The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fus...
With the continuous deepening of educational reform, a large number of educational policies, programs, and research reports have emerged, bringing a heavy burden of information processing and management to educators. Traditional manual classification...
Retinal blood vessels are affected by a variety of eye diseases, including hypertensive retinopathy (HR) and diabetic retinopathy (DR). A person with HR needs to be sure to check their eyes regularly, which requires the use of computer vision methods...
The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommenda...
Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there i...
The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa...
This study investigates relevant diagnosis information for arrhythmia classification from previously collected cardiac data. Discrimination ability of various time-domain attributes and rules were discussed for automatic diagnosis of arrythmia using...
Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in pra...
Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancemen...
Screening procedures in road blackspot detection are essential tools for road authorities for quickly gathering insights on the safety level of each road site they manage. This paper suggests a road blackspot screening procedure for two-lane rural ro...
The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study,...
Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oil...
Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation o...
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important fo...
As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, a...
The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The par...
The magnetic and electric fields of electrostatic discharges are assessed using the Naïve Bayes algorithm, a machine learning technique. Laboratory data from electrostatic discharge generators were used for the implementation of this algorithm....
Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estim...
This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Est...
Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which ca...
As an important improvement to naive Bayes, Tree-Augmented Naive Bayes (TAN) exhibits excellent classification performance and efficiency since it allows that every attribute depends on at most one other attribute in addition to the class variable. H...
Reorganizing city bus routes is generally accomplished by designing bus supply methods to meet passenger demand. The bus supply method involves establishing bus routes and planning their schedules. The actual bus route reorganization decisions are no...
University students, as a special group, face multiple psychological pressures and challenges, making them susceptible to social anxiety disorder. However, there are currently no articles using machine learning algorithms to identify predictors of so...
13 February 2023
The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are dat...
The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine lear...
Deep learning methods based on neural network models have been widely applied to bearing fault classification. Although they can achieve high accuracy, they also come with significant complexity. Bearing faults often generate impact vibrations, which...
Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing li...
Accurate forecasting and management are crucial as obesity represents a global health issue linked to many chronic diseases. This research examines the use of machine learning algorithms such as Decision Tree, Random Forest, K-NN (K Nearest Neighbor)...
While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while fa...
The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon...
Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved k-NN data mi...
Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS a...
The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selectio...
Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by...
The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza&...
Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in naive Bayes (NB), its assumption of the conditional independence of features is often violated and, therefore, red...
Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler...
Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance b...
The rapid development of AI Technology has significantly influenced digital art and triggered widespread discussion on social media platforms, particularly X. The use of AI in generating visual artworks and digital content has elicited diverse public...
Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigati...
Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive...
Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identific...
The Bayesian network is a directed, acyclic graphical model that can offer a structured description for probabilistic dependencies among random variables. As powerful tools for classification tasks, Bayesian classifiers often require computing joint...
In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. T...
Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer t...
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...
Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-heal...
Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We ap...
With the advancement of computer vision and image processing technologies, scene recognition has gradually become a research hotspot. However, in practical applications, it is necessary to detect the categories and locations of objects in images whil...
A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes in...
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