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15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015

作者： Pippa, Evangelia Mporas, Iosif Megalooikonomou, Vasileios Dept. of Computer Engineering and Informatics University of Patras Rion-Patras26500 Greece

ISBN: (纸本)9781467379830

In this paper, we propose a computationally efficient method to estimate the optimal order of the autoregressive (AR) modeling of electroencephalographic (EEG) signals in order to use the AR coefficients as features for the analysis of EEG signals and the automatic detection of epileptic seizures. The estimation of the optimal AR-order is made using regression analysis of statistical features extracted from the samples of the EEG signals. The proposed method was evaluated in both background and ictal EEG segments using recordings from 10 epileptic patients. The experimental evaluation showed that the mean absolute error of the estimated optimal AR order is approximately 4 units. © 2015 IEEE.

关键词： Regression analysis

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A new solution representation for the rectilinear block packing problem

A new solution representation for the rectilinear block pack...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Ken Matsushita Yannan Hu Hideki Hashimoto Shinji Imahori Mutsunori Yagiura Dept. Computer Science and Mathematical Informatics Nagoya University Nagoya Japan Dept. Logistics and Information Engineering Tokyo University of Marine Science and Technology Tokyo Japan Dept. Information and System Engineering Chuo University Tokyo Japan

ISBN: (纸本)9781509036660

The rectilinear block packing problem is a problem of packing a set of rectilinear blocks into a larger rectangular container, where a rectilinear block is a polygonal block whose interior angles are either 90° or 270°. This problem has many applications, such as VLSI design and timber cutting. In this paper, we propose a new solution representation, based on an order of items, to decide a layout of rectilinear blocks whose x-coordinates are fixed. When the shapes of given items have certain characteristics, our representation guarantees optimality. We also generalize this result to the case of general shapes. We then propose an exact algorithm that iteratively generates x-coordinates of items and finds the corresponding optimal layout. The computational results show that our algorithm obtains five exact and one heuristic solutions for six instances and it improves the running time of an existing algorithm.

关键词： Layout Containers Shape Strips Very large scale integration Heuristic algorithms IP networks

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Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach

Optimal medication dosing from suboptimal clinical examples:...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shamim Nemati Mohammad M. Ghassemi Gari D. Clifford Emory University Atlanta GA US Dept. of Electrical Engineering and Computer Science MIT Cambrige MA Dept. of Biomedical Informatics Emory University Atlanta GA Dept. of Biomedical Engineering Georgia Institute of Technology Atlanta GA

Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep reinforcement learning algorithm that learns an optimal heparin dosing policy from sample dosing trails and their associated outcomes in large electronic medical records. Using separate training and testing datasets, our model was observed to be effective in proposing heparin doses that resulted in better expected outcomes than the clinical guidelines. Our results demonstrate that a sequential modeling approach, learned from retrospective data, could potentially be used at the bedside to derive individualized patient dosing policies.

关键词： Hospitals Training Time series analysis Learning (artificial intelligence) Drugs Feature extraction Cost function

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Search for a light charged Higgs boson in t → H ^± b decays, with H ^± → cb, in the lepton+jets final state in proton-proton collisions at √s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2023年第9期2023卷 4页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes Unive

A search for a charged Higgs boson, H ±, produced in top-quark decays, t → H ± b, is presented. The search targets H ± decays into a bottom and a charm quark, H ± → cb. The analysis focuses o... 详细信息

A search for a charged Higgs boson, H ^±, produced in top-quark decays, t → H ^± b, is presented. The search targets H ^± decays into a bottom and a charm quark, H ^± → cb. The analysis focuses on a selection enriched in top-quark pair production, where one top quark decays into a leptonically decaying W boson and a bottom quark, and the other top quark decays into a charged Higgs boson and a bottom quark. This topology leads to a lepton-plus-jets final state, characterised by an isolated electron or muon and at least four jets. The search exploits the high multiplicity of jets containing b-hadrons, and deploys a neural network classifier that uses the kinematic differences between the signal and the background. The search uses a dataset of proton-proton collisions collected at a centre-of-mass energy s = 13 TeV between 2015 and 2018 with the ATLAS detector at CERN’s Large Hadron Collider, amounting to an integrated luminosity of 139 fb⁻¹. Observed (expected) 95% confidence-level upper limits between 0.15% (0.09%) and 0.42% (0.25%) are derived for the product of branching fractions B (t → H ^± b) × B(H ^± → cb) for charged Higgs boson masses between 60 and 160 GeV, assuming the SM production of the top-quark pairs. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

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Search for high-mass Wγ and Zγ resonances using hadronic W/Z boson decays from 139 fb⁻¹ of pp collisions at √s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2023年第7期2023卷 125页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes

A search for high-mass charged and neutral bosons decaying to Wγ and Zγ final states is presented in this paper. The analysis uses a data sample of s = 13 TeV proton-proton collisions with an integrated luminosity of 139 fb⁻¹ collected by the ATLAS detector during LHC Run 2 operation. The sensitivity of the search is determined using models of the production and decay of spin-1 charged bosons and spin-0/2 neutral bosons. The range of resonance masses explored extends from 1.0 TeV to 6.8 TeV. At these high resonance masses, it is beneficial to target the hadronic decays of the W and Z bosons because of their large branching fractions. The decay products of the high-momentum W/Z bosons are strongly collimated and boosted-boson tagging techniques are employed to improve the sensitivity. No evidence of a signal above the Standard Model backgrounds is observed, and upper limits on the production cross-sections of these bosons times their branching fractions to Wγ and Zγ are derived for various boson production models. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

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Sensitivity Analysis of Small Cells and DAS Techno-economic Models in Mobile 5G

Sensitivity Analysis of Small Cells and DAS Techno-economic ...

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IEEE Wireless Communications and Networking Conference

作者： Christos Bouras Anastasia Kollia Andreas Papazois Computer Technology Institute & Press "Diophantus" Computer Engineering & Informatics Dept. University of Patras

ISBN: (纸本)9781467398152

Next generation of mobile technologies demands high date rates and low round trip times. Therefore, we analyze deployments that offer these performance capabilities. We consider the fundamental issue of the expenses of the network's installation, maintenance and operation. We calculate the costs of Distributed Antenna Systems (DAS) and small cells by describing updated cost models for each one. We present a Sensitivity Analysis of certain types of cost variables and parameters including the existing bandwidth, the running costs, the interest rate, the base station, the equipment, the power consumption, the backhaul, the implementation costs and Throughput Density. We include a variable cost provision for the year 2016 and beyond, and conduce several experiments, based on the upper analysis, by considering many different parameter prices and introducing the Throughput Density. We calculate the Total Cost of Ownership (TCO), the Capital (CAPEX) and the Operational (OPEX) expenditures. Finally, we are led to important conclusions by comparing the two main technologies costs and evaluating the cost parameters and variables that will lead future researchers to suggest ways to reduce the higher costs.

关键词： Small cell Ultra-dense DAS Techno-economics Sensitivity analysis 5G

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Security issues in cloud computing for healthcare

Security issues in cloud computing for healthcare

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Repu Daman Manish M. Tripathi Saroj K. Mishra School of Telemedicine & Biomedical Informatics Sanjay Gandhi Post Graduate Institute of Medical Sciences Lucknow UP INDIA Dept. of Computer Science & Engineering Integral University Lucknow UP India

ISBN: (纸本)9781467394178

ICT infrastructure in healthcare domain is improved with the advancement of technology. Health data consist multi-format data as it has text reports, MRI, CT-SCAN images, & pathological slides. Healthcare domain has a large volume of data which is archived as Electronic Health Record (EHR) and it has to be updated frequently. The data of the patients has to be available whenever and wherever necessary in the future course of treatment as patient history. Such a huge archival of data in the hospital needs Skilled manpower, Information and Communication Technology (ICT) and Civil infrastructure and its recurring maintenance cost. Cloud technology thus provides the necessary infrastructure as pay per usage model and when used in healthcare sector lowers the cost of storage, processing and updating infrastructure with minimum cost. Data of patients which are confidential and private in nature can be stored in cloud by selecting appropriate deployment models as different models have different levels of security.

