PREDICTING CREDIT WORTHINESS IN BANK LOAN APPROVAL PROCESS

Loans make up significant part in bank profits. In a bank, amongst large number of loan applications, it can be lengthy and challenging to choose genuine and eligible applications to approve the loan, if the process is done manually. The system was developed using Python 3.7 & its libraries and Jupyter Notebook cross-platform Integrated Development Environment (IDE). The developed system uses the machine learning algorithms - Naïve Bayes, Support Vector Machine (SVM) and XGBoost (Extreme Gradient Boosting) individually to classify the loan applications to automatically choose genuine and eligible applications. Based on average bank balance of the period and financial background, the system approves the loan amount to the predicted applicants with good credit history. The system’s performance in predicting the credit worthiness of applicants was evaluated. Naive Bayes algorithm predicted the credit worthiness with 79% accuracy, SVM algorithm with 81% and XGBoost algorithm with 84%.

Malaria Disease Prediction and Grading System: A Performance Model of Multinomial Naïve Bayes (MNB) Machine Learning in Nigerian Hospitals

bstract: Malaria disease is the number one cause of death all over the Sub-Sahara world. Data mining can help extract valuable knowledge from available data in the healthcare sector. This allows training a patient health prediction model faster than in a clinical trial. Various implementation of machine learning algorithms such as Bayesian Theorem, Logistic Regression, K-Nearest Neighbor, Support Vector Machine and Multinomial Naïve Bayes (MNB), etc. have been applied on Public Hospital Malaria Disease datasets but there has been a limit to modeling using Multinomial Naïve Bayes Algorithm. This research applied MNB modeling to discover the relationship between 15 relevant attributes of the Public Hospitals data collected from Bwari General Hospital in Bwari Area Council and Maitama Hospital in Abuja Municipal Area Council, Abuja, FCT, and Nigeria. The goal is to examine how dependencies between attributes affect the performance of the classifier. The MNB produces a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios. The model has an accuracy of 97%. It was concluded that the model outperformed the GNB classifier which has an accuracy of 100% and RF which also has an accuracy of 100%.

Timeliness Of Spp Payments At Smk Tritech Infomatika Using Naive Bayes Algorithm

SMK Tritech Informatika is one of the vocational schools located on Jalan Bhayangkara Medan. Smk Tritech plays an important role in education. However, many schools are late in financing their operations. So there are many problems related to school operational payments such as tuition payments. SPP payment is an important problem at Smk Tritech Infomatika because many of the students are late in paying SPP even though the payment deadline has been set. Therefore, it is necessary to evaluate the SPP payment. To overcome this problem, it is necessary to detect the factors that cause late tuition payments using data mining. The data mining technique used is classification with the Naive Bayes algorithm method.

KLASIFIKASI EMOSI PUBLIK TERHADAP LARANGAN PENGGUNAAN OBAT SIRUP MENGGUNAKAN ALGORITMA NAIVE BAYES

In October 2022 BPOM withdrew the circulation of syrup drugs and banned the public from using syrup drugs due to the increasing cases of kidney failure in children with a mortality rate of up to 59%. The phenomenon of mass death has caused psychological trauma that threatens. The purpose of this study was to find out how public comments on Twitter and Instagram social media regarding the prohibition of using syrup drugs by classifying emotions using the Naive Bayes algorithm with Particle Swarm Optimization (PSO) feature selection. The methodology used is the AI Project Cycle which consists of problem scoping, data acquisition, data exploration, modeling and evaluation. The amount of data in this study was 1213 comments with 381 surprised comments, 318 angry comments, 277 sad comments, 137 scared comments, and 100 happy comments. Happy and afraid comments have fewer comments than other emotions, so the imbalance dataset will be handled using SMOTE. The results of this study are to compare the classification results of the application of PSO and SMOTE to the Naive Bayes algorithm to determine the best model. Based on the classification results, the Naive Bayes, PSO, and SMOTE models produced the highest accuracy values of 76.48%, with a recall value of 76.47%, a precision of 76.20%, and a fmeasure of 75.62%.

Multinomial Optimization of Naïve Bayes Through the Implementation of Particle Swarm Optimization

Sentiment analysis is widely used in cases of text processing and comments. One of the case studies is about the analysis of a hotel review by the public. The method used in analyzing a sentiment from comments or reviews of a hotel is the Naïve Bayes Classifier. One that can be used is the Multinomial Naïve Bayes method. In improving the results of the accuracy of the method required an optimization method. There are many optimization methods that can be applied to algorithms in sentiment analysis case studies. One well-known method is Particle Swarm Optimization (PSO). This study aims to determine the effect of PSO optimization on the Multinomial Naïve Bayes algorithm in the case of sentiment analysis. From the results of optimization and model testing, the highest accuracy was obtained in the Multinomial Naïve Bayes test with PSO optimization as hyperparameter tunning and feature selection of 97%.

Wavelet Scattering Transform based Doppler signal classification

Normal blood supply to the human brain may be marred by the presence of a clot inside the blood vessels. This clot structure called emboli inhibits normal blood flow to the brain. It is considered as one of the main sources of stroke. Presence of emboli in human’s can be determined by the analysis of transcranial Doppler signal. Different signal processing and machine learning algorithms have been used for classifying the detected signal as an emboli, Doppler speckle, and an artifact. In this paper, we sought to make use of the wavelet transform based algorithm called Wavelet Scattering Transform, which is translation invariant and stable to deformations for classifying different Doppler signals. With its architectural resemblance to Convolutional Neural Network, Wavelet Scattering Transform works well on small datasets and subsequently was trained on a dataset consisting of 300 Doppler signals.To check the effectiveness of extracted Scattering transform based features for Doppler signal classification, learning algorithms that included multi-class Support vector machine, k-nearest neighbor and Naive Bayes algorithms were trained. Comparative analysis was done with respect to the handcrafted Continuous wavelet transform features extracted from samples and Wavelet scattering with Support vector machine achieved an accuracy of 98.89%. Also, with set of extracted scattering coefficients, Gaussian process regression was performed and a regression model was trained on three different sets of scattering coefficients with zero order scattering coefficients providing least prediction loss of 34.95%.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

Spam filter based on naive bayes algorithm

The widespread use of Electronic Mail (E-mail) has led to a significant increase in spam, which has severely impeded the growth and well-being of the Internet. To mitigate this issue, the implementation of email filtering techniques has become necessary, requiring the use of specific technological tools. Presently, the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) algorithms are commonly used in probability statistical classification methods for email filtering. Among these, the NB algorithm is the most classical, with its rich mathematical theory as the basis, high classification efficiency, and straightforward algorithmic approach. However, the algorithm relies on the conditional independence assumption, making the accuracy susceptible to the correlation between attributes. This study focuses on email filtering techniques based on the NB algorithm, conducting experiments to evaluate the classification accuracy and proposing feasible improvements to weaken the independence assumption. The experimental results demonstrated the effectiveness of the employed method.

A Scoping Review of Adopted Information Extraction Methods for RCTs.

Randomized controlled trials (RCTs) provide the strongest evidence for therapeutic interventions and their effects on groups of subjects. However, the large amount of unstructured information in these trials makes it challenging and time-consuming to make decisions and identify important concepts and valid evidence. This study aims to explore methods for automating or semi-automating information extraction from reports of RCT studies. We conducted a systematic search of PubMed, ACM Digital Library, and Web of Science to identify relevant articles published between January 1, 2010, and 2022. We focused on published Natural Language Processing (NLP), machine learning, and deep learning methods that automate or semi-automate key elements of information extraction in the context of RCTs. A total of 26 publications were included, which discussed the automatic extraction of key characteristics of RCTs using various PICO frameworks (PIBOSO and PECODR). Among these publications, 14 (53.8%) extracted key characteristics based on PICO, PIBOSO, and PECODR, while 12 (46.1%) discussed information extraction methods in RCT studies. Common approaches mentioned included word/phrase matching, machine learning algorithms such as binary classification using the Naïve Bayes algorithm and powerful BERT network for feature extraction, support vector machine for data classification, conditional random field, non-machine-dependent automation, and machine learning or deep learning approaches. The lack of publicly available software and limited access to existing software makes it difficult to determine the most powerful information extraction system. However, deep learning models like Transformers and BERT language models have shown better performance in natural language processing.

Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.