ROC Score Research Articles

Prediction of Credit Default based on the XGBoost Model

Abstract. Loans are an important source of revenue for most banks and lending institutions. Improving the accuracy of personal repayment capacity predictions is particularly crucial. It is of great significance for reducing credit risk, optimizing the credit assessment system, and ensuring market stability. This paper uses various models for the predictions. In terms of evaluation, this paper uses the ROC curve as a criterion to assess the practicality of the models. At the same time, to ensure that the most practical model does not suffer from overfitting or underfitting, this paper also uses learning curves to ensure the usability of the entire model. Experimental results have demonstrated that the XGBoost model outperformed other models in predicting credit defaults, achieving a high ROC score of 0.71. Although it cannot predict very well in actual situations, it can also demonstrate through various assessments that the XGBoost model is highly usable and has great potential. The XGBoost model can become a trend in future prediction models by continuously improving the dataset and conducting more tests. This can help governments or banks to roughly understand which customers will default in the next month and take corresponding actions accordingly.

Machine learning-based classification of Parkinson's disease using acoustic features: Insights from multilingual speech tasks

This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech characteristics, leveraging a comprehensive approach that integrates a voting-based machine learning model. Given the growing prevalence of PD, especially among the elderly population, continuous and efficient diagnosis is of paramount importance. Conventional monitoring methods suffer from limitations related to time, cost, and accessibility, underscoring the need for the development of automated diagnostic tools. In this paper, we present a robust model for classifying speech patterns in Korean PD patients, addressing a significant research gap. Our model employs straightforward preprocessing techniques and a voting-based machine learning approach, demonstrating superior performance, particularly when training data is limited. Furthermore, we emphasize the effectiveness of the eGeMAPSv2 feature set in PD analysis and introduce new features that substantially enhance classification accuracy. The proposed model, achieving an accuracy of 84.73 % and an area under the ROC (AUC) score of 92.18 % on a dataset comprising 100 Korean PD patients and 100 healthy controls, offers a practical solution for automated diagnosis applications, such as smartphone apps. Future research endeavors will concentrate on enhancing the model's performance and delving deeper into the relationship between high-importance features and PD.

Support Vector Machine based Data Hacking Prediction using PMU Data

As global reliance on power systems grows due to increasing energy demands and modern consumption patterns, maintaining the stability and reliability of the power grid has become crucial. Power systems are complex and nonlinear, and their operations are continuously evolving, making it difficult and expensive to ensure stability. Traditionally, power systems are designed to handle a single outage at a time. However, recent years have seen several significant blackouts, each originating from a single failure, which have been extensively reported. These reports are vital for mitigating operational risks by strengthening systems against identified high-risk scenarios. While extensive research has been conducted on these blackouts, cyber- attacks introduce a new dimension of risk. The advent of Phasor Measurement Units (PMUs) has enabled centralized monitoring of power system data, allowing for more effective fault and cyber-attack detection.This paper proposes a machine learning-based approach to detecting cyber-attacks using PMU data. Given the complexity and volume of power system data, traditional mathematical and statistical methods are challenging to implement. Instead, a Support Vector Classification (SVC) algorithm is used for binary classification, distinguishing between 'attack' and 'normal' states. The algorithm is trained on PMU data and evaluated using metrics such as the AUC-ROC curve and confusion matrix, achieving an 82% AUC- ROC score, demonstrating its effectiveness in identifying cyber- attacks.

Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks

Abstract Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image data are trained and evaluated. The models are trained using different data augmentation techniques, learning rates, and optimizers. The data is taken from a private dataset, provided by Cantonal Hospital Aarau. The best result was achieved with a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214.

