Encyclopedia of Information Science and Technology, Third Edition

Barbuti, Nicola; Caldarola, Tommaso

doi:10.4018/978-1-4666-5888-2

Cited by 41 publications

(6 citation statements)

References 102 publications

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“…The rapid development of ICTs in recent years has led to an increase in the production and distribution of multimedia material worldwide (Matsiola et al 2018;Dimoulas et al 2015), in addition to its consumption from and through the Internet-such as via (a) interactive websites and/or weblogs/blogs (e.g., interactive documentary, etc. ); (b) online social networks (OSNs), social media, and platforms (e.g., LinkedIn, Facebook, Twitter, etc.…”

Section: Background and Literature Review And/or Related Workmentioning

confidence: 99%

Media Studies, Audiovisual Media Communications, and Generations: The Case of Budding Journalists in Radio Courses in Greece

Nicolaou

Matsiola

Karypidou

et al. 2021

Journalism and Media

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In this article, the quality of media studies education through effective teaching utilizing audiovisual media technologies and audiovisual content (audiovisual media communications) to budding journalists as adult learners (18 years and older) is researched, with results primarily intended for application in radio lessons at all educational levels and disciplines (including adult education). Nowadays, audiovisual media communications play an important role in the modern and visual-centric way of our life, while they require all of us to possess multiple-multimodal skills to have a successful professional practice and career, and especially those who study media studies, such as tomorrow’s new journalists. Data were collected after three interactive teachings with emphasis on educational effectiveness in technology-enhanced learning, through a specially designed written questionnaire with a qualitative and quantitative form (evaluation form), as case study experiments that applied qualitative action research with quasi-experiments. The results (a) confirmed (i) the theory of audiovisual media in education, as well as (ii) the genealogical characteristics and habits of budding journalists as highlighted in basic generational theory, something which appears to be in agreement with findings of previous studies and research; and (b) showed that (i) teaching methodology and educational techniques aimed primarily at adult learners in adult education kept the interest and attention of the budding journalists through the use of such specific educational communication tools as audiovisual media technologies, as well as (ii) sound/audio media, as audiovisual content may hold a significant part in a lecture.

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Section: Background and Literature Review And/or Related Workmentioning

confidence: 99%

Media Studies, Audiovisual Media Communications, and Generations: The Case of Budding Journalists in Radio Courses in Greece

Nicolaou

Matsiola

Karypidou

et al. 2021

Journalism and Media

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“…Other measures were collected from the trained model installed in the Raspberry Pi 3 Model B. The Response Time to complete the Emotion recognition (RTE) [43] is calculated as the average of time records elapsed between the start and completion of a task of emotion recognition; five executions of a minute in each trained model give us the time records. The Main Memory Utilization (MMU) [44] is measured as the amount of main memory used during trained model execution.…”

Section: Evaluation Methods Of the Dnn Modelmentioning

confidence: 99%

Web application to support evidence of individual emotional impact evoked by COVID-19 pandemic restrictions (Preprint)

Mitre‐Hernandez¹,

Ferro-Perez²,

González-Hernández³

2021

Preprint

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BACKGROUND Mental health effects during COVID-19 quarantine need to be handled because patients, relatives, and healthcare workers are living with negative emotional behaviors. The clinical disorders of depression and anxiety are evoking anger, fear, sadness, disgust, and reducing happiness. Therefore, track emotions with the help of psychologists on online consultations –to reduce the risk of contagion– will go a long way in assisting with mental health. The human micro-expressions can describe genuine emotions of people and can be captured by Deep Neural Networks (DNNs) models. But the challenge is to implement it under the poor performance of a part of society's computers and the low speed of internet connection. OBJECTIVE This study aimed to create a useful and usable web application to record emotions in a patient’s card in real-time, achieving a small data transfer, and a Convolutional Neural Networks (CNN) model with a low computational cost. METHODS To validate the low computational cost premise, firstly, we compare DNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network (ResmoNet) model. Secondly, we compare the trained models' results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion recognition (RTE). Finally, we design a data transfer that includes the raw data of emotions and the basic text information of the patient. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists (experts). RESULTS All CNN models were set up using 150 epochs for training and testing comparing the results for each variable in ResmoNet with the best model. It was obtained that ResmoNet has 115,976 NP less than MobileNet, 243,901 FLOPS less than MobileNet, and 5% less accuracy than EDNN (95%). Moreover, ResmoNet used less MMU than any model, only EDNN overcomes ResmoNet in 0.01 seconds for RTE. Finally, with our model, we develop a web application to collect emotions in real-time during a psychological consultation. For data transfer, the patient’s card and raw emotional data have 2 kb with a UTF-8 encoding approximately. Finally, according to the experts, the web application has good usability (73.8 of 100) and utility (3.94 of 5). CONCLUSIONS A usable and useful web application for psychologists and psychiatrists is presented. This tool includes an efficient and light facial emotion recognition model. Its purpose is to be a complementary tool for diagnostic processes.

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“…Using a classic feedforward multilayer network requires setting up the number of neurons in the input layer, the number of hidden layers including the number of neurons, the number of neurons in the output layer, training algorithms (e.g. gradient descent BP, Levenberg-Marquardt BP, quasi-Newton BP) [87,151], training concept (incremental-weights are updated in each iteration, or batch-weights are updated only when all inputs are available in the network) [87], number of epochs, learning rate (usually a small positive value ranging between 0 and 1) [152], and an activation function (e.g. linear, sigmoid, Gaussian) [151].…”

Section: Artificial Neural Network In Classificationmentioning

confidence: 99%

“…gradient descent BP, Levenberg-Marquardt BP, quasi-Newton BP) [87,151], training concept (incremental-weights are updated in each iteration, or batch-weights are updated only when all inputs are available in the network) [87], number of epochs, learning rate (usually a small positive value ranging between 0 and 1) [152], and an activation function (e.g. linear, sigmoid, Gaussian) [151]. Comert et al [87] achieved accurate fetal state classification by means of the CTG recordings and feed-forward ANN, a structure that included 21 input variables, 10 nodes in a hidden layer, and 3 nodes in an output layer.…”

Section: Artificial Neural Network In Classificationmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Barnova,

Martinek,

Vilimkova Kahankova

et al. 2024

Arch Computat Methods Eng

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Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.

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Encyclopedia of Information Science and Technology, Third Edition

Cited by 41 publications

References 102 publications

Media Studies, Audiovisual Media Communications, and Generations: The Case of Budding Journalists in Radio Courses in Greece

Media Studies, Audiovisual Media Communications, and Generations: The Case of Budding Journalists in Radio Courses in Greece

Web application to support evidence of individual emotional impact evoked by COVID-19 pandemic restrictions (Preprint)

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

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