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145th Audio Engineering Society International Convention, AES 2018

作者： Piotrowska, Magdalena Korvel, Grazina Kurowski, Adam Kostek, Bozena Czyzewski, Andrzej Multimedia System Department Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk80-233 Poland Institute of Data Science and Digital Technologies Vilnius University Akademijos str. 4 VilniusLT-04812 Lithuania Audio Acoustics Laboratory Telecommunications and Informatics Gdansk University of Technology Gdansk80-233 Poland

The purpose of this study is to involve both Convolutional Neural Networks and a typical learning algorithm in the allophone classification process. A list of words including aspirated and non-aspirated allophones pronounced by native and non-native English speakers is recorded and then edited and analyzed. Allophones extracted from English speakers' recordings are presented in the form of two-dimensional spectrogram images and used as input to train the Convolutional Neural Networks. Various settings of the spectral representation are analyzed to determine adequate option for the allophone classification. Then, testing is performed on the basis of non-native speakers' utterances. The same approach is repeated employing learning algorithm but based on feature vectors. The achieved classification results are promising as high accuracy is observed. © 2018 KASHYAP.

关键词： Learning algorithms

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Intelligent water quality monitoring using WSN and Machine Learning Approaches 23

Intelligent water quality monitoring using WSN and Machine L...

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7th International Conference on Information System and data Mining, ICISDM 2023

作者： Belachew, Ermiyas Birihanu Bekele, Mebiratu Beyene Birawo, Birtukan Adamu Elte Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Hungary Department of Software Engineering Wolkite University Ethiopia Bahir Dar Institute of Technology Ict 4D Research Centre Bahir Dar University Ethiopia Applied Informatics Silesian University of Technology Poland

ISBN: (纸本)9798400700637

In recent years, wireless sensor networks (WSN) have received considerable attention in environmental, industrial monitoring, and control applications. It is used to monitor, collect, maintain, and analyse environmental data from a remote place with the help of remote sensing and monitoring technologies. Identification of suitable algorithms and protocol WSNs for water quality monitoring is a challenge due to the limitation of energy, computation capability, and storage resources of the sensor nodes. In this paper, we designed a model for a WSN-based water quality monitoring system that employs a machine learning technique to analyse water data and facilitates decision-making. The suggested model can gather water quality parameters from a water body and send them to a distant data centre, where they will be processed using a machine learning (ML) algorithm. The experiment shown that by adopting WSN and ML approach, we can obtain state-of-the-art 0.0723 RMSE and 0.0052 MAE, indicating that the proposed methodology is appropriate for usage in intelligence water quality monitoring. © 2023 Owner/Author.

关键词： Water quality

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Big data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第3期4卷 583-591页

作者： Henderi, Henderi Irawatia, Ranty Indra, Indra Dewi, Deshinta Arrova Kurniawan, Tri Basuki Department of Informatics Engineering University of Raharja Indonesia Informatics Technical Department Universitas Budi Luhur Indonesia Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Faculty of Science and Technology Universitas Bina Darma Indonesia Faculty of Technology and Information Science Universiti Kebangsaan Malaysia Malaysia

Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. © 2023, Ital Publication. All rights reserved.

关键词： Decision making

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Effects of COVID-19 on the liver:The experience of a single center

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World Journal of Gastroenterology 2022年第39期28卷 5735-5749页

作者： Valentina Liakina Ieva Stundiene Gabriele Milaknyte Ramune Bytautiene Rosita Reivytyte Roma Puronaite Gintare Urbanoviciute Edita Kazenaite Centre of Hepatology Gastroenterology and DieteticsClinic of GastroenterologyNephrourology and SurgeryInstitute of Clinical MedicineFaculty of MedicineVilnius UniversityVilnius 01513Lithuania Department of Chemistry and Bioengineering Faculty of Fundamental SciencesVILNIUS TECHVilnius 10223Lithuania Faculty of Medicine Vilnius UniversityVilnius 01513Lithuania Clinic of Cardiac and Vascular Diseases Institute of Clinical MedicineFaculty of MedicineVilnius UniversityVilnius 01513Lithuania Institute of Data Science and Digital Technologies Faculty of Mathematics and InformaticsVilnius UniversityVilnius 01513Lithuania Department of Pathology Forensic Medicine and PharmacologyInstitute of Biomedical SciencesFaculty of MedicineVilnius UniversityVilnius 01513Lithuania

