Results 31 to 40 of about 9,204,231 (269)

Detecting dense text in natural images

IET Computer Vision, 2020
Most existing text detection methods are mainly motivated by deep learning‐based object detection approaches, which may result in serious overlapping between detected text lines, especially in dense text scenarios.
Dianzhuan Jiang, Shengsheng Zhang, Yaping Huang, Qi Zou, Xingyuan Zhang, Mengyang Pu, Junbo Liu   +6 more
doaj   +1 more source

Object-Guided Remote Sensing Image Scene Classification Based on Joint Use of Deep-Learning Classifier and Detector

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Due to the extremely complex composition of remote sensing scenes, REmote Sensing Image Scene Classification (RESISC) is still a challenging task. To further improve classification accuracy, this article introduces a deep-learning detector into RESISC ...
Xiaoliang Yang, Weidong Yan, Weiping Ni, Xifeng Pu, Han Zhang, Maoyu Zhang   +5 more
doaj   +1 more source

Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms

Applied Sciences, 2021
In ultra-high vacuum systems, obtaining the composition of a mass spectrum is often a challenging task due to the highly overlapping nature of the individual profiles of the gas species that contribute to that spectrum, as well as the high differences in
Carlos Flores-Garrigós, Juan Vicent-Camisón, Juan J. Garcés-Iniesta, Emilio Soria-Olivas, Juan Gómez-Sanchís, Fernando Mateo   +5 more
doaj   +1 more source

Communication and Language Profiles of Children Treated for Posterior Fossa Brain Tumors

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Cognitive and language deficits are frequently reported sequelae of posterior fossa brain tumors (PFBT). Typically, delayed onset impedes prompt assessment and early intervention. This has devastating implications for quality of life.
Zara Sved, Meghan Hesse, Jennie Cusiter, Annabel Webb, Kimberley Docking   +4 more
wiley   +1 more source

Detection and identification of tea leaf diseases based on AX-RetinaNet

Scientific Reports, 2022
The accurate detection and identification of tea leaf diseases are conducive to its precise prevention and control. Convolutional neural network (CNN) can automatically extract the features of diseased tea leaves in the images.
Wenxia Bao, Tao Fan, Gensheng Hu, Dong Liang, Haidong Li   +4 more
doaj   +1 more source

Big Data Analysis

, 2022
Abstract This chapter defines big data and sets out methodological decisions to be made in big data analysis. It offers ways of researching millions of instances of ‘everyday talk’ in the public sphere, on a scale that is impossible using other methods, and thus especially useful for deliberative systems research.
Franco-Guillén, Núria, De Laile, Sebastian, Parkinson, John   +2 more
openaire   +2 more sources

The HIT Network for Children and Adolescents With CNS Tumors Facilitates Improvements of Diagnostic Assessments, Multimodal Treatments, Individual Counseling, and Research in Germany, Austria, and Switzerland

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background The HIT network was established in 2000 to create a population‐based structure aiming to improve survival rates and reduce late effects for children with central nervous system (CNS) tumors by conducting comprehensive clinical trials.
Stefan Rutkowski, Michael C. Frühwald, Gudrun Fleischhack, Stephan Tippelt, Pablo Hernáiz‐Driever, Olaf Witt, Astrid Gnekow, Christof Kramm, Matthias Eyrich, Johannes E. A. Wolf, Hermann Müller, Carsten Friedrich, Uwe R. Kordes, Denise Obrecht‐Sturm, Petra Ketteler, Barbara von Zezschwitz, Pascal Johann, Dominik Sturm, Karoline Nemes, Stefan Schönberger, Till Milde, Brigitte Bison, Monika Warmuth‐Metz, Torsten Pietsch, Ulrich Schüller, Arend Koch, David Capper, Martin Hasselblatt, Christian Thomas, Christian Hagel, Stefan M. Pfister, Felix Sahm, David T. W. Jones, Martin U. Schuhmann, Jürgen Krauss, Pedram Emami, Ahmed El Damaty, Rolf D. Kortmann, Rudolf Schwarz, Jürgen Debus, Mechthild Krause, Fabian Pohl, Holger Ottensmeier, Lisa Bußenius, Katja Baust, Andreas Faldum, Cecile Ronckers, Desiree Grabow, Martin Benesch, Thomas Perwein, Karin Dieckmann, Irene Slavc, Johannes Gojo, Nicolas U. Gerber, André O. von Bueren, Katrin Scheinemann, Gabriele Calaminus, Beate Timmermann, Ulrich‐Wilhelm Thomale, Martin Mynarek   +59 more
wiley   +1 more source

Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras

Applied Sciences
In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series in video format using a multimodal approach.
Alejandro Dionis-Ros, Joan Vila-Francés, Rafael Magdalena-Benedito, Fernando Mateo, Antonio J. Serrano-López   +4 more
doaj   +1 more source

Stressful Events Reported by Childhood Cancer Survivors and Community Controls From the St. Jude Lifetime (SJLIFE) Cohort: A Mixed Method Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Characterizing stressful events reported by childhood cancer survivors experienced throughout the lifespan may help improve trauma‐informed care relevant to the survivor experience. Methods Participants included 2552 survivors (54% female; 34 years of age) and 469 community controls (62% female; 33 years of age) from the St.
Megan E. Ware, Colter K. Clayton, Fang Wang, Sedigheh Mirzaei, Kyla Shelton, Tara M. Brinkman, Victoria Willard, Katianne M. Howard Sharp, Kevin Krull, Lindsay F. Schwartz, Melissa M. Hudson, Gregory T. Armstrong, Kirsten K. Ness, Rachel T. Webster   +13 more
wiley   +1 more source

Extreme Gradient Boosting Beats In-Silico Identification of Proteins Potentially Associated With Alzheimer’s

Applied Computational Intelligence and Soft Computing
Alzheimer’s disease (AD) is a chronic, advanced brain sickness disease that slowly destroys memory and thinking skills and, in the end, the ability to perform routine tasks.
Sadia Khalil, Wajid Arshad Abbasi, Syed Ali Abbas, Maryum Bibi, Saiqa Andleeb, Amsa Shabir   +5 more
doaj   +1 more source