Results 81 to 90 of about 134,020 (300)

The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains [PDF]

Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation.
Mirzaalian Dastjerdi, Houman, Emmert, Johannes, Maier, Andreas, Syben, Christopher, Dastjerdi, Houman Mirzaalian, Mendez, Ronald   +5 more
core   +1 more source

Multimodal Optoelectronic Synapses for Neuromorphic Intelligence: Coupling Light, Electrical, Thermal, and Beyond

Advanced Functional Materials, EarlyView.
This review revisits optoelectronic neuromorphic devices that extend from unimodal sensing toward multimodal perception. Five types of stimuli‐to‐output information flow that are classified based on the device roles within the system and applications are summarized and discussed.
Jihyun Shin, Junhyung Cho, Wangmyung Choi, Jaehyun Hur, Jae Kyeong Jeong, Ulrike Kraft, Hocheon Yoo   +6 more
wiley   +1 more source

A CRITICAL REVIEW OF DEEP LEARNING APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS IN STRUCTURAL ENGINEERING

International Journal for Computational Civil and Structural Engineering
Deep learning (DL), a major part of artificial intelligence (AI) is considered as a transformational technology in different areas of science, such as structural engineering.
Manaf Raid Salman, Marwan Al-Shaikhli, Hasan Ali Abbas, Hussain Hamed Ahmad, Sakhiah Abdul Kudus   +4 more
doaj   +1 more source

Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Research Based on Stock Predicting Model of Neural Networks Ensemble Learning

MATEC Web of Conferences, 2018
Financial time series is always one of the focus of financial market analysis and research. In recent years, with the rapid development of artificial intelligence, machine learning and financial market are more and more closely linked.
Xie Qi, Cheng Gengguo, Xu Xu, Zhao Zixuan   +3 more
doaj   +1 more source

From artificial neural networks to deep learning for music generation: history, concepts and trends [PDF]

Neural Computing and Applications, 2020
The current wave of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation. A growing area of application is the generation
openaire   +5 more sources

Are Deep Learning Approaches Suitable for Natural Language Processing? [PDF]

, 2016
In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human
Alshahrani, S., S. Alshahrani, E. Kapetanios, Kapetanios, E.   +3 more
core   +1 more source

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET

Scientific Journal of Astana IT University
Currently, an increasing amount of research is directed towards solving tasks using computer vision libraries and artificial intelligence tools. Most common are the solutions and approaches utilizing machine and deep learning models of artificial neural
Samat Mukhanov, Raissa Uskenbayeva , Abdul Ahmad Rakhim, Im Cho Young, Aknur Yemberdiyeva, Zhansaya Bekaulova   +5 more
doaj   +1 more source

Artificial Neural Network and Deep Learning: Fundamentals and Theory

CoRR
"Artificial Neural Network and Deep Learning: Fundamentals and Theory" offers a comprehensive exploration of the foundational principles and advanced methodologies in neural networks and deep learning. This book begins with essential concepts in descriptive statistics and probability theory, laying a solid groundwork for understanding data and ...
openaire   +4 more sources