204.674 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine learning methods application for consumer banking
Machine learning (ML) methods are effective tools for analysis of many actual problems in modern banking. Increasing growth of data and rapid digitalization underpin the acceleration of ML implementation. These processes are especially noticeable in consumer banking because banks have millions of…
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Machine learning analysis of TCGA cancer data
In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order…
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The Cardiovascular Disease Prediction Using Machine Learning
…in lab settings. To accurately diagnose myocardial illness, a powerful machine learning technique should be used. Through using recommended method, the effectiveness of cardiovascular disease identification using ECG dataset was evaluated. The reliability, sensitivities, and validity obtained using…
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Machine Learning in Nutritional Follow-up Research
…dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.
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Machine learning in medicine: Addressing ethical challenges.
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians.
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Machine Learning for Drug-Target Interaction Prediction
…interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several…
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Automatic detection of pain using machine learning
…non-invasive, objective pain measurements through biosensors coupled with machine learning algorithms. In the current study, a physiological dataset associated with acute pain induction in healthy adults was leveraged to develop an algorithm capable of detecting pain in real-time and in natural…
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DETECTING VESSELS CARRYING MIGRANTS USING MACHINE LEARNING
Political instability, conflicts and inequalities result into significant flows of people worldwide, moving to different countries in search of a better life, safety or to be reunited with their families. Irregular crossings into Europe via sea routes, despite not being new, have recently increased…
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Machine learning‐based scheduling: a bibliometric perspective
…time varying, multi‐objective, and subject to interruptions. Ergo, the machine learning‐based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives…
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Machine Learning-Based Classification of Dislocation Microstructures
…to link to macroscopic properties on the specimen scale. In this work a machine learning based approach for classification of coarse-grained dislocation microstructures in terms of different dislocation density field variables is used. The performance of the model combined with domain knowledge…
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Unearned premium risk and machine learning techniques
…mainly focused on utilizing statistical models. In this article, we apply machine learning models to calculate PDR. We use an extended warranty dataset, which comes under long-duration P & C insurance contracts to demonstrate our models. Using two statistical and two machine learning models, we…
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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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MiPepid: MicroPeptide identification tool using machine learning
Abstract Background Micropeptides are small proteins with length
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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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Machine learning in petrophysics: Advantages and limitations
Machine learning provides a powerful alternative data-driven approach to accomplish many petrophysical tasks from subsurface data. It can assimilate information from large and rich data bases and infer relations, rules, and knowledge hidden in the data. When the physics behind data becomes…
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Explainable machine learning prediction of ICU mortality
Background: There is a variety of mortality prediction models for patients in intensive care units (ICU) to guide appropriate clinical management. Advances in machine learning methodologies typically employ classifiers such as Neural Network and Random Forest which are often regarded by healthcare…
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