3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects
…and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms…
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What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that…
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A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics
…analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which…
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Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the…
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CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning
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Artikel Jurnal Teks lengkap
Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle and climate change in the global terrestrial ecosystem. AGB estimation based on remote sensing is an effective method for regional scale. In this study, Landsat 8 Operational Land Imager and Sentinel-1A data…
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Artikel Jurnal
A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities
-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.
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Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
…and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.
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Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
…and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily…
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Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
…contribute substantially to uncertainty in climate projections. Machine learning of new parameterizations from high-resolution model output is a promising approach, but such parameterizations have been prone to issues of instability and climate drift, and their performance for different grid…
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Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning
…marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen…
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Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV
…especially for doctors and other medical experts. In this paper, various Machine Learning algorithms such as LR, KNN, SVM, and GBC, together with the GridSearchCV, predict cardiac disease. The system uses a 5-fold cross-validation technique for verification. A comparative study is given for these…
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A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms
Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of…
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Artikel Jurnal
Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges
…and address the current developments of human body cancer detection using machine learning techniques for breast, brain, lung, liver, skin cancer leukemia. The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning…
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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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Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their…
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State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms
…presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging…
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Artikel Jurnal Teks lengkap
A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring
Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition…
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