A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the exi...
Censored data play a pivotal role in life testing experiments since they significantly reduce cost and testing time. Hence, this paper investigates the problem of statistical inference for a system of progressive first-failure censoring data for a ne...
Social media platforms make a significant contribution to modeling and influencing people’s opinions and decisions, including political views and orientation. Analyzing social media content can reveal trends and key triggers that will influence...
This paper develops an approach for two-day-ahead global horizontal irradiance (GHI) forecast using the naïve Bayes classifier (NB). Based on publicly available weather forecasting information about temperature, relative humidity, dew point, and...
The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network...
We define an evolving in-time Bayesian neural network called a Hidden Markov Neural Network, which addresses the crucial challenge in time-series forecasting and continual learning: striking a balance between adapting to new data and appropriately fo...
Parkinson’s disease (PD) is a neurodegenerative disease that impacts the neural, physiological, and behavioral systems of the brain, in which mild variations in the initial phases of the disease make precise diagnosis difficult. The general sym...
In multi-agent domains, dealing with non-stationary opponents that change behaviors (policies) consistently over time is still a challenging problem, where an agent usually requires the ability to detect the opponent’s policy accurately and ado...
In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler. To so...
Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than b...
A new weighted Nadarajah–Haghighi (WNH) distribution, as an alternative competitor model to gamma, standard half-logistic, generalized-exponential, Weibull, and other distributions, is considered. This paper explores both maximum likelihood and...
This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach inc...
The advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, a...
Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and non-destructive prediction of this key quality parameter, t...
India is the world’s leading producer and exporter of turmeric. Indian turmeric is known as the best in the world because of its natural medicinal properties. Different turmeric varieties have different amounts of nutritional value, which resul...
The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis...
Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, pro...
Precipitation modeling holds significant importance in various fields such as agriculture, animal husbandry, weather derivatives, hydrology, and risk and disaster preparedness. Stochastic precipitation generators (SPGs) represent a class of statistic...
This research paper focuses on developing an effective gesture-to-text translation system using state-of-the-art computer vision techniques. The existing research on sign language translation has yet to utilize skin masking, edge detection, and featu...
The ability of common carp to withstand both short-term and long-term oxygen deprivation has been well documented; however, the potential genetic mechanisms behind common carp’s hypoxia response remain unclear. Therefore, to understand the poss...
The accurate forecasting of student disability trends is essential for optimizing educational accessibility and resource distribution in the context of Brazil’s oldest public policy, the National Textbook Program (PNLD). This study applies machine le...
Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain...
Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact o...
Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines an...
DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected ear...
Bayesian methods were studied in this paper using deep neural networks. We are interested in variational autoencoders, where an encoder approaches the true posterior and the decoder approaches the direct probability. Specifically, we applied these au...
This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. Our approach uniquely combines tensor decomposition t...
Heart rate is a nonstationary signal and its variation may contain indicators of current disease or warnings about impending cardiac diseases. Hence, heart rate variation analysis has become a noninvasive tool to further study the activities of the a...
6 April 2021
Background: Female genital mutilation/cutting (FGM/C) is a harmful traditional practice affecting the health and rights of women and girls. This has raised global attention on the implementation of strategies to eliminate the practice in conformity w...
Ink disease, primarily caused by the pathogen Phytophthora xcambivora, significantly threatens the health and productivity of sweet chestnut (Castanea sativa Mill.) orchards, highlighting the need for accurate detection methods. This study investigat...
Previous studies on water quality assessment for watershed management have predominantly focused on specific seasonal or annual average values, rather than considering water quality variations based on flow fluctuations. It is crucial to identify the...
Determining the classification of motor competence is an essential aspect of physical activity that must be carried out during school years. The objective is to evaluate motor competence in schoolchildren using smart bands, generate percentiles of th...
Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print pat...
Alzheimer’s is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain’s anatomy and enables the identification of diagnostic features. Artificial intell...
Background and Objective: Dementia is a broad term for a complex range of conditions that affect the brain, such as Alzheimer’s disease (AD). Dementia affects a lot of people in the elderly community, hence there is a huge demand to better unde...
Background/Objectives: This study investigates the application of machine learning (ML) techniques in diagnosing speech sound disorders (SSDs) in Saudi Arabic-speaking children, with a specific focus on phonological biomarkers, particularly Infrequen...
Background: Genetic diversity provides the basic substrate for evolution. Genetic variation consists of changes ranging from single base pairs (single-nucleotide polymorphisms, or SNPs) to larger-scale structural variants, such as inversions, deletio...