Search results for: SELF-LEARNING SYSTEMS - Bridge of Knowledge

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Search results for: SELF-LEARNING SYSTEMS
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Search results for: SELF-LEARNING SYSTEMS

  • Studies in Self-Access Learning Journal

    Journals

    ISSN: 2185-3762

  • Self-Supervised Learning to Increase the Performance of Skin Lesion Classification

    To successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associated with the shortage of data, the most common of which is transfer learning. However, in many cases, the use of transfer learning as the only remedy is insufficient. In this study,...

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  • Learning Health Systems

    Journals

    ISSN: 2379-6146

  • The use and development of e-learning systems in educational projects

    Publication

    - Year 2014

    The article introduces the problem of usage and development of e-learning systems among Polish universities. Easily accessible internet and IT development led to changes in education. Through the use of IT tools, e-learning has become an increasingly popular form of education. Presently, majority of Polish universities use an e-learning system of their own choosing designed to support the didactic processes. The goal of the article...

  • A novel architecture for e-learning knowledge assessment systems

    Publication

    In this paper we propose a novel e-learning system, dedicated strictly to knowledge assessment tasks. In its functioning it utilizes web-based technologies, but its design differs radically from currently popular e-learning solutions which rely mostly on thin-client architecture. Our research proved that such architecture, while well suited for didactic content distribution systems is ill-suited for knowledge assessment products....

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  • A novel architecture for e-learning knowledge assessment systems

    Publication

    In this paper we propose a novel e-learning system, dedicated strictly to knowledge assessment tasks. In its functioning it utilizes web-based technologies, but its design differs radically from currently popular e-learning solutions which rely mostly on thin-client architecture. Our research proved that such architecture, while well suited for didactic content distribution systems is ill-suited for knowledge assessment products....

  • A novel architecture for e-learning knowledge assessment systems

    Publication

    Abstract. In this paper we propose a novel e-learning system, dedicated strictly to knowledge assessment tasks. In its functioning it utilizes web-based technologies, but its design differs radically from currently popular e-learning solutions which rely mostly on thin-client architecture. Our research proved that such architecture,while well suited for didactic content distribution systems is ill-suited for knowledge assessment...

  • Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis

    We proposed to apply a specific machine learning technique called Self-Organising Maps (SOM) to identify similarities in the performance of muscles around human temporomandibular joint (TMJ). The performance was assessed by measuring muscle activation with the use of surface electromyography (sEMG). SOM algorithm used in the study was able to find clusters of data in sEMG test results. The SOM analysis was based on processed sEMG...

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  • Autonomous, Ground Based, Self-Organizing Radiolocation Systems - AEGIR

    Publication

    This article describes the construction and operation of autonomous ground-based radiolocation system that was developed as a technology demonstrator at the Technical University of Gdansk. Preliminary results and conclusions will be presented as well as analysis of its effectiveness. There will be also described the basic blocks of the system.

  • Machine Learning in Multi-Agent Systems using Associative Arrays

    Publication

    - PARALLEL COMPUTING - Year 2018

    In this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...

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