113.116 hasil untuk machine learning
Menampilkan 441–460 · 3,08 detik · RSS
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Artikel Jurnal Teks lengkap
Classifying grains using behaviour-informed machine learning
Sorting granular materials such as ores, coffee beans, cereals, gravels and pills is essential for applications in mineral processing, agriculture and waste recycling. Existing sorting methods are based on the detection of contrast in grain properties including size, colour, density and chemical…
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Machine learning for microfluidic design and control
…operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and…
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COVID‐19: Machine learning for safe transportation
Entire world has been affected by Covid‐19 pandemic. In fighting against the Covid‐19, social distancing and face mask have a paramount role in freezing the spread of the disease. People are asked to limit their interactions with each other, to reduce the spread of the disease. Here an alert system…
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Prediction of GPCR activity using machine learning
GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure–activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal…
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Designing sensitive viral diagnostics with machine learning
Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome’s conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their…
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Artikel Jurnal Teks lengkap
Stroke Risk Prediction with Machine Learning Techniques
…and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high…
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Artikel Jurnal Teks lengkap
Machine Learning Approaches to TCR Repertoire Analysis
…Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of…
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Machine Learning Models for Inpatient Glucose Prediction
…surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of…
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Advancing Rare-Earth Separation by Machine Learning
…and found that the predicted distribution coefficients from our trained machine-learning model match well with the measured values. Therefore, our machine-learning approach paves the way for accelerating the discovery of new ligands for rare-earth separations.
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Automation of Cephalometrics Using Machine Learning Methods
…to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study's computing procedure to be completed faster. To correlate a probability map with…
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Evaluating Plant Gene Models Using Machine Learning
…arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and…
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PeakBot: machine-learning-based chromatographic peak picking
…and unspecific background signals complicate this task. RESULTS: A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled…
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Application of Machine Learning for Cytometry Data
…dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample…
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COVID and nutrition: A machine learning perspective
…inferred information. Based on this data storage, a series of machine learning experiments was conducted to investigate the relationship between nutrition and the risk of contracting COVID-19. With highly accurate scores, the findings strongly suggest that foods and water sources containing certain…
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Artikel Jurnal Teks lengkap
Multi-modality machine learning predicting Parkinson’s disease
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a…
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