AIMC Topic: Neural Networks, Computer

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Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting.

International journal of neural systems
Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural ne...

Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Reliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN...

Development of neural fractional order PID controller with emulator.

ISA transactions
This paper focuses on tuning parameters of fractional order PID controller (FOPID) by using neural networks (NNs). For tuning the coefficients of the controller and orders of fractional derivative and integrator, five exclusive NNs are employed. More...

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing ...

Amur tiger stripes: individual identification based on deep convolutional neural network.

Integrative zoology
The automatic individual identification of Amur tigers (Panthera tigris altaica) is important for population monitoring and making effective conservation strategies. Most existing research primarily relies on manual identification, which does not sca...

Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals.

Neural networks : the official journal of the International Neural Network Society
Electroencephalogram (EEG) signals accumulate the brain's spiking activities using standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in nature and vary depending upon current physical and/or mental activities. Th...

Predicting protein subcellular location with network embedding and enrichment features.

Biochimica et biophysica acta. Proteins and proteomics
The subcellular location of a protein is highly related to its function. Identifying the location of a given protein is an essential step for investigating its related problems. Traditional experimental methods can produce solid determination. Howeve...

Truncated inception net: COVID-19 outbreak screening using chest X-rays.

Physical and engineering sciences in medicine
Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as wel...

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

Cell systems
Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences ca...

Epigenome-based splicing prediction using a recurrent neural network.

PLoS computational biology
Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is now thought to predominantly take place during transcription. Motivated by emerging data showing th...