|
|
|
|
|
Min-Kyung Lee and Inwon Lee
In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both metho...
ver más
|
|
|
|
|
|
|
|
|
|
|
Gauri Vaidya, Meghana Kshirsagar and Conor Ryan
Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One of the most important factors influencing this ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ashir Javeed, Muhammad Asim Saleem, Ana Luiza Dallora, Liaqat Ali, Johan Sanmartin Berglund and Peter Anderberg
Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clini...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tran Thanh Ngoc, Le Van Dai, Lam Binh Minh
Pág. 258 - 269
This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Roseline Oluwaseun Ogundokun, Sanjay Misra, Mychal Douglas, Robertas Dama?evicius and Rytis Maskeliunas
In today?s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-...
ver más
|
|
|
|
|
|
|
|
|
|
|
Nasir Ayub, Usman Ali, Kainat Mustafa, Syed Muhammad Mohsin and Sheraz Aslam
In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility rev...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guglielmo Daddi, Nicolaus Notaristefano, Fabrizio Stesina and Sabrina Corpino
This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of obstac...
ver más
|
|
|
|
|
|
|
|
|
|
|
Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo, Olukayode Karunwi, Yongsung Kim, Cheng-Chi Lee and Chun-Ta Li
Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the ra...
ver más
|
|
|
|
|
|
|
|
|
|
|
Andrea Menapace, Ariele Zanfei and Maurizio Righetti
The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Un-Chang Jeong
This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal s...
ver más
|
|
|
|
|
|