https://reunir.unir.net/handle/123456789/12419 Tue, 31 Mar 2026 01:47:20 GMT 2026-03-31T01:47:20Z https://reunir.unir.net/handle/123456789/12668 Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education Gaona-García, Paulo Alonso; Montenegro-Marin, Carlos Enrique; Sarría, Íñigo; Restrepo Rodríguez, Andrés Ovidio; Ariza Riaño, Maddyzeth Fine motor skills allow to carry out the execution of crucial tasks in people's daily lives, increasing their independence and self-esteem. Among the alternatives for working these skills, immersive environments are found providing a set of elements arranged to have a haptic experience through gestural control devices. However, generally, these environments do not have a mechanism for evaluation and feedback of the exercise performed, which does not easily identify the objective's fulfillment. For this reason, this study aims to carry out a comparison of image recognition methods such as Convolutional Neural Network (CNN), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM) and Decision Tree (DT), for the purpose of performing an evaluation and feedback of exercises. The assessment of the techniques is carried out using images captured from an immersive environment, calculating metrics such as confusion matrix, cross validation and classification report. As a result of this process, it was obtained that the CNN model has a better supported performance in 82.5% accuracy, showing an increase of 23.5% compared to SVM, 30% compared to K-NN and 25% compared to DT. Finally, it is concluded that in order to implement a method of evaluation and feedback in an immersive environment for academic training in the first school years, a low margin of error must be taken in the percentage of successes of the image recognition technique implemented, to ensure the proper development of these skills considering their great importance in childhood. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T12:35:13Z No. of bitstreams: 1 ijimai20195_7_15_pdf_81774.pdf: 1395952 bytes, checksum: 737430441ba8cd3fad81fd43e759b5ae (MD5); Made available in DSpace on 2022-03-17T12:35:13Z (GMT). No. of bitstreams: 1 ijimai20195_7_15_pdf_81774.pdf: 1395952 bytes, checksum: 737430441ba8cd3fad81fd43e759b5ae (MD5) https://reunir.unir.net/handle/123456789/12668 https://reunir.unir.net/handle/123456789/12667 Voltage Stability Enhancement Based on Optimal Allocation of Shunt Compensation Devices Using Lightning attachment procedure optimization Kamel, Salah; Youssef, Heba This paper proposes a combined approach to determine the optimal allocation of different shunt compensation devices (shunt capacitor, static var compensator, and static synchronous compensator) in power systems. The developed approach is a combination between Lightning Attachment Procedure Optimization (LAPO) and loss sensitivity indices (LSIs). Different objective functions such as enhancement of voltage stability index, improvement of voltage profile and minimization of total power losses are considered. Two loss sensitivity indices (LSIs) are developed to reduce the search space in all buses and the total computation time. The developed algorithm is validated using standard IEEE 14-bus and IEEE 30-bus test systems. The developed algorithm successes to achieve the objective functions with the better performance compared with other wellknown optimization techniques such as Teaching learning-based optimization (TLBO), genetic algorithm (GA) and particle swarm optimization (PSO). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T12:15:19Z No. of bitstreams: 1 ijimai20195_7_13_pdf_13640.pdf: 2038527 bytes, checksum: 1aa38449cb5373ae44a1b27163519f2c (MD5); Made available in DSpace on 2022-03-17T12:15:19Z (GMT). No. of bitstreams: 1 ijimai20195_7_13_pdf_13640.pdf: 2038527 bytes, checksum: 1aa38449cb5373ae44a1b27163519f2c (MD5) https://reunir.unir.net/handle/123456789/12667 https://reunir.unir.net/handle/123456789/12666 Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems Mohamed, Emad; Mohamed, Al-Attar Ali; Mitani, Yasunori This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T11:43:19Z No. of bitstreams: 1 ijimai20195_7_11_pdf_41934.pdf: 2511912 bytes, checksum: 4e25f277a837c0beccc0363d053f0aa6 (MD5); Made available in DSpace on 2022-03-17T11:43:19Z (GMT). No. of bitstreams: 1 ijimai20195_7_11_pdf_41934.pdf: 2511912 bytes, checksum: 4e25f277a837c0beccc0363d053f0aa6 (MD5) https://reunir.unir.net/handle/123456789/12666 https://reunir.unir.net/handle/123456789/12665 SOLAM: A Novel Approach of Spatial Aggregation in SOLAP Systems Djamila, Hamdadou; Zemri, Farah Amina In the context of a data driven approach aimed to detect the real and responsible factors of the transmission of diseases and explaining its emergence or re-emergence, we suggest SOLAM (Spatial on Line Analytical Mining) system, an extension of Spatial On Line Analytical Processing (SOLAP) with Spatial Data Mining (SDM) techniques. Our approach consists of integrating EPISOLAP system, tailored for epidemiological surveillance, with spatial generalization method allowing the predictive evaluation of health risk in the presence of hazards and awareness of the vulnerability of the exposed population. The proposed architecture is a single integrated decision-making platform of knowledge discovery from spatial databases. Spatial generalization methods allow exploring the data at different semantic and spatial scales while reducing the unnecessary dimensions. The principle of the method is selecting and deleting attributes of low importance in data characterization, thus produces zones of homogeneous characteristics that will be merged. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:54:44Z No. of bitstreams: 1 ijimai20195_7_10_pdf_39275.pdf: 924775 bytes, checksum: ddbd11984bcd529d622c6ead4fdb90cd (MD5); Made available in DSpace on 2022-03-17T10:54:44Z (GMT). No. of bitstreams: 1 ijimai20195_7_10_pdf_39275.pdf: 924775 bytes, checksum: ddbd11984bcd529d622c6ead4fdb90cd (MD5) https://reunir.unir.net/handle/123456789/12665 https://reunir.unir.net/handle/123456789/12664 "Hello, is There Anybody Who Reads Me?" Radio Programs and Popular Facebook Posts Laor, Tal Radio stations are increasingly active on social networks, as radio continues to adjust and adapt to online spaces. This research is intended to conceptualize and characterize the success of radio programs beyond their native FM environment, focusing on their attempts at achieving popularity on social networks. Success on social networks is measured by user involvement and interaction with posted content and comments. This study looked at the activity of leading Israeli radio programs on Facebook pages and user engagement, evaluating highly involved posts by coding. It was found that radio program activity on social networks expands the reach of radio stations and promotes higher levels of interaction with listeners beyond broadcast schedule. In addition, integration of various media forms such as videos or images increases the likelihood of a post becoming popular. This research presents the convergence of radio programs in accordance with the theoretical framework of technological determinism. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:50:43Z No. of bitstreams: 1 ijimai20195_7_8_pdf_91929.pdf: 331269 bytes, checksum: e530b48a2143243f9d097b44732b2ea7 (MD5); Made available in DSpace on 2022-03-17T10:50:43Z (GMT). No. of bitstreams: 1 ijimai20195_7_8_pdf_91929.pdf: 331269 bytes, checksum: e530b48a2143243f9d097b44732b2ea7 (MD5) https://reunir.unir.net/handle/123456789/12664 https://reunir.unir.net/handle/123456789/12663 Editor’s Note González-Crespo, Rubén; Verdú, Elena 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. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:46:28Z No. of bitstreams: 1 ijimai20195_7_0_pdf_15825.pdf: 109872 bytes, checksum: 30d22789583811a00c389e334fd851c4 (MD5); Made available in DSpace on 2022-03-17T10:46:28Z (GMT). No. of bitstreams: 1 ijimai20195_7_0_pdf_15825.pdf: 109872 bytes, checksum: 30d22789583811a00c389e334fd851c4 (MD5) https://reunir.unir.net/handle/123456789/12663 https://reunir.unir.net/handle/123456789/12662 Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks González-Crespo, Rubén; Verdú, Elena; Khari, Manju; Garg, Aditya Kumar In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:41:21Z No. of bitstreams: 1 ijimai20195_7_2_pdf_18405.pdf: 804428 bytes, checksum: 8fbd87fdbc446269ca004823bbe73efe (MD5); Made available in DSpace on 2022-03-17T10:41:21Z (GMT). No. of bitstreams: 1 ijimai20195_7_2_pdf_18405.pdf: 804428 bytes, checksum: 8fbd87fdbc446269ca004823bbe73efe (MD5) https://reunir.unir.net/handle/123456789/12662 https://reunir.unir.net/handle/123456789/12661 Social Seducement: Empowering Social Economy Entrepreneurship. The Training Approach Burgos, Daniel; Padilla-Zea, Natalia; Aceto, Stefania Long-term unemployment is a persistent problem in Europe, following the economic crisis suffered in 2008. This situation reveals self-employment as a good option for becoming re-involved in the working life. In this context, this paper presents a gamified educational platform to empower social economy entrepreneurship skills in long-term unemployed people. In particular, we present the training approach underpinning the motivational process supported by gamification, which has been developed using the ADDIE model. The learning path is developed according to a story that guides the work throughout the training process. It is based on the premises of alignment with reality, instruction from didactic material and real-life stories, in-game practice, work in groups and assistance from a facilitator. This approach covers the competence needs identified in a previous study and includes gamification techniques to improve motivation and engagement. Therefore, the training program comprises: (1) a set of materials and real social economy enterprise experiences, which are the basis for learning and getting inspiration; (2) activities to develop a business plan based on concepts learned from the learning materials and from real-life stories, as well as the help of a facilitator who walks with trainees during the process; (3) a set of individual and group, mandatory and optional assessment activities to evaluate the learning achieved; and (4) a three-views scoring system that shows learning progress for individuals and groups, and gives players the opportunity to exchange gamification points for benefits in the game. The results presented in this paper are based on a sample of two pilots run in Italy and Spain and involving five facilitators working with around 60 learners. About 60% of participants indicated their intention to apply knowledge obtained in a real-life entrepreneurship initiative. