Conveners
Computational Intelligence in Engineering: Computational Intelligence in Engineering (4)
- Oksana Hoholyuk (Lviv Polytechnic National University)
Computational Intelligence in Engineering: Computational Intelligence in Engineering (4)
- Pavel Karban (University of West Bohemia)
Computational Intelligence in Engineering: Computational Intelligence in Engineering (3)
- Ladislav Janousek (University of Zilina, Department of Electromagnetics and Biomedical Engineering)
The paper considers the problem of improving
the recognition of not-perfect labeled face images using CNN
networks. The proposed solution is based on relabeling the
samples of images by applying the KNN classification principle
based on the distance between the samples. The original images
are first converted to the features and the KNN principle is
applied to them. The classes of...
This research presents an application of the Mask R-CNN algorithm for apple detection and semantic segmentation, aiming to enhance automation in the agricultural sector. Despite the growing use of deep learning techniques in object detection tasks, their application in agricultural contexts, specifically for fruit detection and semantic segmentation, remains relatively unexplored. This study...
Artifacts that occur during the registration of electroencephalographic signals (EEG) pose a significant problem, hindering the analysis of these signals for medical diagnosis or brain-computer interfaces (BCIs). While some artifacts can be relatively easily removed, others, such as those related to muscle activity, are more challenging to eliminate. Continuous research is being conducted to...
High voltage power lines inspection requires high amount of effort and time and needs to be performed regularly. As high voltage power lines are critical infrastructure reducing the frequency of the inspections is not an option. To reduce effort required, some tasks can be automated. For example assessment of the state of transmission towers and power lines or autonomous UAV flights to gather...
Artifacts pose a significant challenge in the analysis of EEG signals. Physiological artifacts stem from natural activities of the human body, such as swallowing saliva, clenching the jaw, facial grimacing, and eye blinking, among others. Visual evaluation often serves as the basis for artifact elimination. In this study, the authors investigated the impact of artifacts on the detection of...
This study investigates the impact of various signal features on machine learning-based tool condition classification in the milling chipboard process. Different machine learning models such as XGBoost, Gradient Boosting, Decision Tree and Random Forest have been applied and the signal features have been ranked based on their importance. The highest ranking signal was 'DataLow_0', contributing...
This paper presents a comprehensive performance evaluation of various AI architectures for a classification of holes drilled in melamine faced chipboard, including custom Convolutional Neural Network (CNN-designed), five-fold CNN-designed, VGG19, single and five-fold VGG16, an ensemble of CNN-designed, VGG19, and 5xVGG16, and Vision Transformers (ViT). Each model's performance was measured and...
Large Language Models (LLMs) have been gaining tremendous popularity since early 2023 with the release of the GPT-4 system. The new artificial intelligence methods have successes in many fields of activity, including education. This article analyses the applicability of LLMs in supporting the teaching of the "Electromagnetic Fields" course in Electrical Engineering studies. Examples of use in...
Abstract. — The AdaDelta learning process optimization method has been tested for a multilayer neural network with three hidden layers with 28 neurons each, when recognizing printed numbers. Testing the learning error of this neural network was carried out using the mapping function and Fourier spectra of the error function. The mapping function describes the process of doubling the number of...
Specialized game computer system based on Arduino Uno - indie project was proposed and investigated. Based on the analysis of the current state of the gaming industry and global trends, the main theses of a successful product, its implementation, operation and the main stages of development were formed. The solution of key problems at all stages of development was described, the corresponding...
This paper presents the design and development of an intelligent air quality monitoring system that utilizes the widely adopted and versatile Arduino Uno microcontroller as its foundational platform. The system underwent comprehensive testing procedures to ensure its adherence to specified requirements. Moreover, a series of experiments were conducted in diverse areas of a residential...
