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arXiv 2019年

作者： Imai, Takashi Suetani, Hiromichi Aoyagi, Toshio The Center for Data Science Education and Research Shiga University Shiga522-8522 Japan Faculty of Science and Technology Oita University Oita870-1192 Japan Rhythm-Based Brain Information Processing Unit RIKEN Center for Brain Science Saitama351-0198 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan

This paper presents a phase description of chaotic dynamics for the study of chaotic phase synchronization. A prominent feature of the proposed description is that it systematically incorporates the dynamics of the non-phase variables inherent in the system. Taking these non-phase dynamics into account is essential for capturing the complicated nature of chaotic phase synchronization, even in a qualitative manner. We numerically verified the validity of the proposed description in application to the Rössler and Lorenz oscillators, and we found that our method provides an accurate description of the characteristic distorted shapes of the synchronization regions for these chaotic oscillators. Furthermore, the proposed description allows us to systematically identify and describe the origin of this distortion. Copyright © 2019, The Authors. All rights reserved.

关键词： Circuit oscillations

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An overview of the LALA project

An overview of the LALA project

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2020 Workshop on Adoption, Adaptation and Pilots of Learning Analytics in Under-Represented Regions, LAUR 2020

作者： Muñoz-Merino, Pedro J. Kloos, Carlos Delgado Tsai, Yi-Shan Gasevic, Dragan Verbert, Katrien Pérez-Sanagustín, Mar Hilliger, Isabel Zúñiga-Prieto, Miguel Ángel Ortiz-Rojas, Margarita Scheihing, Eliana Department of Telematics Engineering Universidad Carlos III de Madrid Av. Universidad 30 Leganés Madrid28911 Spain School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University 29 Ancora Imparo Way ClaytonVIC3800 Australia Department of Computer Science KU Leuven Celestijnenlaan 200A LeuvenB-3001 Belgium Pontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 Macul Santiago Chile Department Computer Science Universidad de Cuenca Av. 12 de Abril y Av. Loja Cuenca010203 Ecuador Information Technology Center Escuela Superior Politécnica del Litoral ESPOL Km 30.5 Vía Perimetral 09-01-5863 Guayaquil Ecuador Universidad Austral de Chile Valdivia Chile Institute de Recherce en Informatique de Toulouse Université Paul Sabatier Toulouse III 118 Route de Narbonne ToulouseF-31062 France

The LALA project ("Building Capacity to Use Learning Analytics to Improve Higher Education in Latin America") is a project that aims at building capacity about the use of data in education for improving education in Latin America. This article presents a general overview of the LALA project including the LALA framework (as a set of guidelines, recommendations and patterns for enabling adoption of learning analytics), the adaptation of learning analytics tools (mainly three different tools used in Europe) and the pilots with learning analytics experiences. The results of this project could serve as an example for other institutions in the Latin American region or other under-represented regions to adopt Learning Analytics as part of their processes. Copyright 2020 for this paper by its authors.

关键词： Adoption Latin America Learning analytics

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Correction to: Making sense of diabetes medication decisions: a mixed methods cluster randomized trial using a conversation aid intervention

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Endocrine 2023年第1期79卷 221-222页

作者： Marleen Kunneman Megan E Branda Jennifer L Ridgeway Kristina Tiedje Carl R May Mark Linzer Jonathan Inselman Angela L H Buffington Jordan Coffey Deborah Boehm James Deming Sara Dick Holly van Houten Annie LeBlanc Juliette Liesinger Janet Lima Joanne Nordeen Laurie Pencille Sara Poplau Steven Reed Anna Vannelli Kathleen J Yost Jeanette Y Ziegenfuss Steven A Smith Victor M Montori Nilay D Shah Knowledge and Evaluation Research Unit Division of Endocrinology Diabetes Metabolism and Nutrition Mayo Clinic Rochester MN USA. Medical Decision Making Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands. Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado-Denver Anschutz Medical Campus Aurora CO USA. Division of Biomedical Statistics and Informatics Department of Health Sciences Research Mayo Clinic Rochester MN USA. Division of Health Care Policy and Research Department of Health Sciences Research Mayo Clinic Rochester MN USA. Laboratoire d'anthropologie des enjeux contemporains Lyon France. Faculty of Public Health and Policy London School of Hygiene and Tropical Medicine London UK. Department of Medicine Hennepin Healthcare and University of Minnesota Minneapolis MN USA. Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Rochester MN USA. Department of Psychiatry and Psychology Mayo Clinic Health System Mankato MN USA. Department of Family Medicine and Community Health University of Minnesota Medical School Minneapolis MN USA. Practice-Based Research Network Mayo Clinic Rochester MN USA. Center for Translational Science Activities Mayo Clinic Rochester MN USA. Center for Patient and Provider Experience Hennepin County Medical Center Minneapolis MN USA. School of Nursing University of Minnesota Minneapolis MN USA. Decision Partners for Health Richfield MN USA. Mayo Clinic Health System Northwest Wisconsin (dept) Home Health and Hospice Eau Claire WI USA. Department of Family and Emergency Medicine Faculty of Medicine Laval University Quebec QC Canada. Park Nicollet International Diabetes Center St. Louis Park MN USA. Mayo Clinic Health System La Crosse WI USA. Kern Center for the Science of Health Care Deliver Mayo Clinic Rochester MN USA. Office of Professional Worklife Hennepin Healthcare Minne

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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Infall Motions in the Hot Core Associated with Hyper-Compact HII Region G345.0061+01.794 B

arXiv

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arXiv 2023年

作者： Komesh, Toktarkhan Garay, Guido Henkel, Christian Omar, Aruzhan Estalella, Robert Assembay, Zhandos Li, Dalei Guzmán, Andrés Esimbek, Jarken Huang, Jiasheng He, Yuxin Alimgazinova, Nazgul Kyzgarina, Meiramgul Bekdaulet, Shukirgaliyev Zhumabay, Nurman Manapbayeva, Arailym Energetic Cosmos Laboratory Nazarbayev University Astana010000 Kazakhstan Institute of Experimental and Theoretical Physics Al-Farabi Kazakh National University Almaty050040 Kazakhstan Departamento de Astronomia Universidad de Chile Camino el Observatorio 1515 Las Condes Santiago Chile Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China Astronomy Department King Abdulaziz University PO Box 80203 Jeddah21589 Saudi Arabia Faculty of Physics and Technology Al-Farabi Kazakh National University Almaty050040 Kazakhstan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 Barcelona08028 Spain National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Chinese Academy of Sciences South America Center for Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Heriot-Watt International Faculty Zhubanov University 263 Zhubanov brothers str Aktobe030000 Kazakhstan Department of Computation and Data Science Astana IT University 55/11 Mangilik El ave Astana010000 Kazakhstan Fesenkov Astrophysical Institute 23 Observatory str. Almaty050020 Kazakhstan Institute of Mathematics Physics and Informatics Abai Kazakh National Pedagogical University Almaty050010 Kazakhstan

We report high angular resolution observations, made with the Atacama Large Millimeter Array in band 6, of high excitation molecular lines of CH3CN and SO2 and of the H29α radio recombination line towards the G345.0061+01.794 B HC H ii region, in order to investigate the physical and kinematical characteristics of its surroundings. Emission was detected in all observed components of the J=14→13 rotational ladder of CH3CN and in the 304,26 − 303,27 and 324,28 − 323,29 lines of SO2. The peak of the velocity integrated molecular emission is located ∼0 ′′ . 4 northwest of the peak of the continuum emission. The first-order moment images and channel maps show a velocity gradient, of 1.1 km s−1 arcsec−1, across the source, and a distinctive spot of blueshifted emission towards the peak of the zero-order moment. The rotational temperature is found to decrease from 252±24 Kelvin at the peak position to 166±16 Kelvin at its edge, indicating that our molecular observations are probing a hot molecular core that is internally excited. The emission in the H29α line arises from a region of 0 ′′ . 65 in size, where its peak coincides with that of the dust continuum. We model the kinematical characteristics of the"central blue spot" feature as due to infalling motions, suggesting a central mass of 172.8±8.8Mo. Our observations indicate that this HC H ii region is surrounded by a compact structure of hot molecular gas, which is rotating and infalling toward a central mass, that is most likely confining the ionized region. The observed scenario is reminiscent of a"butterfly pattern" with an approximately edge-on torus and ionized gas roughly parallel to its rotation axis. © 2023, CC BY.

关键词： Molecules

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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POS-894 PREDICTING PANDEMIC-RELATED EXCESS-DEATH USING PRE-PANDEMIC RISK OF MORTALITY IN INDIVIDUALS WITH CHRONIC KIDNEY DISEASE

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Kidney International Reports 2022年第2期7卷 S387-S388页

作者： M. Dashtban M. Mizani A. Gonazalez-Izquierdo R. Corbett S. Denaxas J. Quint J.B. Mamza T. Morris H. Hemingway C. Sudlow A. Banerjee University College London Institute of Health Informatics London United Kingdom Imperial College Healthcare NHS Trust Renal and Transplant Centre London United Kingdom Imperial College London Faculty of Medicine- National Heart & Lung Institute London United Kingdom AstraZeneca Medical and Scientific Affairs- BioPharmaceuticals Medical Cambridge United Kingdom University College London Health Data Research UK London United Kingdom British Heart Foundation Data Science Centre BHF CVD-COVID-UK consortium London United Kingdom University College London Hospitals NHS Trust Cardiology London United Kingdom Barts Health NHS Trust Cardiology London United Kingdom

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Correction to: Supporting the classifcation of patients in public hospitals in Chile by designing, deploying and validating a system based on natural language processing

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BMC medical informatics and decision making 2021年第1期21卷 220页

作者： Fabián Villena Jorge Pérez René Lagos Jocelyn Dunstan Center for Mathematical Modeling - CNRS UMI2807 Faculty of Physical and Mathematical Sciences University of Chile Santiago Chile. Center for Medical Informatics and Telemedicine ICBM Faculty of Medicine University of Chile Santiago Chile. Computer Science Department Faculty of Physical and Mathematical Sciences University of Chile Santiago Chile. Millennium Institute for Foundational Research on Data Santiago Chile. Digital Health Unit South East Metropolitan Health Service Santiago Chile. Center for Mathematical Modeling - CNRS UMI2807 Faculty of Physical and Mathematical Sciences University of Chile Santiago Chile. jdunstan@uchile.cl. Center for Medical Informatics and Telemedicine ICBM Faculty of Medicine University of Chile Santiago Chile. jdunstan@uchile.cl.

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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Machine learning applied to aspirated and non-aspirated allophone classification - An approach based on audio fingerprinting 145

Machine learning applied to aspirated and non-aspirated allo...

引用

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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