Artificial Adaptive Systems Using Auto Contractive Maps

Buscema, Paolo Massimo; Massini, Giulia; Breda, Marco; Lodwick, Weldon A.; Newman, Francis; Asadi–Zeydabadi, Masoud

doi:10.1007/978-3-319-75049-1

Cited by 19 publications

(14 citation statements)

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“…The architecture of AutoCM was ideated by Massimo Buscema at Semeion Research Center from 2000 to 2007 [29,[40][41][42].…”

Section: The Artificial Neural Networkmentioning

confidence: 99%

“…Then, the modalities through which it is possible to develop a neural network are described; an Auto Contractive neural network (AutoCM) [29] allows to draw a sort of "nervous system" of the data employed, looking at the correlations among the variables and identifying the most significant factors that characterize patients suffering from migraine.…”

Section: Introductionmentioning

confidence: 99%

“…The Maximally Regular Graph (MRG) [46] the graph whose H Function is the highest, among all the graphs generated adding to the original MST the new connections skipped before to complete the MST itself [29]. The MRG is given by the following equations: The "R" variable is a key variable during the MRG generation.…”

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confidence: 99%

“…Springer International Publishing, doi: 10.1007/ 978-3-319-75049-1 for further insights. The book is cited among the references [29]).…”

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confidence: 99%

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Affective symptoms and postural abnormalities as predictors of headache: an application of artificial neural networks

Gitto¹,

Massini²,

Mennini³

et al. 2020

NNW

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Chronic headache is a major liability in the individuals' quality of life. Identifying in advance the main features common to patients with headache may allow planning a preventive strategy of intervention. An artificial neural network model (Auto Contractive Maps-AutoCM), aimed at analyzing the correlations between patients' characteristics, affective symptoms and posture indicators has been developed in this paper. Patients suffering from chronic headache were observed at a neurological centre in Sicily (Italy). Headache and affective states were measured using the Profile of Mood States (POMS), the Beck Depression Inventory (BDI), the Toronto Alexithymia Scale (TAS-20) and the Repression Scale. Postural evaluations were carried through a stabilometric platform. The method of analysis selected allowed to reconstruct some records that were missing, through a Recirculation AutoAssociative Neural Network, and to obtain sound results. The results showed how some items from TAS-20, Repression and POMS were closely linked. The postural abnormalities were correlated primarily with repression features. The highest scores of the POMS were correlated with the items of the BDI. The results obtained lead to interesting remarks about the common traits to patients with headache. The main conclusion lies in the potentialities offered by the new methodology applied, that may contribute, overall, to a better understanding of the complexity of chronic diseases, where many factors concur to define patients' health conditions.

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“…The architecture of AutoCM was ideated by Massimo Buscema at Semeion Research Center from 2000 to 2007 [29,[40][41][42].…”

Section: The Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…Springer International Publishing, doi: 10.1007/ 978-3-319-75049-1 for further insights. The book is cited among the references [29]).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Affective symptoms and postural abnormalities as predictors of headache: an application of artificial neural networks

Gitto¹,

Massini²,

Mennini³

et al. 2020

NNW

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“…As the algorithm is trained, these weights are updated according to simple feedback rules to improve the accuracy of the classification. 32,33 SVMs apply a multidimensional transform to the input data (variables values). The algorithm then attempts to identify the hyperplane in this higher-dimensional space that best separates the training data into the desired categories (e.g., aGvHD and no aGvHD).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review

et al. 2019

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Background The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged. Objective This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used. Methods A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification algorithms in the process of predicting of aGvHD were considered. Results After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables. Conclusion Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly.

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