Naive Bayes Classification Model Research Articles

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites.

Enriching the Fake News Process of Classifying Model using Deep Learning and Machine Learning Algorithm

Fake news detection has become a pressing issue due to the rapid propagation of misinformation through various online platforms. In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. Information carefulness on Internet, especially on social media, is an increasingly important concern, but web-scale data hampers, ability to identify, evaluate and correct such data, or so called "fake news," present in these platforms. In this paper, we propose a method for "fake news" detection and ways to apply it on Facebook, one of the most popular online social media platforms. This method uses Naive Bayes classification model to predict whether a post on Facebook will be labelled as real or fake.

Application of Naive Bayes to Students’ Performance Classification

Naive Bayes Classifier is a strong tool or model in classifying students' performance based on various factors. Thus, this research developed a classification model that can accurately classify students into different academic performance categories. The study utilized data, collected from 1,422 students at the University of Ibadan, Nigeria. Descriptive statistics and data visualization techniques were used to gain insights into the distribution and relationships among the variables. Subsequently, a Naive Bayes classifier model was built using 70% of the data for training and 30% for testing. In addition, a Support Vector Machine (SVM) model was built to compare with the performance of the Naive Bayes model. The results of the descriptive statistics show that the respondents comprise of 846 females and 576 males. From the female respondents, 144 of them had First Class grade, 432 had Second Class Upper, 252 had Second Class Lower, and the remaining 18 had Third Class. From the male respondents, 144 of them had First Class grade, 198 had Second Class Upper, 216 had Second Class Lower, and the remaining 18 had Third Class. The Naive bayes model achieved an overall accuracy of 87%, while the SVM model achieved an overall accuracy of 85%. The results highlighted that department, grade in the first year, and monthly allowance were the most crucial features for classifying performance outcomes, while gender, age group and whether or not the respondents’ parents are educated, exerted the least significant influence on the models. Thus, on average, the Naive Bayes model outperformed the SVM in the classification of students’ performance based on the data collected. Also, the early academic performance, and financial support are significant factors in determining students' overall performance in the Institution.

Tri-Hybrid Naive Bayes Classification Model for Early Cardiovascular Disease Detection

Tri-Hybrid Naive Bayes Classification Model for Early Cardiovascular Disease Detection

Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection

There is a tendency to decrease the number of speedy customers in the operational area of ​​North Sumatra due to customer dissatisfaction. Termination of employment is carried out by the customer against PT. Telekomunikasi Indonesia, Tbk in North Sumatra. There is no management of customer data classification so that classification information based on certain product purchases cannot be known. Naïve Bayes is a classification algorithm that is easy to use but has weaknesses which result in poor performance, therefore feature selection is needed, the genetic algorithm is an algorithm that is able to select attributes in research, will be selected based on the highest weight so that the accuracy of the prediction results is more optimal. The steps taken in the measurement model using the Naive Bayes Classifier (NBC) approach and the model using the GA-NBC approach obtained accurate results from cross validation measurements, Confusion Matrix, ROC curves for the classification of existing and speedy telephone subscribers. The stages of the Naive Bayes process are: data collection, data preprocessing, processing of the Naive Bayes Classifier algorithm. Then the results are validated and evaluated using the Text Mining Algorithm, and calculating the parameters based on the genetic algorithm. The accuracy produced by the Naive Bayes Classifier model is 85.08%. The accuracy produced by the Naive Bayes Classifier model with the selection of Genetic Algorithm features increased to 89.31%.

An improved software defect prediction model based on grey incidence analysis and Naive Bayes algorithm

This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction model. The grey incidence analysis is used to analyze the relation between software modules and ideal modules. Then, the grey correlation degree is embedded into the Naive Bayes classification model as a feature attribute. According to the comparison and analysis of NASA’s public dataset, the prediction model in this paper improves the prediction accuracy.

Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application, namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through the data cleaning process resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data. furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model through automatic calculations. The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.

Detectsy: A System for Detecting Language from the Text, Images, and Audio Files

Abstract— Language detection is a natural language processing task where we need to identify the language of a text or document. As a human, we can easily detect the languages we know. However, it is not possible for an individual to identify many languages. This is where the language identification task can be used. The proposed solution is a complete system that detects language from the text, images, and audio files. Language identification task from text is carried out by training a Multinomial Naive Bayes classifier model. In the case of image and audio inputs, Python libraries are used to achieve the goal of language detection.

Naive Bayes classification model for isotopologue detection in LC-HRMS data

Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula or instrumental error, respectively. Therefore, a Naive Bayes isotopologue classification model was developed that does not depend on any thresholds or molecular formula information. This classification model uses the elemental mass defects of six elemental ratios and successfully identified isotopologues for both theoretical isotopic patterns and wastewater influent samples, outperforming one of the most commonly used approaches (i.e., 1.0033 ​Da mass difference method - CAMERA). For the theoretical isotopologues, the classification model outperformed an “in-house” mass difference method with a true positive rate (TPr) of 99.0% and false positive rate (FPr) of 1.8% compared to a TPr of 16.2% and an FPr of 0.02%, assuming no error. As for the wastewater influent samples, the classification model, with a TPr of 99.8% and false detection rate (FDr) of 0.5%, again performed better than the mass difference method, with a TPr of 96.3% and FDr of 4.8%. Therefore, it can be concluded that the classification model can be used for isotopologue identification, requiring no thresholds or information on the molecular formula.

Predictive structured\u2013unstructured interactions in EHR models: A case study of suicide prediction

Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p < 0.001), likely due to the RF model’s ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.

Using Machine Learning to Detect Events on the Basis of Bengali and Banglish Facebook Posts

In modern times, ensuring social security has become the prime concern for security administrators. The widespread and recurrent use of social media sites is creating a huge risk for the lives of the general people, as these sites are frequently becoming potential sources of the organization of various types of immoral events. For protecting society from these dangers, a prior detection system which can effectively detect events by analyzing these social media data is essential. However, automating the process of event detection has been difficult, as existing processes must account for diverse writing styles, languages, dialects, post lengths, and et cetera. To overcome these difficulties, we developed an effective model for detecting events, which, for our purposes, were classified as either protesting, celebrating, religious, or neutral, using Bengali and Banglish Facebook posts. At first, the collected posts’ text were processed for language detection, and then, detected posts were pre-processed using stopwords removal and tokenization. Features were then extracted from these pre-processed texts using three sub-processes: filtering, phrase matching of specific events, and sentiment analysis. The collected features were ultimately used to train our Bernoulli Naive Bayes classification model, which was capable of detecting events with 90.41% accuracy (for Bengali-language posts) and 70% (for the Banglish-form posts). For evaluating the effectiveness of our proposed model more precisely, we compared it with two other classifiers: Support Vector Machine and Decision Tree.

Establishment of naive Bayes classifier-based risk prediction model for chemotherapyinduced nausea and vomiting

To establish a risk prediction model of chemotherapy-induced nausea and vomiting based on naive Bayes classifier. We collected the basic information, treatment protocols and follow-up data from 300 patients receiving chemotherapy in the Oncology Department of Second Xiangya Hospital from July to September, 2020. Correlation analysis was carried out between the potential factors related to nausea and vomiting in the treatment plan and the individual characteristics of the patients. For the two characteristics with a correlation coefficient greater than 0.8, their contribution to the area under curve (AUC) was calculated, and the characteristic with a smaller contribution was removed. The naive Bayes classifier in the machine learning library scikit-learn was used as the prediction model of chemotherapy-induced nausea and vomiting, and 10-fold stratified-shuffled-split cross-validation was used to obtain the final result of the model. The machine learning model was trained using 70% of the samples, and 30% of the samples were used as the test set to assess the performance of the model. The sensitivity of the model for predicting the risk of nausea and vomiting due to acute chemotherapy was 0.83±0.04 (95%CI: 0.80-0.86) with a specificity of 0.45±0.03 (95%CI: 0.42-0.47) and an AUC of 0.72±0.04 (95% CI: 0.69-0.75). The sensitivity of the model for predicting the risk of delayed chemotherapy-induced nausea and vomiting was 0.84±0.01 (95%CI: 0.83-0.86) with a specificity of 0.48±0.03 (95%CI: 0.45-0.52) and an AUC of 0.74±0.02 (95%CI: 0.72-0.77). The naive Bayes classifier model has a good performance in predicting the risk of chemotherapy-induced nausea and vomiting in Chinese cancer patients.

