Institution
International Burch University
Education•Sarajevo, 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.
Topics: Population, Support vector machine, Bosnian, AC power, Higher education
Papers
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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
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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
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University of Cambridge1, University of Bologna2, Estonian Biocentre3, University of Bristol4, Harvard University5, King Abdullah University of Science and Technology6, University of Tartu7, University of Montpellier8, Massey University9, University of Auckland10, Pennsylvania State University11, University of California, San Francisco12, Arizona State University13, Biodesign Institute14, University of Southampton15, University of Montana16, National Academy of Sciences of Belarus17, Wellcome Trust Sanger Institute18, Kuban State Medical University19, University of Georgia20, University of Copenhagen21, Griffith University22, Academy of Sciences of Uzbekistan23, L.N.Gumilyov Eurasian National University24, University of Dhaka25, Georgia Institute of Technology26, University of Pennsylvania27, Russian Academy of Sciences28, Academy of Medical Sciences, United Kingdom29, Royal Free Hospital30, University of Kharkiv31, Centre national de la recherche scientifique32, Eijkman Institute for Molecular Biology33, North-Eastern Federal University34, Josip Juraj Strossmayer University of Osijek35, Armenian National Academy of Sciences36, University of Winchester37, University Hospital Heidelberg38, Novosibirsk State University39, Bashkir State University40, International Burch University41, Russian Academy42, University College London43, University of Papua New Guinea44, James Cook University45, Max Planck Society46, University of California, Berkeley47, Estonian Academy of Sciences48
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
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Estonian Biocentre1, University of Tartu2, University of Cambridge3, Arizona State University4, University of California, Berkeley5, Armenian National Academy of Sciences6, Russian Academy of Sciences7, University of Auckland8, Pennsylvania State University9, University of Winchester10, Wellcome Trust Sanger Institute11, University of Copenhagen12, Kazan Federal University13, Bashkir State University14, Georgia Institute of Technology15, University of Pennsylvania16, Centre national de la recherche scientifique17, Eijkman Institute for Molecular Biology18, Massey University19, University of Dhaka20, Aarhus University21, Griffith University22, Josip Juraj Strossmayer University of Osijek23, Academy of Sciences of Uzbekistan24, Kuban State Medical University25, Nazarbayev University26, L.N.Gumilyov Eurasian National University27, North-Eastern Federal University28, Academy of Medical Sciences, United Kingdom29, Anthony Nolan30, University College London31, University of St Andrews32, University of Kharkiv33, International Burch University34, National Academy of Sciences of Belarus35, Radboud University Nijmegen36, King Abdullah University of Science and Technology37, Stanford University38, University of Arizona39, Stony Brook University40, University Hospital of North Norway41, Estonian Academy of Sciences42
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
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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
Authors
Showing all 363 results
Name | H-index | Papers | Citations |
---|---|---|---|
Abdulhamit Subasi | 39 | 175 | 7908 |
Mehmet Uzunoglu | 30 | 76 | 4605 |
Abdul Razaque Memon | 22 | 52 | 1851 |
Almir Badnjevic | 20 | 90 | 1169 |
Azra Korjenic | 19 | 83 | 1362 |
Damir Marjanović | 19 | 105 | 2094 |
Mirsada Hukić | 19 | 85 | 1228 |
Mehmet Orhan | 17 | 73 | 817 |
Lejla Gurbeta | 17 | 41 | 785 |
Mario Cifrek | 15 | 117 | 1480 |
Ratko Magjarević | 15 | 98 | 1262 |
Meliha Handzic | 15 | 85 | 995 |
Teoman Duman | 14 | 42 | 974 |
Jasmin Kevric | 11 | 49 | 907 |
Amina Kurtovic-Kozaric | 11 | 39 | 579 |