https://reunir.unir.net/handle/123456789/16743 2024-10-24T23:28:50Z https://reunir.unir.net/handle/123456789/16747 Editor’s Note Montenegro-Marin, Carlos Enrique This regular issue consists of 16 articles that use artificial intelligence or computational systems to come up with new solutions and solve problems more effectively. The issue showcases the use of Artificial Intelligence or computational systems that contribute to new knowledge with innovative applications. In this issue you can find different articles on game theory, models for collaborative filtering, text classification, fake news detection system, identification system, semi eager classifier, longitudinal segmented analysis, etc. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-06-14T11:07:13Z No. of bitstreams: 1 Editor’s Note.pdf: 56673 bytes, checksum: 6eb61f727eb6292a27ecad841527463a (MD5); Made available in DSpace on 2024-06-14T11:07:13Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 56673 bytes, checksum: 6eb61f727eb6292a27ecad841527463a (MD5) https://reunir.unir.net/handle/123456789/16570 Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition Dhahbi, Sami; Saleem, Nasir; Gunawan, Teddy Surya; Bourouis, Sami; Ali, Imad; Trigui, Aymen; Algarni, Abeer D. Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencies efficiently. This study proposes a lightweight hourglass-shaped model for speech enhancement (SE) and automatic speech recognition (ASR). Simple recurrent units (SRU) with skip connections are implemented where attention gates are added to the skip connections, highlighting the important features and spectral regions. The model operates without relying on future information that is well-suited for real-time processing. Combined acoustic features and two training objectives are estimated. Experimental evaluations using the short time speech intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and word error rates (WERs) indicate better intelligibility, perceptual quality, and word recognition rates. The composite measures further confirm the performance of residual noise and speech distortion. With the TIMIT database, the proposed model improves the STOI and PESQ by 16.21% and 0.69 (31.1%) whereas with the LibriSpeech database, the model improves STOI by 16.41% and PESQ by 0.71 (32.9%) over the noisy speech. Further, our model outperforms other deep neural networks (DNNs) in seen and unseen conditions. The ASR performance is measured using the Kaldi toolkit and achieves 15.13% WERs in noisy backgrounds. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-05-13T16:16:38Z No. of bitstreams: 1 Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition.pdf: 2470761 bytes, checksum: 74a0aaefed5f55e8bfcb6ef7c937544c (MD5); Made available in DSpace on 2024-05-13T16:16:38Z (GMT). No. of bitstreams: 1 Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition.pdf: 2470761 bytes, checksum: 74a0aaefed5f55e8bfcb6ef7c937544c (MD5) https://reunir.unir.net/handle/123456789/16005 Requirements for User Experience Management - A Tertiary Study Hinderks, Andreas; Domínguez Mayo, Francisco José; Escalona, María José; Thomaschewski, Jörg Today’s users expect to be able to interact with the products they own without much effort and also want to be excited about them. The development of a positive user experience must therefore be managed. We understand management in general as a combination of a goal, a strategy, and resources. When applied to UX, user experience management consists of a UX goal, a UX strategy, and UX resources. We conducted a tertiary study and examined the current state of existing literature regarding possible requirements. We want to figure out, what requirements can be derived from the literature reviews with the focus on UX and agile development. In total, we were able to identify and analyse 16 studies. After analysing the studies in detail, we identified different requirements for UX management. In summary, we identified 13 requirements. The most frequently mentioned requirements were prototypes and UX/usability evaluation. Communication between UX professionals and developers was identified as a major improvement in the software development process. In summary, we were able to identify requirements for UX management of People/Social, Technology/Artifacts, and Process/Practice. However, we could not identify requirements for UX management that enabled the development and achievement of a UX goal. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T09:28:40Z No. of bitstreams: 1 ip2024_01_004.pdf: 707717 bytes, checksum: 636dc3688d95f8f658579c6594af7a52 (MD5); Made available in DSpace on 2024-02-06T09:28:40Z (GMT). No. of bitstreams: 1 ip2024_01_004.pdf: 707717 bytes, checksum: 636dc3688d95f8f658579c6594af7a52 (MD5) https://reunir.unir.net/handle/123456789/15785 GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware Deore, Mahendra; Tarambale, Manoj; Raja Kumar, Jambi Ratna; Sakhare, Sachin Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-01-02T08:33:42Z No. of bitstreams: 1 ip2023_12_002_0.pdf: 3980674 bytes, checksum: 452dad3a10f3c7ee92f7982332310e05 (MD5); Made available in DSpace on 2024-01-02T08:33:42Z (GMT). No. of bitstreams: 1 ip2023_12_002_0.pdf: 3980674 bytes, checksum: 452dad3a10f3c7ee92f7982332310e05 (MD5) https://reunir.unir.net/handle/123456789/15339 The Game Theory in Quantum Computers: A Review Pérez-Antón, Raquel; López-Sánchez, José Ignacio; Corbi, Alberto Game theory has been studied extensively in recent centuries as a set of formal mathematical strategies for optimal decision making. This discipline improved its efficiency with the arrival, in the 20th century, of digital computer science. However, the computational limitations related to exponential time type problems in digital processors, triggered the search for more efficient alternatives. One of these choices is quantum computing. Certainly, quantum processors seem to be able to solve some of these complex problems, at least in theory. For this reason, in recent times, many research works have emerged related to the field of quantum game theory. In this paper we review the main studies about the subject, including operational requirements and implementation details. In addition, we describe various quantum games, their design strategy, and the used supporting tools. We also present the still open debate linked to the interpretation of the transformations of classical algorithms in fundamental game theory to their quantum version, with special attention to the Nash equilibrium. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-10-02T15:44:05Z No. of bitstreams: 1 ip2023_09_001.pdf: 1472228 bytes, checksum: 40c31750ef4e8ddfa372dabcd1fe17a8 (MD5); Made available in DSpace on 2023-10-02T15:44:05Z (GMT). No. of bitstreams: 1 ip2023_09_001.pdf: 1472228 bytes, checksum: 40c31750ef4e8ddfa372dabcd1fe17a8 (MD5) https://reunir.unir.net/handle/123456789/14810 Supporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniques Caballero-Hernández, Juan Antonio; Palomo-Duarte, Manuel; Dodero, Juan Manuel; Gaševic, Dragan Learning experiences based on serious games are employed in multiple contexts. Players carry out multiple interactions during the gameplay to solve the different challenges faced. Those interactions can be registered in logs as large data sets providing the assessment process with objective information about the skills employed. Most assessment methods in learning experiences based on serious games rely on manual approaches, which do not scalewell when the amount of data increases. We propose an automated method to analyse students’ interactions and assess their skills in learning experiences based on serious games. The method takes into account not only the final model obtained by the student, but also the process followed to obtain it, extracted from game logs. The assessment method groups students according to their in-game errors and ingame outcomes. Then, the models for the most and the least successful students are discovered using process mining techniques. Similarities in their behaviour are analysed through conformance checking techniques to compare all the students with the most successful ones. Finally, the similarities found are quantified to build a classification of the students’ assessments. We have employed this method with Computer Science students playing a serious game to solve design problems in a course on databases. The findings show that process mining techniques can palliate the limitations of skill assessment methods in game-based learning experiences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T09:53:03Z No. of bitstreams: 1 ip2023_05_002.pdf: 2378538 bytes, checksum: cbb82497cc2949dd439295c221f7095d (MD5); Made available in DSpace on 2023-06-01T09:53:03Z (GMT). No. of bitstreams: 1 ip2023_05_002.pdf: 