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https://www.readbyqxmd.com/read/29346328/comparison-of-svm-rf-and-elm-on-an-electronic-nose-for-the-intelligent-evaluation-of-paraffin-samples
#1
Hong Men, Songlin Fu, Jialin Yang, Meiqi Cheng, Yan Shi, Jingjing Liu
Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin...
January 18, 2018: Sensors
https://www.readbyqxmd.com/read/29346031/2016-new-horizons-lecture-beyond-imaging-radiology-of-tomorrow
#2
Hedvig Hricak
This article is based on the New Horizons lecture delivered at the 2016 Radiological Society of North America Annual Meeting. It addresses looming changes for radiology, many of which stem from the disruptive effects of the Fourth Industrial Revolution. This is an emerging era of unprecedented rapid innovation marked by the integration of diverse disciplines and technologies, including data science, machine learning, and artificial intelligence-technologies that narrow the gap between man and machine. Technologic advances and the convergence of life sciences, physical sciences, and bioengineering are creating extraordinary opportunities in diagnostic radiology, image-guided therapy, targeted radionuclide therapy, and radiology informatics, including radiologic image analysis...
January 18, 2018: Radiology
https://www.readbyqxmd.com/read/29343797/artificial-intelligence-estimation-of-carotid-femoral-pulse-wave-velocity-using-carotid-waveform
#3
Peyman Tavallali, Marianne Razavi, Niema M Pahlevan
In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal processing inputs to this machine learning algorithm are sensor agnostic, the presented method can accompany any medical instrument that provides a calibrated or uncalibrated carotid pressure waveform. Our results show that, for an unseen hold back test set population in the age range of 20 to 69, our model can estimate PWV with a Root-Mean-Square Error (RMSE) of 1...
January 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29342489/correction-artificial-intelligence-may-help-in-predicting-the-need-for-additional-surgery-after-endoscopic-resection-of-t1-colorectal-cancer
#4
Katsuro Ichimasa, Shin-Ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyachi
No abstract text is available yet for this article.
January 17, 2018: Endoscopy
https://www.readbyqxmd.com/read/29341917/a-novel-method-for-the-production-of-core-shell-microparticles-by-inverse-gelation-optimized-with-artificial-intelligent-tools
#5
Rosalia Rodríguez-Dorado, Mariana Landín, Ayça Altai, Paola Russo, Rita P Aquino, Pasquale Del Gaudio
Numerous studies have been focused on hydrophobic compounds encapsulation as oils. In fact, oils can provide numerous health benefits as synergic ingredient combined with other hydrophobic active ingredients. However, stable microparticles for pharmaceutical purposes are difficult to achieve when commonly techniques are used. In this work, sunflower oil was encapsulated in calcium-alginate capsules by prilling technique in co-axial configuration. Core-shell beads were produced by inverse gelation directly at the nozzle using w/o emulsion containing aqueous calcium chloride solution in sunflower oil pumped through the inner nozzle while an aqueous alginate solution, coming out from the annular nozzle, produced the bead shell...
January 13, 2018: International Journal of Pharmaceutics
https://www.readbyqxmd.com/read/29341027/developing-deep-learning-applications-for-life-science-and-pharma-industry
#6
Daniel Siegismund, Vasily Tolkachev, Stephan Heyse, Beate Sick, Oliver Duerr, Stephan Steigele
Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be 'game changing' for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning...
January 16, 2018: Drug Research
https://www.readbyqxmd.com/read/29339817/cooperating-with-machines
#7
Jacob W Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A Goodrich, Iyad Rahwan
Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions...
January 16, 2018: Nature Communications
https://www.readbyqxmd.com/read/29339510/artificial-intelligence-exploration-of-unstable-protocells-leads-to-predictable-properties-and-discovery-of-collective-behavior
#8
Laurie J Points, James Ward Taylor, Jonathan Grizou, Kevin Donkers, Leroy Cronin
Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors...
January 16, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29337803/from-big-data-to-artificial-intelligence-harnessing-data-routinely-collected-in-the-process-of-care
#9
Barret Rush, David J Stone, Leo Anthony Celi
No abstract text is available yet for this article.