A HYBRID ASSAULT DETECTION SYSTEM USING RANDOM FOREST ENABLED XGBOOST-LIGHTGBM TECHNIQUE

This article presents the development of an assault identification system using face recognition in a closed location, by employing a machine learning-based computer vision approach. The proposed model combines algorithms such as Random forest, XGBoost and LightGBM techniques. The objective is to accurately identify and classify instances of assault in real-time based on facial recognition. The proposed approach utilizes machine learning algorithms to analyze facial features and patterns associated with assault activities. By leveraging on a hybrid model, the system can be integrated into closed locations such as schools, workplaces, or public venues to enhance security measures and promptly respond to potential threats. The findings of this research contribute to the field of computer vision-based assault identification systems, in addressing security challenges. Further advancements of the proposed hybrid model can lead higher performance levels in various real-world scenarios and enhancing public safety and security. The system's performance was evaluated using various metrics, including precision, recall, F1 score, accuracy, and ROC score. The results shows that the proposed system outperformed the existing system with its identified weakness and limitations of: Limited Robustness in Handling Complex Variations, Inability to Handle High-Dimensional Data, Limited Scalability, e.tc. The hybrid model achieved impressive results, with a precision of 98%, recall of 98%, F1 score of 97.7%, accuracy of 97.5%, and ROC score of 97.4%. The above findings demonstrated the effectiveness and robustness of the developed system in accurately detecting and recognizing assault instances within a closed location.

Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.

To develop a machine learning model to accurately predict stroke risk based on demographic and clinical data. It also sought to identify the most significant stroke risk factors and determine the optimal machine learning algorithm for stroke prediction. This cross-sectional study analyzed data on 438,693 adults from the 2021 Behavioral Risk Factor Surveillance System. Features encompassed demographics and clinical factors. Descriptive analysis profiled the dataset. Logistic regression quantified risk relationships. Adjusted mutual information evaluated feature importance. Multiple machine learning models were built and evaluated on metrics like accuracy, AUC ROC, and F1 score. Key factors significantly associated with higher stroke odds included older age, diabetes, hypertension, high cholesterol, and history of myocardial infarction or angina. Random forest model achieved the best performance with accuracy of 72.46%, AUC ROC of 0.72, and F1 score of 0.74. Cross-validation confirmed its reliability. Top features were hypertension, myocardial infarction history, angina, age, diabetes status, and cholesterol. The random forest model robustly predicted stroke risk using demographic and clinical variables. Feature importance highlighted priorities like hypertension and diabetes for clinical monitoring and intervention. This could help enable data-driven stroke prevention strategies.

Comparing artificial intelligence guided image assessment to current methods of burn assessment.

Appropriate identification of burn depth and size is paramount. Despite the development of burn depth assessment aids [e.g., laser doppler imaging (LDI)], clinical assessment, which assesses partial thickness burn depth with 67% accuracy, currently remains the most consistent standard of practice. We sought to develop an image-based artificial intelligence system that predicts burn severity and wound margins for use as a triaging tool in thermal injury management. Modified EfficientNet architecture trained by 1684 mobile-device-captured images of different burn depths were previously utilized to create a convoluted neural network (CNN). The CNN was modified to a novel Boundary-Attention Mapping (BAM) algorithm using elements of saliency mapping, which was utilized to recognize the boundaries of burns. For validation, 144 patient charts that included clinical assessment, burn location, total body surface area, and LDI assessment were retrieved for a retrospective study. The clinical images underwent CNN-BAM assessment and were directly compared with the LDI assessment. CNN using a four-level burn severity classification achieved an accuracy of 85% (micro/macro-averaged ROC scores). The CNN-BAM system can successfully highlight burns from surrounding tissue with high confidence. CNN-BAM burn area segmentations attained a 91.6% accuracy, 78.2% sensitivity, and 93.4% specificity, when compared to LDI methodology. Results comparing the CNN-BAM outputs to clinical and LDI assessments have shown a high degree of correlation between the CNN-BAM burn severity predictions to those extrapolated from LDI healing potential (66% agreement). CNN-BAM algorithm gives equivalent burn-depth detection accuracy as LDI with a more economical and accessible application when embedded in a mobile device.

A novel signature incorporating genes related to lipid metabolism and immune for prognostic and functional prediction of breast cancer.