2378538 bytes, checksum: cbb82497cc2949dd439295c221f7095d (MD5) https://reunir.unir.net/handle/123456789/14809 Graffiti Identification System Using Low-Cost Sensors García García, Miguel; González Arrieta, María Angélica; Rodríguez González, Sara; Márquez-Sánchez, Sergio; Da Silva Ramos, Carlos Fernando This article introduces the possibility of studying graffiti using a colorimeter developed with Arduino hardware technology according to the Do It Yourself (DIY) philosophy. Through the obtained Red Green Blue (RGB) data it is intended to study and compare the information extracted from each of the graffiti present on different walls. The same color can be found in different parts of a single graffiti, but also in other graffiti that could a priori be of different authorship. Nevertheless, graffiti may be related, and it may be possible to group graffiti artists and "gangs" that work together. The methodology followed for the construction of the colorimeter and its real application in a practical case are described in four case studies. The case studies describe how graffiti were identified and recognized and they provide a comparison of the collected color samples. The results show the added value of the colorimeter in the graffiti recognition process, demonstrating its usefulness on a functional level. Finally, the contributions of this research are outlined, and an analysis is carried out of the changes to be made to the proposed method in the future, for improved graffiti color identification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T09:42:02Z No. of bitstreams: 1 ip2023_05_001.pdf: 4129896 bytes, checksum: 1327ead89f0c7692e534ae14e61b9f4b (MD5); Made available in DSpace on 2023-06-01T09:42:02Z (GMT). No. of bitstreams: 1 ip2023_05_001.pdf: 4129896 bytes, checksum: 1327ead89f0c7692e534ae14e61b9f4b (MD5) https://reunir.unir.net/handle/123456789/14594 Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems Bobadilla, Jesús; Dueñas-Lerín, Jorge; Ortega, Fernando; Gutiérrez, Abraham Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T10:32:26Z No. of bitstreams: 1 ip2023_04_008.pdf: 6298241 bytes, checksum: 78a2b02200b2d8058f71baecdc8062ba (MD5); Made available in DSpace on 2023-05-03T10:32:26Z (GMT). No. of bitstreams: 1 ip2023_04_008.pdf: 6298241 bytes, checksum: 78a2b02200b2d8058f71baecdc8062ba (MD5) https://reunir.unir.net/handle/123456789/14588 PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos Tomar, A.; Kumar, S.; Pant, B. Traditional crowd counting (optical flow or feature matching) techniques have been upgraded to deep learning (DL) models due to their lack of automatic feature extraction and low-precision outcomes. Most of these models were tested on surveillance scene crowd datasets captured by stationary shooting equipment. It is very challenging to perform people counting from the videos shot with a head-mounted moving camera; this is mainly due to mixing the temporal information of the moving crowd with the induced camera motion. This study proposed a transfer learning-based PeopleNet model to tackle this significant problem. For this, we have made some significant changes to the standard VGG16 model, by disabling top convolutional blocks and replacing its standard fully connected layers with some new fully connected and dense layers. The strong transfer learning capability of the VGG16 network yields in-depth insights of the PeopleNet into the good quality of density maps resulting in highly accurate crowd estimation. The performance of the proposed model has been tested over a self-generated image database prepared from moving camera video clips, as there is no public and benchmark dataset for this work. The proposed framework has given promising results on various crowd categories such as dense, sparse, average, etc. To ensure versatility, we have done self and cross-evaluation on various crowd counting models and datasets, which proves the importance of the PeopleNet model in adverse defense of society. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T09:09:31Z No. of bitstreams: 1 ip2023_04_002_.pdf: 6031442 bytes, checksum: fd5da7b99c7eabd3a996764acecebd7f (MD5); Made available in DSpace on 2023-05-03T09:09:31Z (GMT). No. of bitstreams: 1 ip2023_04_002_.pdf: 6031442 bytes, checksum: fd5da7b99c7eabd3a996764acecebd7f (MD5) https://reunir.unir.net/handle/123456789/14369 Longitudinal Segmented Analysis of Internet Usage and Well-Being Among Older Adults Cervantes, Alejandro; Quintana, David; Saez, Yago; Isasi, Pedro The connection between digital literacy and the three core dimensions of psychological well-being is not yet well understood, and the evidence is controversial. We analyzed a sample of 2,314 individuals, aged 50 years and older, that participated in the English Longitudinal Study of Aging. Participants were clustered according to drivers of psychological well-being using Self-Organizing Maps. The resulting groups were subsequently studied separately using generalized estimating equations fitted on 2-year lagged repeated measures using three scales to capture the dimensions of well-being and Markov models. The clustering analysis suggested the existence of four different groups of participants. Statistical models found differences in the connection between internet use and psychological well-being depending on the group. The Markov models showed a clear association between internet use and the potential for transition among groups of the population characterized, among other things, by higher levels of psychological well-being. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T14:19:08Z No. of bitstreams: 1 ip2022_05_002.pdf: 742179 bytes, checksum: 9e2c29ca131f0bcbd368e0c12f8e11cd (MD5); Made available in DSpace on 2023-03-15T14:19:08Z (GMT). No. of bitstreams: 1 ip2022_05_002.pdf: 742179 bytes, checksum: 9e2c29ca131f0bcbd368e0c12f8e11cd (MD5) https://reunir.unir.net/handle/123456789/14364 Modulating the Gameplay Challenge Through Simple Visual Computing Elements: A Cube Puzzle Case Study Ribelles, Jose; Lopez, Angeles; Traver, V. Javier Positive player’s experiences greatly rely on a balanced gameplay where the game difficulty is related to player’s skill. Towards this goal, the gameplay can be modulated to make it easier or harder. In this work, a modulating mechanism based on visual computing is explored. The main hypothesis is that simple visual modifications of some elements in the game can have a significant impact on the game experience. This concept, which is essentially unexplored in the literature, has been experimentally tested with a web-based cube puzzle game where participants played either the original game or the visually modified game. The analysis is based on players’ behavior, performance, and replies to a questionnaire upon game completion. The results provide evidence on the effectiveness of visual computing on gameplay modulation. We believe the findings are relevant to game researchers and developers because they highlight how a core gameplay can be easily modified with relatively simple ingredients, at least for some game genres. Interestingly, the insights gained from this study also open the door to automate the game adaptation based on observed player’s interaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T09:14:27Z No. of bitstreams: 1 ip2022_05_001.pdf: 5360016 bytes, checksum: cad2037fa92328670eb0032ba3e802f1 (MD5); Made available in DSpace on 2023-03-15T09:14:27Z (GMT). No. of bitstreams: 1 ip2022_05_001.pdf: 5360016 bytes, checksum: cad2037fa92328670eb0032ba3e802f1 (MD5) https://reunir.unir.net/handle/123456789/14363 Drug Target Interaction Prediction Using Machine Learning Techniques – A Review Suruliandi, A.; Idhaya, T.; Raja, S. P. Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T08:58:46Z No. of bitstreams: 1 ip2022_11_002_0.pdf: 920478 bytes, checksum: 045180cf6b9ca6c626f93afc48d23879 (MD5); Made available in DSpace on 2023-03-15T08:58:46Z (GMT). No. of bitstreams: 1 ip2022_11_002_0.pdf: 920478 bytes, checksum: 045180cf6b9ca6c626f93afc48d23879 (MD5) https://reunir.unir.net/handle/123456789/14362 OBOE: an Explainable Text Classification Framework del Águila Escobar, Raúl A.; Suárez-Figueroa, Mari Carmen; Fernández-López, Mariano Explainable Artificial Intelligence (XAI) has recently gained visibility as one of the main topics of Artificial Intelligence research due to, among others, the need to provide a meaningful justification of the reasons behind the decision of black-box algorithms. Current approaches are based on model agnostic or ad-hoc solutions and, although there are frameworks that define workflows to generate meaningful explanations, a text classification framework that provides such explanations considering the different ingredients involved in the classification process (data, model, explanations, and