February 2018: Critical Care Medicine
https://www.readbyqxmd.com/read/29336344/-the-urologist-of-the-future-and-new-technologies
#10
Francois Peinado, Atanasio Fernández, Fernando Teba, Guillermo Celada, Marco Antonio Acosta
The last 25 years have brought about revolutionary changes for medicine and in particular for urology: internet was only in its infancy, medical records were written on paper, searches for medical information were done in the hospital library, medical articles were photocopied and our relationship with patients only existed face to face. Social networks had not yet appeared and even Google did not exist. Just imagine what might happen during the next 25 years, we're going to see even more radical changes. The urologist of the future is going to see the arrival of artificial intelligence, collaborative medicine, telemedicine, machine learning, the Internet of Things and personalized robotics; in the meantime, social media will continue to transform the interaction between physician and patient...
January 2018: Archivos Españoles de Urología
https://www.readbyqxmd.com/read/29335825/application-of-artificial-intelligence-using-a-convolutional-neural-network-for-detecting-gastric-cancer-in-endoscopic-images
#11
Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto, Soichiro Ishihara, Satoki Shichijo, Tsuyoshi Ozawa, Tatsuya Ohnishi, Mitsuhiro Fujishiro, Keigo Matsuo, Junko Fujisaki, Tomohiro Tada
BACKGROUND: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. METHODS: A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer...
January 15, 2018: Gastric Cancer
https://www.readbyqxmd.com/read/29333732/artificial-intelligence-will-reduce-the-need-for-clinical-medical-physicists
#12
Xiaoli Tang, Brian Wang, Yi Rong
No abstract text is available yet for this article.
January 2018: Journal of Applied Clinical Medical Physics
https://www.readbyqxmd.com/read/29327117/graphical-classification-of-dna-sequences-of-hla-alleles-by-deep-learning
#13
Jun Miyake, Yuhei Kaneshita, Satoshi Asatani, Seiichi Tagawa, Hirohiko Niioka, Takashi Hirano
Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence "Deep Learning (Stacked autoencoder)". Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimensional representation and were plotted. Profiles of the two-dimensional plots indicate that the alleles can be classified as clusters are formed. The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles...
January 11, 2018: Human Cell
https://www.readbyqxmd.com/read/29325922/the-cost-effectiveness-of-using-paro-a-therapeutic-robotic-seal-to-reduce-agitation-and-medication-use-in-dementia-findings-from-a-cluster-randomized-controlled-trial
#14
Merehau C Mervin, Wendy Moyle, Cindy Jones, Jenny Murfield, Brian Draper, Elizabeth Beattie, David H K Shum, Siobhan O'Dwyer, Lukman Thalib
OBJECTIVES: To examine the within-trial costs and cost-effectiveness of using PARO, compared with a plush toy and usual care, for reducing agitation and medication use in people with dementia in long-term care. DESIGN: An economic evaluation, nested within a cluster-randomized controlled trial. SETTING: Twenty-eight facilities in South-East Queensland, Australia. PARTICIPANTS: A total of 415 residents, all aged 60 years or older, with documented diagnoses of dementia...
January 9, 2018: Journal of the American Medical Directors Association
https://www.readbyqxmd.com/read/29324970/watson-for-oncology-and-breast-cancer-treatment-recommendations-agreement-with-an-expert-multidisciplinary-tumor-board
#15
S P Somashekhar, M-J Sepúlveda, S Puglielli, A D Norden, E H Shortliffe, C Rohit Kumar, A Rauthan, N Arun Kumar, P Patil, K Rhee, Y Ramya
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer...
January 9, 2018: Annals of Oncology: Official Journal of the European Society for Medical Oncology
https://www.readbyqxmd.com/read/29324240/cognitive-science-in-the-era-of-artificial-intelligence-a-roadmap-for-reverse-engineering-the-infant-language-learner
#16
Emmanuel Dupoux
Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e., building an effective system that mimics infant's achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible...
January 8, 2018: Cognition
https://www.readbyqxmd.com/read/29319225/de-novo-design-of-bioactive-small-molecules-by-artificial-intelligence
#17
Daniel Merk, Lukas Friedrich, Francesca Grisoni, Gisbert Schneider
Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model...
January 10, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29309734/methodologic-guide-for-evaluating-clinical-performance-and-effect-of-artificial-intelligence-technology-for-medical-diagnosis-and-prediction
#18
Seong Ho Park, Kyunghwa Han
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics...
January 8, 2018: Radiology
https://www.readbyqxmd.com/read/29306756/a-loop-based-neural-architecture-for-structured-behavior-encoding-and-decoding
#19
Thomas Gisiger, Mounir Boukadoum
We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections...
December 8, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29305341/a-pilot-study-of-biomedical-text-comprehension-using-an-attention-based-deep-neural-reader-design-and-experimental-analysis
#20
Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang
BACKGROUND: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain...
January 5, 2018: JMIR Medical Informatics
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