Impact Factors on Posterior Modified Transfacet Debridement for Thoracic Spinal Tuberculosis Basing on Regression and Classification Analysis

Posterior transfacet approach has been proved to be a safe and effective access to treat thoracic disc herniation. However, the influencing factors of posterior modified transarticular debridement for thoracic tuberculosis have not been reported in the clinical literature. From 2009 to 2014, 37 patients with TST underwent a posterior modified transfacet debridement, interbody fusion following posterior instrumentation, under the cover of 18 months of antituberculosis chemotherapy. The patients were evaluated preoperatively and postoperatively in terms of Frankel Grade, visual analog scale (VAS) pain score, kyphotic Cobb angle, and bone fusion. Blood loss (positive correlation) and focal debridement (positive correlation) could affect operative time. Operative time (positive correlation) could affect blood loss. While, age (positive correlation), PostE (negative correlation), and T_FocalDebridement (positive correlation) could affect bone fusion. The accuracy of naive bayes classifier model is 86.11%. Our preliminary results show that blood loss and focal debridement could affect operative time; operative time could affect blood loss; age, PostE, and T_FocalDebridement could affect bone fusion; the naive Bayes classifier model can predict the KirkaldyWillis accurately.

Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine

The review of the users of one application is of great help to development in improving the quality of the application and may be the means for assessments that users feel satisfied or not. The study conducted a sentiment analysis of the Ruangguru application by testing the three classification models such as N aive B ayes , R andom F orest and Support V ectors M achine . The study has yielded results that from Random Forest classification model 97,16% by using Cross Validation and an AUC score of 0.996. Then accuracy with the model of Support V ector Machine classification support results in accuracy rate of 96.01% to an AUC value of 0.543 and accuracy in the testing of Naive Bayes classification model was 94,16% of AUC score 0,999. This study shows that an increase in accuracy from previous studies of 7.16% with Random Forest’s final cut as a Random Forest classification model with the best performance.

A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model

A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model

Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets

In many real world classification tasks, all data classes are not represented equally. This problem, known also as the curse of class imbalanced in data sets, has a potential impact in the training procedure of a classifier by learning a model that will be biased in favor of the majority class. In this work at hand, an under-sampling approach is proposed, which leverages the usage of a Naive Bayes classifier, in order to select the most informative instances from the available training set, based on a random initial selection. The method starts by learning a Naive Bayes classification model on a small stratified initial training set. Afterwards, it iteratively teaches its base model with the instances that the model is most uncertain about and retrains it until some criteria are satisfied. The overall performance of the proposed method has been scrutinized through a rigorous experimental procedure, being tested using six multimodal data sets, as well as another forty-four standard benchmark data sets. The empirical results indicate that the proposed under-sampling method achieves comparable classification performance in contrast to other resampling techniques, regarding several proper metrics and having performed a suitable statistical testing procedure.