BACKGROUND The coronavirus disease 2019(COVID-19)was perhaps the most severe global health crisis in living *** respiratory symptoms,elevated liver enzymes,abnormal liver function,and even acute liver failure were reported in patients suffering from severe acute respiratory disease coronavirus 2 ***,the precise triggers of these forms of liver damage and how they affect the course and outcomes of COVID-19 itself remain *** To analyze the impact of liver enzyme abnormalities on the severity and outcomes of COVID-19 in hospitalized *** In this study,684 depersonalized medical records from patients hospitalized with COVID-19 during the 2020-2021 period were ***-19 was diagnosed according to the guidelines of the National Institutes of Health(2021).Patients were assigned to two groups:those with elevated liver enzymes(Group 1:603 patients),where at least one out of four liver enzymes were elevated(following the norm of hospital laboratory tests:alanine aminotransferase(ALT)≥40,aspartate aminotransferase(AST)≥40,gamma-glutamyl transferase≥36,or alkaline phosphatase≥150)at any point of hospitalization,from admission to discharge;and the control group(Group 2:81 patients),with normal liver enzymes during ***-19 severity was assessed according to the interim World Health Organization guidance(2022).data on viral pneumonia complications,laboratory tests,and underlying diseases were also collected and *** In total,603(88.2%)patients produced abnormal liver test *** and AST levels were elevated by a factor of less than 3 in 54.9%and 74.8%of cases with increased enzyme levels,*** in Group 1 had almost double the chance of bacterial viral pneumonia complications[odds ratio(OR)=1.73,P=0.0217],required oxygen supply more often,and displayed higher biochemical inflammation indices than those in Group *** differences in other COVID-19 complications or underlying disea

关键词： COVID-19 SARS-CoV-2 Liver enzymes Complications Underlying disease Disease severity

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Technical light-field setup for 3D imaging of the human nerve head validated with an eye model

Technical light-field setup for 3D imaging of the human nerv...

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作者： Schramm, Stefan Dietzel, Alexander Blum, Maren-Christina Link, Dietmar Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally refocusing. To provide depth information in the human optic nerve head such as in glaucoma diagnostics, a mydriatic fundus camera was upgraded with an LF imager. The aim of the study presented here was the validation of the technical setup and resulting depth estimations with an appropriate eye model. The technical setup consisted of a mydriatic fundus camera (FF450, Carl Zeiss Meditec AG, Jena, Germany) and an LF imager (R12, Raytrix GmbH, Kiel, Germany). The field of view was set to 30°. The eye model (24.65 mm total length) consisted of a two-lens optical system and interchangeable fundus models with papilla excavations from 0.2 to 1 mm in steps of 0.2 mm. They were coated with red acrylic lacquer and vessels were drawn with a thin brush. 15 images were taken for each papilla depth illuminated with green light (wavelength 520 nm ± 20 nm). Papilla depth was measured from the papilla ground to the surrounding flat region. All 15 measurements for each papilla depth were averaged and compared to the printed depth. It was possible to perform 3D fundus imaging in an eye model by means of a novel LF-based optical setup. All LF images could be digitally refocused subsequently. Depth estimation in the eye model was successfully performed over a 30° field of view. The measured virtual depth and the printed model papilla depth is linear correlated. The presented LF setup allowed high-quality 3D one-shot imaging and depth estimation of the optic nerve head in an eye model. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Optical systems

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Optimal half-metal band structure for large thermoelectric performance

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Physical Review B 2024年第3期110卷 035150-035150页

作者： Finantius E. M. Rahangiar Adam B. Cahaya Melania S. Muntini Isa Anshori Eddwi H. Hasdeo Department of Physics Research Center for Quantum Physics Department of Physics Faculty of Mathematics and Natural Sciences Department of Physics Faculty of Science and Data Analytics School of Electrical Engineering and Informatics Research Center for Nanosciences and Nanotechnology Department of Physics and Material Science

Half-metal ferromagnets were predicted [Cahaya, Tretiakov, and Bauer, IEEE Trans. Magn. 51, 1 (2015)] to give large thermoelectric performance in antiparallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin valve enhancement factor. We test this theory and find a mismatch of parameters that gives large thermoelectric performance and large spin valve enhancement factor. As a result, we show that the spin valve setup is useful only for half-metal ferromagnets with initially poor thermoelectric performance. To obtain the largest thermoelectric performance, one still needs to open the band gap.

