Results 41 to 50 of about 652,316 (194)

Deep API learning

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016
Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences.
Xiaodong Gu, Dongmei Zhang, Sunghun Kim, Hongyu Zhang   +3 more
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Deep learning? What deep learning?

South African Journal of Higher Education, 2003
In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability than those who adopt a surface ...
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Quantum deep learning

Quantum Information and Computation, 2016
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers.
Ashish Kapoor, Nathan Wiebe, Krysta M. Svore   +2 more
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Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning

Biomimetics
In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring.
Mingen Wang, Panliang Yuan, Pengfei Hu, Zhengrong Yang, Shuai Ke, Longliang Huang, Pai Zhang   +6 more
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Cracking the genetic code with neural networks

Frontiers in Artificial Intelligence, 2023
The genetic code is textbook scientific knowledge that was soundly established without resorting to Artificial Intelligence (AI). The goal of our study was to check whether a neural network could re-discover, on its own, the mapping links between codons ...
Marc Joiret, Marine Leclercq, Gaspard Lambrechts, Francesca Rapino, Pierre Close, Gilles Louppe, Liesbet Geris, Liesbet Geris, Liesbet Geris   +8 more
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Deep Learning in Neuroradiology [PDF]

American Journal of Neuroradiology, 2018
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging.
G. Zaharchuk, E. Gong, M. Wintermark, D. Rubin, C.P. Langlotz   +4 more
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Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree

Computers, 2022
Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical ...
Venkataramana Veeramsetty, Modem Sai Pavan Kumar, Surender Reddy Salkuti   +2 more
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Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Computers, 2022
Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design.
Venkataramana Veeramsetty, Srividya Srinivasula, Surender Reddy Salkuti   +2 more
doaj   +1 more source

Deep Learning

Encyclopedia with Semantic Computing and Robotic Intelligence, 2017
Seminar about deep learning and ...
Arjona, Gorchs, Hernández-Ramírez
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Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm

Engineering Reports
Machine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of ...
Jannatul Mauya, Saad Sahriar, Sanjida Akther, Ruhul Amin, Sabba Ruhi, Md. Shamim Reza   +5 more
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