214.423 hasil untuk machine learning
Menampilkan 421–440 · 1,31 detik · RSS
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Machine learning reveals orbital interaction in materials
We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies…
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How machine learning is shaping cognitive neuroimaging
…uncover new aspects of cognitive organization. We also give a statistical learning perspective on these progresses and on the remaining gaping holes.
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Skull-Stripping with Machine Learning Deformable Organisms
…these may be easily incorporated into the plan. In addition, we use a machine learning method to make our segmentation more accurate. RESULTS: Our method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error…
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Machine Learning Algorithms Implemented in Image Analysis
…photographs, histological stains, DAB immunohistochemistry, etc. We used machine learning algorithms as implemented in FIJI to isolate specific features in longitudinal retinal photographs of non-human primates. Images acquired over several years with different technologies, cameras and skills were…
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Revisiting Warfarin Dosing Using Machine Learning Techniques
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which…
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Distinguishing Asthma Phenotypes Using Machine Learning Approaches
…phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique—latent class analysis—and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It…
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Machine learning in nephrology: scratching the surface
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology…
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Machine learning techniques in cardiac risk assessment
BACKGROUND: The objective of this study was to predict the mortality risk of patients during or shortly after cardiac surgery by using machine learning techniques and their learning abilities from collected data. METHODS: The dataset was obtained from Acıbadem Maslak Hospital. Risk factors of the…
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Artificial Intelligence/Machine Learning in Diabetes Care
Artificial intelligence/Machine learning (AI/ML) is transforming all spheres of our life, including the healthcare system. Application of AI/ML has a potential to vastly enhance the reach of diabetes care thereby making it more efficient. The huge burden of diabetes cases in India represents a…
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MiPepid: MicroPeptide identification tool using machine learning
BACKGROUND: Micropeptides are small proteins with length
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Artificial Intelligence and Machine Learning in Anesthesiology
Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis and self-learning. Progress had historically languished in these areas…
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Pediatric Severe Sepsis Prediction Using Machine Learning
…methods are needed to facilitate this early detection. Objective: Can a machine-learning based prediction algorithm using electronic healthcare record (EHR) data predict severe sepsis onset in pediatric populations? Methods: EHR data were collected from a retrospective set of de-identified…
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Pullout Strength Predictor: A Machine Learning Approach
…feeling of insertion torque. The objective of the paper was to develop a machine learning based model using density of foam, insertion angle, insertion depth, and reinsertion to predict the pullout strength of pedicle screw. METHODS: To predict the pullout strength of pedicle screw, an experimental…
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Machine Learning Approach for Prescriptive Plant Breeding
We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row…
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Machine learning in resting-state fMRI analysis
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning…
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Machine Learning in Psychometrics and Psychological Research
…complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather…
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Attention in Psychology, Neuroscience, and Machine Learning
…including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This…
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Preparing Medical Imaging Data for Machine Learning
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical…
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