Showing papers by "Oklahoma State University–Stillwater published in 2016"
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) as discussed by the authors was used to estimate the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015.
5,050 citations
TL;DR: The Global Burden of Disease 2015 Study provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015, finding several countries in sub-Saharan Africa had very large gains in life expectancy, rebounding from an era of exceedingly high loss of life due to HIV/AIDS.
4,804 citations
Nicholas J Kassebaum1, Megha Arora1, Ryan M Barber1, Zulfiqar A Bhutta2 +679 more•Institutions (268)
TL;DR: In this paper, the authors used the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015.
1,533 citations
Nicholas J Kassebaum1, Ryan M Barber1, Zulfiqar A Bhutta2, Zulfiqar A Bhutta3 +613 more•Institutions (272)
TL;DR: In this article, the authors quantified maternal mortality throughout the world by underlying cause and age from 1990 to 2015 for ages 10-54 years by systematically compiling and processing all available data sources from 186 of 195 countries and territories.
641 citations
Haidong Wang1, Zulfiqar A Bhutta2, Zulfiqar A Bhutta3, Matthew M Coates1 +610 more•Institutions (263)
TL;DR: The Global Burden of Disease 2015 Study provides an analytical framework to comprehensively assess trends for under-5 mortality, age-specific and cause-specific mortality among children under 5 years, and stillbirths by geography over time and decomposed the changes in under- 5 mortality to changes in SDI at the global level.
591 citations
TL;DR: In this paper, the authors argue that CSR can be a response to leaders' personal needs for attention and image reinforcement and hypothesize that CEO narcissism has positive effects on levels and profile of organizational CSR; additionally, they find support for their ideas with a sample of Fortune 500 CEOs.
Abstract: This study builds on insights from both upper echelons and agency perspectives to examine the effects on corporate social responsibility (CSR) practices of CEO's narcissism. Drawing on prior theory about CEO narcissism, we argue that CSR can be a response to leaders' personal needs for attention and image reinforcement and hypothesize that CEO narcissism has positive effects on levels and profile of organizational CSR; additionally, CEO narcissism will reduce the effect of CSR on performance. We find support for our ideas with a sample of Fortune 500 CEOs, operationalizing CEO narcissism with a novel media-based measurement technique that uses third-party ratings of CEO characteristics with validated psychometric scales. Copyright © 2014 John Wiley & Sons, Ltd.
548 citations
TL;DR: This report provides national estimates of levels and trends of HIV/AIDS incidence, prevalence, coverage of antiretroviral therapy (ART), and mortality for 195 countries and territories from 1980 to 2015.
522 citations
TL;DR: The November 2008 election of Barack Obama as 44th President of the United States created great optimism among supporters of many progressive causes, including environmental protection and action on climate change as mentioned in this paper.
Abstract: The November 2008 election of Barack Obama as 44th President of the United States created great optimism among supporters of many progressive causes, including environmental protection and action o...
455 citations
TL;DR: In this article, the performance of the ATLAS muon identification and reconstruction using the first LHC dataset recorded at s√ = 13 TeV in 2015 was evaluated using the Monte Carlo simulations.
Abstract: This article documents the performance of the ATLAS muon identification and reconstruction using the first LHC dataset recorded at s√ = 13 TeV in 2015. Using a large sample of J/ψ→μμ and Z→μμ decays from 3.2 fb−1 of pp collision data, measurements of the reconstruction efficiency, as well as of the momentum scale and resolution, are presented and compared to Monte Carlo simulations. The reconstruction efficiency is measured to be close to 99% over most of the covered phase space (|η| 2.2, the pT resolution for muons from Z→μμ decays is 2.9% while the precision of the momentum scale for low-pT muons from J/ψ→μμ decays is about 0.2%.
440 citations
University of California, San Francisco1, Spelman College2, Brown University3, San Francisco State University4, California State University, Los Angeles5, University of California, Los Angeles6, Oklahoma State University–Stillwater7, University of California, Berkeley8, Northwestern University9, Norfolk State University10, Morgan State University11, University of Maryland, Baltimore County12, University of New Mexico13, California State University, Northridge14
TL;DR: Members of the Joint Working Group on Improving Underrepresented Minorities Persistence in Science, Technology, Engineering and Mathematics (STEM), utilizing Kurt Lewin's planned approach to change, describe five recommendations to increase URM persistence in STEM at the undergraduate level.
