3.327 hasil untuk machine learning
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Artikel Jurnal
Artificial intelligence and machine learning in energy systems: A bibliographic perspective
…for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential…
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COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
…Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short…
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Machine learning-based approach: global trends, research directions, and regulatory standpoints
The field of machine learning (ML) is sufficiently young that it is still expanding at an accelerating pace, lying at the crossroads of computer science and statistics, and at the core of artificial intelligence (AI) and data science. Recent progress in ML has been driven both by the development of…
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Artikel Jurnal
A Review of Android Malware Detection Approaches Based on Machine Learning
…and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe…
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SuperPred 3.0: drug classification and target prediction—a machine learning approach
…both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has…
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Opportunities of Artificial Intelligence and Machine Learning in the Food Industry
…Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. This article is going to explain the…
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Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Abstract
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Predicting Standardized Streamflow index for hydrological drought using machine learning models
…Vector Regression; CC: correlation coefficient; ELM: extreme learning machine; GEP: gene Expression Programming; GP: genetic Programming; GPR: Gaussian process regression; KNN: k-nearest neighbor; LSSVM: least squares Support Vector Machine; LSSVR: least support vector regression; MAE: mean…
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Ethical limitations of algorithmic fairness solutions in health care machine learning
Artificial intelligence has exposed pernicious bias within health data that constitutes substantial ethical threat to the use of machine learning in medicine.1,2 Solutions of algorithmic fairness have been developed to create neutral models: models designed to produce non-discriminatory predictions…
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COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
…(SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist…
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Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields
…screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and…
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Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization
In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and…
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Artificial intelligence and machine learning for medical imaging: A technology review
…the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous…
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Machine learning approaches to drug response prediction: challenges and recent progress
…partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges…
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Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are…
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COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning
…have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other…
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Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms
This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability…
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Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were…
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Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity
For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and…
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