Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

Forradellas, Ricardo Francisco Reier; Alonso, Sergio Luis Náñez; Jorge-Vázquez, Javier; Rodriguez, Marcela Laura

doi:10.3390/socsci10010004

Cited by 23 publications

(9 citation statements)

References 33 publications

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“…It has also been studied in Malaysia (Hossain 2017). A similar methodology has been employed in the study of other areas of the social sciences (Reier Forradellas et al 2021).…”

Section: Discussionmentioning

confidence: 99%

From the Great Recession to the COVID-19 Pandemic: The Risk of Expansionary Monetary Policies

Fernández

Alonso

Forradellas

et al. 2022

Risks

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Central banks have been pursuing an expansionary monetary policy since before the pandemic, although the health and economic crisis of COVID-19 has boosted asset purchase programmes. After the Great Recession, a new phase began, characterised by low interest rates and liquidity injections. These policies spilled over into financial markets and are leading to higher inflation. These policies stabilised the situation in the short term, but if they continue indefinitely there is a risk of debt overhang, investment mistakes and high inflation in the future. The aim of this article is to analyse monetary policy developments from the Great Recession to the COVID-19 crisis. Correlations between different macroeconomic variables will be shown through IBM SPSS Statistics. For this purpose, bi-variate correlations were used. For the predictions and confidence of the model data, Tableau Desktop Edition was used, which in turn was used for the generation of the graphs. There is a strong correlation between the growth of monetary aggregates and public debt and stock market capitalisation for the selected indicators. The main contribution of this research is the analysis of the long-term effects of a monetary policy.

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“…It has also been studied in Malaysia (Hossain 2017). A similar methodology has been employed in the study of other areas of the social sciences (Reier Forradellas et al 2021).…”

Section: Discussionmentioning

confidence: 99%

From the Great Recession to the COVID-19 Pandemic: The Risk of Expansionary Monetary Policies

Fernández

Alonso

Forradellas

et al. 2022

Risks

Self Cite

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“…As we have shown in the study, by means of convolutional networks we can identify and classify the images so that the classification of the materials among glass, paper, plastic, and organic matter can be done automatically [31]. The next step would be to ensure that each identified element is classified where it belongs.…”

Section: Discussionmentioning

confidence: 99%

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

Alonso

Forradellas

Morell³

et al. 2021

Sustainability

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The great advances produced in the field of artificial intelligence and, more specifically, in deep learning allow us to classify images automatically with a great margin of reliability. This research consists of the validation and development of a methodology that allows, through the use of convolutional neural networks and image identification, the automatic recycling of materials such as paper, plastic, glass, and organic material. The validity of the study is based on the development of a methodology capable of implementing a convolutional neural network to validate a reliability in the recycling process that is much higher than simple human interaction would have. The method used to obtain this better precision will be transfer learning through a dataset using the pre-trained networks Visual Geometric Group 16 (VGG16), Visual Geometric Group 19 (VGG19), and ResNet15V2. To implement the model, the Keras framework is used. The results conclude that by using a small set of images, and thanks to the later help of the transfer learning method, it is possible to classify each of the materials with a 90% reliability rate. As a conclusion, a model is obtained with a performance much higher than the performance that would be reached if this type of technique were not used, with the classification of a 100% reusable material such as organic material.

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“…Different with other data set, Forradellas et al [29] analyzed the crime data of Buenos Aires, and a system named SEMMA was proposed. SEMMA means the methods processing, and they are sample, explore, modify, model and asses.…”

Section: The Models Based On Traditional Machine Learning Methodsmentioning

confidence: 99%

Crime Prediction Methods Based on Machine Learning: A Survey

Yin¹

2023

Computers, Materials &Amp; Continua

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The objective of crime prediction, one of the most important technologies in social computing, is to extract useful information from many existing criminal records to predict the next process-related crime. It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas. With the rapid growth of big data, the Internet of Things, and other technologies, as well as the increasing use of artificial intelligence in forecasting models, crime prediction models based on deep learning techniques are accelerating. Therefore, it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms. Existing crime prediction methods can be roughly divided into two categories: those based on conventional machine learning and those based on contemporary deep learning. This survey analyses the fundamental theories and procedures. The most frequently used data sets are then enumerated, and the fundamental procedures of various algorithms are also analyzed in this paper. In light of the insufficient scale of existing data in this field, the ambiguity of data types used to predict crimes, and the absence of public data sets that have a significant impact on the research of algorithm models, this survey proposes the construction of a machine learning-based big data research model to address these issues. Future researchers who will enter this field are provided with a guide to the direction of future research development.

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Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

Cited by 23 publications

References 33 publications

From the Great Recession to the COVID-19 Pandemic: The Risk of Expansionary Monetary Policies

From the Great Recession to the COVID-19 Pandemic: The Risk of Expansionary Monetary Policies

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

Crime Prediction Methods Based on Machine Learning: A Survey

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