Editor's Note

The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI –provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances in Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present regular issue comprises different topics as generative AI, brain and main inspired computing, bird species identification, spam detection, recommendation systems, synthetic aperture radar automatic target recognition, hand gestures recognition, anomalies detection for video surveillance systems, disease detection, social networks analysis, or user experience. The collection of articles shows the wide use of deep learning techniques, although classical machine learning techniques, among others, are also present

Editor’s Note

The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. This regular issue presents research works based on different AI methods such as deep networks, genetic algorithms or classification trees algorithms. These methods are applied into many and various fields as video surveillance, forgery detection, facial recognition, activity recognition, hand written character recognition, clinical decision, marketing, renewable energy or social networking

Editor’s Note

The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. After its recent tenth anniversary, the journal has achieved an important milestone. From 2015 to 2018 IJIMAI was indexed at Web of Science through Emerging Science Citation Index. This meant a great increase in visibility and number of received papers. This year, Clarivate Analytics has accepted the inclusion of IJIMAI in the Journal Citation Reports. Specifically, IJIMAI is being indexed and abstracted in Science Citation Index Expanded, Journal Citation Reports/Science Edition and Current Contents®/Engineering Computing and Technology. The Web of Science Categories in which IJIMAI is included are “Computer Science, Artificial Intelligence” and “Computer Science, Interdisciplinary Applications”. This way, IJIMAI is indexed in Science Citation Index Expanded beginning with vol.4(3) March 2017 so that the journal will be listed in the 2019 Journal Citation Reports with a Journal Impact Factor when released in June 2020. Given this great achievement, IJIMAI Editorial Board has to thank authors for all the papers sent and all the papers published, as well as reviewers for their support to obtain high-quality in papers, and specially our readers because without them this milestone would not have been possible. The present regular issue includes research works based on different AI methods such as convolutional neural networks, genetic algorithms, lightning attachment procedure optimization, or those of multi-agent systems. These methods are applied into various fields as video surveillance, gesture recognition, sentiment analysis, territory planning, search engines, epidemiological surveillance or robotics

ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System

Video surveillance for real-world anomaly detection and prevention using deep learning is an important and difficult research area. It is imperative to detect and prevent anomalies to develop a nonviolent society. Realworld video surveillance cameras automate the detection of anomaly activities and enable the law enforcement systems for taking steps toward public safety. However, a human-monitored surveillance system is vulnerable to oversight anomaly activity. In this paper, an automated deep learning model is proposed in order to detect and prevent anomaly activities. The real-world video surveillance system is designed by implementing the ResNet-50, a Convolutional Neural Network (CNN) model, to extract the high-level features from input streams whereas temporal features are extracted by the Convolutional GRU (ConvGRU) from the ResNet-50 extracted features in the time-series dataset. The proposed deep learning video surveillance model (named ConvGRUCNN) can efficiently detect anomaly activities. The UCF-Crime dataset is used to evaluate the proposed deep learning model. We classified normal and abnormal activities, thereby showing the ability of ConvGRU-CNN to find a correct category for each abnormal activity. With the UCF-Crime dataset for the video surveillance-based anomaly detection, ConvGRU-CNN achieved 82.22% accuracy. In addition, the proposed model outperformed the related deep learning models

Clustering of LMS Use Strategies with Autoencoders

Learning Management Systems provide teachers with many functionalities to offer materials to students, interact with them and manage their courses. Recognizing teachers’ instructing styles from their course designs would allow recommendations and best practices to be made. We propose a method that determines teaching style in an unsupervised way from the course structure and use patterns. We define a course classification approach based on deep learning and clustering. We first use an autoencoder to reduce the dimensionality of the input data, while extracting the most important characteristics; thus, we obtain a latent representation of the courses. We then apply clustering techniques to the latent data to group courses based on their use patterns. The results show that this technique improves the clustering performance while avoiding the manual data pre-processing work. Furthermore, the obtained model defines seven course typologies that are clearly related to different use patterns of Learning Management Systems

Big data and artificial intelligence in earth science: recent progress and future advancements

[abstract not available

Policonsumo de benzodiacepinas y opioides: Una revisión sistemática

Trabajo Fin de Grado en Psicología de 6 créditos (150 horas) de formación específica en drogodependenciasEl policonsumo de benzodiacepinas y opioides se ha convertido en uno de los problemas más alarmantes de la actualidad. La escasez de recursos y medidas dirigidas a prevenir y paliar las posibles consecuencias negativas asociadas a esta forma de adicción es notable. Por dicha razón, esta revisión sistemática se ha centrado en identificar los principales factores psicosociales que permiten detectar posibles abusadores de sustancias, así como examinar los efectos a corto y largo plazo y analizar los tratamientos y medidas de prevención disponibles. El enfoque se dirige particularmente a las personas que presentan un diagnóstico psicológico, antecedentes de abuso de sustancias, problemas de insomnio o dolor crónico. Se subraya, por tanto, la importancia de invertir en investigación y aplicar medidas integrales y efectivas que abordar esta problemática de manera más efectiva.Universidad de Granada. Departamento de Psicología Social. Grado en Psicologí

Follicular adenomatoid odontogenic tumor : immunohistochemical study

Adenomatoid odontogenic tumor (AOT) is an uncommon benign odontogenic lesion that affects young patients, with female predominance, mainly in second decade, showing a radiolucent unilocular image associated with an unerupted tooth, usually a canine. In spite of previous and confusing denominations, such as adenoameloblastoma or adenomatoid ameloblastic tumor, AOT is a benign tumor with a very low rate of recurrence, that show a peculiar morphological picture (basaloid appearance with glandular-like structures, calcifying areas, and amiloid-like material) that allow its histopathological recognition. We present a clinicopathological analysis of a case of follicular AOT affecting the mandible in a 9 years-old female patient associated with unerupted lower left canine. Immunohistochemical study showed some data previously unrecognised. All cellular types that composed AOT showed nuclear positivity for p63 indicating a basal characterization in the different cellular components. According to its benign character and low potential for recurrence, AOT revealed a scant proliferative activity (2-3% nuclei showed Ki-67 positivity) limited to some epithelial nodules (AE1-3 +) of fusiform appearance. Absence of reactivity for hormonal receptors (RE and RPg) excluded a possible hormonodependence in AOT that could explain the observed female predominance

L'adquisició del paràmetre del subjecte nul en català

Aquest treball s'enquadra dintre de la disciplina de l'adquisició del llenguatge, encarregada d'estudiar els diferents processos que l'ésser humà du a terme en el desenvolupament de la parla. En aquest cas, s'estudiarà l'adquisició del subjecte nul en català, i es partirà de la següent qüestió: els xiquets catalanoparlants menors de 5 anys, han fixat correctament el paràmetre del subjecte nul? Per resoldre aquesta qüestió, partirem de la hipòtesi del VEPS (Very Early Parmeter Settings), que afirma que els paràmetres (una de les dimensions de la variació estructural del llenguatge humà) es fixen abans de l'estadi de dues paraules. Per realitzar l'estudi, comptabilitzarem els subjectes nuls en frases subordinades i no subordinades per a 4 adults i 8 xiquets parlants del català, material que extraurem de la base de dades CHILDES. Finalment compararem els nostres resultats amb els de l'anglés de Valian (1991), llengua que no admet l'omissió de subjecte