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:00:18Z No. of bitstreams: 1 ijimai20195_7_14_pdf_17956.pdf: 1146968 bytes, checksum: f84cdd111f796e007016a5385ab48743 (MD5); Made available in DSpace on 2022-03-17T10:00:18Z (GMT). No. of bitstreams: 1 ijimai20195_7_14_pdf_17956.pdf: 1146968 bytes, checksum: f84cdd111f796e007016a5385ab48743 (MD5) https://reunir.unir.net/handle/123456789/12661 https://reunir.unir.net/handle/123456789/12660 Genetic Operators Applied to Symmetric Cryptography Rodríguez, Jefferson; Corredor, Brayan; Suárez, César In this article, a symmetric-key cryptographic algorithm for text is proposed, which applies Genetic Algorithms philosophy, entropy and modular arithmetic. An experimental methodology is used over a deterministic system, which redistributes and modifies the parameters and phases of the genetic algorithm that directly affect its behavior, carrying out a constant evaluation using the fitness function, in order to optimize the results. An independent encryption is established for the auxiliary key, using a main key, in charge of increasing security. The tests are performed over different text sizes, manipulating the parameters and criteria proposed to obtain their appropriate values. Finally, a comparison is presented against the following cryptographic algorithms DES (Data Encryption Standard), RSA (Rivest, Shamir and Adleman) and AES (Advanced Encryption Standard), exposing factors such as processing time, scalability, key size, etc. It is shown that the proposed algorithm has a better performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T09:32:46Z No. of bitstreams: 1 ijimai20195_7_4_pdf_53926.pdf: 976073 bytes, checksum: 3b679d987a5f013782acb8188f2ae9e9 (MD5); Made available in DSpace on 2022-03-17T09:32:46Z (GMT). No. of bitstreams: 1 ijimai20195_7_4_pdf_53926.pdf: 976073 bytes, checksum: 3b679d987a5f013782acb8188f2ae9e9 (MD5) https://reunir.unir.net/handle/123456789/12660 https://reunir.unir.net/handle/123456789/12659 Automatic Irony Detection using Feature Fusion and Ensemble Classifier Harish, B S; Kumar, Keerthi With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T09:20:48Z No. of bitstreams: 1 ijimai20195_7_7_pdf_17438.pdf: 709467 bytes, checksum: 981c45b73fefee6cba736a86cc0cb17a (MD5); Made available in DSpace on 2022-03-17T09:20:48Z (GMT). No. of bitstreams: 1 ijimai20195_7_7_pdf_17438.pdf: 709467 bytes, checksum: 981c45b73fefee6cba736a86cc0cb17a (MD5) https://reunir.unir.net/handle/123456789/12659 https://reunir.unir.net/handle/123456789/12658 Design and Validation of a Framework for the Creation of User Experience Questionnaires Schrepp, Martin; Thomaschewski, Jörg Existing user experience questionnaires have a fixed number of scales. Each of these scales measures a distinct aspect of user experience. These questionnaires can be used with little effort and provide a number of useful support materials that make the application of such a questionnaire quite easy. However, in practical evaluation scenarios it can happen that none of the existing questionnaires contains all scales necessary to answer the research question. It is of course possible to combine several UX questionnaires in such cases, but due to the variations of item formats this is also not an optimal solution. In this paper, we describe the development and first validation studies of a modular framework that allows the creation of user experience questionnaires that fit perfectly to a given research question. The framework contains several scales that measure different UX aspects. These scales can be combined to cover the relevant research questions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:59:38Z No. of bitstreams: 1 ijimai20195_7_9_pdf_19082.pdf: 524794 bytes, checksum: c85ff8af2e38703e807aacd0d00a4f60 (MD5); Made available in DSpace on 2022-03-17T08:59:38Z (GMT). No. of bitstreams: 1 ijimai20195_7_9_pdf_19082.pdf: 524794 bytes, checksum: c85ff8af2e38703e807aacd0d00a4f60 (MD5) https://reunir.unir.net/handle/123456789/12658 https://reunir.unir.net/handle/123456789/12657 Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network Kamel, Salah; Jurado, Francisco; Rashad, Ahmed; Ibrahim, Yousry; Nasrat, Loai Recently, power systems are confronting a lot of challenges. Increasing the dependence on renewable energy sources especially wind energy and its impact on the stability of electrical systems are the most important challenges. Flexible alternating current transmission systems (FACTS) can be used to improve the relationship between wind farms and electrical grids. The performance of these FACTS depends on the parameters of its control system. These parameters can be tuned using modern methods like Artificial Neural Network (ANN). In this paper, ANN is used to improve the performance of static synchronous series compensator (SSSC) integrated into combined wind farm (CWF). This CWF is composed of squirrel cage induction generators (SCIG) and doubly fed induction generators (DFIG) wind turbines. This wind farm is collecting the advantage of SCIG and DFIG wind turbines. To view out the motivation of this paper, a comparison is done among the performances of combined wind farm (CWF) with ANN-SSSC, CWF with ordinary SSSC and CWF with SSSC tune by Multi-objective genetic algorithm (MOGA SSSC). The root mean square Error (RMSE) is used to evaluate the results. The results illustrate that the performance of CWF can be improved using SSSC adjusted by ANN. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:46:28Z No. of bitstreams: 1 ijimai20195_7_12_pdf_71211.pdf: 1459034 bytes, checksum: 53163579f1b2f2aa8d98ec065c66b161 (MD5); Made available in DSpace on 2022-03-17T08:46:28Z (GMT). No. of bitstreams: 1 ijimai20195_7_12_pdf_71211.pdf: 1459034 bytes, checksum: 53163579f1b2f2aa8d98ec065c66b161 (MD5) https://reunir.unir.net/handle/123456789/12657 https://reunir.unir.net/handle/123456789/12656 MWAND: A New Early Termination Algorithm for Fast and Efficient Query Evaluation Mansouria, Zemani Imene; Lougmiri, Zekri; Mohamed, Senouci Nowadays, current information systems are so large and maintain huge amount of data. At every time, they process millions of documents and millions of queries. In order to choose the most important responses from this amount of data, it is well to apply what is so called early termination algorithms. These ones attempt to extract the Top-K documents according to a specified increasing monotone function. The principal idea behind is to reach and score the most significant less number of documents. So, they avoid fully processing the whole documents. WAND algorithm is at the state of the art in this area. Despite it is efficient, it is missing effectiveness and precision. In this paper, we propose two contributions, the principal proposal is a new early termination algorithm based on WAND approach, we call it MWAND (Modified WAND). This one is faster and more precise than the first. It has the ability to avoid unnecessary WAND steps. In this work, we integrate a tree structure as an index into WAND and we add new levels in query processing. In the second contribution, we define new fine metrics to ameliorate the evaluation of the retrieved information. The experimental results on real datasets show that MWAND is more efficient than the WAND approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:39:49Z No. of bitstreams: 1 ijimai20195_7_6_pdf_83440.pdf: 2146533 bytes, checksum: a76d69fb4a8d38700081a88613da8f40 (MD5); Made available in DSpace on 2022-03-17T08:39:49Z (GMT). No. of bitstreams: 1 ijimai20195_7_6_pdf_83440.pdf: 2146533 bytes, checksum: a76d69fb4a8d38700081a88613da8f40 (MD5) https://reunir.unir.net/handle/123456789/12656 https://reunir.unir.net/handle/123456789/12655 Humanoid Localization on Robocup Field using Corner Intersection and Geometric Distance Estimation Sudin, M N; Abdullah, Sheikh; Nasudin, M F In the humanoid competition field, identifying landmarks for localizing robots in a dynamic environment is of crucial importance. By convention, state-of-the-art humanoid vision systems rely on poles located outside the middle of the field as an indicator for generating landmarks. However, in compliance with the recent rules of Robocup, the middle pole has been discarded to deliberately provide less prior information for the humanoid vision system to strategize its winning tactics on the field. Previous localization method used middle poles as a landmark. Therefore, robot localization tasks should apply accurate corner and distance detection simultaneously to locate the positions of goalposts. State-of-the-art corner detection algorithms such as the Harris corner and mean projection transformation are excessively sensitive to image noise and suffer from high processing times. Moreover, despite their prevalence in robot motor log and fish-eye lens calibration for humanoid localization, current distance estimation techniques nonetheless remain highly dependent on multiple poles as vision landmarks, apart from being prone to huge localization errors. Thus, we propose a novel localization method consisting of a proposed corner extraction algorithm, namely, the contour intersection algorithm (CIA), and a distance estimation algorithm, namely, analytic geometric estimation (AGE), for efficiently identifying salient goalposts. At first, the proposed CIA algorithm, which is based on linear contour intersection using a projection matrix, is utilized to extract corners of a goalpost after performing an adaptive binarization process. Then, these extracted corner features are fed into our proposed AGE algorithm to estimate the real-word distance using analytic geometry methods. As a result, the proposed localization vision system and the state-of-the-art method obtained approximately 3-4 and 7-23 centimeter estimation errors, respectively. This demonstrates the capability of the proposed localization algorithm to outperform other methods, which renders it more effective in indoor task localization for further actions such as attack or defense strategies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:24:45Z No. of bitstreams: 1 ijimai20195_7_5_pdf_15069.pdf: 1680591 bytes, checksum: 7a57d4336eeb3eb7d67e1435c36a3b83 (MD5); Made available in DSpace on 2022-03-17T08:24:45Z (GMT). No. of bitstreams: 1 ijimai20195_7_5_pdf_15069.pdf: 1680591 bytes, checksum: 7a57d4336eeb3eb7d67e1435c36a3b83 (MD5) https://reunir.unir.net/handle/123456789/12655 https://reunir.unir.net/handle/123456789/12654 A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis Djamila, Hamdadou; Khiat, Sofiane Decision support consists of proposing tasks and projects by taking into account temporal constraints and the use of resources with the aim of finding a compromise solution between several alternatives. Indeed, on the one hand, centralized resolution systems and methods are generally inappropriate to the real case because of the local unavailability of decision makers. On the other hand, the data of the decisional problem are generally poorly expressed in a negotiation environment. Other techniques and approaches treat the same decision-making problem and impose a distributed vision for coherent decisions. For this purpose, Multi-Agent Systems (MAS) allow modeling a distributed resolution of the group decision support problem. In this article, we propose a new model of a multi-criteria group decision support system based on a multi-agent system modeling a spatial problem. We consider that each decision maker is assimilated to an agent that has a decision-making autonomy, in which he interacts with other agents in the debate through a negotiation process in order to reach an acceptable compromise. In this study, we propose coordination mechanisms among agents to highlight the simulated negotiation. Therefore, the proposed system finds a solution before fixed deadlines’ time expire. We experiment the suggested negotiation model to solve the decisional problem of spatial localization in territory planning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:14:26Z No. of bitstreams: 1 ijimai20195_7_1_pdf_15237.pdf: 2047630 bytes, checksum: 9c0e0b3f38ca199198c6be541319464f (MD5); Made available in DSpace on 2022-03-17T08:14:26Z (GMT). No. of bitstreams: 1 ijimai20195_7_1_pdf_15237.pdf: 2047630 bytes, checksum: 9c0e0b3f38ca199198c6be541319464f (MD5) https://reunir.unir.net/handle/123456789/12654 https://reunir.unir.net/handle/123456789/12653 A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems Joshi, Rakesh Chandra; Singh, Adithya Gaurav; Joshi, Mayank; Mathur, Sanjay In the development of intelligent video surveillance systems for tracking a vehicle, occlusions are one of the major challenges. It becomes difficult to retain features during occlusion especially in case of complete occlusion. In this paper, a target vehicle tracking algorithm for Smart Video Surveillance (SVS) is proposed to track an unidentified target vehicle even in case of occlusions. This paper proposes a computationally efficient approach for handling occlusions named as Kalman Filter Assisted Occlusion Handling (KFAOH) technique. The algorithm works through two periods namely tracking period when no occlusion is seen and detection period when occlusion occurs, thus depicting its hybrid nature. Kanade-Lucas-Tomasi (KLT) feature tracker governs the operation of algorithm during the tracking period, whereas, a Cascaded Object Detector (COD) of weak classifiers, specially trained on a large database of cars governs the operation during detection period or occlusion with the assistance of Kalman Filter (KF). The algorithm’s tracking efficiency has been tested on six different tracking scenarios with increasing complexity in real-time. Performance evaluation under different noise variances and illumination levels shows that the tracking algorithm has good robustness against high noise and low illumination. All tests have been conducted on the MATLAB platform. The validity and practicality of the algorithm are also verified by success plots and precision plots for the test cases. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:00:52Z No. of bitstreams: 1 ijimai20195_7_3_pdf_11888.pdf: 5039071 bytes, checksum: d35310ded982d480ace8aa56fdae377c (MD5); Made available in DSpace on 2022-03-17T08:00:53Z (GMT). No. of bitstreams: 1 ijimai20195_7_3_pdf_11888.pdf: 5039071 bytes, checksum: d35310ded982d480ace8aa56fdae377c (MD5) https://reunir.unir.net/handle/123456789/12653 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 https://reunir.unir.net/handle/123456789/12534 Contour Enhancement Algorithm for Improving Visual Perception of Deutan and Protan Dichromats Ribeiro, Madalena; Gomes, Abel A variety of recoloring methods has been proposed in the literature to remedy the problem of confusing red-green colors faced by dichromat people (as well by other color-blinded people). The common strategy to mitigate this problem is to remap colors to other colors. But, it is clear this does not guarantee neither the necessary contrast to distinguish the elements of an image, nor the naturalness of colors learnt from past experience of each individual. In other words, the individual’s perceptual learning may not hold under color remapping. With this in mind, we introduce the first algorithm primarily focused on the enhancement of object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. This is particularly adequate to increase contrast in images where we find adjacent regions that are color-indistinguishable from the dichromacy’s point of view. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T12:53:19Z No. of bitstreams: 1 ijimai_5_5_10_pdf_53220.pdf: 2599974 bytes, checksum: d3cfc67d7ff776095cae39795af2fa3e (MD5); Made available in DSpace on 2022-02-28T12:53:19Z (GMT). No. of bitstreams: 1 ijimai_5_5_10_pdf_53220.pdf: 2599974 bytes, checksum: d3cfc67d7ff776095cae39795af2fa3e (MD5) https://reunir.unir.net/handle/123456789/12534 https://reunir.unir.net/handle/123456789/12533 Editor’s Note Verdú, Elena 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. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T12:26:40Z No. of bitstreams: 1 ijimai_5_5_0_pdf_42920.pdf: 179636 bytes, checksum: bb3a08eac40ec4a0ee8f3668c0274858 (MD5); Made available in DSpace on 2022-02-28T12:26:40Z (GMT). No. of bitstreams: 1 ijimai_5_5_0_pdf_42920.pdf: 179636 bytes, checksum: bb3a08eac40ec4a0ee8f3668c0274858 (MD5) https://reunir.unir.net/handle/123456789/12533 https://reunir.unir.net/handle/123456789/12532 Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing Lies, Jan This contribution reviews the vast scope of digital application areas, which shape the digital marketing landscape and coin the present term “marketing intelligence” from a marketing technique point of view. Additionally, marketing intelligence as social engineering techniques are described. The review ranges from digital IT- and big data marketing until marketing 5.0 as digitalized trust marketing. The multiplicity of applications and interdependencies of the digital and social techniques reviewed should show that big data and marketing intelligence have already become a marketing reality. It becomes clear that marketing is witnessing a methodological, technical and cultural paradigm shift that augments and amplifies traditional outbound marketing with inbound marketing. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T12:18:06Z No. of bitstreams: 1 ijimai_5_5_16_pdf_13983.pdf: 287444 bytes, checksum: c6acfcd1dc39d1736b3d6ce2e13ad815 (MD5); Made available in DSpace on 2022-02-28T12:18:06Z (GMT). No. of bitstreams: 1 ijimai_5_5_16_pdf_13983.pdf: 287444 bytes, checksum: c6acfcd1dc39d1736b3d6ce2e13ad815 (MD5) https://reunir.unir.net/handle/123456789/12532 https://reunir.unir.net/handle/123456789/12531 Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models Waheeb, Waddah; Ghazali, Rozaida In this paper, a new application of ridge polynomial based neural network models in multivariate time series forecasting is presented. The existing ridge polynomial based neural network models can be grouped into two groups. Group A consists of models that use only autoregressive inputs, whereas Group B consists of models that use autoregressive and moving-average (i.e., error feedback) inputs. The well-known Box-Jenkins gas furnace multivariate time series was used in the forecasting comparison between the two groups. Simulation results show that the models in Group B achieve significant forecasting performance as compared to the models in Group A. Therefore, the Box-Jenkins gas furnace data can be modeled better using neural networks when error feedback is used. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:51:12Z No. of bitstreams: 1 ijimai_5_5_15_pdf_10586.pdf: 1192665 bytes, checksum: 342da9c69270f6bb01bd1e616bdf2c04 (MD5); Made available in DSpace on 2022-02-28T11:51:12Z (GMT). No. of bitstreams: 1 ijimai_5_5_15_pdf_10586.pdf: 1192665 bytes, checksum: 342da9c69270f6bb01bd1e616bdf2c04 (MD5) https://reunir.unir.net/handle/123456789/12531 https://reunir.unir.net/handle/123456789/12530 A Recent Trend in Individual Counting Approach Using Deep Network Ghazvini, Anahita; Abdullah, Siti Norul Huda Sheikh; Ayob, Masri In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:45:42Z No. of bitstreams: 1 ijimai_5_5_1_pdf_11035.pdf: 274933 bytes, checksum: d70947019a084a20d5065c9d467967a2 (MD5); Made available in DSpace on 2022-02-28T11:45:42Z (GMT). No. of bitstreams: 1 ijimai_5_5_1_pdf_11035.pdf: 274933 bytes, checksum: d70947019a084a20d5065c9d467967a2 (MD5) https://reunir.unir.net/handle/123456789/12530 https://reunir.unir.net/handle/123456789/12529 Optimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm Kamel, Salah; Jurado, Francisco; Elkasem, Ahmed; Rashad, Ahmed The main purpose of this paper is allowing doubly fed induction generator wind farms (DFIG), which are connected to power system, to effectively participate in feeding electrical loads. The oscillation in power system is one of the challenges of the interconnection of wind farms to the grid. The model of DFIG contains several gains which need to be achieved with optimal values. This aim can be accomplished using an optimization algorithm in order to obtain the best performance. The multi-objective optimization algorithm is used to determine the optimal control system gains under several objectives. In this paper, a multi-objective genetic algorithm is applied to the DFIG model to determine the optimal values of the gains of DFIG control system. In order to point out the contribution of this work; the performance of optimized DFIG model is compared with the non-optimized model of DFIG. The results show that the optimized model of DFIG has better performance over the non-optimized DFIG model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:25:50Z No. of bitstreams: 1 ijimai_5_5_6_pdf_17227.pdf: 915651 bytes, checksum: da8f20b0a8735d65c463d86cbb119b5c (MD5); Made available in DSpace on 2022-02-28T11:25:50Z (GMT). No. of bitstreams: 1 ijimai_5_5_6_pdf_17227.pdf: 915651 bytes, checksum: da8f20b0a8735d65c463d86cbb119b5c (MD5) https://reunir.unir.net/handle/123456789/12529 https://reunir.unir.net/handle/123456789/12528 GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment Ullah, S; Raees, M Three Dimensional (3D) interaction is the plausible human interaction inside a Virtual Environment (VE). The rise of the Virtual Reality (VR) applications in various domains demands for a feasible 3D interface. Ensuring immersivity in a virtual space, this paper presents an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers; thumb and index. The two fingertip-thimbles made of paper are used to trace states and positions of the fingers by an ordinary camera. Based on the positions of the fingers, the basic interaction tasks; selection, scaling, rotation, translation and navigation are performed by intuitive gestures of the fingers. Without keeping a gestural database, the features-free detection of the fingers guarantees speedier interactions. Moreover, the system is user-independent and depends neither on the size nor on the color of the users’ hand. With a case-study project; Interactions by the Gestures of Fingers (IGF) the technique is implemented for evaluation. The IGF application traces gestures of the fingers using the libraries of OpenCV at the back-end. At the front-end, the objects of the VE are rendered accordingly using the Open Graphics Library; OpenGL. The system is assessed in a moderate lighting condition by a group of 15 users. Furthermore, usability of the