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1

A deep ensemble learning framework for forecasting root zone soil moisture in semi-arid regions of South Bihar

Patel Ravi, Kumar Aditya, Koch Niharika, Singh Gurpreet, Yadav Jainath

Acta Geophysica

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2026

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vol. 74, no. 3

art. no. 168, s. 1--23

EN

Accurate forecasting of root zone soil moisture (RZSM) is crucial for effective groundwater management, irrigation planning, and drought mitigation in semi-arid agrarian regions such as South Bihar. Traditional hydrological models mostly struggle to figure out the nonlinear temporal dynamics inherent in soil moisture data. This study proposes a hybrid deep ensemble learning framework that leverages the strengths of six deep neural networks (LSTM, Bi-LSTM, GRU, Bi-GRU, RNN, and CNN) as base models, with eXtreme Gradient Boosting (XGBoost) employed as a meta-learner in a stacked ensemble architecture. Each model is independently trained on Groundwater Root Zone Soil Wetness (GWETROOT) time series data (Jan 1985 to June 2025), and their predictions are aggregated using XGBoost to generate a robust final forecast. The study utilizes daily RZSM data (GWETROOT, surface to 100 cm depth) obtained from the NASA POWER project, which provides satellite-and model-based gridded estimates. The performance of all models was evaluated across five districts in Bihar (Arwal, Aurangabad, Gaya, Jehanabad, and Nawada) using standard statistical metrics including MAE, MAPE, RMSE, MSE, and R2. Results demonstrate that the proposed ensemble approach consistently outperformed individual models, offering improved accuracy (R2 = 0.99) and generalizability. The findings highlight the effectiveness of integrating deep learning with ensemble techniques for soil moisture forecasting and offer a scalable solution for climate-resilient water resource management.

2

Improving Threat Detection in Information Security with Ensemble Learning

Sanmorino Ahmad, Gustriansyah Rendra, Puspasari Shinta, Afriyani Fauziah

Applied Cybersecurity & Internet Governance

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2025

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Vol. 4, No. 1

art. no. 8, s. 1-15

EN

In the face of increasingly complex and frequent cyberattacks, traditional rule-based threat detection systems often fail to identify evolving malicious behaviours. This study addresses the challenge by leveraging ensemble learning to enhance intrusion detection in information security. By integrating three distinct machine learning models – Support Vector Machine (SVM), Random Forest, and Deep Neural Network (DNN) – the proposed approach capitalises on their strengths while mitigating their weaknesses. The primary goal is to enhance detection accuracy, minimise false positives, and ensure reliable performance across various attack types. Using benchmark datasets, such as NSL-KDD and CICIDS2017, each model is trained and evaluated separately before being combined through a voting mechanism. Results from 10-fold cross-validation show that while baseline models perform well individually, the ensemble demonstrates more balanced and robust detection, achieving 94.00% accuracy, 95.10% precision, and a high area under the curve score of 0.77. These findings highlight the value of ensemble methods in producing consistent and dependable threat classification. The contribution of this work lies in demonstrating how a multi-model ensemble strategy can significantly strengthen cybersecurity defences, offering a scalable solution adaptable to real-world security environments.

3

Utilizing ensemble learning in the classifications of ductile and brittle failure modes of UHPC strengthened RC members

Taffese Woubishet Zewdu, Zhu Yanping, Chen Genda

Archives of Civil and Mechanical Engineering

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2024

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Vol. 24, no. 2

art. no. e86, 2024

EN

This study aims to achieve the swift and precise classification of ductile and brittle failure modes in flexural reinforced concrete (RC) members, specifically those with tension sides strengthened by ultrahigh performance concrete (UHPC). Employing six ensemble learning techniques - Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and LightGBM - the authors utilize a comprehensive dataset comprising 14 features, which include manually labeled failure modes obtain from load-deflection curves. The model training spans four scenarios, varying in the inclusion or exclusion of features describing the cross-sectional area of RC members and moment resistance. XGBoost emerges as the most effective classifier, achieving an impressive 84% accuracy with high confidence. Additionally, the study employs the Shapley Additive Explanation (SHAP) technique on the best-performing model to illuminate the significance and impacts of various features in UHPC-strengthened flexural members’ failure modes. Notably, moment resistance and UHPC tensile strength surface as the most influential factors in predicting failure modes. Increased rebar yield strength, UHPC compressive strength, UHPC reinforcement ratio, and steel fiber volume in UHPC contribute to enhanced ductility in flexural members, while heightened moment resistance and UHPC layer thickness, along with a robust RC-UHPC interface, tend to induce brittleness. The introduction of such an effective failure modes classification model, coupled with the model’s explainability, instills trust in its predictions and facilitates seamless integration into real-world applications, particularly in seismic areas. The model’s ability to operate without the need for pre-experimental tests marks a significant advancement in the field.