A supervised machine learning approach using naive Gaussian Bayes classification for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy (PALS)

Abstract The acquisition of high-quality and non-artefact afflicted positron lifetime spectra is crucial for a profound analysis, i.e. the correct lifetime spectra decomposition for retrieving the true information. Since the introduction of digital positron lifetime spectrometers, this is generally realized by applying detector pulse discrimination with the help of software-based pulse filtering regarding area and/or shape of the detector pulses. Here, we present a novel approach for shape-sensitive detector pulse discrimination applying supervised machine learning (ML) based on a naive Bayes classification model using a normally distributed likelihood. In general, naive Bayes methods find wide application for many real-world problems such as famously applied for email spam filtering, text categorization or document classification. Their algorithms are relatively simple to implement and, moreover, perform extremely fast compared to more sophisticated methods in training and predicting on high-dimensional datasets, i.e. detector pulses. In this study we show that a remarkable low number of less than 20 labelled training pulses is sufficient to achieve comparable results as of applying physically filtering. Hence, our approach represents a potential alternative.

Predictive Value of Opto-magnetic Imaging Spectroscopy in Discriminating Oral Squamous Cell Carcinoma from Non-tumor Tissue in Surgical Margins

Negative surgical margins status is of extreme importance for better prognosis and lower recurrence rate in patients undergoing surgical treatment for oral squamous cell carcinoma (OSCC). Standard histopathology is time consuming and current optical procedures come with various limitations. We examined the potential of a new imaging technique—opto-magnetic imaging spectroscopy (OMIS) in discriminating tumor (OSCC) from adjacent non-tumor oral cavity tissue. A total of 46 samples from 21 patients were included in this study. Samples were taken from a fresh surgical specimen of primary OSCC. Digital imaging of samples was performed within 60 min of resection using OMIS device. Images were processed through a specifically designed convolution algorithm based on light-matter interaction. As a result a convolution spectrum was generated. Predictive value of spectral data was assessed using correlation tests and Naive Bayes classification model. There was no significant correlation in distribution of characteristic peaks between tumor and non-tumor tissue (P > 0.05). Tumor tissue showed more magnetic activity compared to non-tumor tissue. Naive Bayes classifier with kernel density estimation discriminated tumor from non-tumor tissue with the accuracy of 82.61%, 86.96% sensitivity, 78.26% specificity and the AUC value of 0.917. OMIS seems to be a promising optical method for ex vivo characterization of OSCC and non-tumor tissue. Further investigation is necessary to determine how tissue type and level of pathological transformation impact OMIS results. Ultimately, this could aid surgeons in using this method as an in vivo indicator for surgical resection with safe margins.

A Frame Work for Hospital Readmission Based on Deep Learning Approach and Naive Bayes Classification Model

A Frame Work for Hospital Readmission Based on Deep Learning Approach and Naive Bayes Classification Model

Comparison of Accuracy between Convolutional Neural Networks and Naïve Bayes Classifiers in Sentiment Analysis on Twitter

The needs and demands of the community for the ease of accessing information encourage the increasing use of social media tools such as Twitter to share, deliver and search for information needed. The number of large tweets shared by Twitter users every second, making the collection of tweets can be processed into useful information using sentiment analysis. The need for a large number of tweets to produce information encourages the need for a classifier model that can perform the analysis process quickly and provide accurate results. One algorithm that is currently popular and is widely used today to build classifier models is Deep Learning. Sentiment analysis in this research was conducted on English-language tweets on the topic Turkey Crisis 2018 by using one of the Deep Learning algorithms, Convolutional Neural Network (CNN). The resulting of CNN classifier model will then be compared with the Naive Bayes Classifier (NBC) classifier model to find out which classifier model can provide better accuracy in sentiment analysis. The research methods that will be carried out in this research are data retrieval, pre-processing, model design and training, model testing and visualization. The results obtained from this research indicate that the CNN classifier model produces an accuracy of 0.88 or 88% while the NBC classifier model produces an accuracy of 0.78 or 78% in the testing phase of the data test. Based on these results it can be concluded that the classifier model with Deep Learning algorithm produces better accuracy in sentiment analysis compared to the Naive Bayes classifier model.