https://reunir.unir.net/handle/123456789/12627 Fri, 08 Nov 2024 23:04:05 GMT 2024-11-08T23:04:05Z https://reunir.unir.net/handle/123456789/12636 Editor’s Note. Towards an Intelligent Society: Advances in Marketing and Neuroscience Mochón, Francisco; Baldominos Gómez, Alejandro This Special Issue focuses in cases that explore the relationship between Artificial Intelligence and marketing, as well as neuroscience. AI can be combined with specific neuroscience techniques to achieve a more successful and profitable neuromarketing. For this Special Issue, we have found that descriptions of successful use cases are highly valuable to help researchers identify fields where novel applications of AI can enhance the outcome of digital marketing and neuroscience. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T10:28:31Z No. of bitstreams: 1 ijimai20195_6_0_pdf_19590.pdf: 101699 bytes, checksum: 61e7a6f67a50657da0e326306647f5b8 (MD5); Made available in DSpace on 2022-03-15T10:28:31Z (GMT). No. of bitstreams: 1 ijimai20195_6_0_pdf_19590.pdf: 101699 bytes, checksum: 61e7a6f67a50657da0e326306647f5b8 (MD5) https://reunir.unir.net/handle/123456789/12636 https://reunir.unir.net/handle/123456789/12635 MOBEEZE. Natural Interaction Technologies, Virtual Reality and Artificial Intelligence for Gait Disorders Analysis and Rehabilitation in Patients with Parkinson's Disease Lombardo, Juan Manuel; Lopez, Miguel Angel; Miron, Felipe; López, Mabel; León, Mónica; Arambarri, Jon; Álvarez, David Parkinson's Disease (PD) is the most common degenerative disorder after Alzheimer's disease. Generally affecting elderly groups, it has a strong limiting effect on physical functioning and performance of roles, vitality and general perception of health. Since the disease is progressive, the patient knows he's going to get worse. The deterioration is significant not only in mobility but also in pain, social isolation, and emotional reactions. Freezing is a phenomenon associated with this disease and it is characterized by a motor disorder that leaves the patient literally stuck to the ground. Mobeeze is designed with the main objective of providing health personnel with a tool to analyse, evaluate and monitor the progress of patients’ disorders as well as the personalization and adaptation of rehabilitation sessions in patients with Parkinson's disease. Based on the characteristics measured in real time which will allow the strengthening effects of rehabilitation and help to assimilate them in the long term. The creation of Mobeeze allows the constitution of a system of analysis and evaluation of march disorders in real time, through natural interaction, virtual reality and artificial intelligence. In this project, we will analyse if these non-invasive technologies reduce the stress induced to the patient when he is feeling evaluated. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T10:17:16Z No. of bitstreams: 1 ijimai20195_6_7_pdf_12627.pdf: 371762 bytes, checksum: 29b65e04186ac4647b90cbaad23b59f5 (MD5); Made available in DSpace on 2022-03-15T10:17:16Z (GMT). No. of bitstreams: 1 ijimai20195_6_7_pdf_12627.pdf: 371762 bytes, checksum: 29b65e04186ac4647b90cbaad23b59f5 (MD5) https://reunir.unir.net/handle/123456789/12635 https://reunir.unir.net/handle/123456789/12634 The Promotion of Graduate Programs through Clustering Prospective Students Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio; Castillo-Villar, Fernando-Rey The promotion of academic programs, particularly at graduate levels, emerges as a response to market changes. In general, graduate programs are not a first order necessity which makes necessary the right promotion of such programs guarantee the attraction of prospective students, which enroll in some of them, which is essential for the financial sustainability of universities. Notably, the last one is a crucial problem for private universities. In this paper, we analyze the prospective students that enroll in a private to design better promotion strategies by using on data gathered by online sources. Specifically, we use clustering techniques to define marketing strategies based on segments of students. We find that age and city are crucial to promoting graduate programs while marital status and sex does not impact the decision of students in the university that we analyze. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:49:53Z No. of bitstreams: 1 ijimai20195_6_3_pdf_12770.pdf: 517809 bytes, checksum: de199a529087534d70e3bdc39990ce1a (MD5); Made available in DSpace on 2022-03-15T09:49:53Z (GMT). No. of bitstreams: 1 ijimai20195_6_3_pdf_12770.pdf: 517809 bytes, checksum: de199a529087534d70e3bdc39990ce1a (MD5) https://reunir.unir.net/handle/123456789/12634 https://reunir.unir.net/handle/123456789/12633 Model for Prediction of Progression in Multiple Sclerosis Pruenza, Cristina; Díaz, Julia; Solano, María Teresa; Arroyo, Rafael; Izquierdo, Guillermo Multiple sclerosis is an idiopathic inflammatory disease of the central nervous system and the second most common cause of disability in young adults. Choosing an effective treatment is crucial to preventing disability. However, response to treatment varies greatly between patients. Because of this, accurate and timely detection of individual response to treatment is an essential requisite of efficient personalised multiple sclerosis therapy. Nowadays, there is a lack of comprehensive predictive models of response to individual treatment.This paper arises from the clinical need to improve this situation. To achieve it, all patient's information was used to evaluate the effectiveness of demographic, clinical and paraclinical variables of individual response to fourteen disease-modifying therapies in MSBase, an international cohort. A personalized prediction model to three stages of disease, as a support tool in clinical decision making for each MS patient, was developed applying machine learning and Big Data techniques. These techniques were also used to reduce the data set and define a minimum set of characteristics for each patient. Best predictors for the response to treatment were identified to refine the predictive model. Fourteen relevant variables were selected. A web application was implemented to be used to support the specialist neurologist in real time. This tool provides a prediction of progression in EDSS from the last relapse of an individual patient, and a report for the medical expert. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:36:47Z No. of bitstreams: 1 ijimai20195_6_6_pdf_58400.pdf: 506098 bytes, checksum: 54cb90b44ee937d966e2bd9c2cf8b3e2 (MD5); Made available in DSpace on 2022-03-15T09:36:47Z (GMT). No. of bitstreams: 1 ijimai20195_6_6_pdf_58400.pdf: 506098 bytes, checksum: 54cb90b44ee937d966e2bd9c2cf8b3e2 (MD5) https://reunir.unir.net/handle/123456789/12633 https://reunir.unir.net/handle/123456789/12632 Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations Herrera-Viedma, Enrique; Carrasco, Ramón Alberto; Moreno, Caio Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:23:29Z No. of bitstreams: 1 ijimai20195_6_1_pdf_17622.pdf: 1061408 bytes, checksum: ba43ba351ab50685925b88f828b9a460 (MD5); Made available in DSpace on 2022-03-15T09:23:29Z (GMT). No. of bitstreams: 1 ijimai20195_6_1_pdf_17622.pdf: 1061408 bytes, checksum: ba43ba351ab50685925b88f828b9a460 (MD5) https://reunir.unir.net/handle/123456789/12632 https://reunir.unir.net/handle/123456789/12631 A User-centered Smartphone Application for Wireless EEG and its Role in Epilepsy Ahufinger, Sofía; Balugo, Paloma; González, María Mercedes; Pequeño, Elías; González, Henar; Herrero, Pilar Electroencephalography is well-known for its importance in the diagnosis and treatment of mental and neurological disorders and abnormalities, being especially noted in critically ill patients who suffer a variety of cerebral injuries and altered states of consciousness. However, there is an important lack of adapted equipment and applications designed to suit the clinical and research needs. Hence, patients, physicians and researchers suffer, in most cases, from a restricted mobility due to non-portable devices and wires which keep them attached to the bed, leading to an uncomfortable patient experience or difficulties during the recording. In addition, nowadays, both physicians and researchers need to access the recordings and patient information from different places such as different units or hospitals. To solve this problem, this paper presents the design and evaluation of the high-fidelity prototype of a wireless EEG smartphone application based on a user-centred design, including expert panel guidance, paper and high-fidelity prototyping and usability testing, which confirm the accuracy of the defined context of use and the validity of the prototyped application to suit the clinical and research needs. In fact, since the EEG is the most efficient and specific way to define the epileptogenic cortex, we will focus on the possible use of the presented App in epilepsy diagnosis, which is one of the main targets in the field. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:18:04Z No. of bitstreams: 1 ijimai20195_6_5_pdf_21239.pdf: 293153 bytes, checksum: aae82f3c0f5d0731e4c32d521dc88c15 (MD5); Made available in DSpace on 2022-03-15T09:18:04Z (GMT). No. of bitstreams: 1 ijimai20195_6_5_pdf_21239.pdf: 293153 bytes, checksum: aae82f3c0f5d0731e4c32d521dc88c15 (MD5) https://reunir.unir.net/handle/123456789/12631 https://reunir.unir.net/handle/123456789/12630 Do Women and Men Perceive User Experience Differently? Schrepp, Martin; chewski, Jörg Thomas; Aufderhaar, Kristina We study three web sites to see whether there are systematic differences between women and men in their rating of the user experience of the sites. One of the sites addresses especially the target group of women, another the target group of men, whereas the third site is neutral in this respect. The selection of the sites was safeguarded with gender screening. The participants in the study rated the three chosen websites with the questionnaires UEQ and VISAWI-S. The results indicate that there are no substantial differences in the perception of the UX between men and women. Personal attitudes and preferences seem to have a substantially greater influence than sex. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:12:13Z No. of bitstreams: 1 ijimai20195_6_8_pdf_16167.pdf: 728501 bytes, checksum: fbd608ea8834dd64e79bc337260d3170 (MD5); Made available in DSpace on 2022-03-15T09:12:13Z (GMT). No. of bitstreams: 1 ijimai20195_6_8_pdf_16167.pdf: 728501 bytes, checksum: fbd608ea8834dd64e79bc337260d3170 (MD5) https://reunir.unir.net/handle/123456789/12630 https://reunir.unir.net/handle/123456789/12629 Voice Analysis Using PRAAT Software and Classification of User Emotional State Magdin, Martin; Sulka, T; Tomanová, J; Vozár, M During the last decades the field of IT has seen an incredible and very rapid development. This development has shown that it is important not only to shift performance and functional boundaries but also to adapt the way human-computer interaction to modern needs. One of the interaction possibilities is a voice control which nowadays can‘t be restricted only to direct commands. The goal of adaptive interaction between man and computer is the human needs understanding. The paper deals with the user's emotional state classification based on the voice track analysis, it describes its own solution - the measurement and the selection process of appropriate voice characteristics using ANOVA analysis and the use of PRAAT software for many voice aspects analysis and for the implementation of own application to classify the user's emotional state from his/her voice. In the paper are presented the results of the created application testing and the possibilities of further expansion and improvement of this solution. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:07:53Z No. of bitstreams: 1 ijimai20195_6_4_pdf_80526.pdf: 470562 bytes, checksum: fa5d3148fdcbe8f347d7ef3660cf6b20 (MD5); Made available in DSpace on 2022-03-15T09:07:53Z (GMT). No. of bitstreams: 1 ijimai20195_6_4_pdf_80526.pdf: 470562 bytes, checksum: fa5d3148fdcbe8f347d7ef3660cf6b20 (MD5) https://reunir.unir.net/handle/123456789/12629 https://reunir.unir.net/handle/123456789/12628 An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study Goli, Alireza; Zare, Hassan Khademi; Tavakkoli-Moghaddam, Reza; Sadeghieh, Ahmad This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T08:55:38Z No. of bitstreams: 1 ijimai20195_6_2_pdf_10531.pdf: 649398 bytes, checksum: f98b30f13053b5c86fc063c76e4ec77f (MD5); Made available in DSpace on 2022-03-15T08:55:38Z (GMT). No. of bitstreams: 1 ijimai20195_6_2_pdf_10531.pdf: 649398 bytes, checksum: f98b30f13053b5c86fc063c76e4ec77f (MD5) https://reunir.unir.net/handle/123456789/12628