This paper describes a solution for the image density classification problem (DCP) using an entirely distributed system with only local processing of information named cellular automata (CA). The proposed solution uses two cellular automata’s f...
High-quality, annotated datasets are crucial for enhancing the application of natural language processing tasks. Nevertheless, in the educational domain, they are limited compared to the vast amount of learning content. In the context of smart learni...
With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy a...
Classification yards are crucial nodes of railway freight transportation network, which plays a vital role in car flow reclassification and new train formation. Generally, a modern yard covers an expanse of several square kilometers and costs billion...
In this paper, we propose a novel binary classification method called the kernel-free quadratic surface minimax probability machine (QSMPM), that makes use of the kernel-free techniques of the quadratic surface support vector machine (QSSVM) and inhe...
Background: This study aims to evaluate the inter-rater reliability and perceived usability of a newly developed drug-related problem (DRP) classification system for use by pharmacists in the intermediate and long-term care (ILTC) setting in Singapor...
One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in...
Fixed-point theory plays many important roles in real-world problems, such as image processing, classification problem, etc. This paper introduces and analyzes a new, accelerated common-fixed-point algorithm using the viscosity approximation method a...
This paper investigates the problems of locating yards and allocating works of train formation among yards based on the train service network. It not only involves the modifications of the scales of existing yards, either improving, or downsizing or...
We consider the problem of designing a diffractive neural network (DNN) consisting of a set of sequentially placed phase diffractive optical elements (DOEs) and intended for the optical solution of several given classification problems at different o...
In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neu...
Data imbalance is a common problem in most practical classification applications of machine learning, and it may lead to classification results that are biased towards the majority class if not dealt with properly. An effective means of solving this...
In the development of algorithms for convex optimization problems, symmetry plays a very important role in the approximation of solutions in various real-world problems. In this paper, based on a fixed point algorithm with the inertial technique, we...
In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the st...
Tolerance-rough-set-based classifiers (TRSCs) are known to operate effectively on real-valued attributes for classification problems. This involves creating a tolerance relation that is defined by a distance function to estimate proximity between any...
In this paper, we propose a new accelerated common fixed-point algorithm for two countable families of G-nonexpansive mappings. Weak convergence results are obtained in the context of directed graphs in real Hilbert spaces. As applications, we apply...
This paper proposes an optimal path type classification scheme for the three-point Dubins problem. It allows us to directly extract the shortest path type from a Dubins set, evaluating only the relative initial and final configurations with the via p...
Artificial Neural Networks (ANNs) are models that learn patterns in input-output data. Since traditional optimization methods often get trapped in local optima when determining weight and bias values, identifying optimal parameters and enhancing netw...
A poor quality diet may be a common risk factor for both obesity and dental problems such as caries. The aim of this paper is to use classification tree analysis (CTA) to identify predictors of dental problems in a nationally representative cohort of...
This study examined the reversal error phenomenon in Chinese students’ algebra word problem-solving, focusing on how sentence structures influence performance and problem representation strategies across educational levels. Using a person-cente...
This article introduces an iterative algorithm that is created by integrating the
Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially si...
A new accelerated algorithm for approximating the common fixed points of a countable family of G-nonexpansive mappings is proposed, and the weak convergence theorem based on our main results is established in the setting of Hilbert spaces with a symm...
Transportation planning has been established as a key topic in the literature and social production practices. An increasing number of researchers are studying vehicle routing problems (VRPs) and their variants considering real-life applications and...
The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP...
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS cl...
The current paper presents a novel method for reducing a multiclass confusion matrix into a
The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to compreh...
The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision...
Deep learning is successful in providing adequate classification results in the field of traffic classification due to its ability to characterize features. However, malicious traffic captures insufficient data and identity tags, which makes it diffi...
Various machine learning algorithms have been applied to network intrusion classification problems, including both binary and multi-class classifications. Despite the existence of numerous studies involving unbalanced network intrusion datasets, such...
The problem of self-collisions of manipulators with several links installed on a robot can arise when they work together in one zone. To prevent self-collisions, it is necessary to develop methods for their detection and their subsequent inclusion in...
The incapability to move the facial muscles is known as facial palsy, and it affects various abilities of the patient, for example, performing facial expressions. Recently, automatic approaches aiming to diagnose facial palsy using images and machine...
Class imbalance (CI) poses a significant challenge in machine learning, characterized by a substantial disparity in sample sizes between majority and minority classes, leading to a pronounced “long-tail effect” in statistical distribution...
Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhance...
Twenty years ago, Rota posed the problem of finding all possible algebraic identities that can be satisfied by a linear operator on an algebra, named Rota’s Classification Problem later. Rota’s Classification Problem has proceeded two ste...
This study assesses the level of agreement on medication therapy problem (MTP) identification and classification between primary care, ambulatory care pharmacists within a health-system that recently implemented system-wide pharmacist provision of co...
This study proposes a double inertial technique integrated with the Mann algorithm to address the fixed-point problem. Our method is further employed to tackle the split-equilibrium problem and perform classification using a urinary tract infection d...
We introduce a new class of split inverse problems, termed the Split Pseudomonotone Equilibrium Problem with Multiple Output Sets, which generalizes classical equilibrium formulations to accommodate multiple decision outputs and pseudomonotonicity. T...
The article formulates the Dictionary Recognition problem, which is relevant for a wide range of applied problems: word recognition in a noisy audio signal for natural language processing tasks or in a noisy electromagnetic signal, recognition of vis...
With the promotion of an ecological civilization philosophy and a sustainable development strategy, solid waste classification and collection has become an emerging issue in China. Based on the three dimensions of sustainable development, namely econ...
Data imbalance is a common problem in classification tasks. The Mahalanobis-Taguchi system (MTS) has proven to be promising due to its lack of requirements for data distribution. The MTS is a binary classifier. However, multi-classification problems...
Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only op...
Recently, a standard dataset namely SCADI (Self-Care Activities Dataset) based on the International Classification of Functioning, Disability, and Health for Children and Youth framework for self-care problems identification of children with physical...
Hyperspectral image (HSI) classification has recently been successfully explored by using deep learning (DL) methods. However, DL models rely heavily on a large number of labeled samples, which are laborious to obtain. Therefore, finding a way to eff...
In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are still insufficiently addressed, such as the receptive field problem, smal...
Machine learning algorithms are frequently used for classification problems on tabular datasets. In order to make informed decisions about model selection and design, it is crucial to gain meaningful insights into the complexity of these datasets. Fe...
At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and fa...
The paper addresses the issue of classification machine learning algorithm performance based on a novel probabilistic confusion matrix concept. The paper develops a theoretical framework which associates the proposed confusion matrix and the resultin...
In this paper, a method is presented in order to conduct data classification through the use of a Self-Organizing Map (SOM) Artificial Neural Network (ANN). At first, the performance of the K-means algorithm in relation to the classification of the d...
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