4

Plant-traits: how citizen science and artificial intelligence can impact natural science

Ignesti Giacomo, Moroni Davide, Martinelli Massimo

Annals of Computer Science and Information Systems

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2024

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Vol. 39

625--630

EN

Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models. However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection. This study investigates the application of AI techniques to address these issues within natural science. We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data. Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on open- access platforms such as iNaturalist. By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.

5

Exploring Stability and Performance of hybrid Gradient Boosting Classification and Regression Models in Sectors Stock Trend Prediction: A Tale of Preliminary Success and Final Challenge

Liu Ming, Cen Ling, Ruta Dymitr, Vu Quang Hieu

Annals of Computer Science and Information Systems

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2024

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Vol. 39

761--766

EN

In the dynamic field of financial analytics, the ability to predict stock market trends is crucial for effective trading strategies which is the task for FedCSIS 2024 Data Science Challenge: Predicting Stock Trends. This paper presents a comprehensive study on the use of hybrid gradient boosting models, incorporating both classification and regression approaches, to forecast stock trends across different sectors of the S&P 500. Utilizing a rich dataset comprising key financial indicators for 300 companies over a decade, our research aims to unravel the complexities of sector-specific trend predictions. The model leverages 58 financial indicators per company, along with their 1-year change metrics, to predict the future stock movements. In the preliminary phase of the competition, our hybrid model demonstrated promising results, achieving a score of 0.5941, ranking first among competitors. However, despite the initial success, the final phase of the model evaluation revealed a decline in performance, with a score of only 0.841500. This discrepancy highlights potential issues in model stability and generalized-ability when transitioning from a controlled to a more varied testing environment. This work not only underscores the complexities of predictive modeling in finance but also sets the stage for future research into creating more resilient AI-driven trading systems.

6

A novel ensemble learning technique of shallow models applied on a COVID-19 dataset

Babuc D.

Annals of Computer Science and Information Systems

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2024

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Vol. 39

537--542

EN

Our lives were affected by the COVID-19 pandemic. In order to face this crisis, we provided a novel ensemble learning strategy to tackle the COVID-19 prediction and classification problems. Because of their capacity to handle the complex and varied nature of COVID-19 data, a range of shallow models, including K-Nearest Neighbors, Decision Trees, Support Vector Machines, Classification and Regression Trees, and Extreme Gradient Boost, are included in our method. Using a COVID-19 dataset, each model is trained independently and then ensemble learning techniques are used to integrate the predictions of the models. We use strict model validation and hyperparameter optimization to improve performance. Comparing our ensemble method to a single model or traditional ensemble techniques, our results show considerable improvements in classification performance and prediction accuracy.

7

Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning

Maher Asmaa, Mian Qaisar Saeed, Salankar N., Jiang Feng, Tadeusiewicz Ryszard, Pławiak Paweł, Abd El-Latif Ahmed A., Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

463--475

EN

The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybridBCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and ‘‘Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are ‘‘Fractal Dimension” (FD), ‘‘Higher Order Spectra” (HOS), ‘‘Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the ‘‘Naïve Bayes” (NB), ‘‘Support Vector Machine” (SVM), ‘‘Random Forest” (RF), and ‘‘K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.

8

Efficient Deep Learning Approach for Olive Disease Classification

Bruno Antonio, Moroni Davide, Martinelli Massimo

Annals of Computer Science and Information Systems

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2023

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Vol. 35

889--894

EN

Olive culture is one of the most important for the Mediterranean countries. In recent years, the role of Artificial Intelligence in agriculture is increasing: its use ranges from monitoring of cultivated soil, to irrigation management, to yield prediction, to autonomous agricultural robots, to weed and pest classification and management for example by taking pictures using a standard smartphone or a unmanned aerial vehicle, and all this eases human work and makes it even more accessible. In this work, we propose a method for olive diseases classification, based on an adaptive ensemble of two EfficientNet-b0 models, that improves the state-of-the-art accuracy on a publicly available dataset by 1.6-2.6%. Both in terms of number of parameters and on number of operations, our method reduces complexity roughly by 50% and 80\% respectively, that is a level not seen in at least a decade. Due to its efficiency, this method is also embeddable into a smartphone application for real-time processing.

