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https://www.readbyqxmd.com/read/28110106/fast-learning-method-for-convolutional-neural-networks-using-extreme-learning-machine-and-its-application-to-lane-detection
#1
Jihun Kim, Jonghong Kim, Gil-Jin Jang, Minho Lee
Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection...
December 10, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28108092/the-analysis-and-application-of-a-new-hybrid-pollutants-forecasting-model-using-modified-kolmogorov-zurbenko-filter
#2
Peizhi Li, Yong Wang, Qingli Dong
Cities in China suffer from severe smog and haze, and a forecasting system with high accuracy is of great importance to foresee the concentrations of the airborne particles. Compared with chemical transport models, the growing artificial intelligence models can simulate nonlinearities and interactive relationships and getting more accurate results. In this paper, the Kolmogorov-Zurbenko (KZ) filter is modified and firstly applied to construct the model using an artificial intelligence method. The concentration of inhalable particles and fine particulate matter in Dalian are used to analyze the filtered components and test the forecasting accuracy...
January 17, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28106594/big-data-and-nursing-implications-for-the-future
#3
Maxim Topaz, Lisiane Pruinelli
Big data is becoming increasingly more prevalent and it affects the way nurses learn, practice, conduct research and develop policy. The discipline of nursing needs to maximize the benefits of big data to advance the vision of promoting human health and wellbeing. However, current practicing nurses, educators and nurse scientists often lack the required skills and competencies necessary for meaningful use of big data. Some of the key skills for further development include the ability to mine narrative and structured data for new care or outcome patterns, effective data visualization techniques, and further integration of nursing sensitive data into artificial intelligence systems for better clinical decision support...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28105917/proclat-a-new-bioinformatics-tool-for-in-silico-protein-reclassification-case-study-of-drab-a-protein-coded-from-the-dratgb-operon-in-azospirillum-brasilense
#4
Elisa Terumi Rubel, Roberto Tadeu Raittz, Nilson Antonio da Rocha Coimbra, Michelly Alves Coutinho Gehlen, Fábio de Oliveira Pedrosa
BACKGROUND: Azopirillum brasilense is a plant-growth promoting nitrogen-fixing bacteria that is used as bio-fertilizer in agriculture. Since nitrogen fixation has a high-energy demand, the reduction of N2 to NH4(+) by nitrogenase occurs only under limiting conditions of NH4(+) and O2. Moreover, the synthesis and activity of nitrogenase is highly regulated to prevent energy waste. In A. brasilense nitrogenase activity is regulated by the products of draG and draT. The product of the draB gene, located downstream in the draTGB operon, may be involved in the regulation of nitrogenase activity by an, as yet, unknown mechanism...
December 15, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28087372/the-future-of-artificial-intelligence-and-interpretative-specialization-in-clinical-biochemistry
#5
Christopher McCudden
No abstract text is available yet for this article.
January 10, 2017: Clinical Biochemistry
https://www.readbyqxmd.com/read/28080147/bringing-more-transparency-to-artificial-intelligence
#6
Andrea Gaggioli
No abstract text is available yet for this article.
January 2017: Cyberpsychology, Behavior and Social Networking
https://www.readbyqxmd.com/read/28071987/a-computer-aided-diagnosis-system-using-artificial-intelligence-for-the-diagnosis-and-characterization-of-thyroid-nodules-on-ultrasound-initial-clinical-assessment
#7
Young Jun Choi, Jung Hwan Baek, Hye Sun Park, Woo Hyun Shim, Tae Yong Kim, YoungKee Shong, Jeong Hyun Lee
Background We describe an initial clinical assessment of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intelligence (AI) for thyroid ultrasound, and evaluate its performance in the diagnosis of malignant thyroid nodules and categorization of nodule characteristics. Methods This prospective study protocol was reviewed and approved by the institutional review board. Patients with thyroid nodules with decisive diagnosis, whether benign or malignant on the basis of cytopathologic or US results, were consecutively enrolled from November 2015 to February 2016...
January 10, 2017: Thyroid: Official Journal of the American Thyroid Association
https://www.readbyqxmd.com/read/28069962/content-analysis-of-150-years-of-british-periodicals
#8
Thomas Lansdall-Welfare, Saatviga Sudhahar, James Thompson, Justin Lewis, Nello Cristianini
Previous studies have shown that it is possible to detect macroscopic patterns of cultural change over periods of centuries by analyzing large textual time series, specifically digitized books. This method promises to empower scholars with a quantitative and data-driven tool to study culture and society, but its power has been limited by the use of data from books and simple analytics based essentially on word counts. This study addresses these problems by assembling a vast corpus of regional newspapers from the United Kingdom, incorporating very fine-grained geographical and temporal information that is not available for books...
January 9, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28064478/automated-phase-mapping-with-agilefd-and-its-application-to-light-absorber-discovery-in-the-v-mn-nb-oxide-system
#9
Santosh K Suram, Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Lan Zhou, R Bruce van Dover, Carla P Gomes, John M Gregoire
Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial X-ray diffraction data sets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of X-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition...
January 9, 2017: ACS Combinatorial Science
https://www.readbyqxmd.com/read/28055930/deep-learning-for-health-informatics
#10
Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu Perez, Benny Lo, Guang-Zhong Yang
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data...
December 29, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28055847/show-and-tell-lessons-learned-from-the-2015-mscoco-image-captioning-challenge
#11
Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions...
July 7, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28048233/su-g-201-09-evaluation-of-a-novel-machine-learning-algorithm-for-permanent-prostate-brachytherapy-treatment-planning
#12
A Nicolae, L Lu, G Morton, H Chung, J Helou, M Al Hanaqta, A Loblaw, E Heath, A Ravi
PURPOSE: A novel, automated, algorithm for permanent prostate brachytherapy (PPB) treatment planning has been developed. The novel approach uses machine-learning (ML), a form of artificial intelligence, to substantially decrease planning time while simultaneously retaining the clinical intuition of plans created by radiation oncologists. This study seeks to compare the ML algorithm against expert-planned PPB plans to evaluate the equivalency of dosimetric and clinical plan quality. METHODS: Plan features were computed from historical high-quality PPB treatments (N = 100) and stored in a relational database (RDB)...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28043026/venturing-into-the-uncanny-valley-of-mind-the-influence-of-mind-attribution-on-the-acceptance-of-human-like-characters-in-a-virtual-reality-setting
#13
Jan-Philipp Stein, Peter Ohler
For more than 40years, the uncanny valley model has captivated researchers from various fields of expertise. Still, explanations as to why slightly imperfect human-like characters can evoke feelings of eeriness remain the subject of controversy. Many experiments exploring the phenomenon have emphasized specific visual factors in connection to evolutionary psychological theories or an underlying categorization conflict. More recently, studies have also shifted away focus from the appearance of human-like entities, instead exploring their mental capabilities as basis for observers' discomfort...
December 30, 2016: Cognition
https://www.readbyqxmd.com/read/28040748/how-the-strengths-of-lisp-family-languages-facilitate-building-complex-and-flexible-bioinformatics-applications
#14
Bohdan B Khomtchouk, Edmund Weitz, Peter D Karp, Claes Wahlestedt
We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code...
December 31, 2016: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/28029644/the-cornucopia-of-meaningful-leads-applying-deep-adversarial-autoencoders-for-new-molecule-development-in-oncology
#15
Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, Alex Zhavoronkov
Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule...
December 22, 2016: Oncotarget
https://www.readbyqxmd.com/read/28026791/intelligent-non-invasive-diagnosis-of-aneuploidy-raw-values-and-highly-imbalanced-dataset
#16
Andreas Neocleous, Kypros Nicolaides, Christos Schizas
The objective of this work is to introduce a noninvasive diagnosis procedure for aneuploidy and minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets1 with a total of 122362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus...
December 22, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28025341/can-we-replace-curation-with-information-extraction-software
#17
REVIEW
Peter D Karp
Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can...
2016: Database: the Journal of Biological Databases and Curation
https://www.readbyqxmd.com/read/28003527/ni%C3%A3-pce-bell-or-turing-how-to-test-odour-reproduction
#18
REVIEW
David Harel
Decades before the existence of anything resembling an artificial intelligence system, Alan Turing raised the question of how to test whether machines can think, or, in modern terminology, whether a computer claimed to exhibit intelligence indeed does so. This paper raises the analogous issue for olfaction: how to test the validity of a system claimed to reproduce arbitrary odours artificially, in a way recognizable to humans. Although odour reproduction systems are still far from being viable, the question of how to test candidates thereof is claimed to be interesting and non-trivial, and a novel method is proposed...
December 2016: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/28002840/suprasegmental-characteristics-of-spontaneous-speech-produced-in-good-and-challenging-communicative-conditions-by-talkers-aged-9-14-years
#19
Valerie Hazan, Outi Tuomainen, Michèle Pettinato
Purpose: This study investigated the acoustic characteristics of spontaneous speech by talkers aged 9-14 years and their ability to adapt these characteristics to maintain effective communication when intelligibility was artificially degraded for their interlocutor. Method: Recordings were made for 96 children (50 female participants, 46 male participants) engaged in a problem-solving task with a same-sex friend; recordings for 20 adults were used as reference. The task was carried out in good listening conditions (normal transmission) and in degraded transmission conditions...
December 1, 2016: Journal of Speech, Language, and Hearing Research: JSLHR
https://www.readbyqxmd.com/read/28000444/diagnosis-and-classification-of-17-diseases-from-1404-subjects-via-pattern-analysis-of-exhaled-molecules
#20
Morad K Nakhleh, Haitham Amal, Raneen Jeries, Yoav Y Broza, Manal Aboud, Alaa Gharra, Hodaya Ivgi, Salam Khatib, Shifaa Badarneh, Lior Har-Shai, Lea Glass-Marmor, Izabella Lejbkowicz, Ariel Miller, Samih Badarny, Raz Winer, John Finberg, Sylvia Cohen-Kaminsky, Frédéric Perros, David Montani, Barbara Girerd, Gilles Garcia, Gérald Simonneau, Farid Nakhoul, Shira Baram, Raed Salim, Marwan Hakim, Maayan Gruber, Ohad Ronen, Tal Marshak, Ilana Doweck, Ofer Nativ, Zaher Bahouth, Da-You Shi, Wei Zhang, Qing-Ling Hua, Yue-Yin Pan, Li Tao, Hu Liu, Amir Karban, Eduard Koifman, Tova Rainis, Roberts Skapars, Armands Sivins, Guntis Ancans, Inta Liepniece-Karele, Ilze Kikuste, Ieva Lasina, Ivars Tolmanis, Douglas Johnson, Stuart Z Millstone, Jennifer Fulton, John W Wells, Larry H Wilf, Marc Humbert, Marcis Leja, Nir Peled, Hossam Haick
We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined...
December 21, 2016: ACS Nano
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