Breast cancer prognosis and functioning have not been thoroughly examined in relation to immunological and lipid metabolism. However, there is a lack of prognostic and functional analyses of the relationship between lipid metabolism and immunity in breast cancer. DEGs in breast cancer were obtained from UCSC database, and lipid metabolism and immune-related genes were obtained from GSEA and Immune databases. A predictive signature was constructed using univariate Cox and LASSO regression on lipid metabolism and immune-related DEGs. The signature's prognostic significance was assessed using Kaplan-Meier, time-dependent ROC, and risk factor survival scores. Survival prognosis, therapeutic relevance, and functional enrichment were used to mine model gene biology. We selected IL18, which has never been reported in breast cancer before, in the signature to learn more about its function, potential to predict outcome, and immune system role. RT-PCR was performed to verify the true expression level of IL18. A total of 136 DEGs associated with breast cancer responses to both immunity and lipid metabolism. Nine key genes (CALR, CCL5, CEPT, FTT3, CXCL13, FLT3, IL12B, IL18, and IL24, p < 1.6e-2) of breast cancer were identified, and a prognostic was successfully constructed with a good predictive ability. IL18 in the model also had good clinical prognostic guidance value and immune regulation and therapeutic potential. Furthermore, the expression of IL18 was higher than that in paracancerous tissue. A unique predictive signature model could effectively predict the prognosis of breast cancer, which can not only achieve survival prediction, but also screen out key genes with important functional mechanisms to guide clinical drug experiments.

Performance Analysis of Random Forest Algorithm for Network Anomaly Detection using Feature Selection

As the volume and complexity of computer network traffic continue to increase, network administrators face a growing challenge in monitoring and discovering unusual activity. To keep the network safe and functioning, detecting anomalies is essential. Machine learning-based anomaly detection techniques have become increasingly popular in recent years. This is due to the fact that conventional anomaly detection methods make it difficult to detect unknown and complex attacks. This research aims to conduct a performance analysis of two feature selection methods using the random forest algorithm using the UNSW-NB15 dataset to determine which model is most effective in detecting network traffic anomalies. The models evaluated were random forest with the filter method and random forest with the wrapper method. A number of metrics used for model performance assessment are accuracy, F1-score, receiver operating characteristic curve, and precision-recall. Dataset collection, data pre-processing, feature selection, model construction, and evaluation are the main components of the research methodology. The research results show that the Random Forest approach with the Filter method has an accuracy of 0.8950, F1-score of 0.8333, ROC score of 0.8928, and a precision-recall value of 0.8347. Meanwhile, the approach using the Wrapper method obtained an accuracy of 0.9151, F1-score of 0.8510, ROC score of 0.9136, and a precision-recall value of 0.8637. This shows that the performance of Random Forest with the Wrapper method is superior in all assessment metrics. Random Forest with the Wrapper Method is the right choice of model for detecting network traffic anomalies because of its stable performance and ability to handle complex patterns

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model’s focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.

Integrating IoT and Artificial intelligent for Enhanced Electric Vehicle Charging and Autonomous Driving for sustainable transportation

This research explores the confluence of Internet of things and artificial intelligence towards fostering electric vehicle charging and autonomous driving for sustainable transportation. By meticulously collecting and preprocessing, and executing multiple machine learning models such as Artificial Neural Network , Decision Trees , Naive Bayes , and Random Forest , the research delves into charging infrastructure and easy autonomous driving. Particularly, real-time datasets, such as EV station data, road geography and mapping data, weather and telematics data, were properly collected and processed . Then, the preprocessing stage, which included data cleaning, normalization, and feature engineering had improved the datasets’ predictability. Consequently, the datasets such as charging recommendation, predicting energy use, predicting of optimal route, had produced highly accurate training and testing for forecasting elelctric vehicles’ demands. The ML models indicated their proficiency in predicting not only the optimal charging schedules, but also predictive energy consumption, and predictive routes. Specifically, ANN with 0.945 held the highest precision, while DT 0.912, NB 0.887, and RF 0.819 were almost identical . Likewise, the recall scores, F1 scores, and AUC ROC scores also coincided the prized outcome of the models. The findings of the research are groundbreaking for both transportation sector, policymakers, as well as urban planners and stakeholders at large. Following the data-fed recommendations would not be excessively challenging, and they would yield favourable results whether the stakeholders are dealing with EV charging infrastructure, or dealing with traffic congestions, or are establishing their own sustainable solution for future mobility via not just electric vehicles but also other tools and devices. Hence, we are hopeful that our research serves as a lighthouse for the transportation domain towards a greener future.

