Bayes Algorithm Research Articles

Enhancing Automative Safety System for Driver Fatigue Detection by Using Mediapipe’s Framework

Traffic accidents caused by tiredness, exhaustion, and distracted driving are a big concern on a global scale. As per global Road Safety reports drowsy driving was reason for 91,000 road accidents in Tamil Nadu in the year 2023. In this study, I suggest a collective deep learning architecture for automatic driver sleepiness detection while some earlier works are suggested extracting the lips and eyes movements to identify sleepiness, more contemporary computer vision-based systems have performed only somewhat well. This is because they either use extremely large deep learning models with still-poor performance or they use hand-crafted features with traditional methods like K-NN Algotithm and openCV. To overcome such issues in this project I am planing to create a Driver Drowsiness Detection and Alerting System using Mediapipe‘s Face Mesh solution in Python. These systems assess the driver‘s alertness and warn the driver if needed. Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. we will create a drowsy driver detection system to address such an issue. For this, we will use Mediapipe‘s Face Mesh solution in python and the Eye Aspect ratio formula using by Navie Bayes Algorithm. My goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time. Keyword: Navie Baye’s, Mediapipe Framework.

Analisis Sentimen pada Ekspedisi Kurir Online di Indonesia Menggunakan Algoritma Naive Bayes

This journal focuses on sentiment analysis of four popular online courier expeditions in Indonesia, namely JNT, JNE, Shoope Express, and Anteraja. The purpose of this research is to understand the opinions and responses of users towards the online courier expedition service. The research was conducted using the sentiment analysis method, which utilizes data from user reviews contained in each online courier application on the Playstore platform. In this study, the Naive Bayes algorithm is used to perform sentiment analysis. This algorithm was chosen with the aim of producing higher accuracy results in determining positive and negative sentiments from user reviews. By using this method, this research hopes to provide a deeper understanding of the perceptions and experiences of users of the online courier service under study. The results of the study show that there are variations in user sentiment for each online courier expedition. User reviews include aspects of satisfaction with service, desired complaints, and problems that often arise when using the online courier application. These findings provide valuable insights for online courier service providers, as it can help them improve service quality and customer satisfaction. By understanding user opinions and feedback, online courier service providers can identify deficiencies in their services and make necessary improvements.

Bayesian approaches to assigning the source of an odour detected by an electronic nose

After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes’ theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source ‘none of the above’, which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient’s breath), and three sources of unpleasant odours in a meat processing plant.

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques.

The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.

Utilizing Radiomics of Peri-Lesional Edema in T2-FLAIR Subtraction Digital Images to Distinguish High-Grade Glial Tumors From Brain Metastasis.

Differentiating high-grade glioma (HGG) and isolated brain metastasis (BM) is important for determining appropriate treatment. Radiomics, utilizing quantitative imaging features, offers the potential for improved diagnostic accuracy in this context. To differentiate high-grade (grade 4) glioma and BM using machine learning models from radiomics data obtained from T2-FLAIR digital subtraction images and the peritumoral edema area. Retrospective. The study included 1287 patients. Of these, 602 were male and 685 were female. Of the 788 HGG patients included in the study, 702 had solitary masses. Of the 499 BM patients included in the study, 112 had solitary masses. Initially, the model was developed and tested on solitary masses. Subsequently, the model was developed and tested separately for all patients (solitary and multiple masses). Axial T2-weighted fast spin-echo sequence (T2WI) and T2-weighted fluid-attenuated inversion recovery sequence (T2-FLAIR), using 1.5-T and 3.0-T scanners. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the area of peritumoral edema. The maximum relevance-minimum redundancy (mRMR) method was then used for dimensionality reduction. The naive Bayes algorithm was used in model development. The interpretability of the model was explored using SHapley Additive exPlanations (SHAP). Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. The performance metrics include area under curve (AUC), sensitivity (SENS), and specificity (SPEC). The mean age of HGG patients was 61.4 ± 13.2 years and 61.7 ± 12.2 years for BM patients. In the external validation cohort, the model achieved AUC: 0.991, SENS: 0.983, and SPEC: 0.922. The external cohort results for patients with solitary lesions were AUC: 0.987, SENS: 0.950, and SPEC: 0.922. The artificial intelligence model, developed with radiomics data from the peritumoral edema area in T2-FLAIR digital subtraction images, might be able to differentiate isolated BM from HGG. 3 TECHNICAL EFFICACY: Stage 2.

