204,674 results for machine learning
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Journal Article
Machine learning in industrialization: a bibliometric analysis
Machine learning is currently emerging as one of the most rapidly advancing technologies, with a recent upward trend in its use for process automation across industrial processes. The objective of this study was to conduct a bibliometric analysis to identify research trends in machine learning. The…
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Journal Article
Machine learning for DCO-OFDM based LiFi.
…appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language…
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Journal Article
Network modal innovation for distributed machine learning
Distributed machine learning, as a popular computing architecture for artificial intelligence, still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios…
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Journal Article
Predicting preterm birth using machine learning methods
…multifactorial. The objective of this study was to develop and compare machine learning models for predicting the risk of preterm birth. Data were collected from 50 patients in a maternity ward, with an analysis performed based on the timing of delivery (preterm vs. term). The applicability of…
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Journal Article
Double nanowire quantum dots and machine learning
Abstract We present an approach to estimate the single-particle energies in double InAs/InP nanowire quantum dots by combining an atomistic tight-binding approach with machine learning. The method works particularly well with a neural network and transfer learning, where we can accurately recover…
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Journal Article
Coverage bias in small molecule machine learning
Abstract Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end models that avoid explicit domain…
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Journal Article
Machine Learning Models for Spring Discharge Forecasting
…models derived from artificial intelligence studies. Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was…
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Journal Article Full text
Assessing Ships’ Environmental Performance Using Machine Learning
…to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships’ environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal…
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Journal Article
Machine learning models for water safety enhancement
…the samples, thereby contributing to the overall safety of the water. The Machine Learning (ML) models employed in this study provided accurate predictions, ensuring reliability across various demographic groups and reinforcing the robustness of the findings. Overall, the results suggest that…
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Journal Article
Machine learning in obsessive-compulsive disorder medications
…The aim of this study is to classify the medications of OCD with machine learning (ML) methods and to compare the classification performances of the decision tree (DT), chi-square automatic interaction detection (CHAID) algorithm, and linear model in ML methods. This research is a descriptive…
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Journal Article
Machine Learning-Driven Density Prediction for Nanomaterials
This paper provides a machine learning method that uses band gap and chemical composition data from the Materials Project database to predict the density of nanomaterials. We developed an improved Random Forest Regressor and compared it against several baseline models, including Linear regression…
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Journal Article Full text
Brain Signals to Actions Using Machine Learning
This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided…
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Journal Article Full text
Causal Economic Machine Learning (CEML): “Human AI”
This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral…
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Journal Article Full text
Machine learning estimation of rock masses displacement
This paper presents a comprehensive analysis of the factors affecting landslide occurrence in Iran based on a dataset containing information on more than 4000 landslide cases. Both natural (slope, height, rainfall, distance to rivers and faults) and anthropogenic (type of land use) factors were…
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Journal Article Full text
An overview of machine learning classification techniques
Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers…
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Journal Article
Data-driven decarbonization framework with machine learning
Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw materials to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the…
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Journal Article Full text
Emergency Floor Plan Digitization Using Machine Learning
An increasing number of special-use and high-rise buildings have presented challenges for efficient evacuations, particularly in fire emergencies. At the same time, however, the use of autonomous vehicles within indoor environments has received only limited attention for emergency scenarios. To…
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Journal Article
Reconstructing S-matrix Phases with Machine Learning
…analytically and numerical approaches are required. We apply modern machine learning techniques to studying the unitarity constraint. We find that for a given modulus, when a phase exists it can generally be reconstructed to good accuracy with machine learning. Moreover, the loss of the…
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Journal Article
Sign Language Classifier based on Machine Learning
…are integrated to help with the image pre-processing part. By doing so, a machine learning model was developed that does not require a lot of processing power because instead of using the images themselves, it uses extracted data from them to connect this model to a mobile interface that the users…
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