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1

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.

2

A comparative study on performance of basic and ensemble classifiers with various datasets

Gunakala Archana, Shahid Afzal Hussain

Applied Computer Science

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2023

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Vol. 19, no 1

107--132

EN

Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.

3

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Otoom Mohammad Mahmood, Sattar Khalid Nazim Abdul, Al Sadig Mutasim

Advances in Science and Technology. Research Journal

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2023

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Vol. 17, no 2

322--329

EN

Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.

4

Research on the risk classification of cruise ship fires based on an attention-BP neural network

Xiong Zhenghua, Xiang Bo, Chen Ye, Chen Bin

Polish Maritime Research

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2022

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nr 3

61--68

EN

Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.

5

An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data

Nayak Sachin, Vincent Shweta, Sumathi K., Kumar Om Prakash, Pathan Sameena

International Journal of Electronics and Telecommunications

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2022

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Vol. 68, No. 2

251--257

EN

Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.

6

A Systematic Review of Ensemble Techniques for Software Defect and Change Prediction

Khanna Megha

e-Informatica Software Engineering Journal

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2022

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Vol. 16, nr 1

art. no. 220105

EN

Background: The use of ensemble techniques have steadily gained popularity in several software quality assurance activities. These aggregated classifiers have proven to be superior than their constituent base models. Though ensemble techniques have been widely used in key areas such as Software Defect Prediction (SDP) and Software Change Prediction (SCP), the current state-of-the-art concerning the use of these techniques needs scrutinization. Aim: The study aims to assess, evaluate and uncover possible research gaps with respect to the use of ensemble techniques in SDP and SCP. Method: This study conducts an extensive literature review of 77 primary studies on the basis of the category, application, rules of formulation, performance, and possible threats of the proposed/utilized ensemble techniques. Results: Ensemble techniques were primarily categorized on the basis of similarity, aggregation, relationship, diversity, and dependency of their base models. They were also found effective in several applications such as their use as a learning algorithm for developing SDP/SCP models and for addressing the class imbalance issue. Conclusion: The results of the review ascertain the need of more studies to propose, assess, validate, and compare various categories of ensemble techniques for diverse applications in SDP/SCP such as transfer learning and online learning.

7

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.

8

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.

9

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.

10

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.

11

Applying a neural network ensemble to intrusion detection

Ludwig Simone A.

Journal of Artificial Intelligence and Soft Computing Research

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2019

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Vol. 9, No. 3

177--178

EN

An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

12

Projekt badawczy, uczenie zespołowe, teoria ugruntowana

Janiszewski A.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2018

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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.

13

A machine learning approach for the segmentation of driving maneuvers and its application in autonomous parking

Notomista G., Botsch M.

Journal of Artificial Intelligence and Soft Computing Research

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2017

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Vol. 7, No. 4

243--255

EN

A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle–dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to excellent performance in parallel-, cross- and oblique-parking maneuvers. The work shows that segmentation using classifies and open-loop control are an efficient approach in autonomous driving for the implementation of driving maneuvers.

14

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

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

Biocybernetics and Biomedical Engineering

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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.

15

Classifier ensembles using structural features for spammer detection in online social networks

Abulaish M., Bhat S. Y.

Foundations of Computing and Decision Sciences

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2015

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Vol. 40, No. 2

89--105

EN

As the online social network technology is gaining all time high popularity and usage, the malicious behavior and attacks of spammers are getting smarter and difficult to track. The newer spamming approaches using the social engineering concepts are making traditional spam and spammer detection techniques obsolete. Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult. Newer approaches for spammer detection using topological features are gaining attention. Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features of legitimate users and spammers in online social networks. The proposed method aims to identify topological and community-based features from users’ interaction network and uses popular classifier ensembles – bagging and boosting to identify spammers in online social networks. Experimental evaluation of the proposed method is done over a real-world data set with artificial spammers that follow a behavior as reported in earlier literature. The experimental results reveal that the identified features are highly discriminative to identify spammers in online social networks.