A Proposal for the Organisational Measure in Intelligent Systems

Román, Jesús-Ángel; Delgado, María Luisa Pérez

doi:10.3390/app10051806

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…On the other hand, the goal of machine learning is to develop techniques that allow a computer system to learn autonomously [4]. Artificial neural networks (ANNs) are a specialized AI tool used to solve a wide variety of problems [5][6][7][8][9][10][11][12]. An ANN is a trainable system made up of a set of interconnected neurons organized in layers.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Used to Date Photographs

2022

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Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type of device that they were taken with, and thus be able to locate the photograph in a specific context. This is not the case with analog photographs, which lack any information regarding the date they were taken. Extracting this information is a complicated task, so classifying each photograph according to the date it was taken is a laborious task for a human expert. Artificial intelligence techniques make it possible to determine the characteristics and classify the images automatically. Within the field of artificial intelligence, convolutional neural networks are one of the most widely used methods today. This article describes the application of convolutional neural networks to automatically classify photographs according to the year they were taken. To do this, only the photograph is used, without any additional information. The proposed method divides each photograph into several segments that are presented to the network so that it can estimate a year for each segment. Once all the segments of a photograph have been processed, a general year for the photograph is calculated from the values generated by the network for each of its segments. In this study, images taken between 1960 and 1999 were analyzed and classified using different architectures of a convolutional neural network. The computational results obtained indicate that 44% of the images were classified with an error of less than 5 years, 20.25% with a marginal error between 5 and 10 years, and 35.75% with a higher marginal error of more than 10 years. Due to the complexity of the problem, the results obtained are considered good since 64.25% of the photographs were classified with an error of less than 10 years. Another important result of the study carried out is that it was found that the color is a very important characteristic when classifying photographs by date. The results obtained show that the approach given in this study is an important starting point for this type of task and that it allows placing a photograph in a specific temporal context, thus facilitating the work of experts dedicated to scientific and forensic photography.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Used to Date Photographs

2022

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Age Analysis with Convolutional Neural Networks

Delgado

Gallego

2023

Advances in Intelligent Systems and Computing

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Designing an Intelligent Virtual Educational System to Improve the Efficiency of Primary Education in Developing Countries

et al. 2022

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Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on primary education in developing countries, showing the design of an intelligent virtual educational system to improve the efficiency of primary education through recommendations based on reliable data. The intelligent system is formed of four subsystems: data warehousing, analytical data processing, monitoring process and recommender system for educational agents. To illustrate this, the paper contains two dashboards that analyze, respectively, the digital resources usage time and an aggregate profile of teachers’ digital skills, in order to infer new activities that improve efficiency. These intelligent virtual educational systems focus the teaching–learning process on new forms of interaction on an educational future oriented to personalized teaching for the students, and new evaluation and teaching processes for each professor.

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A Proposal for the Organisational Measure in Intelligent Systems

Cited by 3 publications

References 69 publications

Convolutional Neural Networks Used to Date Photographs

Convolutional Neural Networks Used to Date Photographs

Age Analysis with Convolutional Neural Networks

Designing an Intelligent Virtual Educational System to Improve the Efficiency of Primary Education in Developing Countries

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