Abstract: Members of the Joint Working Group on Improving Underrepresented Minorities (URMs) Persistence in Science, Technology, Engineering, and Mathematics (STEM)-convened by the National Institute of General Medical Sciences and the Howard Hughes Medical Institute-review current data and propose deliberation about why the academic "pathways" leak more for URM than white or Asian STEM students. They suggest expanding to include a stronger focus on the institutional barriers that need to be removed and the types of interventions that "lift" students' interests, commitment, and ability to persist in STEM fields. Using Kurt Lewin's planned approach to change, the committee describes five recommendations to increase URM persistence in STEM at the undergraduate level. These recommendations capitalize on known successes, recognize the need for accountability, and are framed to facilitate greater progress in the future. The impact of these recommendations rests upon enacting the first recommendation: to track successes and failures at the institutional level and collect data that help explain the existing trends.
436 citations
University of Nevada, Reno1, Purdue University2, Monsanto3, Old Dominion University4, North Carolina State University5, University College London6, Oklahoma State University–Stillwater7, Spanish National Research Council8, National Institutes of Health9, University of Cambridge10, Wellcome Trust11, J. Craig Venter Institute12, Leidos13, Broad Institute14, University of Notre Dame15, University of Nevada, Las Vegas16, University of Barcelona17, Carlos III Health Institute18, University of Massachusetts Medical School19, University of Connecticut20, University of Lausanne21, University of Oxford22, West Virginia University23, Virginia Tech24, Indiana University25, University of Maryland, Baltimore26, Texas A&M University27, Kansas State University28, University of Minnesota29, University of Manchester30, National University of Singapore31, University of California, San Francisco32, Iowa State University33, Colorado State University34, Pennsylvania State University35, University of California, Riverside36, Max Planck Society37, ANSES38, University of Santiago de Compostela39, Pompeu Fabra University40, California State Polytechnic University, Pomona41, University of Queensland42, University of the Sunshine Coast43, Swiss Institute of Bioinformatics44, University of Geneva45, University of Copenhagen46, University of Tennessee Health Science Center47, Wellcome Trust Sanger Institute48, University of Vigo49, University of Illinois at Urbana–Champaign50, Quinnipiac University51, International Livestock Research Institute52
TL;DR: Insights from genome analyses into parasitic processes unique to ticks, including host ‘questing', prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival are reported.
Abstract: Ticks transmit more pathogens to humans and animals than any other arthropod. We describe the 2.1 Gbp nuclear genome of the tick, Ixodes scapularis (Say), which vectors pathogens that cause Lyme disease, human granulocytic anaplasmosis, babesiosis and other diseases. The large genome reflects accumulation of repetitive DNA, new lineages of retro-transposons, and gene architecture patterns resembling ancient metazoans rather than pancrustaceans. Annotation of scaffolds representing ∼57% of the genome, reveals 20,486 protein-coding genes and expansions of gene families associated with tick-host interactions. We report insights from genome analyses into parasitic processes unique to ticks, including host 'questing', prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival. We identify proteins associated with the agent of human granulocytic anaplasmosis, an emerging disease, and the encephalitis-causing Langat virus, and a population structure correlated to life-history traits and transmission of the Lyme disease agent.
TL;DR: In this paper, the authors suggest that persons who are attracted by, selected into, and persist in entrepreneurship may be relatively high in the capacity to tolerate or effectively manage stress, whereas those who are relatively low in this capacity tend to exit from entrepreneurship either voluntarily or involuntarily.
TL;DR: In this paper, an independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b tagging algorithm used in the online trigger are also presented.
Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.
TL;DR: This article examined the extent of a left-right ideological divide on climate change views via Eurobarometer survey data on the publics of 25 EU countries before the 2008 global financial crisis, the 2009 ‘climategate’ controversy and COP-15 in Copenhagen, and an increase in organized climate change denial campaigns.
Abstract: There is a strong political divide on climate change in the US general public, with Liberals and Democrats expressing greater belief in and concern about climate change than Conservatives and Republicans. Recent studies find a similar though less pronounced divide in other countries. Its leadership in international climate policy making warrants extending this line of research to the European Union (EU). The extent of a left–right ideological divide on climate change views is examined via Eurobarometer survey data on the publics of 25 EU countries before the 2008 global financial crisis, the 2009 ‘climategate’ controversy and COP-15 in Copenhagen, and an increase in organized climate change denial campaigns. Citizens on the left consistently reported stronger belief in climate change and support for action to mitigate it than did citizens on the right in 14 Western European countries. There was no such ideological divide in 11 former Communist countries, likely due to the low political salience of...
