<abstract xmlns="http://www.w3.org/1999/xhtml">

Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.
</abstract>

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

<abstract xmlns="http://www.w3.org/1999/xhtml">

Anomaly pattern detection in a data stream aims to detect a time point where outliers begin to occur abnormally. Recently, a method for anomaly pattern detection has been proposed based on binary classification for outliers and statistical tests in the data stream of binary labels of normal or an outlier. It showed that an anomaly pattern can be detected accurately even when outlier detection performance is relatively low. However, since the anomaly pattern detection method is based on the binary classification for outliers, most well-known outlier detection methods, with the output of real-valued outlier scores, can not be used directly. In this paper, we propose an anomaly pattern detection method in a data stream using the transformation to multiple binary-valued data streams from real-valued outlier scores. By using three outlier detection methods, Isolation Forest(IF), Autoencoder-based outlier detection, and Local outlier factor(LOF), the proposed anomaly pattern detection method is tested using artificial and real data sets. The experimental results show that anomaly pattern detection using Isolation Forest gives the best performance.
</abstract>

Anomaly Pattern Detection in Streaming Data Based on the Transformation to Multiple Binary-Valued Data Streams

<abstract xmlns="http://www.w3.org/1999/xhtml">

Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.
</abstract>

Department of Oncology and Hemato-oncology

Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks

<abstract xmlns="http://www.w3.org/1999/xhtml">

The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of bio-metric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data.
</abstract>

A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic

<abstract xmlns="http://www.w3.org/1999/xhtml">

Deep Neural Networks (DNNs) have shown great success in many fields. Various network architectures have been developed for different applications. Regardless of the complexities of the networks, DNNs do not provide model uncertainty. Bayesian Neural Networks (BNNs), on the other hand, is able to make probabilistic inference. Among various types of BNNs, <italic>Dropout as a Bayesian Approximation</italic> converts a Neural Network (NN) to a BNN by adding a dropout layer after each weight layer in the NN. This technique provides a simple transformation from a NN to a BNN. However, for DNNs, adding a dropout layer to each weight layer would lead to a strong regularization due to the deep architecture. Previous researches [1, 2, 3] have shown that adding a dropout layer after each weight layer in a DNN is unnecessary. However, how to place dropout layers in a ResNet for regression tasks are less explored. In this work, we perform an empirical study on how different dropout placements would affect the performance of a Bayesian DNN. We use a regression model modified from ResNet as the DNN and place the dropout layers at different places in the regression ResNet. Our experimental results show that it is not necessary to add a dropout layer after every weight layer in the Regression ResNet to let it be able to make Bayesian Inference. Placing Dropout layers between the stacked blocks i.e. Dense+Identity+Identity blocks has the best performance in Predictive Interval Coverage Probability (PICP). Placing a dropout layer after each stacked block has the best performance in Root Mean Square Error (RMSE).
</abstract>

Evaluating Dropout Placements in Bayesian Regression Resnet

AHEAD OF PRINT

Volume 14 (2024): Issue 2 (March 2024)

Volume 14 (2024): Issue 1 (January 2024)

Volume 13 (2023): Issue 4 (October 2023)

Volume 13 (2023): Issue 3 (June 2023)

Volume 13 (2023): Issue 2 (March 2023)

Volume 13 (2023): Issue 1 (January 2023)

Volume 12 (2022): Issue 4 (October 2022)

Volume 12 (2022): Issue 3 (July 2022)

Volume 12 (2022): Issue 2 (April 2022)

Volume 12 (2022): Issue 1 (January 2022)

Volume 11 (2021): Issue 4 (October 2021)

Volume 11 (2021): Issue 3 (July 2021)

Volume 11 (2021): Issue 2 (April 2021)

Volume 11 (2021): Issue 1 (January 2021)

Volume 10 (2020): Issue 4 (October 2020)

Volume 10 (2020): Issue 3 (July 2020)

Volume 10 (2020): Issue 2 (April 2020)

Volume 10 (2020): Issue 1 (January 2020)

Volume 9 (2019): Issue 4 (October 2019)

Volume 9 (2019): Issue 3 (July 2019)

Volume 9 (2019): Issue 2 (April 2019)

Volume 9 (2019): Issue 1 (January 2019)

Volume 8 (2018): Issue 4 (October 2018)

Volume 8 (2018): Issue 3 (July 2018)

Volume 8 (2018): Issue 2 (April 2018)

Volume 8 (2018): Issue 1 (January 2018)

Volume 7 (2017): Issue 4 (October 2017)

Volume 7 (2017): Issue 3 (July 2017)

Volume 7 (2017): Issue 2 (April 2017)

Volume 7 (2017): Issue 1 (January 2017)

Volume 6 (2016): Issue 4 (October 2016)

Volume 6 (2016): Issue 3 (July 2016)

Volume 6 (2016): Issue 2 (April 2016)

Volume 6 (2016): Issue 1 (January 2016)

Volume 5 (2015): Issue 4 (October 2015)

Volume 5 (2015): Issue 3 (July 2015)

Volume 5 (2015): Issue 2 (April 2015)

Volume 5 (2015): Issue 1 (January 2015)

Volume 4 (2014): Issue 4 (October 2014)

Volume 4 (2014): Issue 3 (July 2014)

Volume 4 (2014): Issue 2 (April 2014)

Volume 4 (2014): Issue 1 (January 2014)

Volume 3 (2013): Issue 4 (October 2013)

Volume 3 (2013): Issue 3 (July 2013)

Volume 3 (2013): Issue 2 (April 2013)

Volume 3 (2013): Issue 1 (January 2013)

Journal of Artificial Intelligence and Soft Computing Research

 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz  Editor-in-Chief: Prof. Leszek Rutkowski, IEEE FellowFrequency of publishing: quarterlyMonths of publication: January, April, July, October  ISSN (print): 2083-2567ISSN (on-line): 2449-6499  Journal of Artificial Intelligence and Soft Computing Research is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.  Why JAISCR?   Premier source of high quality research,  We have practice in publishing innovative articles dealing with various aspects of artificial intelligence methods and soft computing,  Extensive scope,  We are focused on permanent development of our journal.   JAISCR research areas  JAISCR publishes high quality, innovative research results in various areas of artificial intelligence and soft computing. These areas include, but are not limited to:   AI in Modelling and Simulation,  AI in Scheduling and Optimization,  Automated Reasoning and Inference,  Bioinformatics,  Cognitive Aspects of AI,  Computer Vision and Speech Understanding,  Data Mining,  Distributed Intelligent Processing,  Evolutionary Design,  Expert Systems,  Fuzzy Modelling and Control,  Hardware Implementations,  Heuristic Search,  Hybrid Models,  Information Retrieval,  Intelligent Database Systems,  Knowledge Engineering,  Mechatronics,  Multi-agent Systems,  Natural Language Processing,  Neural Network Theory and Architectures,  Neuro-informatics and Bio-inspired Models,  Pattern Recognition,  Robotics and Related Fields,  Rough Sets Theory: Foundations and Applications,  Supervised and Unsupervised Learning,  Swarm Intelligence and Systems,  Web Intelligence Applications &amp; Search,  Various Applications.   Submitting to JAISCR - a great advantage for the authorsWhy?   Fair and thorough peer review (at least three referees are required for the acceptance of each article),  Fast and efficient publication process,  Polishing the structure and technical format of each submission,  Open access (all articles published in JAISCR are open access - freely available on Sciendo platform),  Permanence (because of the open access form, permanent accessibility is assured),  High visibility within the field (the author's work is freely accessible to a global audience. In addition, articles are available through Sciendo),  Promotion of the published papers in the scientific community.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 