AIMC Topic: Neural Networks, Computer

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Comprehensive Machine Learning Prediction of Extensive Enzymatic Reactions.

The journal of physical chemistry. B
New enzyme functions exist within the increasing number of unannotated protein sequences. Novel enzyme discovery is necessary to expand the pathways that can be accessed by metabolic engineering for the biosynthesis of functional compounds. According...

Shape-Texture Debiased Training for Robust Template Matching.

Sensors (Basel, Switzerland)
Finding a template in a search image is an important task underlying many computer vision applications. This is typically solved by calculating a similarity map using features extracted from the separate images. Recent approaches perform template mat...

A Nonlinear Finite-Time Robust Differential Game Guidance Law.

Sensors (Basel, Switzerland)
In this paper, a robust differential game guidance law is proposed for the nonlinear zero-sum system with unknown dynamics and external disturbances. First, the continuous-time nonlinear zero-sum differential game problem is transformed into solving ...

Appliance-Level Anomaly Detection by Using Control Charts and Artificial Neural Networks with Power Profiles.

Sensors (Basel, Switzerland)
Nowadays, the development of the Internet of Things (IoT) concept has increased the interest in some technologies, one of which is the detection of anomalies in home appliances before they occur. In this work, in order to contribute to the works that...

CNN-based two-branch multi-scale feature extraction network for retrosynthesis prediction.

BMC bioinformatics
BACKGROUND: Retrosynthesis prediction is the task of deducing reactants from reaction products, which is of great importance for designing the synthesis routes of the target products. The product molecules are generally represented with some descript...

Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach.

Journal of molecular modeling
The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of ...

A novel scaled-gamma-tanh (SGT) activation function in 3D CNN applied for MRI classification.

Scientific reports
Activation functions in the neural network are responsible for 'firing' the nodes in it. In a deep neural network they 'activate' the features to reduce feature redundancy and learn the complex pattern by adding non-linearity in the network to learn ...

Auditory Speech Based Alerting System for Detecting Dummy Number Plate via Video Processing Data sets.

Computational intelligence and neuroscience
Spectrum of applications in computer vision use object detection algorithms driven by the power of AI and ML algorithms. State of art detection models like faster Region based convolutional Neural Network (RCNN), Single Shot Multibox Detector (SSD), ...

Detection Method of Athlete Joint Injury Based on Deep Learning Model.

Computational and mathematical methods in medicine
The research on accurate and intelligent segmentation of knee joint MRI images is of great significance to reduce the work intensity of clinical doctors and nurses. In order to solve the problem that knee joint MRI image segmentation model needs a la...

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts ...