Bayesian Classifier Research Articles

Classification of Liver Lesion Stages using pyRadiomics Features Combined with 3D-CNN in 3D-CT and US Images

Objectives: The goal of this study is to identify the various stages in liver diseases employing pyRadiomics features by analyzing the 3-dimensional CT and US images. Methods: The study uses Gray level Run Length Matrix for feature extraction and 3D-CNN for classification and compared with methods like Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) classifiers to assess the efficiency of the proposed model. The suggested algorithm is implemented using the library functions in Tensorflow and Keras packages in Python. Findings: The study utilizes radiomic feature extraction and two-phase classification to detect and classify liver disorders. In the first phase, binary classification is used to classify US and CT images as normal or abnormal, with accuracy rates of 95.4% and 97.9%. This is followed by the second phase classification, which classifies the lesion stages for both US and CT aberrant images. CT images have been shown to be more accurate in categorizing lesion stages than US imaging. CT images have a much lower misclassification rate at each step than US images. Finally, the effectiveness of the 3D-CNN model is compared with SVM, RF, and NB and the suggested method outperforms the other methods. Novelty: The suggested architecture employs radionics feature extraction with two-phase CNN for classification and the suggested method is found to be more efficient than SVM, RF, and NB. Keywords: Radiomics; Feature Extraction; Convolutional Neural Networks (CNN); Classification; Liver Diseases

Predicting Customer Success in Digital Marketing with Data Mining and Naive Bayes Classifier Using Google Analytics

Predicting Customer Success in Digital Marketing with Data Mining and Naive Bayes Classifier Using Google Analytics

Comparative Evaluation of Sentiment Analysis Methods: From Traditional Techniques to Advanced Deep Learning Models

Abstract. Sentiment evaluation plays a crucial role in deciphering public perception and consumer responses in today's digital landscape. This investigation offers a thorough assessment of diverse sentiment evaluation techniques, contrasting conventional machine learning methodologies with cutting-edge deep learning frameworks. In particular, the research scrutinizes the efficacy of Bidirectional Encoder Representations from Transformers (BERT)-derived architectures (BERT-Base and Robustly Optimized BERT Pretraining Approach (RoBERTa)), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Support Vector Machines (SVM), and Naive Bayes classifiers. The study gauges these approaches based on their precision, recall, F1-metric, overall accuracy, and computational efficiency using an extensive sentiment evaluation dataset. The results reveal that BERT-based models, particularly RoBERTa, achieve the highest accuracy (87.44%) and F1-score (0.8746), though they also require the longest training time (approximately 3 hours). CNN and LSTM models strike a balance between performance and efficiency, while traditional methods like SVM and Naive Bayes offer faster training and deployment with moderate accuracy. The insights gained from this study are valuable for both researchers and practitioners, highlighting the trade-offs between model performance, computational demands, and practical deployment considerations in sentiment analysis applications.

A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method

Annually, many people worldwide lose their lives due to breast cancer, making it one of the most prevalent cancers in the world. Since the disease is becoming more common, early detection of breast cancer is essential to avoiding serious complications and possibly death as well. This research provides a novel Breast Cancer Discovery (BCD) strategy to aid patients by providing prompt and sensitive detection of breast cancer. The two primary steps that form the BCD are the Breast Cancer Discovery Step (BCDS) and the Pre-processing Step (P2S). In the P2S, the needed data are filtered from any non-informative data using three primary operations: data normalization, feature selection, and outlier rejection. Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. The primary contribution of this research is the novel outlier rejection technique known as the Combined Outlier Rejection Technique (CORT). CORT is divided into two primary phases: (i) the Quick Rejection Phase (QRP), which is a quick phase utilizing a statistical method, and (ii) the Accurate Rejection Phase (ARP), which is a precise phase using an optimization method. Outliers are rapidly eliminated during the QRP using the standard deviation, and the remaining outliers are thoroughly eliminated during ARP via Binary Harris Hawk Optimization (BHHO). The P2S in the BCD strategy indicates that data normalization is a pre-processing approach used to find numeric values in the datasets that fall into a predetermined range. Information Gain (IG) is then used to choose the optimal subset of features, and CORT is used to reject incorrect training data. Furthermore, based on the filtered data from the P2S, an Ensemble Classification Method (ECM) is utilized in the BCDS to identify breast cancer patients. This method consists of three classifiers: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Wisconsin Breast Cancer Database (WBCD) dataset, which contains digital images of fine-needle aspiration samples collected from patients’ breast masses, is used herein to compare the BCD strategy against several contemporary strategies. According to the outcomes of the experiment, the suggested method is very competitive. It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature.

A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a retrospective study).

In circumstances where antemortem information concerning the deceased individual is unavailable, forensic experts prepare biological profiling for unidentified human remains that aids in narrowing the search for identity. Biological profiling includes basic demographic information such as sex, age, stature, and ethnicity. Sex identification is the first and key step in the biological profiling of unidentified human remains, as it effectively reduces potential matches by excluding nearly one-half of the suspected cases and facilitates the subsequent stages. This study was conducted to assess the accuracy of artificial intelligence (AI) in predicting sex by analysing mandibular canine dimensions, mandibular intercanine distance (MICD), and mandibular canine index (MCI) obtained from three-dimensional (3D) digital impressions captured by using an intraoral scanner (IOS). The results of the receiver operating characteristic (ROC) test indicated that mean mandibular canine width (MeanMCW) had the highest sexual dimorphism with the area under the curve (AUC) of 0.912, and the Gaussian Naive Bayes (GNB) classifier demonstrated the highest testing accuracy among all machine learning (ML) models, achieving an accuracy of 92.5%. While the outcomes of this study are promising, further studies are imperative to validate these findings with larger sample sizes in different ethnic populations.

QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning.

Prediction of mortality in hospitalized patients is a crucial and important problem. Several severity scoring systems over the past few decades and machine learning models for mortality prediction have been developed to predict in-hospital mortality. Our aim in this study was to apply machine learning (ML) algorithms using QTc interval to predict in-hospital mortality in ACS patients and compare them to the validated conventional risk scores. This study was retrospective, using supervised learning, and data mining. Out of a cohort of 500 patients admitted to a tertiary care hospital from September 2018 to August 2020, who presented with ACS. Prediction models for in-hospital mortality in ACS patients were developed using 3 ML algorithms. We employed the ensemble learning random forest (RF) model, the Naive Bayes (NB) model and the rule-based projective adaptive resonance theory (PART) model. These models were compared to one another and to two conventional validated risk scores; the Global Registry of Acute Coronary Events (GRACE) risk score and Thrombolysis in Myocardial Infarction (TIMI) risk score. Out of the 500 patients included in our study, 164 (32.8%) patients presented with unstable angina, 148 (29.6%) patients with non-ST-elevation myocardial infarction (NSTEMI) and 188 (37.6%) patients were having ST-elevation myocardial infarction (STEMI). 64 (12.8%) patients died in-hospital and the rest survived. Performance of prediction models was measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.83 to 0.93 using all available variables compared to the GRACE score (0.9 SD 0.05) and the TIMI score (0.75 SD 0.02). Using QTc as a stand-alone variable yielded (0.67 SD 0.02) with a cutoff value 450 using Bazett's formula, whereas using QTc in addition to other variables of personal and clinical data and other ECG variables, the result was 0.8 SD 0.04. Results of RF and NB models were almost the same, but PART model yielded the least results. There was no significant difference of AUC values after replacing the missing values and applying class balancer. The proposed method can effectively predict patients at high risk of in-hospital mortality early in the setting of ACS using only clinical and ECG data. Prolonged QTc interval can be used as a risk predictor of in-hospital mortality in ACS patients.

An Optimized Approach for Detection and Classification of Spam Email’s Using Ensemble Methods

AbstractSince the advent of email services, spam emails have been a major concern because users’ security depends on the classification of emails as ham or spam. It’s a malware attack that has been used for spear phishing, whaling, clone phishing, website forgery, and other harmful activities. However, various ensemble Machine Learning (ML) algorithms used for the detection and filtering of spam emails have been less explored. In this research, we offer a ML-based optimized algorithm for detecting spam emails that have been enhanced using Hyper-parameter tuning approaches. The proposed approach uses two feature extraction modules, namely Count-Vectorizer and TFIDF-Vectorizer that provide the most effective classification results when we apply them to three different publicly available email data sets: Ling Spam, UCI SMS Spam, and the Proposed dataset. Moreover, to extend the performance of classifiers we used various ML methods such as Naive Bayes (NB), Logistic Regression (LR), Extra Tree, Stochastic Gradient Descent (SGD), XG-Boost, Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perception (MLP), and parameter optimization approaches such as Manual search, Random search, Grid search, and Genetic algorithm. For all three data sets, the SGD outperformed other algorithms. All of the other ensembles (Extra Tree, RF), linear models (LR, Linear-SVC), and MLP performed admirably, with relatively high precision, recall, accuracies, and F1-score.

Abstract 4140120: Artificial intelligence-driven morbidity prediction in acute kidney injury after acute type A aortic dissection surgery

Background: Acute kidney injury (AKI) often complicates acute type A aortic dissection (ATAAD), with elevated comorbidity rates and a significant tie to in-hospital mortality. Identifying risk factors early can mitigate AKI severity. Research Questions: This research endeavors to develop and corroborate predictive models leveraging Machine Learning (ML) techniques from Artificial Intelligence to forecast AKI occurrences in ATAAD-afflicted individuals. Methods: The study employed various machine learning (ML) algorithms including Gradient Boosting Machine (GBM), LightGBM, Random Forest (RF), K-Nearest Neighbors (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Naive Bayes (NB), Logistic Regression (LR), and ensemble methods (combining LR&LightGBM), employing tenfold cross-validation. Model performance was evaluated using SHapley Additive exPlanations (SHAP). A web-based tool for predicting AKI incidence was developed using Streamlit, based on the most effective model. The analysis involved 1350 ATAAD patients, among whom 586 (43.4%) developed post-operative AKI. Patients were divided into two cohorts: 85% for training and 15% for testing, with 126 features included in the predictive model. Results: Incorporating top 10 features, LightGBM (AUROC=0.886, 95% CI 0.841-0.930) excelled in predictive accuracy, calibration, and clinical utility, identifying key factors such as ventilation time in ICU, hourly urine output post-surgery, diuretic use, Scr, heart rate, urea, administration of recombinant human brain natriuretic peptide and ebrantil, MCHC, and blood glucose as associated with ATAAD-AKI. Conclusion(s): These ML models are robust tools for predicting AKI in ATAAD patients, with LightGBM's superior predictive ability standing out. They offer valuable support for clinical decision-making in ATAAD management, helping optimize postoperative strategies to minimize AKI occurrence after surgery.

Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques

This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar pressure data were collected and the plantar temperature was measured both immediately before and after the walk. Each participant completed the trial three times, and the average data between the trials were calculated. The research was divided into three experiments: the first evaluated the correlations between the plantar pressure and temperature data; the second focused on predicting diabetes using each data type independently; and the third combined both data types and assessed the effect of such to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification algorithms were employed, and the performance was evaluated using a five-fold cross-validation strategy. The outcomes of the initial set of experiments indicated that the machine learning models were significant correlations between the thermal data and pressure estimates. This was consistent with the findings from the prior correlation analysis, which showed weak relationships between these two data modalities. However, a shift in focus towards predicting diabetes by aggregating the temperature and pressure data led to encouraging results, demonstrating the effectiveness of this approach in accurately predicting the presence of diabetes. The analysis revealed that, while several classifiers demonstrated reasonable metrics when using standalone variables, the integration of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at 68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model’s predictive accuracy. This can indicate the importance of multimodal data integration and their potentials in diabetes prediction.

Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches.

To compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) and further evaluate the stratified effect of the radiomics model on disease-free survival (DFS) and overall survival (OS) of LSCC patients. In all, 106 LSCC patients (40 with inflamed and 66 with non-inflamed immunophenotyping) were randomly assigned into a training (n = 53) and testing (n = 53) cohort. Briefly, 750 radiomics features from contrast-enhanced CT images were respectively extracted from the whole tumor and two Otsu method-derived subregions. Intraclass correlation coefficients (ICCs) were calculated to evaluate the reproducibility. The radiomics models for predicting immunophenotyping were respectively created using K-nearest neighbors (KNN), logistic regression (LR), and Naive bayes (NB) classifiers. The performance of models in the testing cohort were compared using area under the curve (AUC). The prognostic value of the optimal model was determined by survival analysis. The radiomics features derived from whole tumor showed better reproducibility than those derived from habitats. The best model for the whole tumor (LR classifier) showed superior performance than that for the habitats (KNN classifier) in the testing cohort, but there were no significant differences (AUC: 0.741 vs. 0.611, p = 0.112). Multivariable Cox regression analysis showed that the immunophenotyping predicted by the optimal model was an independent risk factor of unfavorable DFS (p = 0.009) and OS (p = 0.008) in LSCC patients. Whole tumor-based CT radiomics could serve as a potential predictive biomarker of immunophenotyping and outcome prediction in LSCC patients.

Predicting Student Dropout Rates in Higher Education: A Comparative Study of Machine Learning Algorithms

Recent years have seen a significant increase in study interest in the areas of predicting student performance, avoiding failure, and identifying the variables affecting student dropout. One important indicator in online and open distance learning courses is the student dropout rate. We purpose the naive bayes classification method to construct the student dropout prediction using naive bayes. This work examines the critical topic of forecasting student dropout rates in higher education using machine learning approaches, with a particular emphasis on the random forest algorithm and the naive bayes algorithm. The study's goal is to properly anticipate dropout rates using data mining methods and machine learning algorithms after conducting a thorough evaluation of existing literature and approaches. The systematic method consists of data collection from a Kaggle dataset, data preparation to solve class imbalance via SMOTE oversampling, and algorithm selection. Random forest and naive Bayes approaches outperform other machine learning algorithms in terms of accuracy, sensitivity, specificity, and precision. The study underscores the importance of considering diverse factors such as demographic data, socioeconomic factors, and academic performance in dropout prediction models. The implications of this research extend beyond academia, with the potential to inform proactive interventions and support systems, ultimately leading to improved student outcomes and institutional effectiveness. According to this paper, the paper outputs that for the binary classification on the data set used in this project has best performed with Naive Bayes and Random Forest Algorithm with SMOTE oversampling. Keywords- SMOTE oversampling, machine learning, Random forest, naive bayes.

SENTIMENT ANALYSIS OF PUBLIC OPINION ON PRESIDENTIAL ADVISORY APPOINTMENTS USING NAIVE BAYES CLASSIFICATION

Social media platforms such as Twitter, Facebook, and YouTube have become significant channels for public discourse, where users freely express opinions, including negative sentiments and hate speech. To better understand public opinion, particularly in politically charged contexts, sentiment analysis can classify user comments as either positive or negative. This study aims to analyze public sentiment regarding the formation of a special advisory team for President Jokowi, using a sentiment classification approach. The study employed a Naïve Bayes classifier to analyze sentiment from 3,000 comments gathered from Twitter, Facebook, and YouTube. The dataset was divided into 80% training data (used to train the model with known sentiment) and 20% test data. The Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling large datasets in text classification tasks. The Naive Bayes classification on sentiment analysis of public opinion regarding the appointment of presidential advisors achieved an overall accuracy of 71% in classifying the test data. Negative sentiment was classified with an accuracy of 71%, while positive sentiment was classified with an accuracy of 70%. The results demonstrate that the Naïve Bayes classifier is a viable method for sentiment analysis in political discourse, although the model's performance indicates room for improvement. The novelty of this research lies in its focus on sentiment analysis of public opinion specifically related to presidential advisory appointments, an area not yet extensively explored in sentiment analysis studies. This study contributes to the field by providing insights into the public’s perception of political decisions using machine learning techniques. The implications for future research include refining classification methods for better accuracy and applying the model to other political or governmental topics.

Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers

The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.

Application of Machine Learning for the Prediction of Absorption, Distribution, Metabolism and Excretion (ADME) Properties from Cichorium intybus Plant Phytomolecules

Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.

A data-driven ISM-BN model for safety analysis of inland shipping in the Pearl River Basin

Inland shipping of the Pearl River plays an important role in the Chinese shipping system. To ensure navigation safety, we collect reports of maritime accidents from 2015 to 2022 in the Pearl River basin. This article extracts influencing factors by collecting the experience of inland waterway safety navigation and analyzing accident reports. Then, this paper uses the interpretative structural modeling method (ISM) to build a correlation model. Using a data-driven Bayesian network (BN), it analyzes the impact of various factors on the safety navigation in the Pearl River. The model validation is completed by compared with tree augmented naive Bayes classifiers (TAN) network using the same validation samples, through validation with the test set, the prediction accuracy has improved by 25%. The results indicate factors such as vessel type, accident month, accident day and time, etc. have a significant impact on the safety of navigation in the inland Pearl River waterway. The method used can identify important risk factors for accidents and the average predictive probability of validation samples reaches 87.03%. These research results could be extended to maritime management efforts in the Pearl River Basin.

Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.

Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while Naïve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.

Enhancing sound source localization and music teaching through integrated computational resource allocation

In contemporary educational and computational settings, the incorporation of cutting-edge technologies like sound source localization and personalized music teaching helps in offering an effective resource allocation strategies. Previous systems for sound localization and music teaching frequently lacked real-time flexibility and effective resource use, reducing their efficiency in dynamic learning settings and tasks involving computation. To overcome these shortcomings, the SoundLocMusicTeachRA (SLMTRA) algorithm is presented, a single, integrated platform made to maximize sound localization accuracy, improve music teaching efficiency, and enhance computational resource oversight. However, the existing study did not highlight the importance of computation resource allocation but this proposed algorithm will address it. SLMTRA uses a new Bagging ensemble approach incorporating Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), with hyperparameter tuning to enhance the effectiveness of the approach. These classifiers are trained utilizing sound localization datasets from recordings made with microphones, music teaching feedback datasets from data on student performance, and resource allocation datasets from metrics for computer utilization. Experimental findings indicate SLMTRA’s high accuracy in sound source localization, improved music teaching feedback capacities, as well as effective resource allocation tactics, guaranteeing the best performance of the system. The implementation of SLMTRA represents a noteworthy development in combining sound localization, music teaching, and resource allocation within a unified computational framework, offering a more flexible and effective system compared to previous methodologies.

Research on Climactic Chapter Recognition of a Chinese Long Novel Based on Plot Description

Many readers continue to pursue Chinese long novels in the past several decades because of diverse characters and fascinating plots. The climactic chapter is an important part of a Chinese long novel, where the key conflict develops to the extreme point. However, how to quickly and accurately recognize the climactic chapter remains a common problem for many readers in their reading choices. This paper conducts research on recognizing the climactic chapter of a Chinese long novel by accurately describing its plot. The proposed method consists of two parts; one is the extraction of key elements, such as viewpoint paragraphs, non-viewpoint paragraphs, chapter keywords, major characters etc. The other part is the climactic chapter recognition, which applies the Bidirectional Gate Recurrent Unit (BiGRU) model and the multi-head attention to recognize the climactic chapter, on the basis of the chapter plot description matrix. Comparative experiments on the corpus named The Condor Trilogy show that the proposed method in this paper has a better recognition performance compared with the existing models, such as Naive Bayesian (NB), Support Vector Machine (SVM), Roberta-large, and the Bidirectional Long-Short Term Memory (BiLSTM) network. Ablation experiments validated the effectiveness of primary components in the proposed method.

Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.

Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors. This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. Further, the study employed multivariable Cox regression analysis and Kaplan-Meier survival curves, along with subgroup analysis, to explore the relationship between the machine learning-based score and 30-day mortality. Through LASSO regression and recursive feature elimination (RFE), 25 optimal feature variables are selected. The XGBoost model performed best, with an area under the curve (AUC) of 0.881, a sensitivity of 0.5714, a specificity of 0.9651 and an F1 score of 0.64. The first six most important feature variables were the use of vasopressor, high Charlson comorbidity index, low blood oxygen saturation, history of malignant tumor, hyperglycemia and high APSIII score. Based on the optimal threshold of 0.62, patients were divided into high and low-risk groups, and the 30-day survival rate in the high-risk group decreased significantly. COX regression analysis further confirmed the positive correlation between high-risk scores and 30-day mortality. In the subgroup analysis, the model showed good risk stratification ability in patients with different gender, renal replacement therapy and with or without a history of malignant tumor, but it was not effective in predicting peripheral vascular disease. the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.