3,327 results for machine learning
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Journal Article
Predicting Employee Attrition Using Machine Learning Techniques
There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent…
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The Role of Machine Learning in Cybersecurity
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such…
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Crop Prediction Model Using Machine Learning Algorithms
Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential…
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Mitigating bias in machine learning for medicine
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications. Vokinger et al…
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Machine learning for metabolic engineering: A review
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and…
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Journal Article
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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Small data machine learning in materials science
Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data…
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Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems…
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Journal Article
Machine learning–enabled high-entropy alloy discovery
…often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with…
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Perspectives in machine learning for wildlife conservation
…can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require…
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Machine learning advances for time series forecasting
Abstract In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of…
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Machine learning in the Chinese stock market
We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for the US market, liquidity emerges as the most important…
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Breast Cancer Type Classification Using Machine Learning
…cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. METHODS: We performed analysis of RNA-Sequence data from 110 triple…
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Journal Article Full text
Breast cancer detection by leveraging Machine Learning
…a novel method to detect breast cancer by employing techniques of Machine Learning. The authors carried out an experimental analysis on a dataset to evaluate the performance. The proposed method has produced highly accurate and efficient results when compared to the existing methods.
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