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Erratum to: International workshop on insecticide resistance in vectors of arboviruses, December 2016, Rio de Janeiro, Brazil

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Parasites & vectors 2017年第1期10卷 391页

作者： Vincent Corbel Dina M Fonseca David Weetman João Pinto Nicole L Achee Fabrice Chandre Mamadou B Coulibaly Isabelle Dusfour John Grieco Waraporn Juntarajumnong Audrey Lenhart Ademir J Martins Catherine Moyes Lee Ching Ng Kamaraju Raghavendra Hassan Vatandoost John Vontas Pie Muller Shinji Kasai Florence Fouque Raman Velayudhan Claire Durot Jean-Philippe David Institut de Recherche pour le Développement (IRD) Maladies Infectieuses et Vecteurs Ecologie Génétique Evolution et Contrôle (MIVEGEC UM1-CNRS 5290-IRD 224) B.P. 64501 911 Avenue Agropolis 34394 Montpellier Cedex 5 France. vincent.corbel@ird.fr. Center for Vector Biology Rutgers University (RU) 180 Jones Avenue New Brunswick NJ 08901 USA. Department of Vector Biology Liverpool School of Tropical Medicine (LSTM) Pembroke Place Liverpool L35QA UK. Global Health and Tropical Medicine GHTM Instituto de Higiene e Medicina Tropical IHMT Universidade Nova de Lisboa UNL Rua da Junqueira 100 1349-008 Lisbon Portugal. Department of Biological Sciences University of Notre Dame (UND) Eck Institute for Global Health 239 Galvin Life Science Center Notre Dame Indiana 46556 USA. Institut de Recherche pour le Développement (IRD) Maladies Infectieuses et Vecteurs Ecologie Génétique Evolution et Contrôle (MIVEGEC UM1-CNRS 5290-IRD 224) B.P. 64501 911 Avenue Agropolis 34394 Montpellier Cedex 5 France. Malaria Research and Training Center (MRTC) Point G Bamako B.P. 1805 Mali. Institut Pasteur de la Guyane (IPG) 23 avenue Pasteur B.P. 6010 97306 Cayenne Cedex French Guiana. Department of Entomology Kasetsart University (KU) 50 Ngam Wong Wan Rd Ladyaow Bangkok Chatuchak 10900 Thailand. Center for Global Health/Division of Parasitic Diseases and Malaria/Entomology Branch U.S. Centers for Disease Control and Prevention (CDC) 1600 Clifton Rd. NE MS G-49 Bldg. 23 Atlanta GA 30329 USA. Instituto Oswaldo Cruz (Fiocruz) Avenida Brasil 4365 Rio de Janeiro/RJ CEP Manguinhos 21040-360 Brazil. Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford Oxford OX3 7LF UK. Environmental Health Institute (EHI) National Environment Agency (NEA) 11 Biopolis Way Helios Block #04-03/04 & #06-05 /08 Singapore Republic of Singapore. Department of Health Research National Institute of Malaria Research (NIMR) GoI Sector 8 Dwarka Delh

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Erratum to: Methods for evaluating medical tests and biomarkers

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Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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Advanced IoT Sensors, Networks and Systems 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Ashwani Kumar Dubey Vijayan Sugumaran Peter Han Joo Chong

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Machine Learning in Chemoinformatics and Medicinal Chemistry

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Biomedical data science 1000年第1期5卷 43-65页

作者： Raquel Rodríguez-Pérez Filip Miljković Jürgen Bajorath 2Current affiliation: Novartis Institutes for Biomedical Research Novartis Campus Basel Switzerland 1Department of Life Science Informatics B-IT (Bonn-Aachen International Center for Information Technology) Chemical Biology and Medicinal Chemistry Program Unit LIMES (Life and Medical Sciences Institute) Rheinische Friedrich-Wilhelms-Universität Bonn Germany email: bajorath@bit.uni-bonn.de 3Current affiliation: Data Science and AI Imaging and Data Analytics Clinical Pharmacology and Safety Sciences R&D AstraZeneca Gothenburg Sweden

In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains.

关键词： machine learning deep learning chemoinformatics medicinal chemistry data structures learning strategies

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Hybrid Artificial Intelligent Systems 1

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丛书名： Lecture Notes in Computer science

1000年

作者： Pablo García Bringas Hilde Pérez García Francisco Javier Martínez de Pisón José Ramón Villar Flecha Alicia Troncoso Lora Enrique A. de la Cal Álvaro Herrero Francisco Martínez Álvarez Giuseppe Psaila Héctor Quintián Emilio Corchado

ISBN: (数字)9783031154713

ISBN: (纸本)9783031154706

This book constitutes the refereed proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022, held in Salamanca, Spain, in September 2022.;The 43 full papers presented in this book were carefully reviewed and selected from 67 submissions. They were organized in topical sections as follows: bioinformatics; data mining and decision support systems; deep learning; evolutionary computation; HAIS applications; image and speech signal processing; and optimization techniques.

关键词： Artificial Intelligence Computer Applications data Mining and Knowledge Discovery

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Application of Intelligent Systems in Multi-modal Information analytics 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Vijayan Sugumaran Zheng Xu Huiyu Zhou

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Stop the Hate, Spread the Hope: An Ensemble Model for Hope Speech Detection in English and Dravidian Languages

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Deepawali Sharma Vedika Gupta Vivek Kumar Singh Bharathi Raja Chakravarthi Department of Computer Science Banaras Hindu University Faculty of Science Varanasi India School of Computer Science Engineering and Technology (SCSET) Bennett University Noida India Jindal Global Business School OP Jindal Global University Sonipat India Department of Computer Science University of Delhi New Delhi India School of Computer Science National University of Ireland Galway Galway Ireland Unit for Linguistic Data Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Galway Ireland

The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to detect harmful speech such as hate speech, cyberbullying, and abuse. Recently, another type of speech referred to as ‘Hope Speech’ has gained attention from the research community. Hope speech consists of positive affirmations or words of reassurance, encouragement, consolation or motivation offered to the affected individual/ community during the lean periods of life. However, there has been relatively less research focused on the detection of hope speech, more particularly in low-resource languages. This paper, therefore, attempts to develop an ensemble model for detecting hope speech in some low-resource languages. data for four different languages, namely English, Kannada, Malayalam and Tamil are obtained and experimented with different deep learning-based models. An ensemble model is proposed to combine the advantages of the better performing models. Experimental results demonstrate the superior performance of the proposed Ensemble (LSTM, mBERT, XLM-RoBERTa) model compared to individual models based on data from all four languages (weighted average F1-score for English is 0.93; for Kannada is 0.74; for Malayalam is 0.82; and for Tamil is 0.60). Thus, the proposed ensemble model proves to be a suitable approach for hope speech detection in the given low resource languages.

关键词： CNN Deep Learning GloVe embedding Hate Speech Hope Speech Low Resource Language LSTM mBERT XLM-RoBERTa

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Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment

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ACM Transactions on Sensor Networks 1000年

作者： Yongzhe Jia Bowen Liu Xuyun Zhang Fei Dai Arif Khan Lianyong Qi Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland College of Computer Science and Technology China University of Petroleum East China - Qingdao Campus Qingdao China

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.

关键词： Federated learning split learning model pruning edge computing

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Application of Intelligent Systems in Multi-modal Information analytics 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Vijayan Sugumaran Zheng Xu Huiyu Zhou

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