关键词： Handheld computers Decision support systems Conferences

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Neuroimaging Biomarkers of Cognitive Decline in Healthy Older Adults via Unified Learning

Neuroimaging Biomarkers of Cognitive Decline in Healthy Olde...

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IEEE Symposium Series on Computational Intelligence

作者： Tayo Obafemi-Ajayi Khalid Al-Jabery Lauren Salminen David Laidlaw Ryan Cabeen Donald Wunsch Robert Paul Engineering Program Missouri State University Springfield MO Dept. of Electrical and Computer Eng. Missouri University of Science and Technology Rolla MO University of Southern California Imaging Genetics Center Los Angeles CA Dept. of Computer Science Brown University Providence RI USC Stevens Neuroimaging and Informatics Institute Los Angeles CA Dept. of Psychological Sciences University of Missouri St. Louis MO

Cognitive aging in healthy adults exhibits significant and heterogeneous variability. In this study, we apply a robust unified learning framework to cluster subgroups using neuroimaging data (brain volume and white matter), to identify neurological phenotypes that can sort out the heterogeneity in cognitive aging and help identify potential risk factors for suboptimal brain aging. Using machine learning analytics, results revealed two unique subgroups in healthy older adults with different patterns of white matter integrity and brain volumetric measures. The classification of phenotypical subgroups in healthy older adults may inform the understanding of the complexity of brain changes before the onset of clinical symptoms. The identified neuroimaging features that defined group classification are recognized as important structures that subserve cognitive performance. Further analysis of these potential biomarkers that help predict trajectory of cognitive decline in symptom free individuals could lead to the detection of early stages of neurodegenerative diseases.

关键词： Neuroimaging Feature extraction Aging Clustering algorithms Algorithm design and analysis Predictive models Biological system modeling

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CASSANDRA - A simulation-based, decision-support tool for energy market stakeholders 8

CASSANDRA - A simulation-based, decision-support tool for en...

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8th EAI International Conference on Simulation Tools and Techniques, SIMUTOOLS 2015

作者： Vavliakis, Konstantinos N. Chrysopoulos, Anthony C. Chatzidimitriou, Kyriakos C. Symeonidis, Andreas L. Mitkas, Pericles A. Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Informatics and Telematics Institute CERTH Thessaloniki Greece

ISBN: (纸本)9781631900792

Energy gives personal comfort to people, and is essential for the generation of commercial and societal wealth. Nevertheless, energy production and consumption place considerable pressures on the environment, such as the emission of greenhouse gases and air pollutants. They contribute to climate change, damage natural ecosystems and the man-made environment, and cause adverse effects to human health. Lately, novel market schemes emerge, such as the formation and operation of customer coalitions aiming to improve their market power through the pursuit of common benefits. In this paper we present CASSANDRA, an open source1, expandable software platform for modelling the demand side of power systems, focusing on small scale consumers. The structural elements of the platform are a) the electrical installations (i.e. households, commercial stores, small industries etc.), b) the respective appliances installed, and c) the electrical consumption-related activities of the people residing in the installations. CASSANDRA serves as a tool for simulation of real demand-side environments providing decision support for energy market stakeholders. The ultimate goal of the CASSANDRA simulation functionality is the identification of good practices that lead to energy efficiency, clustering electric energy consumers according to their consumption patterns, and the studying consumer change behaviour when presented with various demand response programs. Copyright © 2015 ICST.

关键词： Climate change

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