users) is still missing. With the intention of covering this research gap, in this paper we present a text classification framework called OBOE (explanatiOns Based On concEpts), in which such ingredients play an active role to open the black-box. OBOE defines different components whose implementation can be customized and, thus, explanations are adapted to specific contexts. We also provide a tailored implementation to show the customization capability of OBOE. Additionally, we performed (a) a validation of the implemented framework to evaluate the performance using different corpora and (b) a user-based evaluation of the explanations provided by OBOE. The latter evaluation shows that the explanations generated in natural language express the reason for the classification results in a way that is comprehensible to non-technical users. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T08:40:46Z No. of bitstreams: 1 ip2022_11_01.pdf: 1457451 bytes, checksum: 5fd3f5aeda8a86102d1df7f228ab1c4a (MD5); Made available in DSpace on 2023-03-15T08:40:46Z (GMT). No. of bitstreams: 1 ip2022_11_01.pdf: 1457451 bytes, checksum: 5fd3f5aeda8a86102d1df7f228ab1c4a (MD5) https://reunir.unir.net/handle/123456789/14357 Brain Tumor Classification Using a Pre-Trained Auxiliary Classifying Style-Based Generative Adversarial Network Kumaar, M. Akshay; Samiayya, Duraimurugan; Rajinikanth, Venkatesan; Raj Vincent P M, Durai; Kadry, Seifedine Computer Vision's applications and their use cases in the medical field have grown vastly in the past decade. The algorithms involved in these critical applications have helped doctors and surgeons perform procedures on patients more precisely with minimal side effects. However, obtaining medical data for developing large scale generalizable and intelligent algorithms is challenging in the real world as multiple socio-economic, administrative, and demographic factors impact it. Furthermore, training machine learning algorithms with a small amount of data can lead to less accuracy and performance bias, resulting in incorrect diagnosis and treatment, which can cause severe side effects or even casualties. Generative Adversarial Networks (GAN) have recently proven to be an effective data synthesis and augmentation technique for training deep learning-based image classifiers. This research proposes a novel approach that uses a Style-based Generative Adversarial Network for conditional synthesis and auxiliary classification of Brain Tumors by pre-training. The Discriminator of the pre-trained GAN is fine-tuned with extensive data augmentation techniques to improve the classification accuracy when the training data is small. The proposed method was validated with an open-source MRI dataset which consists of three types of tumors - Glioma, Meningioma, and Pituitary. The proposed system achieved 99.51% test accuracy, 99.52% precision score, and 99.50% recall score, significantly higher than other approaches. Since the framework can be made adaptive using transfer learning, this method also benefits new and small datasets of similar distributions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T11:08:15Z No. of bitstreams: 1 ip2023_02_008_0.pdf: 2381727 bytes, checksum: 316091571fa76e03f42a6c6be5902ef2 (MD5); Made available in DSpace on 2023-03-14T11:08:15Z (GMT). No. of bitstreams: 1 ip2023_02_008_0.pdf: 2381727 bytes, checksum: 316091571fa76e03f42a6c6be5902ef2 (MD5) https://reunir.unir.net/handle/123456789/14339 An Efficient Fake News Detection System Using Contextualized Embeddings and Recurrent Neural Network Ali Reshi, Junaid; Ali, Rashid Fake news is detrimental for society and individuals. Since the information dissipation through online media is too quick, an efficient system is needed to detect and counter the propagation of fake news on social media. Many studies have been performed in last few years to detect fake news on social media. This study focusses on the efficient detection of fake news on social media, through a Natural Language Processing based approach, using deep learning. For the detection of fake news, textual data have been analyzed in unidirectional way using sequential neural networks, or in bi-directional way using transformer architectures like Bidirectional Encoder Representations from Transformers (BERT). This paper proposes ConFaDe - a deep learning based fake news detection system that utilizes contextual