Algorithms, Volume 16, Issue 9

Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous...

In this paper, a novel global optimization approach in the form of an adaptive hyper-heuristic, namely HyperDE, is proposed. As the naming suggests, the method is based on the Differential Evolution (DE) heuristic, which is a well-established optimiz...

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving inter...

This paper presents the development of a robust control algorithm to be applied in a knee and ankle joint exoskeleton designed for rehabilitation of flexion/extension movements. The goal of the control law is to follow the trajectory of a straight le...

Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet (UV) radiation in the nocturnal atmosphere of Earth from the International Space Station. Meteors are among multiple phenomena that manifest themselves not only in the visibl...

The carousel greedy algorithm (CG) was proposed several years ago as a generalized greedy algorithm. In this paper, we implement CG to solve linear regression problems with a cardinality constraint on the number of features. More specifically, we int...

This article reviews several implementation aspects in estimating regularized single-group and multiple-group structural equation models (SEM). It is demonstrated that approximate estimation approaches that rely on a differentiable approximation of n...

Optical Coherence Tomography (OCT) is a light-based imaging modality that is used widely in the diagnosis and management of eye disease, and it is starting to become used to evaluate for ear disease. However, manual image analysis to interpret the an...

In this paper, a new nonsmooth optimization-based algorithm for solving large-scale regression problems is introduced. The regression problem is modeled as fully-connected feedforward neural networks with one hidden layer, piecewise linear activation...

Nonlinear (NL) and multilinear (ML) systems play a fundamental role in engineering and science. Over the last two decades, active research has been carried out on exploiting the intrinsically multilinear structure of input–output signals and/or...

In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobi...

Plant growth is inevitably affected by diseases, and one effective method of disease detection is through the observation of leaf changes. To solve the problem of disease detection in complex backgrounds, where the distinction between plant diseases...

This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which...

The paper outlines an algorithm for the rapid aerodynamic evaluation of winglet geometries using the TORNADO Vortex Lattice Method. It is a very useful tool to obtain a first approximation of the aerodynamic properties and for performing an optimizat...

Visualization techniques for understanding and explaining machine learning models have gained significant attention. One such technique is the decision map, which creates a 2D depiction of the decision behavior of classifiers trained on high-dimensio...

Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these sit...

Enormous amounts of data collected from social networks or other online platforms are being published for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have motivated the wo...

The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for l...

The matrix logarithm function has applicability in many engineering and science fields. Improvements in its calculation, from the point of view of both accuracy and/or execution time, have a direct impact on these disciplines. This paper describes a...

Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of th...

This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues...

Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative a...

Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in rece...

Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is...

Modeling of physical processes as partial differential equations (PDEs) is often carried out with computationally expensive numerical solvers. A common, and important, process to model is that of laser interaction with biological tissues. Physics-inf...

Confidence regions for the Weibull parameters with minimum areas among all those based on the Conditionality Principle are constructed using an equivalent diffuse Bayesian approach. The process is valid for scenarios involving standard failure and pr...

Jurisdictions currently provide information on winter road conditions through qualitative descriptors like bare and fully snow-covered. Ideally, these descriptors are meant to warn drivers beforehand about hazardous roads. In practice, however, disce...

Decision making in real-world applications frequently calls for taking into account multiple goals to come up with viable solutions [...]

As the largest open social medium on the Internet, Reddit is widely studied in the scientific literature. Due to its structured form and division into topical subfora (subreddits), conducted research often concerns connections and interactions betwee...

Efforts across diverse domains like economics, energy, and agronomy have focused on developing predictive models for time series data. A spectrum of techniques, spanning from elementary linear models to intricate neural networks and machine learning...

Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur...

Traffic forecasting is an important research topic in intelligent transportation systems and smart cities [...]

This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) mo...

Timely information about the need to thin forests is vital in forest management to maintain a healthy forest while maximizing income. Currently, very-high-spatial-resolution remote sensing data can provide crucial assistance to experts when evaluatin...

This paper establishes a novel fractional-order version of a recently expanded form of the Susceptible-Exposed-Infectious-Recovery (SEIR) Mpox model. This model is investigated by means of demonstrating some significant findings connected with the st...

Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possi...

The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes...

This paper examines the relationship between user pageview (PV) histories and their itemchoice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user–item pair. We propose a s...

The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using...

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimizatio...

For mobile cleaning robot navigation, it is crucial to not only base the motion decisions on the ego agent’s capabilities but also to take into account other agents in the shared environment. Therefore, in this paper, we propose a deep reinforc...

This paper presents a solution for an autonomously driven vehicle (a robotic car) based on artificial intelligence using a supervised learning method. A scaled-down robotic car containing only one camera as a sensor was developed to participate in th...

A neural-network-assisted numerical method is proposed for the solution of Laplace and Poisson problems. Finite differences are applied to approximate the spatial Laplacian operator on nonuniform grids. For this, a neural network is trained to comput...

With the function RooTri(), we present a simple and robust calculation method for the approximation of the intersection points of a scalar field given as an unstructured point cloud with a plane oriented arbitrarily in space. The point cloud is appro...

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on ste...

This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray...

Flow-shop scheduling problems are classic examples of multi-resource and multi-operation scheduling problems where the objective is to minimize the makespan. Because of the high complexity and intractability of the problem, apart from some exceptiona...

Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem...

Permanent magnet synchronous motor (PMSM) drive systems are commonly utilized in mobile electric drive systems due to their high efficiency, high power density, and low maintenance cost. To reduce the tracking error of the permanent magnet synchronou...

It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modificatio...