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Machine learning for microbiologists
Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human...
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Maximizing reusability of learning objects through machine learning techniques
Maximizing the reusability of learning objects through machine learning techniques has significantly transformed the landscape of e-learning systems....
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Understanding machine translation fit for language learning: The mediating effect of machine translation literacy
The use of machine translation has become a topic of debate in language learning, which highlights the need to thoroughly examine the appropriateness...
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Research trends in deep learning and machine learning for cloud computing security
Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles...
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Confidentiality of Machine Learning Models
AbstractThis article is about ensuring the confidentiality of models using machine learning systems. The aim of this study is to ensure the...
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Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease Detection
A progressive neurodegenerative ailment called Parkinson's disease (PD) is marked by the death of dopamine-producing cells in the substantia nigra...
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Mitigating Bias in Clinical Machine Learning Models
Purpose of reviewIdentifying the risk for and addressing bias in clinical machine learning models is essential to reap its full benefits and ensure...
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HASM quantum machine learning
The miniaturization of transistors led to advances in computers mainly to speed up their computation. Such miniaturization has approached its...
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Machine Learning
Machine learning is useful to identify rules hidden in given data and to predict unknown data using the identified rules. It has been increasingly...
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Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach
Evaluation of slope failures, which cause significant loss of life and property comparable to natural disasters such as earthquakes, floods and...
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Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques
Social media platforms have revolutionized traditional communication techniques by allowing people to connect instantaneously, openly, and...
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Blockchain meets machine learning: a survey
Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology...
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Application of machine learning and deep learning for cancer vaccine (rapid review)
Cancer is a common and dangerous disease based on the World Health Organization. Much research has been done on new and effective cancer treatments,...
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Machine learning with a reject option: a survey
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support...
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Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection
Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease...
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Machine learning and non-machine learning methods in mathematical recognition systems: Two decades’ systematic literature review
Tools based on machine learning (ML) have seen widespread application in the prediction and categorization of mathematical symbols and phrases. The...
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Diabetes detection based on machine learning and deep learning approaches
The increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies....
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Target adaptive extreme learning machine for transfer learning
Extreme learning machines (ELM) have been applied in several fields due to their simplicity and computational efficiency. However, ELM hurts the...
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Automated machine learning: past, present and future
Automated machine learning (AutoML) is a young research area aiming at making high-performance machine learning techniques accessible to a broad set...
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Adversarial machine learning phases of matter
We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that...
