3.327 hasil untuk machine learning
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Artikel Jurnal
Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future
…diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time…
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A machine learning based credit card fraud detection using the GA algorithm for feature selection
…card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature…
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Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
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Identifying who has long COVID in the USA: a machine learning approach using N3C data
…(N3C) electronic health record repository, we developed XGBoost machine learning models to identify potential patients with long COVID. We defined our base population (n=1 793 604) as any non-deceased adult patient (age ≥18 years) with either an International Classification of Diseases-10-Clinical…
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LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer
…cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely studied. MATERIALS AND METHODS: Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically…
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Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education
Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing…
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Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms
…to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the…
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Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
…techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of…
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Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models…
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Applied Machine Learning for Prediction of CO 2 Adsorption on Biomass Waste-Derived Porous Carbons
…527 data points collected from peer-reviewed publications and applied machine learning to systematically map CO 2 adsorption as a function of the textural and compositional properties of BWDPCs and adsorption parameters. Various tree-based models were devised, where the gradient boosting decision…
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Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health
In Low- and Middle- Income Countries (LMICs), machine learning (ML) and artificial intelligence (AI) offer attractive solutions to address the shortage of health care resources and improve the capacity of the local health care infrastructure. However, AI and ML should also be used cautiously, due…
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Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion
…laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. The highly accurate data labeling enabled by…
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UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat
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Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring
…of accurate physical models. In recent years, physics-informed machine learning (PIML) has emerged as a promising approach for condition monitoring, combining the strengths of physics-based modelling and data-driven machine learning. This study presents a comprehensive overview of PIML techniques…
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Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China
…concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual…
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Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
…of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making…
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Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment
Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The…
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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
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