Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning
Applied SciencesHazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health.
Mihaela Tita, Ion Onutu, Bogdan Doicin
doaj +1 more source
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms [PDF]
, 2012 Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.
Hoos, Holger H. +3 more
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A trust-region method for stochastic variational inference with applications to streaming data
, 2015 Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and ...
Hoffman, Matthew D., Theis, Lucas
core
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
, 2017 Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
Bartels, Simon +4 more
core
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
, 2017 Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the ...
Gurevych, Iryna, Reimers, Nils
core
A Novel Approach to Detect Drones Using Deep Convolutional Neural Network Architecture
SensorsOver the past decades, drones have become more attainable by the public due to their widespread availability at affordable prices. Nevertheless, this situation sparks serious concerns in both the cyber and physical security domains, as drones can be ...
Hrishi Rakshit, Pooneh Bagheri Zadeh
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The Safety Risks of AI-Driven Solutions in Autonomous Road Vehicles
World Electric Vehicle JournalAt present Deep Neural Networks (DNN) have a dominant role in the AI-driven Autonomous driving approaches. This paper focuses on the potential safety risks of deploying DNN classifiers in Advanced Driver Assistance System (ADAS) systems.
Farshad Mirzarazi +2 more
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Dynamic Display of Changing Posterior in Bayesian Survival Analysis: The Software [PDF]
We consider the problem of estimating an unknown distribution function in the presence of censoring under the conditions that a parametric model is believed to hold approximately.
Balasubramanian Narasimhan, Hani J. Doss
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Electrical, Control and Communication Engineering, 2018 An attempt of finding an appropriate number of convolutional layers in convolutional neural networks is made. The benchmark datasets are CIFAR-10, NORB and EEACL26, whose diversity and heterogeneousness must serve for a general applicability of a rule ...
Romanuke Vadim V.
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Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
Applied SciencesThe large number of images in the different areas and the possibilities of technologies lead to various solutions in automatization using image data. In this paper, tattoo detection and identification were analyzed.
Gabija Pocevičė +3 more
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