AIMC Topic: Neural Networks, Computer

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Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer.

Food science and technology international = Ciencia y tecnologia de los alimentos internacional
The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying pr...

Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.

BMC cancer
BACKGROUND: Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for ...

Stabilizing patterns in time: Neural network approach.

PLoS computational biology
Recurrent and feedback networks are capable of holding dynamic memories. Nonetheless, training a network for that task is challenging. In order to do so, one should face non-linear propagation of errors in the system. Small deviations from the desire...

Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs.

IEEE/ACM transactions on computational biology and bioinformatics
Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different...

A novel method for predicting kidney stone type using ensemble learning.

Artificial intelligence in medicine
The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of t...

Spatiotemporal Bayesian networks for malaria prediction.

Artificial intelligence in medicine
Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been use...

Bullying incidences identification within an immersive environment using HD EEG-based analysis: A Swarm Decomposition and Deep Learning approach.

Scientific reports
Bullying is an everlasting phenomenon and the first, yet difficult, step towards the solution is its detection. Conventional approaches for bullying incidence identification include questionnaires, conversations and psychological tests. Here, unlike ...

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks.

PloS one
The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases an...

Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter.

Journal of applied toxicology : JAT
It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathwa...

Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

Neural computation
We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with struc...