Artificial Neural Networks

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

doi:10.1007/978-3-319-75049-1_2

Cited by 31 publications

(21 citation statements)

References 8 publications

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“…These variables could have been eliminated. The network would have gained in simplicity and processing speed, but would have decreased in predictive capacity, which is why it was decided to include these variables as well [40]. On the other hand, the synaptic weights of the different types of roles in the network invite reflection on the relevance of teachers as positive models [12,[27][28][29]36,37].…”

Section: Discussionmentioning

confidence: 99%

“…It is closely interconnected with other neurons in the network. The interconnections can be more or less intense depending on their contribution to the network through the synaptic weight of each one [40].…”

Section: Introductionmentioning

confidence: 99%

“…ANNs are based on the concept of artificial intelligence and are closely related to "big data", "machine learning" and how it is possible to reproduce systems capable of learning on their own [40]. ANNs have been used in many branches of science to predict data because of their usefulness in managing large databases.…”

Section: Introductionmentioning

confidence: 99%

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What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

Ramón

Rodríguez

2020

Sustainability

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The aggressor sets in motion dysfunctional and violent behaviors with others in the dynamic of bullying. These behaviors can be understood as misfit coping strategies in response to environmental demands perceived as stressful, putting at risk the quality of education. The aim of this study was to develop a predictive model based on artificial neural networks (ANN) to forecast a violent coping strategy based on perceived stress, resilience, other coping strategies and various socio-demographic variables. For this purpose, the Stress Coping Questionnaire (SCQ), the Perceived Stress Scale (PSS) and the Brief Resilient Coping Scale (BRCS) were administered to 283 participants from the educational field (71.5% women). The design was cross-sectional. An inferential analysis (multilayer perception ANN) was performed with SPSS version 24. The results showed a predictive model that took into consideration the subject’s stress levels, personal assessment and strategies such as negative self-targeting or avoidance to predict open emotional expression (a coping strategy defined by violent behaviors) in approximately four out of five cases. The conclusions emphasis the need for considering problem solving, stress management and coping skills to prevent school violence and improve the social environment through sustainable psychological measures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

Ramón

Rodríguez

2020

Sustainability

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“…Artificial Neural networks (ANNs) are well suited for classification and prediction [20]. ANNs enable problem-solving by changing the structure of interconnected components [21]. The reason for the popularity of ANNs is that is they are non-parametric statistical models, which do not need any assumptions between input and output variables [22], and that they have the ability to learn from experience and enhance their functions to improve classification and prediction accuracy [23].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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“…Conversely, cognitive systems, and in particular artificial neural networks (ANNs), extract the relationship directly from data as an input/output (domain/range) pair by imposing a priori architecture (the NN) rather than an existing model. The advantage of this approach is that, under the appropriate conditions, ANNs are able to model a complicated relationship for the modelling to adapt to the different problems [38,39], solving engineering issues in industrial applications, such as in-process control [40,41], and optimisation procedures [42][43][44], as well as in non-destructive evaluation [45]. Moreover, ANNs can allow robust models even in the presence of a high noise ratio [38,46,47].…”

Section: Introductionmentioning

confidence: 99%

An integrated approach for the modelling of silicon carbide components laser milling process

Leone

Genna

Tagliaferri

2021

Int J Adv Manuf Technol

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The paper deals with characterisation and modelling of laser milling process on silicon carbide hard ceramic. To this end, a Yb:YAG pulsed fiber laser was adopted to mill silicon carbide bars. Square pockets, 5×5 mm2 in plane dimension, were machined at the maximum nominal average power (30W), under different laser process parameters: pulse frequency, scan speed, hatching distance, repetitions and scanning strategy. After machining, the achieved depth and the roughness parameters were measured by way of digital microscopy and 3D surface profiling, respectively. In addition, the material removal rate was calculated as the ratio between the removed volume/process time. Analysis of variance (ANOVA) was adopted to assess the effect of the process parameters on the achieved depth, the material removal rate (MRR) and roughness parameters, while response surface methodology (RSM) and artificial neuronal networks (ANNs) were adopted to model the process behaviours. Results show that both RSM and ANNs fault in MRR and RSm roughness parameters modelling. Thus, an integrated approach was developed to overcome the issue; the approach is based on the use of the RSM model to obtain a hybrid Training dataset for the ANNs. The results show that the approach can allow improvement in model accuracy.

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Artificial Neural Networks

Cited by 31 publications

References 8 publications

What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

An integrated approach for the modelling of silicon carbide components laser milling process

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