Nanjing Agricultural University1, University of Florida2, Potsdam Institute for Climate Impact Research3, University of Bonn4, Columbia University5, University of Chicago6, Stanford University7, SupAgro8, Goddard Institute for Space Studies9, Institut national de la recherche agronomique10, International Maize and Wheat Improvement Center11, Oklahoma State University–Stillwater12, Prince of Songkla University13, Michigan State University14, James Hutton Institute15, University of Leeds16, Counterintelligence Field Activity17, University of Córdoba (Spain)18, Ludwig Maximilian University of Munich19, International Institute for Applied Systems Analysis20, University of Hohenheim21, Washington State University22, University of Guelph23, Texas A&M University24, University of Maryland, College Park25, Aarhus University26, United States Department of Agriculture27, Indian Agricultural Research Institute28, University of Arizona29, Kansas State University30, University of Birmingham31, Karlsruhe Institute of Technology32, University of Göttingen33, University of Natural Resources and Life Sciences, Vienna34, Rothamsted Research35, Netherlands Environmental Assessment Agency36, Wageningen University and Research Centre37, Chinese Academy of Sciences38, Commonwealth Scientific and Industrial Research Organisation39, China Agricultural University40
TL;DR: This paper showed that grid-based and point-based simulations and statistical regressions, without deliberate adaptation or CO 2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales.
Abstract: The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO 2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify 'method uncertainty' in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
TL;DR: The methods employed in the ATLAS experiment to correct for the impact of pile-up on jet energy and jet shapes, and for the presence of spurious additional jets, are described, with a primary focus on the large 20.3 kg-1 data sample.
Abstract: The large rate of multiple simultaneous protonproton interactions, or pile-up, generated by the Large Hadron Collider in Run 1 required the development of many new techniques to mitigate the advers ...
06 Sep 2016
TL;DR: The critical importance of good datasets for model learning, testing, and evaluation is discussed and several public open source synthetic datasets for various radio machine learning tasks are introduced.
Abstract: This paper surveys emerging applications of Machine Learning (ML) to the Radio Signal Processing domain. Provides some brief background on enabling methods and discusses some of the potential advancements for the field. It discusses the critical importance of good datasets for model learning, testing, and evaluation and introduces several public open source synthetic datasets for various radio machine learning tasks. These are intended to provide a robust common baselines for those working in the field and to provide a benchmark measure against which many techniques can be rapidly evaluated and compared.
TL;DR: A survey of the photophysical data and the diversity of transformations that may be accomplished utilizing commercially available photocatalysts is contained herein.
TL;DR: In this paper, a multilevel model that examines the effects of employee involvement climate on the individual-level process linking employee regulatory focus (promotion and prevention) to innovation via thriving was proposed and tested.
TL;DR: The luminosity determination for the ATLAS detector at the LHC during pp collisions at s√= 8 TeV in 2012 is presented in this article, where the evaluation of the luminosity scale is performed using several luminometers.
Abstract: The luminosity determination for the ATLAS detector at the LHC during pp collisions at s√= 8 TeV in 2012 is presented. The evaluation of the luminosity scale is performed using several luminometers ...
TL;DR: The results suggest that the ridge in pp collisions arises from the same or similar underlying physics as observed in p+Pb collisions, and that the dynamics responsible for the ridge has no strong sqrt[s] dependence.
Abstract: ATLAS has measured two-particle correlations as a function of relative azimuthal-angle, $\Delta \phi$, and pseudorapidity, $\Delta \eta$, in $\sqrt{s}$=13 and 2.76 TeV $pp$ collisions at the LHC using charged particles measured in the pseudorapidity interval $|\eta|$<2.5. The correlation functions evaluated in different intervals of measured charged-particle multiplicity show a multiplicity-dependent enhancement at $\Delta \phi \sim 0$ that extends over a wide range of $\Delta\eta$, which has been referred to as the "ridge". Per-trigger-particle yields, $Y(\Delta \phi)$, are measured over 2<$|\Delta\eta|$<5. For both collision energies, the $Y(\Delta \phi)$ distribution in all multiplicity intervals is found to be consistent with a linear combination of the per-trigger-particle yields measured in collisions with less than 20 reconstructed tracks, and a constant combinatoric contribution modulated by $\cos{(2\Delta \phi)}$. The fitted Fourier coefficient, $v_{2,2}$, exhibits factorization, suggesting that the ridge results from per-event $\cos{(2\phi)}$ modulation of the single-particle distribution with Fourier coefficients $v_2$. The $v_2$ values are presented as a function of multiplicity and transverse momentum. They are found to be approximately constant as a function of multiplicity and to have a $p_{\mathrm{T}}$ dependence similar to that measured in $p$+Pb and Pb+Pb collisions. The $v_2$ values in the 13 and 2.76 TeV data are consistent within uncertainties. These results suggest that the ridge in $pp$ collisions arises from the same or similar underlying physics as observed in $p$+Pb collisions, and that the dynamics responsible for the ridge has no strong $\sqrt{s}$ dependence.
