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Institution

International Burch University

EducationSarajevo, Bosnia and Herzegovina
About: International Burch University is a education organization based out in Sarajevo, Bosnia and Herzegovina. It is known for research contribution in the topics: Population & Support vector machine. The organization has 356 authors who have published 567 publications receiving 6917 citations.


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Journal ArticleDOI
TL;DR: In this work, a versatile signal processing and analysis framework for Electroencephalogram (EEG) was proposed and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients.
Abstract: In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.

1,010 citations

Journal ArticleDOI
TL;DR: A novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy and validate the superiority of the SVM method compared to conventional machine learning methods.

422 citations

Journal ArticleDOI
Luca Pagani1, Luca Pagani2, Luca Pagani3, Daniel Lawson4, Evelyn Jagoda1, Evelyn Jagoda5, Alexander Mörseburg1, Anders Eriksson6, Anders Eriksson1, Mario Mitt7, Florian Clemente8, Florian Clemente1, Georgi Hudjashov3, Georgi Hudjashov9, Georgi Hudjashov10, Michael DeGiorgio11, Lauri Saag3, Jeffrey D. Wall12, Alexia Cardona1, Reedik Mägi7, Melissa A. Wilson Sayres13, Melissa A. Wilson Sayres14, Sarah Kaewert1, Charlotte E. Inchley1, Christiana L. Scheib1, Mari Järve3, Monika Karmin10, Monika Karmin7, Monika Karmin3, Guy S. Jacobs15, Tiago Antao16, Florin Mircea Iliescu1, Alena Kushniarevich17, Alena Kushniarevich3, Qasim Ayub18, Chris Tyler-Smith18, Yali Xue18, Bayazit Yunusbayev3, Kristiina Tambets3, Chandana Basu Mallick3, Lehti Saag7, Elvira Pocheshkhova19, George Andriadze20, Craig Muller21, Michael C. Westaway22, David M. Lambert22, Grigor Zoraqi, Shahlo Turdikulova23, Dilbar Dalimova23, Zhaxylyk Sabitov24, Gazi Nurun Nahar Sultana25, Joseph Lachance26, Joseph Lachance27, Sarah A. Tishkoff27, Kuvat T. Momynaliev, Jainagul Isakova, Larisa Damba28, Marina Gubina28, Pagbajabyn Nymadawa29, Irina Evseeva30, L. A. Atramentova31, Olga Utevska31, François-Xavier Ricaut32, Nicolas Brucato32, Herawati Sudoyo33, Thierry Letellier32, Murray P. Cox9, Nikolay A. Barashkov34, Vedrana Škaro35, Lejla Mulahasanovic, Dragan Primorac, Hovhannes Sahakyan36, Hovhannes Sahakyan3, Maru Mormina37, Christina A. Eichstaedt1, Christina A. Eichstaedt38, Daria V. Lichman39, Daria V. Lichman28, S M Abdullah, Gyaneshwer Chaubey3, Joseph Wee, Evelin Mihailov7, A. S. Karunas40, Sergei Litvinov40, Sergei Litvinov3, Rita Khusainova40, N. V. Ekomasova40, V. L. Akhmetova, I. M. Khidiyatova40, Damir Marjanović41, Levon Yepiskoposyan36, Doron M. Behar3, Elena Balanovska28, Andres Metspalu7, Miroslava Derenko28, Boris Malyarchuk28, Mikhail Voevoda39, Mikhail Voevoda28, Mikhail Voevoda42, Sardana A. Fedorova34, Ludmila P. Osipova28, Ludmila P. Osipova39, Marta Mirazón Lahr1, Pascale Gerbault43, Matthew Leavesley44, Matthew Leavesley45, Andrea Bamberg Migliano43, Michael D. Petraglia46, Oleg Balanovsky28, Elza Khusnutdinova40, Ene Metspalu3, Ene Metspalu7, Mark G. Thomas43, Andrea Manica1, Rasmus Nielsen47, Richard Villems3, Richard Villems7, Richard Villems48, Eske Willerslev21, Toomas Kivisild3, Toomas Kivisild1, Mait Metspalu3 
13 Oct 2016-Nature
TL;DR: A genetic signature in present-day Papuans that suggests that at least 2% of their genome originates from an early and largely extinct expansion of anatomically modern humans (AMHs) out of Africa earlier than 75,000 years ago is found.
Abstract: High-coverage whole-genome sequence studies have so far focused on a limited number of geographically restricted populations, or been targeted at specific diseases, such as cancer. Nevertheless, the availability of high-resolution genomic data has led to the development of new methodologies for inferring population history and refuelled the debate on the mutation rate in humans. Here we present the Estonian Biocentre Human Genome Diversity Panel (EGDP), a dataset of 483 high-coverage human genomes from 148 populations worldwide, including 379 new genomes from 125 populations, which we group into diversity and selection sets. We analyse this dataset to refine estimates of continent-wide patterns of heterozygosity, long- and short-distance gene flow, archaic admixture, and changes in effective population size through time as well as for signals of positive or balancing selection. We find a genetic signature in present-day Papuans that suggests that at least 2% of their genome originates from an early and largely extinct expansion of anatomically modern humans (AMHs) out of Africa. Together with evidence from the western Asian fossil record, and admixture between AMHs and Neanderthals predating the main Eurasian expansion, our results contribute to the mounting evidence for the presence of AMHs out of Africa earlier than 75,000 years ago.

336 citations

Journal ArticleDOI
Monika Karmin1, Monika Karmin2, Lauri Saag2, Lauri Saag1, Mário Vicente3, Melissa A. Wilson Sayres4, Melissa A. Wilson Sayres5, Mari Järve1, Ulvi Gerst Talas2, Siiri Rootsi1, Anne-Mai Ilumäe1, Anne-Mai Ilumäe2, Reedik Mägi2, Mario Mitt2, Luca Pagani3, Tarmo Puurand2, Zuzana Faltyskova3, Florian Clemente3, Alexia Cardona3, Ene Metspalu2, Ene Metspalu1, Hovhannes Sahakyan1, Hovhannes Sahakyan6, Bayazit Yunusbayev1, Bayazit Yunusbayev7, Georgi Hudjashov8, Georgi Hudjashov1, Michael DeGiorgio9, Eva Liis Loogväli1, Christina A. Eichstaedt3, Mikk Eelmets2, Mikk Eelmets1, Gyaneshwer Chaubey1, Kristiina Tambets1, S. S. Litvinov1, S. S. Litvinov7, Maru Mormina10, Yali Xue11, Qasim Ayub11, Grigor Zoraqi, Thorfinn Sand Korneliussen12, Thorfinn Sand Korneliussen5, Farida Akhatova13, Farida Akhatova14, Joseph Lachance15, Joseph Lachance16, Sarah A. Tishkoff16, Kuvat T. Momynaliev, François-Xavier Ricaut17, Pradiptajati Kusuma17, Pradiptajati Kusuma18, Harilanto Razafindrazaka17, Denis Pierron17, Murray P. Cox19, Gazi Nurun Nahar Sultana20, Rane Willerslev21, Craig Muller12, Michael C. Westaway22, David M. Lambert22, Vedrana Škaro23, Lejla Kovacevic, Shahlo Turdikulova24, Dilbar Dalimova24, Rita Khusainova7, Rita Khusainova14, N. N. Trofimova1, N. N. Trofimova7, V. L. Akhmetova7, I. M. Khidiyatova14, I. M. Khidiyatova7, Daria V. Lichman, Jainagul Isakova, Elvira Pocheshkhova25, Zhaxylyk Sabitov26, Zhaxylyk Sabitov27, Nikolay A. Barashkov28, Pagbajabyn Nymadawa29, Evelin Mihailov2, Joseph Wee Tien Seng, Irina Evseeva30, Andrea Bamberg Migliano31, S M Abdullah, George Andriadze32, Dragan Primorac, L. A. Atramentova33, Olga Utevska33, Levon Yepiskoposyan6, Damir Marjanović34, Alena Kushniarevich35, Alena Kushniarevich1, Doron M. Behar1, Christian Gilissen36, Lisenka E.L.M. Vissers36, Joris A. Veltman36, Elena Balanovska7, Miroslava Derenko7, Boris Malyarchuk7, Andres Metspalu2, Sardana A. Fedorova28, Anders Eriksson37, Anders Eriksson3, Andrea Manica3, Fernando L. Mendez38, Tatiana M. Karafet39, Krishna R. Veeramah40, Neil Bradman, Michael F. Hammer39, Ludmila P. Osipova, Oleg Balanovsky7, Elza Khusnutdinova14, Elza Khusnutdinova7, Knut Johnsen41, Maido Remm2, Mark G. Thomas31, Chris Tyler-Smith11, Peter A. Underhill38, Eske Willerslev12, Rasmus Nielsen5, Mait Metspalu1, Mait Metspalu2, Richard Villems2, Richard Villems42, Richard Villems1, Toomas Kivisild1, Toomas Kivisild3 
TL;DR: A study of 456 geographically diverse high-coverage Y chromosome sequences, including 299 newly reported samples, infer a second strong bottleneck in Y-chromosome lineages dating to the last 10 ky, and hypothesize that this bottleneck is caused by cultural changes affecting variance of reproductive success among males.
Abstract: It is commonly thought that human genetic diversity in non-African populations was shaped primarily by an out-of-Africa dispersal 50-100 thousand yr ago (kya). Here, we present a study of 456 geographically diverse high-coverage Y chromosome sequences, including 299 newly reported samples. Applying ancient DNA calibration, we date the Y-chromosomal most recent common ancestor (MRCA) in Africa at 254 (95% CI 192-307) kya and detect a cluster of major non-African founder haplogroups in a narrow time interval at 47-52 kya, consistent with a rapid initial colonization model of Eurasia and Oceania after the out-of-Africa bottleneck. In contrast to demographic reconstructions based on mtDNA, we infer a second strong bottleneck in Y-chromosome lineages dating to the last 10 ky. We hypothesize that this bottleneck is caused by cultural changes affecting variance of reproductive success among males.

325 citations

Journal ArticleDOI
TL;DR: Results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair.

320 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
20227
202169
202064
201992
201866