Task of clustering, that is data division into homogeneous groups represents one the elementary problems contemporary mining. Cluster analysis can be approached through variety methods based on statistical inference or heuristic techniques. Recently algorithms employing novel meta-heuristics are special interest — as they effectively tackle problem under consideration which known to NP-hard. The paper studies application nature-inspired Flower Pollination Algorithm for clustering with...

In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose this article compare KHA optimization algorithm used for learning an artificial neural network (ANN), with other heuristic methods and more conventional procedures. proposed ANN training method has verified classification task. For benchmark examples drawn...

Abstract The Transformer is an important addition to the rapidly increasing list of different Artificial Neural Networks (ANNs) suited for extremely complex automation tasks. It has already gained position tool choice in automatic translation many business solutions. In this paper, we present automated approach optimizing structure based upon Simulated Annealing, algorithm widely recognized both its simplicity and usability optimization tasks where search space may be highly complex....

Abstract Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning metaheuristic algorithms offer more efficient approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase input pulses, different approaches including a genetic algorithm, particle swarm optimizer, simulated annealing. find that...

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging that offer a wide range of applications, including mine prevention, power grid maintenance, pipeline monitoring, upstream oil monitoring. MI-based (WUSNs), utilizing small antenna coils, viable solution by providing consistent channel conditions. The cross-layer protocols address the specific challenges WUSNs, leading improved...

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area interest. It offers significant potential to fortify demand forecasting fine-tune inventory management. This paper presents an innovative application Generative Adversarial Networks (GANs) generate synthetic transaction data, specifically focusing on novel system architecture that combines behavior modeling with stock-keeping unit (SKU)...

Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem analysis.It can be solved with broad range methods employing statistical approaches or heuristic procedures.The latter often include mechanisms from nature they are to serve useful components effective optimizers.The paper investigates the possibility using novel nature-inspired technique -Grasshopper Optimization Algorithm (GOA) -to generate accurate clusterings.As...

This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically satellite classification. We compare fuzzy-inference method with two other methods, decision trees and neural networks, using case study classification from images. Further, an unsupervised approach based on k-means clustering has been also taken consideration comparison. The includes training the classifier...

Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Attention (GAT), Convolutional (GCN), Recurrent (GRN). An increase usability computer vision is also observed. The number applications this field continues to expand; it includes video analysis understanding, action behavior recognition, computational photography, image synthesis from zero...

Analysing action scenes in soccer is a challenging task due to the complex and dynamic nature of game, as well interactions between players. This article provides comprehensive overview this divided into recognition, spotting, spatio-temporal localization soccer, with particular emphasis on modalities used multimodal methods. We explore publicly available data sources metrics evaluate models' performance. The reviews recent state-of-the-art methods that leverage deep learning techniques...

Abstract The aim of this paper is to present a Complete Gradient Clustering Algorithm, its applicational aspects and properties, as well illustrate them with specific practical problems from the subject bioinformatics (the categorization grains for seed production), management design marketing support strategy mobile phone operator) engineering synthesis fuzzy controller). main property Algorithm that it does not require strict assumptions regarding desired number clusters, which allows...

Abstract The paper deals with the issue of reducing dimension and size a data set (random sample) for exploratory analysis procedures. concept algorithm investigated here is based on linear transformation to space smaller dimension, while retaining as much possible same distances between particular elements. Elements matrix are computed using metaheuristics parallel fast simulated annealing. Moreover, elimination or decrease in importance performed those elements which have undergone...

Particle swarm optimization constitutes currently one of the most important nature-inspired metaheuristics, used successfully for both combinatorial and continuous problems.Its popularity has stimulated emergence various variants swarm-inspired techniques, based in part on concept pairwise communication numerous members solving problem hand.This paper overviews some examples such namely Fully Informed Swarm Optimization (FIPSO), Firefly Algorithm (FA) Glowworm (GSO).It underlines...

Recent growth of metaheuristic search strategies has brought a huge progress in the domain computational optimization. The breakthrough started since well-known Particle Swarm Optimization algorithm had been introduced and examined. technique presented this contribution mimics process flower pollination. It is build on foundation first kind—known as Flower Pollination Algorithm (FPA). In paper, its simplified improved version, obtained after extensive performance testing, presented. based...

This study explores the potential of super-resolution techniques in enhancing object detection accuracy football. Given sport's fast-paced nature and critical importance precise (e.g. ball, player) tracking for both analysis broadcasting, could offer significant improvements. We investigate how advanced image processing through impacts reliability algorithms football match footage. Our methodology involved applying state-of-the-art to a diverse set videos from SoccerNet, followed by using...

The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial networks, one crucial aspect innovative is suggesting a novel architecture. Currently used architectures have mostly been developed manually by human experts, which time-consuming error-prone process. this work, we use pure Genetic Programming Approach CNNs, employs only operation,...