3.327 hasil untuk machine learning
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SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
…neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets…
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An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
…cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be…
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Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network
Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches. Although FHE schemes are suitable as tools for implementing PPML models, previous PPML models based on FHE…
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The Role of Machine Learning in the Understanding and Design of Materials
…approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by…
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Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
…prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions…
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Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
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Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction
…generate additional insights. Objective: To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes. Design, Setting, and Participants: This cohort study…
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Insect classification and detection in field crops using modern machine learning techniques
…classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbors (KNN), naive bayes (NB) and convolutional neural network (CNN) model. This paper presents the insect pest…
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Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
…The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized…
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Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans…
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Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine
…nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics…
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Integrated phenology and climate in rice yields prediction using machine learning methods
…method (MLR, multiple linear regression), and more advanced three machine learning (ML) methods: backpropagation neural network (BP), support vector machine (SVM) and random forest (RF). The results showed that ML methods were more precise than MLR method. The combination using the integrated…
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Deep machine learning approach to develop a new asphalt pavement condition index
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Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
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Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit
…of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This…
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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0
Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation…
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IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review
…potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be…
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Machine learning for medical imaging: methodological failures and recommendations for the future
Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this…
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Educational data mining: prediction of students' academic performance using machine learning algorithms
…students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The performances of the random forests, nearest neighbour, support vector machines…
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