TL;DR: In this article, a search for a high-mass Higgs boson in the,,, and decay modes using the ATLAS detector at the CERN Large Hadron Collider is presented.
Abstract: A search is presented for a high-mass Higgs boson in the , , , and decay modes using the ATLAS detector at the CERN Large Hadron Collider. The search uses proton-proton collision data at a centre-of-mass energy of 8 TeV corresponding to an integrated luminosity of 20.3 fb. The results of the search are interpreted in the scenario of a heavy Higgs boson with a width that is small compared with the experimental mass resolution. The Higgs boson mass range considered extends up to for all four decay modes and down to as low as 140 , depending on the decay mode. No significant excess of events over the Standard Model prediction is found. A simultaneous fit to the four decay modes yields upper limits on the production cross-section of a heavy Higgs boson times the branching ratio to boson pairs. 95 % confidence level upper limits range from 0.53 pb at GeV to 0.008 pb at GeV for the gluon-fusion production mode and from 0.31 pb at GeV to 0.009 pb at GeV for the vector-boson-fusion production mode. The results are also interpreted in the context of Type-I and Type-II two-Higgs-doublet models.
TL;DR: This approach exploits the transfer learning technique as a tool to generate an effective initial population pool via reusing past experience to speed up the evolutionary process, and at the same time any population-based multiobjective algorithms can benefit from this integration without any extensive modifications.
Abstract: One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing the "experiences" to construct a prediction model via statistical machine learning approaches. However most of the existing methods ignore the non-independent and identically distributed nature of data used to construct the prediction model. In this paper, we propose an algorithmic framework, called Tr-DMOEA, which integrates transfer learning and population-based evolutionary algorithm for solving the DMOPs. This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this, we incorporate the proposed approach into the development of three well-known algorithms, nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and the regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), and then employ twelve benchmark functions to test these algorithms as well as compare with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed method through exploiting machine learning technology.
TL;DR: In this paper, a measurement of the inelastic proton-proton cross section using 60''μb^{-1} of pp collisions at a center-of-mass energy sqrt[s] of 13'TeV with the ATLAS detector at the LHC is presented.
Abstract: This Letter presents a measurement of the inelastic proton-proton cross section using 60 μb^{-1} of pp collisions at a center-of-mass energy sqrt[s] of 13 TeV with the ATLAS detector at the LHC. Inelastic interactions are selected using rings of plastic scintillators in the forward region (2.07 10^{-6}, where M_{X} is the larger invariant mass of the two hadronic systems separated by the largest rapidity gap in the event. In this ξ range the scintillators are highly efficient. For diffractive events this corresponds to cases where at least one proton dissociates to a system with M_{X}>13 GeV. The measured cross section is compared with a range of theoretical predictions. When extrapolated to the full phase space, a cross section of 78.1±2.9 mb is measured, consistent with the inelastic cross section increasing with center-of-mass energy.
TL;DR: In this article, the authors describe a model-agnostic search for pairs of jets (dijets) produced by resonant and non-resonant phenomena beyond the Standard Model.
TL;DR: This paper proposes a privacy-preserving scheme for IDR programs in the smart grid, which enables the DR provider to compute individual demand curtailments and DR rewards while preserving customer privacy.
Abstract: The advanced metering infrastructure (AMI) in the smart grid provides real-time information to both grid operators and customers, exploiting the full potential of demand response (DR). However, it introduces new privacy threats to customers. Prior works have proposed privacy-preserving methods in the AMI, such as temporal or spatial aggregation. A main assumption in these works is that fine-grained data do not need to be attributable to individuals. However, this assumption does not hold in incentive-based demand response (IDR) programs where fine-grained metering data are required to analyze individual demand curtailments, and hence, need to be attributable. In this paper, we propose a privacy-preserving scheme for IDR programs in the smart grid, which enables the DR provider to compute individual demand curtailments and DR rewards while preserving customer privacy. Moreover, a customer can reveal his/her identity and prove ownership of his/her power usage profile in certain situations, such as legal disputes. We achieve both privacy and efficiency in our scheme through a combination of several cryptographic primitives, such as identity-committable signatures and partially blind signatures. As far as we know, we are the first to identify and address privacy issues for IDR programs in the smart grid.