Comparative Analysis of K-Means and Naïve Bayes Algorithms for Predicting Students' Academic Performance

A student's performance is a victory statistic in higher education. The university's exceptional academic record strengthens its position as one of the prerequisites for a prestigious university. Teachers need to forecast and analyze student performance to pinpoint areas of weakness and improve academic standing. In academic settings, Coordination of computational tactics to improve workforce management and academic attainment is achieved through Educational Data Mining (EDM), a theory-based approach. Classification is a broadly connected technique in forecasting student performance based on diverse criteria. Machine learning algorithms are fundamental to knowledge disclosures, permitting precise performance projection and early student-identifiable proof. This study examines how well students perform academically using the Naïve Bayes classifier (NBC) model and the K-Means clustering approach. From supervised and unsupervised machine learning, two (2) algorithms with comparable operational capacity were selected. The labeled classes in the classifier correspond to the grades in the dataset. Records were gathered from 178 students (400 levels) in Adamawa State University Mubi's computer science departments in the 2022–2023 academic sessions. The training and testing sets of the dataset are divided into two groups, each with a percentage ratio of 30% and 70%. According to the results, the Naïve Bayes model has an accuracy of 92.6%, while the K-Means model has an accuracy of 38.9%.

Microblog Negative Comments Data Analysis Model Based on Multi-scale Convolutional Neural Network and Weighted Naive Bayes Algorithm

As a form of public supervision, Microblog’s negative reviews allow people to share their opinions and experiences and express dissatisfaction with unfair and unreasonable phenomena. This form of supervision has the potential to promote social fairness, drive governments, businesses, and individuals to correct mistakes and enhance transparency. To characterize the sentiment trend and determine the influence of Microblog negative reviews, we propose a multi-scale convolutional neural network and weighted naive bayes algorithm (MCNN–WNB). We define the feature vector characterization index for Microblog negative review data and preprocess the data accordingly. We quantify the relationship between attributes and categories using the weighted Naive Bayes method and use the quantification value as the weighting coefficient for the attributes, addressing the issue of decreased classification performance in traditional methods. We introduce a sentiment classification model based on word vector representation and a multi-scale convolutional neural networks to filter out Microblog negative review data. We conduct simulation experiments using real data, analyzing key influencing parameters such as convergence time, training set sample size, and number of categories. By comparing with K-means, Naive Bayes algorithm, Spectral Clustering algorithm and Autoencoder algorithm, we validate the effectiveness of our proposed method. We discover that the convergence time of the MCNN–WNB algorithm increases as the number of categories increases. The average classification accuracy of the algorithm remains relatively stable with varying test iterations. The algorithm’s precision increases with the number of training set samples and eventually stabilizes.

Performance Evaluation of Machine Learning Algorithms for Iris Dataset Classification

The problem of classifying iris flowers is a well-known issue in the fields of machine learning and pattern recognition. The Iris dataset is widely recognized as a standard for evaluating the effectiveness of classification algorithms in machine learning. This paper offers a comparative assessment of different machine learning algorithms when applied to the Iris dataset. We implement and assess the performance of k-Nearest Neighbors, Logistic Regression, Decision Tree, Linear SVC methods, Random Forest, Gaussian Naïve Bayes and AdaBoost algorithms based on their accuracy in classification. We examine diverse classification methods, appraise their effectiveness, and discuss our findings. The outcomes reveal the accuracy of these models in correctly categorizing the Iris species. To achieve high accuracy, k-Nearest Neighbors, Logistic Regression, Linear SVC methods,Decision Tree,, Random Forest and AdaBoost classifiers are utilised.

Predicting customer churn in telecommunication sector using Naïve Bayes algorithm

The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM)need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning(ML)have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector usingNaïveBayes(NB) model is presented. The performance of presentedNBalgorithmis evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches.

Machine Learning Approach of Predicting Airline Flight Delay using Naïve Bayes Algorithm

Machine Learning Approach of Predicting Airline Flight Delay using Naïve Bayes Algorithm

Cuneiform Text Dialect Identification Using Machine Learning Algorithms and Natural Language Processing (NLP)

Due to a lack of resources and the tokenization issue, it is challenging to identify the languages inscribed in cuneiform symbols. Sumerian and six dialects of the Akkadian language-Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian-are among the seven languages and dialects written in cuneiform that need to be identified. This problem is addressed by the Cuneiform Language Identification task in VarDial 2019. This paper presents ten machine learning algorithms derived from four types of machine learning that were used (supervised, ensemble, instance-based, and Artificial Neural Network) learnings. The Support Vector Machine (SVM), Na Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) algorithms within supervised learning, the K-Nearest Neighbors algorithm (KNN) within instance- based learning, the Random Forest (RF), Adaptive Boosting (Adaboost), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) algorithms within ensemble learning. Also, one of the natural language processing algorithms, n-gram, is used to identify the cuneiform dialect. The best result belongs to an ensemble of Random Forest classifiers working on character-level features with a macro averaged F1 score of 96%, and the best outcome for the n-grams algorithm is 0.82% of di-gram.

