The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee co...
Radio mean labeling of a connected graph G is an assignment of distinct positive integers to the vertices of G satisfying a mathematical constraint called radio mean condition. The maximum label assigned to any vertex of G is called the
Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features fo...
In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborh...
In this work, we propose a novel Biased Random-Key Genetic Algorithm (BRKGA) to solve the Maximum Flow with Minimum Number of Labels (MF-ML) problem, a challenging NP-Complete variant of the classical Maximum Flow problem defined on graphs in which a...
Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point...
This study presents an algorithm for shrimp counting using a small-scale labeling model. The aim is to enhance shrimp farming efficiency, reduce biosecurity risks, and minimize manual counting errors. Experimental evaluations were conducted using Lit...
The Electric Fish Optimization (EFO) algorithm is inspired by the predation behavior and communication of weak electric fish. It is a novel meta-heuristic algorithm that attracts researchers because it has few tunable parameters, high robustness, and...
During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled r...
Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning met...
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...
In the realm of personalized federated learning, some current methods substitute shared parameters with shared samples created by Generative Adversarial Networks (GANs). This enables each client to independently design the architecture of their neura...
Legal Judgment Prediction aims to automatically predict judgment outcomes based on descriptions of legal cases and established law articles, and has received increasing attention. In the preliminary work, several problems still have not been adequate...
The non-parametric Gaussian mixture of regressions (NPGMRs) model serves as a flexible approach for the determination of latent heterogeneous regression relationships. This model assumes that the component means, variances and mixing proportions are...
In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and...
In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between p...
Quantitative analysis of intracranial vessel segments typically requires the identification of the vessels’ centerlines, and a path-finding algorithm can be used to automatically detect vessel segments’ centerlines. This study compared th...
Advances in deep learning are impressive in various fields and have achieved performance beyond human capabilities in tasks such as image classification, as demonstrated in competitions such as the ImageNet Large Scale Visual Recognition Challenge. N...
Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to us...
This paper introduces a new memory-efficient algorithm for connected-components labeling in binary images, which is based on run-length encoding. Unlike conventional pixel-based methods that scan and label individual pixels using global buffers or di...
Multi-class classification is a very important technique in engineering applications, e.g., mechanical systems, mechanics and design innovations, applied materials in nanotechnologies, etc. A large amount of research is done for single-label classifi...
It is very challenging to design the capacity-approaching labeling schemes for large constellations, such as 32-QAM, in delayed bit-interleaved coded modulation (DBICM). In this paper, we investigate the labeling design for 32-QAM DBICM with various...
As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilay...
In this paper, we study the problem of developing new combinatorial generation algorithms. The main purpose of our research is to derive and improve general methods for developing combinatorial generation algorithms. We present basic general methods...
According to Chomsky’s Labeling Algorithm the merger of two phrases, i.e., {XP, YP}, is labeled either via feature sharing between the two elements or by ignoring the lower copies of movement chains. It is not immediately clear, within this app...
Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the co...
Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation...
We provide an algorithm to efficiently compute bisimulation for probabilistic labeled transition systems, featuring non-deterministic choice as well as discrete probabilistic choice. The algorithm is linear in the number of transitions and logarithmi...
The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem...
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common dr...
This paper puts forward an innovative theory and method to calculate the canonical labelings of graphs that are distinct to
This paper presents a novel theory and method to calculate the canonical labelings of digraphs whose definition is entirely different from the traditional definition of Nauty. It indicates the mutual relationships that exist between the canonical lab...
In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak tar...
Electric vehicles play a key role for developing an eco-sustainable transport system. One critical component of an electric vehicle is its battery, which can be quickly charged or exchanged before it runs out. The problem of electric vehicle dispatch...
It has been shown that identifying the structural holes in social networks may help people analyze complex networks, which is crucial in community detection, diffusion control, viral marketing, and academic activities. Structural holes bridge differe...
In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm...
Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage...
Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Rece...
Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched...
Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction to...
This study investigates a variant of the shortest path problem (SPP) tailored for plug-in hybrid electric vehicles (PHEVs), incorporating two practical features: hybrid energy mode switching and partial charging. A novel modeling framework is propose...
The algorithm MLS (Maximal Label Search) is a graph search algorithm that generalizes the algorithms Maximum Cardinality Search (MCS), Lexicographic Breadth-First Search (LexBFS), Lexicographic Depth-First Search (LexDFS) and Maximal Neighborhood Sea...
Analyzing the solution space structure and evolution of 3-satisfiability (3-SAT) problem is an important way to study the difficulty of the solving satisfiability (SAT) problem. However, there is no unified analysis model for the spatial structure an...
This study addresses a specialized variant of the full-truckload delivery problem inspired by a Turkish logistics firm that operates in the liquid transportation sector. An exact algorithm is proposed for the relevant problem, to which no exact appro...
Component Labeling, as a fundamental preprocessing task in image understanding and pattern recognition, is an indispensable task in digital image processing. It has been proved that it is one of the most time-consuming tasks within pattern recognitio...
Background and objectives: The front-of-pack label Nutri-Score has met a lot of scientific opposition [...]
Label Propagation Algorithm (LPA) is a fast community detection algorithm. However, since each node is randomly assigned a different label at first, there is serious randomness in the label updating process of LPA, resulting in great instability of d...
The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or m...
In abstract argumentation frameworks, the computation of stable extensions is an important semantic task for evaluating the acceptability of arguments. The current approaches for the computation of stable extensions are typically conducted through me...
While semantic parsing has been an important problem in natural language processing for decades, recent years have seen a wide interest in automatic generation of code from text. We propose an alternative problem to code generation: labelling the alg...
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