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs). We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations. Experimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier. Our study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps. This study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients’ historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis

BackgroundLong-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting.MethodsWe constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People’s Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets.ResultsThe results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process.ConclusionsOur diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.

Machine learning-based ransomware classification of Bitcoin transactions

Ransomware presents a significant threat to the security and integrity of cryptocurrency transactions. This research paper explores the intricacies of ransomware detection in cryptocurrency transactions using the Bitcoinheist dataset. The dataset encompasses 28 distinct families classified into three ransomware categories: Princeton, Montreal, and Padua, along with a white category representing legitimate transactions. We propose a novel hybrid supervised and semi-supervised multistage machine learning framework to tackle this challenge. Our framework effectively classifies known ransomware families by leveraging ensemble learning techniques such as Decision Tree, Random Forest, XGBoost, and Stacking. Additionally, we introduce a novel semi-supervised approach to accurately identify previously unseen ransomware instances within the dataset. Through rigorous evaluation employing comprehensive classification metrics, including accuracy, precision, recall, F1 score, RoC score, and prediction time, our proposed approach demonstrates promising results in ransomware detection within cryptocurrency transactions.

A Learning Approach for The Identification of Network Intrusions Based on Ensemble XGBoost Classifier

The limitation of signature-based Intrusion Detection systems has given rise for the popularity of Machine learning (ML) approaches r for building such intrusion detection systems (IDSs). ML is a sub-filed of Artificial Intelligence that enables algorithms to learn from data and its applications have been widely accepted and used in many domains. To achieve a promising ML-based model that can identify attacks and intrusions in networks and the cyber space, different stages of machine learning approach like pre-processing, attribute selection, model building, hyper parameter tuning can be very important. CICIDS2017 intrusion dataset was used for all the experimentations. This study focuses on building cyber threat detection model based on the ensemble feature selection and classification method. Innovative approaches were used for the analysis and pre-processing of the dataset. Thereafter, XGboost algorithm was used for selecting relevant features from the default input attributes in each of the captures. Thereafter, the reduced features were employed in the identification of cyber intrusions. The average accuracy achieved in the 8 captures of the dataset is 98% while precision is 0.98. Also, recall is 0.98, f1-score is 0.98 while AUC ROC score is 0.99. The study concluded that XGBoost-based model was able to achieve promising results based on the proper dataset encoding, feature importance-based feature selection and tuning of the algorithm for intrusion identification.&#x0D;

Acoustic COVID-19 Detection Using Multiple Instance Learning.

In the COVID-19 pandemic, a rigorous testing scheme was crucial. However, tests can be time-consuming and expensive. A machine learning-based diagnostic tool for audio recordings could enable widespread testing at low costs. In order to achieve comparability between such algorithms, the DiCOVA challenge was created. It is based on the Coswara dataset offering the recording categories cough, speech, breath and vowel phonation. Recording durations vary greatly, ranging from one second to over a minute. A base model is pre-trained on random, short time intervals. Subsequently, a Multiple Instance Learning (MIL) model based on self-attention is incorporated to make collective predictions for multiple time segments within each audio recording, taking advantage of longer durations. In order to compete in the fusion category of the DiCOVA challenge, we utilize a linear regression approach among other fusion methods to combine predictions from the most successful models associated with each sound modality. The application of the MIL approach significantly improves generalizability, leading to an AUC ROC score of 86.6% in the fusion category. By incorporating previously unused data, including the sound modality 'sustained vowel phonation' and patient metadata, we were able to significantly improve our previous results reaching a score of 92.2%.

Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means

Hepatitis is a serious disease that causes death throughout the world. It is responsible for inflammation in the human liver. If we manage to detect this life-threatening disease early, we can save many lives from it. In this research paper, we predict hepatitis disease using data mining techniques. We have attempted to propose a feasible approach to improve the performance of our prediction models in our research. We address the problem of missing values in the dataset by replacing them with the mean value. Nine algorithms were applied to the hepatitis disease dataset to calculate prediction accuracy. We measure accuracy, precision, recall, ROC and best score, and we compare them with random search hyperparameter tuning. It is hoped that by using them we will find the optimal combination of hyperparameters to improve the performance of machine learning models which helps us compare the performance of classification models.

Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification

Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient.