Algorithms, Volume 17, Issue 8

Malfatti’s problem involves three circles (called Malfatti circles) that are tangent to each other and two sides of a triangle. In this study, our objective is to extend the problem to find 6, 10, … $\sum _1^{n}i$ (n > 2) circles inside the...

In this paper, algorithms for different types of trigonometric power sums are developed and presented. Although interesting in their own right, these trigonometric power sums arise during the creation of an algorithm for the four types of twisted tri...

One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as...

This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan)...

In the rapidly evolving domain of mobile edge–cloud computing (MECC), the proliferation of Internet of Things (IoT) devices and mobile applications poses significant challenges, particularly in dynamically managing computational demands and use...

The bipartite polarization problem is an optimization problem where the goal is to find the highest polarized bipartition on a weighted and labeled graph that represents a debate developed through some social network, where nodes represent user&rsquo...

In this paper, we aimed to identify the dynamics of a crude distillation unit (CDU) using closed-loop data with NARX−NN and the Koopman operator in both linear (KL) and bilinear (KB) forms. A comparative analysis was conducted to assess the per...

Ballast serves a vital structural function in supporting railroad tracks under continuous loading. The degradation of ballast can result in issues such as inadequate drainage, lateral instability, excessive settlement, and potential service disruptio...

The discrete cosine transform (DCT) is widely used for image and video compression. Lossy algorithms such as JPEG, WebP, BPG and many others are based on it. Multiple modifications of DCT have been developed to improve its performance. One of them is...

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (...

Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-i...

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in...

Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithm...

This paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in...

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook s...

The clique transversal problem is a critical concept in graph theory, focused on identifying a minimum subset of vertices that intersects all maximal cliques in a graph. This problem and its variations—such as the k-fold clique, $\left\{k\right\}$-clique, min...

We consider situations in which the clustering of some multivariate data is desired, which establishes an ordering of the clusters with respect to an underlying latent variable. As our motivating example for a situation where such a technique is desi...

Data labeling systems are designed to facilitate the training and validation of machine learning algorithms under the umbrella of crowdsourcing practices. The current paper presents a novel approach for designing a customized data labeling system, em...

Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated...

Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing computat...

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to e...

Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, r...

This study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived...

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project siz...

This paper introduces a novel non-smooth numerical optimization approach for solving the Three-Point Dubins Problem (3PDP). The 3PDP requires determining the shortest path of bounded curvature that connects given initial and final positions and orien...

In recent years, nonsmooth optimization and analysis have seen remarkable advancements, significantly impacting various scientific and engineering disciplines [...]

Ore hauling operations are crucial within the mining industry as they supply essential minerals to production plants. Conducted with sophisticated and high-cost operational equipment, these operations demand meticulous planning to ensure that product...

Pinhole corrosions on oil and gas pipelines are difficult to detect and size and, therefore, pose a significant challenge to the pipeline integrity management practice. This study develops two convolutional neural network (CNN) models to identify pin...

Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments...

Operator precedence grammars (OPG) are context-free grammars (CFG) that are characterized by the absence of two adjacent non-terminal symbols in the body of each production (right-hand side). Operator precedence languages (OPL) are deterministic and...

We implement an inversion-based deblending method in the common midpoint gathers (CMP) as an alternative to the standard common receiver gather (CRG) domain methods. The primary advantage of deblending in the CMP domain is that reflections from dippi...

In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in...

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 llumi...

The minimum error entropy (MEE) criterion finds extensive utility across diverse applications, particularly in contexts characterized by non-Gaussian noise. However, its computational demands are notable, and are primarily attributable to the double...

The gas-gathering pipeline network is a critical infrastructure for collecting and conveying natural gas from the extraction site to the processing facility. This paper introduces a design optimization model for a star–tree gas-gathering pipeli...

We demonstrate how to evaluate stepwise hedge automata (Shas) with subhedge projection while completely projecting irrelevant subhedges. Since this requires passing finite state information top-down, we introduce the notion of downward stepwise hedge...

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven alg...

This paper describes the design, implementation, and usage of a Python package called Hyperspectral Python (HypPy). Proprietary software for processing hyperspectral images is expensive, and tools developed using these packages cannot be freely distr...

Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes whe...

Color distortion in an image presents a challenge for machine learning classification and regression when the input data consists of pictures. As a result, a new algorithm for color standardization of photos is proposed, forming the foundation for a...

Amazons is a computerized board game with complex positions that are highly challenging for humans. In this paper, we propose an efficient optimization of the Monte Carlo tree search (MCTS) algorithm for Amazons, fusing the ‘Move Groups’...

Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LST...

Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address c...

Delivery apps are experiencing significant growth, requiring efficient algorithms to coordinate transportation and generate profits. One problem that considers the goals of delivery apps is the multi-vehicle profitable pickup and delivery problem (MV...

This article introduces Lester, a novel method to automatically synthesize retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames are processed with the Segment A...

This paper appraises convolutional neural network (CNN) models’ capabilities in emotion detection from facial expressions, seeking to aid the diagnosis of psychosomatic illnesses, typically made in clinical setups. Using the FER-2013 dataset, t...

The Discrete Cosine Transform (DCT) is fundamental to high-capacity data hiding schemes due to its ability to condense signals into a few significant coefficients while leaving many high-frequency coefficients relatively insignificant. These high-fre...

This paper presents a quantum algorithm for solving the one-dimensional heat equation with Dirichlet boundary conditions. The algorithm utilizes discretization techniques and employs quantum gates to emulate the heat propagation operator. Central to...

In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise know...

The k shortest paths problem finds applications in multiple fields. Of particular interest in the transportation field is the variant of finding k simple shortest paths (KSSP), which has a higher complexity. This research presents a novel label-setti...