9

A high-performance arrhythmic heartbeat classification using ensemble learning method and PSD based feature extraction approach

Yakut Önder, Bolat Emine Doğru

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

667--680

EN

Health problems, directly or indirectly caused by cardiac arrhythmias, may threaten life. The analysis of electrocardiogram (ECG) signals is an important diagnostic tool for assessing cardiac function in clinical research and disease diagnosis. Until today various Soft Computing methods and techniques have been proposed for the analysis of ECG signals. In this study, a new Ensemble Learning based method is proposed that automatically classifies the arrhythmic heartbeats of ECG signal according to the category-based and patient-based evaluation plan. A two-stage median filter was used to remove the baseline wander from the ECG signal. The locations of fiducial points of the ECG signal were determined using the developed QRS complex detection method. Within the scope of this study, four different feature extraction methods were utilized. A new feature extraction technique based on the Power Spectral Density has been proposed. Hybrid sub-feature sets were constructed using a Wrapper-based feature selection algorithm. A new method based on Ensemble Learning (EL) has been proposed by using a stacking algorithm. Multi-layer Perceptron (MLP) and Random Forest (RF) as base learners and Linear Regression (LR) as meta learner were utilized. Average performance values for the category-based arrhythmic heartbeat classification of the proposed new method based on Ensemble Learning; accuracy was 99,88%, sensitivity was 99,08%, specificity was 99,94% and positive predictivity (+P) was 99,08%. Average performance values for patient-based arrhythmic heartbeat classification were 99,72% accuracy, 99,30% sensitivity, 99,83% specificity and 99,30% positive predictivity (+P). Thus, it is concluded that the proposed method has higher performance results than similar studies in the literature.

10

Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task

Trostianchyn A.M., Izonin I.V., Duriagina Z.A., Tkachenko R.O., Kulyk V.V., Havrysh B.M.

Archives of Materials Science and Engineering

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2022

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Vol. 116, nr 2

71--80

EN

Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.

11

Diversified gradient boosting ensembles for prediction of the cost of forwarding contracts

Ruta Dymitr, Liu Ming, Cen Ling, Vu Quang Hieu

Annals of Computer Science and Information Systems

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2022

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Vol. 30

431--436

EN

A common business practice for transportation forwarders is to bid for shipping contracts at the transport or freight exchanges. Based on the detailed contract requirements they try to estimate the total expected cost of its execution and accordingly bid with the fixed price in advance for delivering such shipping service at the prescribed specification and schedule. The capability to accurately predict the cost of contract execution is the critical factor deciding about the profitability of offered shipping services as well as the amount of business drawn from freight exchanges. However, given highly volatile nature of the transport services ecosystem, it is difficult to simultaneously account for countless dynamically changing factors like fuel prices, currency exchange rates, temporal and spatial multitude of routing and implied traffic risks, the properties of cargo and shipping vehicles etc., which leads to big cost under- or over-estimation resulting with loss-making contracts or equally painful missed revenue opportunities. In the context of FedCSIS 2022 data mining competition we propose an accurate and robust predictor of the cost of forwarding contracts built upon the detailed contract data using the ensemble of the state-of-the-art gradient boosting-based regression models. Our established feature engineering framework combined with deep parametric optimization of the individual models and multi-faceted diversification techniques guiding hybrid final model ensembles were instrumental to outperform all the competitive predictors and win the FedCSIS 2022 contest.

12

Application of Diversified Ensemble Learning in Real-life Business Problems: The Case of Predicting Costs of Forwarding Contracts

Trajanoska Milena, Gjorgovski Pavel, Zdravevski Eftim

Annals of Computer Science and Information Systems

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2022

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Vol. 30

437--446

EN

Finding an optimal machine learning model that can be applied to a business problem is a complex challenge that needs to provide a balance between multiple requirements, including a high predictive performance of the model, continuous learning and deployment, and explainability of the predictions. The topic of the FedCSIS 2022 Challenge: ‘Predicting the Costs of Forwarding Contracts' is related to the challenges logistics and transportation companies are facing. To tackle this challenge, we utilized the provided datasets to establish an entire Machine Learning framework which includes domain-specific feature engineering and enrichment, generic feature transformation and extraction, model hyper-parameter tuning, and creating ensembles of traditional and deep learning models. Our contributions additionally include an analysis of the types of models which are suitable for the case of predicting a multimodal continuous target variable, as well as explainable analysis of the features which have the largest impact on predicting the value of these costs. We further show that ensembles created by combining multiple different models trained with different algorithms can improve the performance on unseen data. In this particular dataset, the experiments showed that such a combination improves the score by 3% compared to the best performing individual model.

13

Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network

Żelasko Dariusz, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 1

135--153

EN

Due to a continuous increase in the use of computer networks, it has become important to ensure the quality of data transmission over the network. The key issue in the quality assurance is the translation of parameters describing transmission quality to a certain rating scale. This article presents a technique that allows assessing transmission quality parameters. Thanks to the application of machine learning, it is easy to translate transmission quality parameters, i.e., delay, bandwidth, packet loss ratio and jitter, into a scale understandable by the end user. In this paper we propose six new ensembles of classifiers. Each classification algorithm is combined with preprocessing, cross-validation and genetic optimization. Most ensembles utilize several classification layers in which popular classifiers are used. For the purpose of the machine learning process, we have created a data set consisting of 100 samples described by four features, and the label which describes quality. Our previous research was conducted with respect to single classifiers. The results obtained now, in comparison with the previous ones, are satisfactory—high classification accuracy is reached, along with 94% sensitivity (overall accuracy) with 6/100 incorrect classifications. The suggested solution appears to be reliable and can be successfully applied in practice.

14

Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images

Anoop B. N., Pavan Rakesh, Girish G. N., Kothari Abhishek R., Rajan Jeny

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1343--1358

EN

Segmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods.

15

Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection

Ksiazek Wojciech, Hammad Mohamed, Plawiak Pawel, Acharya U. Rajendra, Tadeusiewicz Ryszard

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1512--1524

EN

The most common type of liver cancer is hepatocellular carcinoma (HCC), which begins in hepatocytes. The HCC, like most types of cancer, does not show symptoms in the early stages and hence it is difficult to detect at this stage. The symptoms begin to appear in the advanced stages of the disease due to the unlimited growth of cancer cells. So, early detection can help to get timely treatment and reduce the mortality rate. In this paper, we proposes a novel machine learning model using seven classifiers such as K-nearest neighbor (KNN), random forest, Naïve Bayes, and other four classifiers combined to form stacking learning (ensemble) method with genetic optimization helping to select the features for each classifier to obtain highest HCC detection accuracy. In addition to preparing the data and make it suitable for further processing, we performed the normalization techniques. We have used KNN algorithm to fill in the missing values. We trained and evaluated our developed algorithm using 165 HCC patients collected from Coimbra's Hospital and University Centre (CHUC) using stratified cross-validation techniques. There are total of 49 clinically significant features in this dataset, which are divided into two groups such as quantitative and qualitative groups. Our proposed algorithm has achieved the highest accuracy and F1-score of 0.9030 and 0.8857, respectively. The developed model is ready to be tested with huge database and can be employed in cancer screening laboratories to aid the clinicians to make an accurate diagnosis.

16

Real-time equipment condition assessment for a class-imbalanced dataset based on heterogeneous ensemble learning

Chen Xiaohui, Zhang Zhiyao, Zhang Ze

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 1

68--80

EN

This study proposes an ensemble learning model for the purpose of performing a real-time equipment condition assessment. This model makes it possible to plan desired preventive maintenance activities before an unexpected failure takes place. This study focuses on the class-imbalanced problem in equipment condition assessment research. In reality, equipment will experience multiple conditions(states), most of the time remaining in the normal condition and relatively rarely being in the critical condition, which means that, from the perspective of data modelling, the distribution of samples is highly imbalanced among different classes(conditions). The majority of samples belong to the normal condition, while the minority belong to the critical condition, which poses a great challenge to the classification performance. To address this problem, a genetic algorithm-based ensemble learning model is presented. Furthermore, a self-updating learning strategy is presented for online monitoring, contributing to adaptability and reliability enhancement along with time. Many previous studies have attempted feature extraction and to set thresholds for equipment health indicators. This study has an advantage of omitting these steps, as it can directly assess the equipment condition through the proposed ensemble learning model. Numerical experiments, including two types of comparison studies, have been conducted. The results show the greater effectiveness of our proposed model over that of previous research in terms of the stability and accuracy of its classification performance.

PL

W pracy przedstawiono model uczenia maszynowego opartego na zespołach niejednorodnych klasyfikatorów (ensemble learning), który pozwala przeprowadzać ocenę stanu sprzętu w czasie rzeczywistym. Model ten umożliwia zaplanowanie niezbędnych czynności konserwacji profilaktycznej przed wystąpieniem niespodziewanego uszkodzenia. Tematem pracy jest zagadnienie niezrównoważonego rozkładu w klasach poruszane w badaniach dotyczących oceny stanu sprzętu. W warunkach rzeczywistych, sprzęt chrakteryzuje wiele różnych stanów, przy czym przez większość czasu pozostaje on w stanie normalnym, a relatywnie rzadko znajduje się w stanie krytycznym, co oznacza, że z punktu widzenia modelowania danych, rozkład prób w poszczególnych klasach (stanach) jest wysoce niezrównoważony. Większość prób należy do stanu normalnego, a mniejszość do stanu krytycznego, co stanowi duże wyzwanie jeśli chodzi o wydajność klasyfikacji. W celu rozwiązania tego problemu, przedstawiono model uczenia zespołowego oparty na algorytmie genetycznym. Ponadto zaprezentowano samoaktualizującą się strategię uczenia wykorzystywaną do monitorowania online, która wraz z upływem czasu zwiększa adaptacyjność i niezawodność modelu . W wielu poprzednich badaniach podejmowano próby ekstrakcji cech oraz ustalania progów dla wskaźników stanu sprzętu. Zaletą przedstawionej metody jest to, że pozwala ona pominąć te etapy i bezpośrednio oceniać stan sprzętu za pomocą proponowanego modelu uczenia zespołowego. Przeprowadzono eksperymenty numeryczne, w tym dwa rodzaje badań porównawczych. Wyniki pokazują większą skuteczność proponowanego modelu w stosunku do poprzednich badań pod względem stabilności i trafności klasyfikacji.

17

Projekt badawczy, uczenie zespołowe, teoria ugruntowana

Janiszewski A.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2018

|

z. 117

187--198

PL

W artykule podjęto tematykę uczenia się w zespole. Omówiono zagadnienia teoretyczne, propozycję budowy narzędzia badawczego oraz wyniki badania wstępnego. Usiłowano uzyskać odpowiedź na pytanie badawcze dotyczące sposobów i środków wykorzystywanych przez członków zespołu badawczego dążących do uzyskania namacalnych wyników pracy w warunkach intensywnych i wielokierunkowych przepływów wiedzy. Uwaga została skoncentrowana na specyfice pracy zespołów realizujących projekt badawczy dofinansowywany przez Narodowe Centrum Nauki. Uzyskane wyniki mogą posłużyć w przyszłości jako podstawa do przeprowadzenia badań związanych z funkcjonowaniem zespołów badawczych NCN w całym kraju, co z uwagi na obecne trendy w zakresie realizacji badań można ocenić jako przedsięwzięcie warte podjęcia.

EN

In the paper author discusses the topic connected with team learning issues. Theoretical problems are analysed as well as the proposition of the way of building of a research tool is presented. Next, results of initial research are shown. The authors tries to find the answer for the research question related to the ways and means which are made use of by team members who deal with intensive and multidirectional knowledge flows in order to obtain some tangible results of their work. The research object which attention is paid to is research teams that carry out research projects financed by National Science Centre. Hitherto obtained results may allow author to conduct research on NCN’s research teams in all country in the future. Taking into account current trends as to ways of conducting research this kind of undertaking appears to be worth implementing.

18

Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Hassan A. R., Bhuiyan M. I. H.

Biocybernetics and Biomedical Engineering

|

2016

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Vol. 36, no. 1

248--255

EN

An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological signal based or yield poor algorithmic performance. In this study, we propound a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal. Decomposing the EEG signal segments using Empirical Mode Decomposition (EMD), we extract various statistical moment based features. The effectiveness of statistical features in the EMD domain is inspected. Statistical analysis is performed for feature selection. We then employ Adaptive Boosting and decision trees to perform classification. The performance of our feature extraction scheme is studied for various choices of classification models. Experimental outcomes manifest that the performance of the proposed sleep staging algorithm is better than that of the state-of-the-art ones. Furthermore, the proposed method's non-REM 1 stage detection accuracy is better than most of the existing works.