Academic Articles & Research Papers

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have ...

Kalman Filtering and Neural Networks

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear appr...

On Calibration of Modern Neural Networks

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...

Dynamic Elliptical Shaping Control for Swarm Robots

Solving the robotic swarm coverage problem for an elliptical area has various applications for exploring novel environments. Solutions for this problem should cover a specified ellipse and seamlessly adapt to changing numbers of robots. Pre...

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Zn–Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn–Ni electrophosphate...

Artificial neural networks

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...

An Introduction to Neural Networks

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain funct...

Neural networks

article Free Access Share on Neural networks: applications in industry, business and science Authors: Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , David E. Rumelhart Stanford Univ., Stanford, CA Sta...

Neural networks and their applications

Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to...

On neural networks.

JIŘÍ BENEŠTypical neural network models representing a broad survey of actual trends are considered in an unifying way as Complexes subject to specific control systems, called Formators which may have a two-level arrangement and incorporate...

Stock market forecasting research based on GA-WOA-LSTM.

With the increasing complexity and prosperity of global financial markets, stock market forecasting plays a critical role in investment decision-making, market regulation, and economic planning. This study proposes a hybrid prediction model...

Applications of deep learning in dentistry

Deep Learning for Sensor-based Human Activity Recognition

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in pra...

Deep learning in electron microscopy

Abstract Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applic...

Deep learning in nano-photonics: inverse design and beyond

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the...

Deep learning in computational mechanics: a review

Abstract The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies w...

Deep Learning

This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising....

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpect...

Applications of deep learning to MRI images: A survey

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tiss...

Deep learning for digital holography: a review

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities ...