3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization
…space. In this work, we merge the topology optimization method with deep learning algorithms, such as adversarial autoencoders, and show substantial improvement of the optimization process in terms of computational time (4900 times faster) and final devices efficiencies (∼98%) by providing…
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Invited review: Machine learning for materials developments in metals additive manufacturing
…electrical engineering, and mechanical engineering. The use of modern machine learning approaches to model these degrees of freedom can reduce the time and cost to elucidate the science of metals AM and to optimize the engineering of these complex, multidisciplinary processes. New machine learning…
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Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines
…digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict…
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Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer
…participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40)…
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Machine learning and deep learning approach for medical image analysis: diagnosis to detection
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A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis
Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment…
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Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning
…to ensure the accuracy and timeliness of detection. This paper researches machine learning and deep learning for intrusion detection in imbalanced network traffic. It proposes a novel Difficult Set Sampling Technique(DSSTE) algorithm to tackle the class imbalance problem. First, use the Edited…
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Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions
In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial…
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Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset…
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Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician
BACKGROUND: Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection. OBJECTIVES: We summarise recent and potential future applications of AI and its relevance to…
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer…
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A review on machine learning in 3D printing: applications, potential, and challenges
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Image-based profiling for drug discovery: due for a machine-learning upgrade?
…results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in…
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Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
…dynamic operation, and cold start, are briefly explained in this work. Machine learning and artificial intelligence (AI) have received increasing attention in material/energy development. This review also discusses their applications and potential in the development of fundamental knowledge and…
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Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
…Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics…
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Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
…"COVID-19," "quarantine") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. RESULTS: Popular unigrams included "virus," "lockdown," and…
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Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature
…difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a…
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Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes…
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Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
…known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with…
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