Machine learning approaches to predict the need forintensive care unitadmission among Iranian COVID-19 patients based on ICD-10: A cross-sectional study.

Timely identification of the patients requiring intensive care unit admission (ICU) could be life-saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID-19 patients. We screened all patients with COVID-19 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID-19 patients (≥18 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Naıve Bayes, logistic regression, lightGBM, decision tree, and K-Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic-Area Under the Curve (AUC) were used to compare the prediction performance of different models. Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza & pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID-19 patients. All six models achieved an AUC greater than 0.60. Naıve Bayes achieved the best predictive performance (AUC = 0.71). Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID-19 patients. Machine learning models could help quickly identify high-risk patients upon entry and reduce mortality and morbidity among COVID-19 patients.

Optimization of Chinese film and television communication technology in Portuguese-speaking areas under the application of digital Internet of Things

With the continuous change and progress of the world system, cultural communication has become a vital topic in today's society. This study aims to optimize the communication effect of Chinese film in Portuguese-speaking countries and promote the development of cultural communication in China through deep learning (DL) technology. First, this study utilizes DL technology to design film feature recognition and classification models to provide technical support for Chinese film communication. Second, by exploring the principle, development, and function of the digital Internet of Things, the study analyzes the evolution and spread of Chinese film in Portuguese-speaking countries and uses the Bayes algorithm to optimize the model. The results show that the calculation time of the designed model is shorter and the accuracy is higher, which offers an important reference for the effective communication of Chinese films in Portuguese-speaking countries. Hence, this study not only provides technical support for the improvement of the international communication effect of Chinese film but also contributes to the development of cultural communication in China.

Acoustic feature-based emotion recognition and curing using ensemble learning and CNN

Emotion recognition and understanding plays a crucial role in various domains, including healthcare, human-computer interaction, and mental well-being. In this context, this paper proposes a methodology for recognizing and curing emotions using acoustic features and machine learning algorithms. The approach involves extracting acoustic features from the signals using diverse signal processing techniques. These features are then utilized as inputs for machine learning and deep learning algorithms, including the Random Forest classifier, XG Boost classifier, Convolutional Neural Network (CNN), and an ensemble algorithm. The ensemble algorithm combines Random Forest and XG Boost as base classifiers, with the Naïve Bayes algorithm serving as the meta classifier. We also propose a novel model that generates personalized curing strategies for individuals based on emotion recognition, so they can keep their emotional state positive. With the help of an ensemble learning model the proposed model achieved an emotion recognition accuracy of 92 % by combining three publicly available datasets containing emotional speech recordings. In the neutral and positive emotion classifications, the Receiver Operating Characteristic curve (ROC) had a 98 % accuracy rate while negative emotion classifications had a 91 % true positive rate. The effectiveness of the proposed curing methodology model has also been demonstrated by conducting experiments on a group of individuals and comparing the results with a state-of-the-art Generative Pre-Trained Transformer-3 (GPT-3) and ChatGPT, it was inferred that 89.35 % of the test group preferred the responses of the proposed curing model, over the GPT models The results of our experiments show that our proposed methodology can significantly boost the emotional state of an individual, thereby highlighting its potential for use in clinical settings.

Predicting pharmaceutical supply chain disruptions before and during the COVID-19 pandemic.

Disruptions to the pharmaceutical supply chain (PSC) have negative implications for patients, motivating their prediction to improve risk mitigation. Although data analytics and machine learning methods have been proposed to support the characterization of probabilities to inform decisions and risk mitigation strategies, their application in the PSC has not been previously described. Further, it is unclear how well these models perform in the presence of emergent events representing deep uncertainty such as the COVID-19 pandemic. This article examines the use of data-driven models to predict PSC disruptions before and during the COVID-19 pandemic. Using data on generic drugs from the pharmacy supply chain division of a Fortune 500 pharmacy benefit management firm, we have developed predictive models based on the naïve Bayes algorithm, where the models predict whether a specific supplier or whether a specific product will experience a supply disruption in the next time period. We find statistically significant changes in the relationships of nearly all variables associated with product supply disruptions during the pandemic, despite pre-pandemic stability. We present results showing how the sensitivity, specificity, and false positive rate of predictive models changed with the onset of the COVID-19 pandemic and show the beneficial effects of regular model updating. The results show that maintaining model sensitivity is more challenging than maintaining specificity and false positive rates. The results provide unique insight into the pandemic's effect on risk prediction within the PSC and provide insight for risk analysts to better understand how surprise events and deep uncertainty affect predictive models.