TL;DR: Ability to overwinter on living annual and perennial hosts in southern sorghum-producing areas and wind-aided movement of alate aphids appear to be the main factors in its impressive geographic spread in North America.
Abstract: In 2013, the sugarcane aphid, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae), a new invasive pest of sorghum species in North America, was confirmed on sorghum in 4 states and 38 counties in the United States. In 2015, the aphid was reported on sorghum in 17 states and over 400 counties as well as all sorghum-producing regions in Mexico. Ability to overwinter on living annual and perennial hosts in southern sorghum-producing areas and wind-aided movement of alate aphids appear to be the main factors in its impressive geographic spread in North America. Morphological characteristics of the sugarcane aphid include dark tarsi, cornicles, and antennae, allowing easy differentiation from other aphids on the crop. Sugarcane aphid damages sorghum by removing sap and covering plants with honeydew, causing general plant decline and yield loss. Honeydew and sooty mold can disrupt harvesting. The aphid's high reproductive rate on susceptible sorghum hybrids has resulted in reports of yield loss ranging from 10% to greater than 50%. In response, a combination of research-based data and field observations has supported development of state extension identification, scouting, and treatment guides that aid in initiating insecticide applications to prevent yield losses. Highly efficacious insecticides have been identified and when complemented by weekly scouting and use of thresholds, economic loss by sugarcane aphid can be minimized. Some commercial sorghum hybrids are partially resistant to the aphid, and plant breeders have identified other lines with sugarcane aphid resistance. A very diverse community of predators and parasitoids of sugarcane aphid has been identified, and their value to limit sugarcane aphid population growth is under investigation.
TL;DR: In this paper, a data-driven risk-averse stochastic unit commitment model is proposed, where risk aversion stems from the worst-case probability distribution of the renewable energy generation amount, and the corresponding solution methods to solve the problem are developed.
Abstract: Considering recent development of deregulated energy markets and the intermittent nature of renewable energy generation, it is important for power system operators to ensure cost effectiveness while maintaining the system reliability To achieve this goal, significant research progress has recently been made to develop stochastic optimization models and solution methods to improve reliability unit commitment run practice, which is used in the day-ahead market for ISOs/RTOs to ensure sufficient generation capacity available in real time to accommodate uncertainties Most stochastic optimization approaches assume the renewable energy generation amounts follow certain distributions However, in practice, the distributions are unknown and instead, a certain amount of historical data are available In this research, we propose a data-driven risk-averse stochastic unit commitment model, where risk aversion stems from the worst-case probability distribution of the renewable energy generation amount, and develop the corresponding solution methods to solve the problem Given a set of historical data, our proposed approach first constructs a confidence set for the distributions of the uncertain parameters using statistical inference and solves the corresponding risk-averse stochastic unit commitment problem Then, we show that the conservativeness of the proposed stochastic program vanishes as the number of historical data increases to infinity Finally, the computational results numerically show how the risk-averse stochastic unit commitment problem converges to the risk-neutral one, which indicates the value of data
Mississippi State University1, University of Kentucky2, Iowa State University3, South Dakota State University4, Michigan State University5, University of Arkansas6, Pennsylvania State University7, Oklahoma State University–Stillwater8, Ohio State University9, University of Florida10, University of Costa Rica11, University of Maryland, College Park12, Louisiana State University13, Texas A&M University14, Kansas State University15, University of Tennessee16, University of Georgia17, University of Delaware18, North Carolina State University19, University of Minnesota20, North Dakota State University21, Virginia Tech22, Clemson University23, DuPont Pioneer24, Auburn University25, University of Wisconsin-Madison26, Ontario Ministry of the Environment27, Purdue University28, University of Missouri29
TL;DR: Annual decreases in corn yield caused by diseases were estimated by surveying members of the Corn Disease Working Group in 22 corn-producing states in the United States and in Ontario, Canada from 2012 through 2015 by finding foliar diseases commonly caused the largest estimated yield loss.
Abstract: Annual decreases in soybean (Glycine max L. Merrill) yield caused by diseases were estimated by surveying university-affiliated plant pathologists in 28 soybean-producing states in the United State...
TL;DR: Results show that the proposed method can be used to design an accurate classification system for epilepsy diagnosis, and high accuracy rates were obtained.
Abstract: The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out this study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks. The evaluation of the proposed method is conducted using the k -fold cross-validation methodology, reporting on classification accuracy, sensitivity, and specificity. The proposed method is tested using a benchmark EEG dataset, and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epilepsy diagnosis.