204.674 hasil untuk machine learning
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Artikel Jurnal
Thalamocortical dysrhythmia detected by machine learning
Thalamocortical dysrhythmia has been proposed to occur in a number of neurological and psychiatric disorders. Here, the authors use a data-driven approach to demonstrate thalamocortical dysrhythmia occurs in individuals with Parkinson’s disease, neuropathic pain, tinnitus, and depression.
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Artikel Jurnal
Ethically Responsible Machine Learning in Fintech
Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation…
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Artikel Jurnal
Machine-learning algorithms in regulatory practice
A growing body of literature discusses the impact of machine-learning algorithms on regulatory processes. This paper contributes to the predomi- nantly legal and technological literature by using a sociological-institutional perspective to identify nine organisational challenges for using…
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Artikel Jurnal Teks lengkap
Machine Learning in Agriculture: A Review
Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine…
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Artikel Jurnal
Covert Channel Detection: Machine Learning Approaches
…and developments, with a particular focus on detection techniques using machine learning (ML) approaches. It provides a thorough review of the most common covert channels and ML techniques that are used to counter them, as well as addressing their achievements and limitations. In addition, this…
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Artikel Jurnal
Quantum Machine Learning Tensor Network States
…to simulate many-body physics and have a further range of applications in machine learning. Finding and contracting tensor network states is a computational task, which may be accelerated by quantum computing. We present a quantum algorithm that returns a classical description of a rank-r tensor…
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Artikel Jurnal
Machine learning partners in criminal networks
…networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among…
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Artikel Jurnal
Explainable machine learning in materials science
Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research…
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Artikel Jurnal
Machine learning in the string landscape
Abstract We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric…
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Artikel Jurnal
Machine learning for morbid glomerular hypertrophy
Abstract A practical research method integrating data-driven machine learning with conventional model-driven statistics is sought after in medicine. Although glomerular hypertrophy (or a large renal corpuscle) on renal biopsy has pathophysiological implications, it is often misdiagnosed as…
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Artikel Jurnal
Modeling Organizational Performance with Machine Learning
…The evolutionary computation method of genetic algorithm and the machine learning method of Bayesian additive regression trees (BART), are used to model the importance of each of the intra- and extra-organizational factors in identifying the firms’ performance as well as employee well-being. The…
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Artikel Jurnal Teks lengkap
Machine Learning Approach to Dysphonia Detection
This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the…
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Artikel Jurnal
Machine learning security and privacy:a survey
As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and privacy issues which are getting more and more…
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Artikel Jurnal
Machine learning in chemical reaction space
Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of…
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Artikel Jurnal
Machine learning in energy storage materials
Abstract With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium‐ion batteries as two representative examples, we review substantial advances of machine…
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Artikel Jurnal Teks lengkap
Transforming health policy through machine learning.
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.
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Artikel Jurnal
Exploring Chemical Space with Machine Learning
…in the area of drug discovery. Here we review our implementations of machine learning in this project, including our use of deep neural networks to enumerate the GDB13 database from a small sample set, to generate analogs of drugs and natural products after training with fragment-size molecules…
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Artikel Jurnal
Pileup Mitigation with Machine Learning (PUMML)
…We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of…
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