A Medical Cyber-Physical System utilizing the Bayes algorithm for post-diagnosis patient supervision

A Medical Cyber-Physical System utilizing the Bayes algorithm for post-diagnosis patient supervision

Machine Learning Techniques for Medicinal Leaf Prediction and Disease Identification

Trees have been a crucial component in humans' lives for hundreds of years, providing food, shelter, and medicine. Some trees have a lot of medicinal properties that cure many diseases. In the old days, Ayurvedic methods were popular for various treatments, but nowadays, the demand for foreign medicine is increasing gradually, which also has side effects. This paper addresses this issue by deciding on the medical conditions corresponding to a symptom and predicting an herb leaf that can be treated it using some modern machine learning techniques. We have used three machine learning methods to accomplish this goal: Multinomial Naive Bayes, Gradient Boosting and Random Forest. These techniques were then used to assess the symptoms and decide the name of the disease and which leaf is appropriate for medicine. The highest accuracy (92%) was produced by the Multinomial Naive Bayes algorithm, thereby showing its capability to predict the right medicinal leaf based on given symptoms. The results show that machine learning algorithms, especially Multinomial Naive Bayes, can identify diseases and recommend suitable medicinal leaves. This approach holds promise for integrating traditional Ayurvedic knowledge with modern technology to offer alternative treatments with potentially fewer side effects.

Analysis and Classification Of Wireless LAN Network Service Quality Using The Naive Bayes Algorithm In The Department Of Informatics and Computer Engineering, Faculty Of Engineering, State University Of Makassar

This study aims to determine the Quality of Service outcomes and the classification results of the Naive Bayes Algorithm on the Wireless LAN network quality in the Department of Informatics Engineering and Computer Faculty of Engineering at Makassar State University. This study uses the Action Research method with a quantitative approach. It conducts 54 tests using Wireshark in two different sessions to be compared for each time conducted during the research. The results of the research indicate that the status of the Wireless LAN network in the Department of Informatics Engineering and Computer Faculty of Engineering at Makassar State University falls into the "Good" category with the highest value of 59.26% , with an algorithm accuracy of 65.43%. Both testing sessions, morning session from 08:30 to 09:00 and afternoon session from 13:30 to 14:00, are categorized as "Good" according to the TIPHON standardization category as well as based on the Naive Bayes Algorithm classification results. Based on the research findings, it is recommended that the measurement of network service quality can utilize software other than Wireshark and increase the amount of test data to improve the accuracy of the Naive Bayes algorithm.

Implementation of the Naive Bayes Method in Looker Studio for data on the achievement of Great IDN in IDN Akhwat School

The IDN Hebat program is an important tool for schools to track and analyze student achievement data. However, with the targeted activities in the IDN program, challenges arise in managing and measuring achievement data efficiently. The research aims to develop a Web Cloud-based data management system for IDN Hisbat achievements at IDN Akhwat School by utilizing Google Looker Studio and the Naive Bayes Algorithm. The data source used in this study is by applying a classification dataset obtained from student achievement information data in the Great IDN Program. The results of this analysis show that the highest accuracy of teaching achievement fell on the status of exceeding the target with a percentage of 89%, and the highest class that placed the status above the target was class 9A with an average percentage of 35%. In addition, the results from this analysis can help coordinators and schools in planning more effective and strategic programs in the future. Overall, this study provides important benefits in improving the quality of teaching and student coaching, as well as supporting data-driven decision-making. This study is expected to enhance the efficiency, accuracy, and effectiveness of managing student achievement, while also supporting the attainment of optimal educational goals for each student to achieve extraordinary results.

Analisis Prediksi Prestasi Siswa UPTD SD Negeri 30 Aek Batu dalam Machine Learning dengan Metode Naive Bayes

This study aims to analyze and predict student achievement at UPTD SD Negeri 30 Aek Batu using a machine learning method with the Naive Bayes algorithm. By collecting data that includes academic grades, attendance, and other factors, the study cleans and processes the data to build a prediction model. The Naive Bayes algorithm is applied to identify patterns and relationships between these variables and student achievement. The results of this model are evaluated to determine its accuracy and ability to predict student performance, thereby providing valuable information for educational improvement strategies and interventions in schools.