Applications of Artificial Neural Networks in Chemical Problems

Matarollo, Vinícius Gonçalves; Honório, Káthia Maria; Silva, Albérico Borges Ferreira da

doi:10.5772/51275

Cited by 12 publications

(5 citation statements)

References 134 publications

(141 reference statements)

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“…3 This unit takes a summated weighted input, applies an activation function, and produces an output if the sum exceeds a certain threshold. 10,12 Building upon this, Rosenblatt further developed the perceptron in the 1960s, which serves as the fundamental unit of an ANN 2 (Figure 3). A perceptron is a simplified computational unit in ANNs, inspired by biological neurons, capable of binary classification.…”

Section: Ann and DL Concepts Functions And Structurementioning

confidence: 99%

“…The concept of an artificial neuron unit was developed by McCulloch and Pitts 3 . This unit takes a summated weighted input, applies an activation function, and produces an output if the sum exceeds a certain threshold 10,12 . Building upon this, Rosenblatt further developed the perceptron in the 1960s, which serves as the fundamental unit of an ANN 2 (Figure 3).…”

Section: Ann and DL Concepts Functions And Structurementioning

confidence: 99%

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The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

Taha,

Morren

2023

Muscle and Nerve

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The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

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Section: Ann and DL Concepts Functions And Structurementioning

confidence: 99%

Section: Ann and DL Concepts Functions And Structurementioning

confidence: 99%

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

Taha,

Morren

2023

Muscle and Nerve

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“…PCA is one of the most popular analyses which have the potential to provide good classification capabilities, nevertheless, due to the nature of our data, there is a more suitable tool to treat complex data: neural networks. They offer a flexible and powerful approach to classification that can handle a wide range of problems and applications (5)(6)(7). Between all the possible architectures, self-organized-maps (SOMs) stand out for their clustering, visualization, and classification capabilities.…”

Section: Introductionmentioning

confidence: 99%

Sieci neuronowe do mapowania archeologicznych artefaktów litowych

Jiménez-Puerto,

Gallello,

Orozco

2024

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An analytical non-destructive strategy to chemically characterize lithic artefacts has been developed. Around 100 archaeological lithic materials found in Neolithic-Chalcolithic sites in the Mediterranean region of the Iberian Peninsula and nowadays stored in different museums of the Valencian Community (Spain), were studied. The materials belong to different typologies of rock (diabase, sillimanite, ophite and amphibolite) and were analysed employing portable energy dispersive X-ray fluorescence spectroscopy (pXRF) directly in the rock surface. The obtained data were processed through neural networks protocol, specifically the so-called Kohonen networks or Self Organised Maps (SOM), to map the geologic samples. This self-organized topological feature maps are suitable to deal with multidimensional representations and map them in a two-dimensional space of neurons, following an unsupervised learning protocol. SOM is used to reduce multidimensional data onto lower-dimensional spaces and clustering procedures. As a result, SOM create spatially organized representations, which enhance the discovery of correlations between data. In this case the method has enabled the evaluation of elemental markers related to each rock type behaving as a fine hidden pattern detector and so understand the possible advantages and disadvantages of the analytical method employed to define provenance issues. The attribution suggested by statistics is mainly driven by the composition of rocks essential minerals which are linked to the different petrogenetic conditions. The results showed that in most of the cases the distribution and dispersion of the chemical profile depend of the kind of rock, and clearly suggest that a good way to identify stone tools raw material procurement is to look for elemental markers, being the prior step to create an approximation to ancient exchange networks and their evolution in a diachronic axis.

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“…A neural network is an algorithm of ML. Inspired by the neural network of the human brain, an artificial neural network can learn from past data and generate a response [ 17 ]. The structure of a typical artificial neural network is usually composed of three essential types of layers, i.e., the input layer, hidden layer(s), and output layer.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

Jiang

Marrone

et al. 2020

Materials

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The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

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Applications of Artificial Neural Networks in Chemical Problems

Cited by 12 publications

References 134 publications

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

Sieci neuronowe do mapowania archeologicznych artefaktów litowych

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

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