关键词： Thermal energy harvesting devices Transport phenomena Boltzmann theory

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Influence of electric field orientation on the effect of ocular current stimulation using full field electroretinogram

Influence of electric field orientation on the effect of ocu...

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作者： Blum, Maren-Christina Leydolph, Lilly Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

Ocular current stimulation (CS) exhibits potential for the treatment of neurodegenerative ocular diseases. For a full field electroretinogram (ffERG) we found no CS effect on the characteristic waves (a-wave, b'-wave, and b-wave). To investigate whether the orientation of the generated electric field has an influence on the CS effect, this study repeated the previous ffERG study with changing one CS electrode position and compared the results of both studies. In the first study 15 (8 m, 27.5 ± 4.5 years) and in the second study 17 (7 m, 22.0 ± 1.9 years) healthy volunteers were stimulated with an anodal, cathodal, and sham direct CS of 800 μA for 5 min in three sessions (randomized, different days). For both studies, a cut-sized ring rubber electrode was placed around the eye. A square rubber electrode was placed for the first study at the ipsilateral temple and for the second study at the visual cortex. Before (ERG 1) and during (ERG 2) the CS, the ffERG was measured. For both studies, the difference between the ERG 1 and ERG 2 measurement (CS effect) for the three characteristic waves was analyzed and compared between the studies. For statistical analysis, the Mann-Whitney U test with Bonferroni correction was applied (α = 0.05). The comparison of the data distribution showed only slight differences between the studies. The Mann-Whitney U test found no significant difference of the CS effect between the studies for all amplitudes and CS groups (p ≥ 0.0055). In the mean, the latency differences were smaller than the time resolution, therefore no statements for latency effects were possible. It can be concluded that the retinal cells generating the ffERG are not affected by ocular CS in either electrode montages tested. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Rubber

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Sentiment based hybrid deep learning for recommender models

Sentiment based hybrid deep learning for recommender models

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Lamia Berkani Ilyes Djerfaf Mouaadh Hamed Abdelouahab Department of Artificial Intelligence & Data Science Faculty of Informatics University of Science and Technology Houari Boumediene (USTHB) Bab Ezzouar Algiers Algeria

To solve the data sparsity problem, we propose in this article an approach based on hybrid deep learning sentiment model, using Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). The word embeddings Glove and Word2Vec were exploited in the RNN-LSTM and CNN-LSTM hybrid combinations. The results of experiments on two datasets from the Amazon database show that our approach significantly outperforms state-of-the-art recommendation models.

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Spectral Generalized Category Discovery by training on combined labels

Spectral Generalized Category Discovery by training on combi...

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Ruixuan Mao Modafar Al-Shouha Gábor Szűcs Department of Data Science Faculty of Informatics Eötvös Loránd University Budapest Hungary Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Hyperspectral imaging poses major challenges for supervised classification methods due to the high dimensionality of the data. This classification was solved in previous works with known classes. In this paper we present a different aim where the goal is to discover and distinguish novel categories for hyperspectral images in scenarios where some classes are unknown. This belongs to Generalized Category Discovery (GCD) problem with a further task for automatic clustering of unlabeled data with partial knowledge. Our contribution is an additional supervised learning stage on combined labels coming from two sources. Our approach consists of three stages, (i) dimension reduction, (ii) clusters assignment with the Hungarian method, and (iii) classification, where a special training set is constructed from ground true labels and the filtered test set with (dummy) labels that are the predicted categories from the clustering part. The proposed method (S-GCD) was evaluated on two datasets, and our experiments demonstrate that classification on combined labels greatly improves both classification and clustering scores.

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Towards Smarter Health Care: Can Artificial Intelligence Help?

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CEUR Workshop Proceedings 2021年 3060卷

作者： Lucas, Peter J. F. Stella, Fabio A. Department of Data Science Faculty of EEMCS University of Twente Enschede Netherlands Department of Informatics Systems and Communication University of Milan-Bicocca Milan Italy

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