Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships bet...
We propose a minimum variance unbiased approximation to the conditional relative entropy of the distribution induced by the observed frequency estimates, for multi-classification tasks. Such approximation is an extension of a decomposable scoring cri...
Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional...
Bayesian network classifiers (BNCs) have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the...
This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED...
Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies h...
To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Targe...
When applied to classification problems, Bayesian networks are often used to infer a class variable when given feature variables. Earlier reports have described that the classification accuracy of Bayesian network structures achieved by maximizing th...
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...
Mapping hydrothermal alteration minerals using multispectral remote sensing satellite imagery provides vital information for the exploration of porphyry and epithermal ore mineralizations. The Ahar-Arasbaran region, NW Iran, contains a variety of por...
Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant depen...
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...
An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and di...
This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probab...
We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarde...
Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the margi...
The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts w...
Causal discovery involves searching intractably large spaces. Decomposing the search space into classes of observationally equivalent causal models is a well-studied avenue to making discovery tractable. This paper studies the topological structure u...
Background/Objectives: This study evaluates whether convolutional neural networks (CNNs) can be trained to determine the primary tumor origin from MRI images alone in patients with metastatic brain lesions. Methods: This retrospective, monocentric st...
Currently, with the satisfaction of people’s material life, sports, like yoga and tai chi, have become essential activities in people’s daily life. For most yoga amateurs, they could only learn yoga by self-study, like mechanically imitat...
Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes’ theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particu...
Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearab...
Unbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that pre...
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...
While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. Ho...
When dealing with complex entrepreneurial network problems, such as sustainable resource flows, the highly uncertainty in environment that brings cognitive bias in entrepreneurs’ decision-making means which entrepreneurs who are expert in using...
As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity. The scoring functions once proposed and widely used for a Bayesian network are not appro...
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...
The rapid growth in data makes the quest for highly scalable learners a popular one. To achieve the trade-off between structure complexity and classification accuracy, the k-dependence Bayesian classifier (KDB) allows to represent different number of...
Uncertainty presents unfamiliar circumstances or incomplete information that may be difficult to handle with a single model of a traditional machine learning algorithm. They are possibly limited by inadequate data, an ambiguous model, and learning pe...
This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area o...
In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the...
Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused...
The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectr...
Studying cloud-to-ground lightning strokes and ground-strike points provides an alternative method of lightning mapping for lightning risk assessment. Various k-means algorithms have been used to verify the ground-strike points from lightning locatin...
In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it...
Software defect prediction is an important area in software engineering because it helps developers identify and fix problems before they become costly and hard-to-fix bugs. Early detection of software defects helps save time and money in the softwar...
Big data applications have tremendously increased due to technological developments. However, processing such a large amount of data is challenging for machine learning algorithms and computing resources. This study aims to analyze a large amount of...
With the rapid development of IoT (Internet of Things), massive data is delivered through trillions of interconnected smart devices. The heterogeneous networks trigger frequently the congestion and influence indirectly the application of IoT. The tra...
The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous...
This paper focuses on gait abnormality type identification—specifically, recognizing antalgic gait. Through experimentation, we demonstrate that detecting an individual’s gait type is a viable biometric that can be used along with other c...
The capabilities of ceramic PZT transducers, allowing for elastic wave excitation in a broad frequency spectrum, made them particularly suitable for the Structural Health Monitoring field. In this paper, the approach to detecting impact damage in com...
As a critical component of the power system, transmission lines play a significant role in ensuring the safe and stable operation of the power grid. To address the challenge of accurately characterizing complex and diverse fault types, this paper pro...
In recent years, deep learning has been applied to many medical imaging fields, including medical image processing, bioinformatics, medical image classification, segmentation, and prediction tasks. Computer-aided detection systems have been widely ad...
Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal p...
A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new...
(1) Background: The high heterogeneity of inflammatory bowel disease (IBD) makes the study of this condition challenging. In subjects affected by Crohn’s disease (CD), extra-intestinal manifestations (EIMs) have a remarkable potential impact on...
Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Thr...
The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of detecting discharges of tracers f...
Executive function is the mental ability to modulate behavior or thinking to accomplish a task. This is developmentally important for children’s academic achievements and ability to adjust to school. We classified executive function difficultie...
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