Introduction: Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale u...

https://tandfonline.com/doi/pdf/10.1080/17434440.2019.1610387

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

/pdf/artificial-neural-network-in-drug-delivery-and-5g3skmtj5n.pdf

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

This study is part of an ongoing project investigating the first stage of the process of student transition to university. This paper aims to contribute to the continuing debates on the possibilities how to reduce the student failure and improve educational processes with the help of data mining techniques, in particular of the artificial neural networks. The population consists of 810 students enrolled for the first time in a health care professions degree course at the University of Genoa in the academic year 2008-09. The research is based on the analysis of data and information originating from primary sources: administrative data related to the careers of students; statistical data collected during the research through an ad hoc survey; data derived from telephone interviews with students who had not completed the enrolment in the subsequent years. The neural network correctly predicted 84 percent of the cases of group 1,81 percent of the cases of group 2, and 76 percent of the cases belonging to group of dropouts. The application of the artificial neural network model can offer a valid tool to design educational interventions to deliver to those who score high in the level of risk.

/pdf/predicting-students-dropout-at-university-using-artificial-4ic4bmzp45.pdf

Predicting students’ dropout at university using Artificial Neural Networks

Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.

Purpose:
The authors propose a new training method for artificial neural networks (ANNs) in two-class classification tasks such as classifying breast lesions on a mammogram as malignant or benign.

Methods:
Whereas the conventional binary training method uses binary training target values based on the diagnostic truth of a lesion being malignant or benign, the authors use multiple training target values based on more detailed histological diagnosis that presumably are related to the posterior probability of a lesion being malignant. The authors performed Monte Carlo simulation studies in which training target values were assigned based on posterior probability, and they also performed a mammography study in which training target values were assigned according to histological subtypes.

Results:
These studies showed that the multitarget training method produced less variability in the ANN outputs than the binary training method. The simulation studies also showed that except for when the number of training cases was extremely large, the multitarget training method produced improved overall classification performance over the binary training method.

Conclusions:
Therefore, the multitarget ANN training method is potentially useful for ANN applications in computer-aided diagnosis of breast cancer.

/pdf/a-multitarget-training-method-for-artificial-neural-network-3fb4wfd2vv.pdf

A multitarget training method for artificial neural network with application to computer-aided diagnosis

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A multitarget training method for artificial neural network with application to computer-aided diagnosis