embeddings generated from a transformer-based model. The model uses Masked Language Modelling and Replaced Token Detection in its pre-training to capture contextual and semantic information in the text. The proposed system outperforms the previously set benchmarks for fake news detection; including state-of-the-art approaches on a real-world fake news dataset, when evaluated using a set of standard performance metrics with an accuracy of 99.9 % and F1 macro of 99.9%. In contrast to the existing state-of-the-art model, the proposed system uses 90 percent less network parameters and is 75 percent lesser in size. Consequently, ConFaDe requires fewer hardware resources and less training time, and yet outperforms the existing fake news detection techniques, a step forward in the direction of Green Artificial Intelligence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T12:06:50Z No. of bitstreams: 1 ip2023_02_007_1.pdf: 1828984 bytes, checksum: 10970f3c192f73e3f2f4b1b0f7068095 (MD5); Made available in DSpace on 2023-03-13T12:06:50Z (GMT). No. of bitstreams: 1 ip2023_02_007_1.pdf: 1828984 bytes, checksum: 10970f3c192f73e3f2f4b1b0f7068095 (MD5) https://reunir.unir.net/handle/123456789/14314 KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals García-Peñalvo, Francisco; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; Sampedro-Gómez, Jesús; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, P. Ignacio; Sánchez, Pedro L. Machine Learning (ML) has extended its use in several domains to support complex analyses of data. The medical field, in which significant quantities of data are continuously generated, is one of the domains that can benefit from the application of ML pipelines to solve specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc. However, while health professionals are experts in their domain, they can lack programming and theoretical skills regarding ML applications. Therefore, it is necessary to train health professionals in using these paradigms to get the most out of the application of ML algorithms to their data. In this work, we present a platform to assist non-expert users in defining ML pipelines in the health domain. The system’s design focuses on providing an educational experience to understand how ML algorithms work and how to interpret their outcomes and on fostering a flexible architecture to allow the evolution of the available components, algorithms, and heuristics. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T15:55:31Z No. of bitstreams: 1 ip2023_01_006.pdf: 1776887 bytes, checksum: 6df5ba28ca606edf3547654db4c11ff0 (MD5); Made available in DSpace on 2023-03-09T15:55:31Z (GMT). No. of bitstreams: 1 ip2023_01_006.pdf: 1776887 bytes, checksum: 6df5ba28ca606edf3547654db4c11ff0 (MD5) https://reunir.unir.net/handle/123456789/14311 Chatbot-Based Learning Platform for SQL Training Balderas, Antonio; Baena-Pérez, Rubén; Person, Tatiana; Mota, José Miguel; Ruiz-Rube, Iván Learning the SQL language for working with relational databases is a fundamental subject for future computer engineers. However, in distance learning contexts or unexpected situations like the COVID-19 pandemic, where students had to follow lectures remotely, they may find it hard to learn. Chatbots are software applications that aim to have conversations with people to help them solve problems or provide support in a specific domain. This paper proposes a chatbot-based learning platform to assist students in learning SQL. A case study has been conducted to evaluate the proposal, with undergraduate computer engineering students using the learning platform to perform SQL queries while being assisted by the chatbot. The results show evidence that students who used the chatbot performed better on the final SQL exam than those who did not. In addition, the research shows positive evidence of the benefits of using such learning platforms to support SQL teaching and learning for both students and lecturers: students use a platform that helps them self-regulate their learning process, while lecturers get interesting metrics on student performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T15:01:20Z No. of bitstreams: 1 ip2022_05_003.pdf: 1402528 bytes, checksum: fa631e1bc6e7d15b9f36bd1e2d12c703 (MD5); Made available in DSpace on 2023-03-09T15:01:20Z (GMT). No. of bitstreams: 1 ip2022_05_003.pdf: 1402528 bytes, checksum: fa631e1bc6e7d15b9f36bd1e2d12c703 (MD5)