Search results for "machine learning."
showing 10 items of 1455 documents
Prognostic Impact of Frozen Section Investigation and Extent of Proximal Safety Margin in Gastric Cancer Resection
2020
Background and aims Guidelines propose different extents of macroscopic proximal margin for gastric cancer and frozen margin investigation in selected cases, but data is lacking. This study was to evaluate the necessary extent of macroscopic proximal margin, accuracy of frozen margin investigation, and prognostic impact of tumor-free proximal margin length in pT2-pT4 gastric cancer. Study design Proximal and distal frozen margins were routinely investigated intraoperatively in all pT2-pT4 gastric cancers resected between 2011 and 2017. Macroscopic and microscopic proximal margin lengths were correlated. For R0-resections, survival analysis was performed for distal gastrectomy (DG) with micr…
Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.
2020
Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and &ldquo
Statistical formats to optimize evidence-based decision making: A behavioral approach
2013
Abstract Statistical information is crucial for managerial decision making. The decision-making literature in psychology and mathematical cognition documents how different statistical formats can facilitate certain types of decisions. The present analysis is the first of its kind to assess the impact of statistical formats in the presentation of data from market research on both the optimality of market decisions and the time required to perform the decision-making process. An economic experiment provides the data for this study. The experiment presents statistical information in simple frequencies and relative frequencies using numerical and pictorial representations in the context of diff…
A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes
2011
Published version of an article from the book: Hybrid artificial intelligent systems, Lecture notes in computer science. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-21219-2_2 There are numerous applications in Artificial Intelligence (AI) and Machine Learning (ML) where the criteria for decisions are based on testing procedures. The most common tools used in such random phenomena involve Random Walks (RWs). The theory of RWs and its applications have gained an increasing research interest since the start of the last century. [1]. In this context, we note that a RW is, usually, defined as a trajectory involving a series of successive ran…
Convolutional Neural Networks for Multispectral Image Cloud Masking
2020
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems. One of their major strengths is that, when enough data is available, CNN perform an end-to-end learning without the need of custom feature extraction methods. In this work, we study the use of different CNN architectures for cloud masking of Proba-V multispectral images. We compare such methods with the more classical machine learning approach based on feature extraction plus supervised classification. Experimental results suggest that CNN are a promising alternative for solving cloud masking problems.
Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification
2021
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant’s age, viewed as prior knowledge. Here, we propose to leverage continuous proxy me…
Which Is Which? Evaluation of Local Descriptors for Image Matching in Real-World Scenarios
2019
Matching with local image descriptors is a fundamental task in many computer vision applications. This paper describes the WISW contest held within the framework of the CAIP 2019 conference, aimed at benchmarking recent descriptors in challenging planar and non-planar real image matching scenarios. According to the contest results, the descriptors submitted to the competition, most of which based on deep learning, perform significantly better than the current state-of-the-art in image matching. Nonetheless, there is still room for improvement, especially in the case of non-planar scenes.
Do trained assessors generalize their knowledge to new stimuli?
2005
Previous work showed that trained assessors are better at discriminating and describing familiar chemico-sensorial stimuli than novices. In this study, we evaluated whether this superiority holds true for new stimuli. We first trained a group of subjects to characterize beer flavors over a two year period. After training was accomplished, we compared the performance of these trained assessors with the performance of novice subjects for discrimination and matching tasks. The tasks were performed using both well-learned and new beers. Trained assessors outperformed novices in the discrimination task for learned beers but not for new beers. But on the matching task, trained assessors outperfor…
Boosting the supercapacitive behavior of CoAl-layered double hydroxides via tuning the metal composition and interlayer space
2020
Layered double hydroxides (LDHs) are promising supercapacitor materials due to their wide chemical versatility, earth abundant metals and high specific capacitances. Many parameters influencing the supercapacitive performance have been studied such as the chemical composition, the synthetic approaches, and the interlayer anion. However, no systematic studies about the effect of the basal space have been carried out. Here, two-dimensional (2D) CoAl-LDHs were synthesized through anion exchange reactions using surfactant molecules in order to increase the interlayer space (ranging from 7.5 to 32.0 Å). These compounds exhibit similar size and dimensions but different basal space to explore excl…
Boosting the Performance of One-Step Solution-Processed Perovskite Solar Cells Using a Natural Monoterpene Alcohol as a Green Solvent Additive
2021
The perovskite film is the core of a perovskite solar cell (PSC), and its quality is crucial for the performance of such devices. The morphology, crystallinity, and surface coverage of the perovskite layer greatly affect the power conversion efficiency (PCE), hysteresis, and long-term stability of PSCs. The incorporation of appropriate solvent additives in the perovskite precursor solution is an effective strategy to control the film morphology and reduce the defects and grain boundaries. However, the commonly used solvent additives are environmentally harmful and highly toxic. In this work, α-terpineol (a nontoxic, eco-friendly, and low-cost monoterpene alcohol) is employed for the first t…