3,327 results for machine learning
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Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley…
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Effect of data encoding on the expressive power of variational quantum-machine-learning models
Quantum computers can be used for supervised learning by treating parametrized quantum circuits as models that map data inputs to predictions. While a lot of work has been done to investigate the practical implications of this approach, many important theoretical properties of these models remain…
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Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions
The way people travel, organise their time, and acquire information has changed due to information technologies. Artificial intelligence (AI) and machine learning (ML) are mechanisms that evolved from data management and developing processes. Incorporating these mechanisms into business is a trend…
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Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
…for a given application. High-throughput computational screening and machine learning are promising routes to efficiently navigate the vast chemical space of MOFs but have rarely been used for the prediction of properties that need to be calculated by quantum mechanical methods. Here, we introduce…
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Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major…
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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
…for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR)…
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Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust…
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Application of machine learning to the monitoring and prediction of food safety: A review
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed…
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Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques
…to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However…
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A review of Machine Learning-based zero-day attack detection: Challenges and future directions
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Energy consumption prediction by using machine learning for smart building: Case study in Malaysia
…a predictive model for energy consumption in Microsoft Azure cloud-based machine learning platform. Three methodologies which are Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbour are proposed for the algorithm of the predictive model. Focusing on real-life application in…
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Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward
…will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications-will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where…
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Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review
…stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction…
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Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyze such scans could transform neuroimaging research. Yet, their potential remains untapped since no automated algorithm is…
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Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing
…closed loop control. In the present work, the application of various Machine Learning (ML) approaches and in-situ monitoring technologies for the purpose of defect detection are reviewed. The potential of these methods for enabling process control implementation is discussed. We provide a critical…
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Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques
…using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and…
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Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework
Climate change and rapid urbanisation exacerbated multiple urban issues threatening urban sustainability. Numerous studies integrated machine learning and remote sensing to monitor urban issues and develop mitigation strategies for sustainability. However, few studies comparatively analysed joint…
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