In South Korea, the rapid proliferation of smartphones has led to an uptick in messenger phishing attacks associated with electronic communication financial scams. In response to this, various phishing detection algorithms have been proposed. However...
A new accelerated common fixed point algorithm is introduced and analyzed for a countable family of nonexpansive mappings and then we apply it to solve some convex bilevel optimization problems. Then, under some suitable conditions, we prove a strong...
In large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance t...
Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Met...
Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, b...
The use of machine-learning techniques is becoming more and more frequent in solving all those problems where it is difficult to rationally interpret the process of interest. Intrusion detection in networked systems is a problem in which, although it...
Classification techniques, reliant on annotated data for autonomous decision training, have become pivotal tools in diverse domains. These techniques rely on models like Backpropagation Neural Networks (BPNNs). However, BPNNs frequently trap local op...
This paper studies the Booking Stays Assignment Problem (BSAP), a large-scale problem in which the Mohammedia Foundation for Social Works of Judges allocates a limited pool of vacation residences among thousands of competing members under priority, q...
In this paper, we introduce a new line search technique, then employ it to construct a novel accelerated forward–backward algorithm for solving convex minimization problems of the form of the summation of two convex functions in which one of th...
The generalization error of conventional support vector machine (SVM) depends on the ratio of two factors; radius and margin. The traditional SVM aims to maximize margin but ignore minimization of radius, which decreases the overall performance of th...
This paper aims to solve the asymmetric problem of sample classification recognition in extreme class imbalance. Inspired by Krawczyk (2016)’s improvement direction of extreme sample imbalance classification, this paper adopts the AdaBoost mode...
Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the g...
Categorical data is a fundamental element of real-world tabular classification problems. In typical machine learning workflows, encoding strategies are used to convert categorical data into numerical representations. These encoding strategies vary in...
The Rosenblatt’s first theorem about the omnipotence of shallow networks states that elementary perceptrons can solve any classification problem if there are no discrepancies in the training set. Minsky and Papert considered elementary perceptr...
With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and produci...
It is shown that the set of all networks of fixed order
We propose a novel method using Locality-Sensitive Hashing (LSH) for solving the optimization problem that arises in the training stage of support vector machines for large data sets, possibly in high dimensions. LSH was introduced as an efficient wa...
Background: This scoping review aimed to identify gaps in the literature regarding medication therapy problems (MTPs) among hospitalized adults with decreased kidney function. Specifically, it aimed to answer the following questions: (1) What types o...
Functional data are nowadays routinely collected and stored in a wide variety of fields. Their adequate use and analysis are a challenge for the scientific community. Mathematically, each function can be understood as a sequence of infinite related n...
For the past few decades, various algorithms have been proposed to solve convex minimization problems in the form of the sum of two lower semicontinuous and convex functions. The convergence of these algorithms was guaranteed under the L-Lipschitz co...
Manual classification of algorithmic problems is costly and inconsistent, although it remains essential for curriculum sequencing, recommendation systems, and quality control on competitive programming platforms. To address this limitation, this stud...
28 July 2026
Hearing-functioning problems contribute substantially to disability and rehabilitation needs globally; however, evidence describing the range and interconnected nature of these problems across African populations remains fragmented. This study aimed...
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., th...
Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers...
In order to improve the problem of overly relying on situational information, high computational power requirements, and weak adaptability of traditional maneuver methods used by hypersonic vehicles (HV), an intelligent maneuver strategy combining de...
We report the results of an in-depth study of 15 variants of five different Convolutional Neural Network (CNN) architectures for the classification of seeds of seven different grass species that possess symmetry properties. The performance metrics of...
Regression analysis with interval-valued outcomes presents a fundamental challenge in modeling data where uncertainty is inherent rather than incidental. Such data, arising naturally in fields ranging from meteorology to finance, require methods that...
Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness...
Researchers using machine learning methods for classification can face challenges due to class imbalance, where a certain class is underrepresented. Over or under-sampling of minority or majority class observations, or solely relying on model selecti...
Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and...
Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributi...
The classic set covering problem (SCP) is an NP-hard binary integer optimization problem with diverse business and industrial applications. Its primary goal is to consolidate resources by selecting a minimal cost subset of columns in a matrix that co...
A generalisation of the Lie symmetry method is applied to classify a coupled system of reaction-diffusion equations wherein the nonlinearities involve arbitrary functions in the limit case in which one equation of the pair is quasi-steady but the oth...
The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in th...
Several behavioural problems exist in office environments, including resource use, sedentary behaviour, cognitive/multitasking, and social media. These behavioural problems have been solved through subjective or objective techniques. Within objective...
Designing or expanding a bicycle-sharing system (BSS) involves addressing the infrastructure’s location of the bicycle stations. Station location is an essential factor for designing and implementing a new system or for its operation. In a complex sp...
Drug-related problems (DRPs), i.e., adverse drug reactions (ADRs) and medication errors (MEs), constitute a serious threat to the patient’s safety. DRPs are often insufficiently captured by clinical routine documentation, and thus, they frequently re...
The density classification (DC) task, a computation which maps global density information to local density, is studied using one-dimensional non-unitary quantum cellular automata (QCAs). Two approaches are considered: one that preserves the number de...
In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decisio...
In this paper, a new model supporting decisions about product allocation in an order-picking shelf warehouse is presented. Industry 4.0 pays attention to inciting the processes, self-analysis and self-optimization of the short response time to market...
In recent years, several methods have emerged for compressing image classification models using CNNs, for example, by applying pruning to the convolutional layers of the network. Typically, each pruning method uses a type of pruning distribution that...
In the context of quantum-inspired machine learning, quantum state discrimination is a useful tool for classification problems. We implement a local approach combining the k-nearest neighbors algorithm with some quantum-inspired classifiers. We compa...
Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditi...
The main goal of the article is the evaluation of usefulness of CORINE Land Cover (CLC) data—acquired predominantly by visual interpretation of Landsat satellite imagery—for monitoring of changes in settlement development and land use. Th...
In this paper we present the first results of a pilot experiment in the interpretation of multimodal observations of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze,...
The mapping of categorical variables into numerical values is common in machine learning classification problems. This type of mapping is frequently performed in a relatively arbitrary manner. We present a series of four assumptions (tested numerical...
Constrained optimization plays a central role in transport and logistics location problems, such as depot siting under geometric or infrastructure-related constraints. In practice, the classification of constrained extrema by classical second-order m...
The modern digital world and associated innovative and state-of-the-art applications that characterize its presence, render the current digital age a captivating era for many worldwide. These innovations include dialogue systems, such as Apple’...
Background/Objectives: Malnutrition and undernutrition are critical health concerns associated with increased mortality and costs. Although these are distinct clinical concepts, they are often used interchangeably in clinical practice and are inconsi...
In this paper, we focus on the redesign of the output layer for the weighted regularized extreme learning machine (WRELM). For multi-classification problems, the conventional method of the output layer setting, named “one-hot method”, is...
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