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Publications (15)
Purpose: The purpose of this study was to design and synthesize the ug46 peptide, incorporate its fibrils into composite materials, and evaluate its structural and antimicrobial properties. Another objective was to utilize spectroscopy and molecular simulation, enhanced by Machine Vision methods, to monitor the aggregation process of the ug46 pepti...
The aim of this research is to create an automated system for identifying soil microorganisms at the genera level based on raw microscopic images of monocultural colonies grown in laboratory environment. The examined genera are: Fusarium, Trichoderma, Verticillium, Purpureolicillium and Phytophthora. The proposed pipeline deals with unprocessed mic...
TfELM introduces an innovative Python framework leveraging TensorFlow for Extreme Learning Machines (ELMs), offering a comprehensive suite for diverse machine learning (ML) tasks. Existing solutions in the ELM landscape lack comprehensive implementations. TfELM fills this gap by consolidating 18 ELM variants (including 14 so-far unimplemented in Py...
The identification of soil microorganisms plays a crucial role in agriculture and horticulture, as it enables the monitoring of beneficial species and early detection of pathogens. In this study, we propose a system that utilizes machine vision and machine learning techniques, specifically Convolutional Neural Networks, to automate the identificati...
The rise in fungal infections, attributed to various factors including medical interventions and compromised immune systems, necessitates rapid and accurate identification methods. While traditional mycological diagnostics are time-consuming, machine learning offers a promising alternative. Nevertheless, the scarcity of well-curated datasets is a s...
This research investigates the integration of Metaheuristic Algorithms (MAs) with the Extreme Learning Machine (ELM) model to optimize parameters of activation function. While MAs have traditionally been employed for weights selection, a methodology that utilizes MA for the selection of activation function parameters was proposed. The performance o...
This work presents a research on Nature Inspired Metaheuristic Algorithms (MA) used as optimizers in training process of Machine Learning method called Extreme Learning Machine (ELM). We tested 19 MA optimizers measuring their performance directly on sample datasets. The impact of input parameters such as number of hidden layer units, optimization...
Extreme Learning Machine (ELM) is a feed-forward neural network with one hidden layer. In its modification called ELM Radial Basis Function the input data is a priori clustered into a number of sets represented by their centroids. The matrix of distances between each sample and centroid is calculated and applied as input data to the neural network....
The main goal of this paper is to construct automated system for accurate identification of soil microorganisms on a genera level based on microscopic images of the monocultural colonies. The microorganisms in question belong to one of the following genera: Fusarium, Trichoderma, Verticillium or Phytophthora. Proposed classification system is fully...
This study investigates the performance of 36 different activation functions applied in Extreme Learning Machine on 10 distinct datasets. Results show that Mish and Sexp activation functions exhibit outstanding generalization abilities and consistently perform well across most datasets, while other functions are more dependent on the characteristic...
Soil bacteria have a significant impact on agriculture and horticulture. These bacteria can be distinguished by the microbiologists based on their microscopic images. In our project this approach is performed with the aid of machine learning and image processing techniques. The implemented fully automated recognition system identifies five bacteria...
Soil bacteria play a fundamental role in plant growth. This paper focuses on developing and testing some techniques designed to identify automatically such microorganisms. More specifically, the recognition performed here deals with the specific five genera of soil bacteria. Their microscopic images are classified with machine learning methods usin...