technique is investigated in games. Outcomes of the evaluations revealed that the approach is suitable for VR applications both in terms of cost and accuracy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:19:40Z No. of bitstreams: 1 ijimai_5_5_14_pdf_29363.pdf: 2694018 bytes, checksum: f9cf545f2eeb77a2cb238eb34e2b88f3 (MD5); Made available in DSpace on 2022-02-28T11:19:40Z (GMT). No. of bitstreams: 1 ijimai_5_5_14_pdf_29363.pdf: 2694018 bytes, checksum: f9cf545f2eeb77a2cb238eb34e2b88f3 (MD5) https://reunir.unir.net/handle/123456789/12528 https://reunir.unir.net/handle/123456789/12527 User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features George, Loay; Hadi, Hend In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:05:36Z No. of bitstreams: 1 ijimai_5_5_7_pdf_30776.pdf: 1234323 bytes, checksum: ae5a77bae2101df0b707f9b23e170f63 (MD5); Made available in DSpace on 2022-02-28T11:05:36Z (GMT). No. of bitstreams: 1 ijimai_5_5_7_pdf_30776.pdf: 1234323 bytes, checksum: ae5a77bae2101df0b707f9b23e170f63 (MD5) https://reunir.unir.net/handle/123456789/12527 https://reunir.unir.net/handle/123456789/12526 Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method Harish, B S; Kumar, Keerthi; Darshan, H K Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naïve Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T10:47:30Z No. of bitstreams: 1 ijimai_5_5_13_pdf_67503.pdf: 456286 bytes, checksum: f5a3f5578df8946e92c8c1e61e2d89fa (MD5); Made available in DSpace on 2022-02-28T10:47:30Z (GMT). No. of bitstreams: 1 ijimai_5_5_13_pdf_67503.pdf: 456286 bytes, checksum: f5a3f5578df8946e92c8c1e61e2d89fa (MD5) https://reunir.unir.net/handle/123456789/12526 https://reunir.unir.net/handle/123456789/12525 IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition Djamila, Hamdadou; Belkhodja, Leila Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T10:36:30Z No. of bitstreams: 1 ijimai_5_5_12_pdf_14881.pdf: 1446164 bytes, checksum: 160008d7c7caea9b08dc5dc1b63adb14 (MD5); Made available in DSpace on 2022-02-28T10:36:30Z (GMT). No. of bitstreams: 1 ijimai_5_5_12_pdf_14881.pdf: 1446164 bytes, checksum: 160008d7c7caea9b08dc5dc1b63adb14 (MD5) https://reunir.unir.net/handle/123456789/12525 https://reunir.unir.net/handle/123456789/12524 Contribution to the Association Rules Visualization for Decision Support: A Combined Use Between Boolean Modeling and the Colored 2D Matrix Atmani, Baghdad; Benhacine, Fatima Zohra; Abdelouhab, Fawzia Zohra In the present paper we aim to study the visual decision support based on Cellular machine CASI (Cellular Automata for Symbolic Induction). The purpose is to improve the visualization of large sets of association rules, in order to perform Clinical decision support system and decrease doctors’ cognitive charge. One of the major problems in processing association rules is the exponential growth of generated rules volume which impacts doctor’s adaptation. In order to clarify it, many approaches meant to represent this set of association rules under visual context have been suggested. In this article we suggest to use jointly the CASI cellular machine and the colored 2D matrices to improve the visualization of association rules. Our approach has been divided into four important phases: (1) Data preparation, (2) Extracting association rules, (3) Boolean modeling of the rules base (4) 2D visualization colored by Boolean inferences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T10:14:59Z No. of bitstreams: 1 ijimai_5_5_5_pdf_16533.pdf: 827642 bytes, checksum: 6bd65e303d0548b2c2f9c5d58e17b265 (MD5); Made available in DSpace on 2022-02-28T10:14:59Z (GMT). No. of bitstreams: 1 ijimai_5_5_5_pdf_16533.pdf: 827642 bytes, checksum: 6bd65e303d0548b2c2f9c5d58e17b265 (MD5) https://reunir.unir.net/handle/123456789/12524 https://reunir.unir.net/handle/123456789/12505 A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network Jha, Sudan; Dey, Anirban; Kumar, Raghvendra; Kumar-Solanki, Vijender Visual Question Answering (VQA) is a stimulating process in the field of Natural Language Processing (NLP) and Computer Vision (CV). In this process machine can find an answer to a natural language question which is related to an image. Question can be open-ended or multiple choice. Datasets of VQA contain mainly three components; questions, images and answers. Researchers overcome the VQA problem with deep learning based architecture that jointly combines both of two networks i.e. Convolution Neural Network (CNN) for visual (image) representation and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM) for textual (question) representation and trained the combined network end to end to generate the answer. Those models are able to answer the common and simple questions that are directly related to the image’s content. But different types of questions need different level of understanding to produce correct answers. To solve this problem, we use faster Region based-CNN (R-CNN) for extracting image features with an extra fully connected layer whose weights are dynamically obtained by LSTMs cell according to the question. We claim in this paper that a single R-CNN architecture can solve the problems related to VQA by modifying weights in the parameter prediction layer. Authors trained the network end to end by Stochastic Gradient Descent (SGD) using pretrained faster R-CNN and LSTM and tested it on benchmark datasets of VQA. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T10:50:56Z No. of bitstreams: 1 ijimai_5_5_4_pdf_36854.pdf: 1645422 bytes, checksum: 2389b1ebfa24f250540f62850dc0ec52 (MD5); Made available in DSpace on 2022-02-24T10:50:56Z (GMT). No. of bitstreams: 1 ijimai_5_5_4_pdf_36854.pdf: 1645422 bytes, checksum: 2389b1ebfa24f250540f62850dc0ec52 (MD5) https://reunir.unir.net/handle/123456789/12505 https://reunir.unir.net/handle/123456789/12504 Detecting Image Brush Editing Using the Discarded Coefficients and Intentions López Hernández, Fernando Carlos; de-la-Fuente-Valentín, Luis; Sarría, Íñigo This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T10:27:11Z No. of bitstreams: 1 ijimai_5_5_2_pdf_18456.pdf: 1323688 bytes, checksum: 9a6025ecf36c634d091e29269c999ff2 (MD5); Made available in DSpace on 2022-02-24T10:27:11Z (GMT). No. of bitstreams: 1 ijimai_5_5_2_pdf_18456.pdf: 1323688 bytes, checksum: 9a6025ecf36c634d091e29269c999ff2 (MD5) https://reunir.unir.net/handle/123456789/12504 https://reunir.unir.net/handle/123456789/12503 Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images Jalal, Ahmad; Kamal, Shaharyar Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T10:01:36Z No. of bitstreams: 1 ijimai_5_5_9_pdf_48446.pdf: 578451 bytes, checksum: d0dd27c2ec3c4c07158deeb3ab61e9ba (MD5); Made available in DSpace on 2022-02-24T10:01:36Z (GMT). No. of bitstreams: 1 ijimai_5_5_9_pdf_48446.pdf: 578451 bytes, checksum: d0dd27c2ec3c4c07158deeb3ab61e9ba (MD5) https://reunir.unir.net/handle/123456789/12503 https://reunir.unir.net/handle/123456789/12502 A Diversity-Accuracy Measure for Homogenous Ensemble Selection Zouggar, Taleb; Adla, A Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose a method called DIversity and ACcuracy for Ensemble Selection (DIACES) using an evaluation function based on both diversity and accuracy. The method is applied on homogenous ensembles composed of C4.5 decision trees and based on a hill climbing strategy. This allows selecting ensembles with the best compromise between maximum diversity and minimum error rate. Comparative studies show that in most cases the proposed method generates reduced size ensembles with better performances than usual ensemble simplification methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T09:54:33Z No. of bitstreams: 1 ijimai_5_5_8_pdf_37634.pdf: 644785 bytes, checksum: 99cc13fa30ac38ba5beec9ab85043c80 (MD5); Made available in DSpace on 2022-02-24T09:54:33Z (GMT). No. of bitstreams: 1 ijimai_5_5_8_pdf_37634.pdf: 644785 bytes, checksum: 99cc13fa30ac38ba5beec9ab85043c80 (MD5) https://reunir.unir.net/handle/123456789/12502 https://reunir.unir.net/handle/123456789/12501 Deep Belief Network and Auto-Encoder for Face Classification Bouchra, Nassih; Mohammed, Ngadi; Nabil, Hmina; Aouatif, Amine The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods which are Deep Belief Network (DBN) and Stacked Auto-Encoder (SAE), to initialize deep supervised Neural Networks (NN), besides of Back Propagation Neural Networks (BPNN) applied to face classification task. Moreover, our contribution is to extract hierarchical representations of face image based on the Deep Learning models which are: DBN, SAE and BPNN. Then, the extracted feature vectors of each model are used as input of NN classifier. Next, to test our approach and evaluate its performance, a simulation series of experiments were performed on two facial databases: BOSS and MIT. Our proposed approach which is (DBN,NN) has a significant improvement on the classification error rate compared to (SAE,NN) and BPNN which we get 1.14% and 1.96% in terms of error rate with BOSS and MIT respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T09:30:17Z No. of bitstreams: 1 ijimai_5_5_3_pdf_64780.pdf: 1185160 bytes, checksum: fa2fcff4722df825ed0f384dc93fa8f9 (MD5); Made available in DSpace on 2022-02-24T09:30:17Z (GMT). No. of bitstreams: 1 ijimai_5_5_3_pdf_64780.pdf: 1185160 bytes, checksum: fa2fcff4722df825ed0f384dc93fa8f9 (MD5) https://reunir.unir.net/handle/123456789/12501 https://reunir.unir.net/handle/123456789/12500 Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving Souhar, Abdelghani; Daldali, M Inspired from human perception and common text documents characteristics based on readability constraints, an Arabic text line segmentation approach is proposed using seam carving. Taking the gray scale of the image as input data, this technique offers better results at extracting handwritten text lines without the need for the binary representation of the document image. In addition to its fast processing time, its versatility permits to process a multitude of document types, especially documents presenting low text-to-background contrast such as degraded historical manuscripts or complex writing styles like cursive handwriting. Even if our focus in this paper was on Arabic text segmentation, this method is language independent. Tests on a public database of 123 handwritten Arabic documents showed a line detection rate of 97.5% for a matching score of 90%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T08:50:00Z No. of bitstreams: 1 ijimai_5_5_11_pdf_12351.pdf: 1591538 bytes, checksum: 5825c7c92e4aa7cb244d81430781128b (MD5); Made available in DSpace on 2022-02-24T08:50:00Z (GMT). No. of bitstreams: 1 ijimai_5_5_11_pdf_12351.pdf: 1591538 bytes, checksum: 5825c7c92e4aa7cb244d81430781128b (MD5) https://reunir.unir.net/handle/123456789/12500 https://reunir.unir.net/handle/123456789/12480 Editor’s Note Mochón, Francisco This special issue has been designed with the primary objective of demonstrating the diversity of fields where AI is used and, consequently, how it is gaining increasing importance as a tool for analysis and research. In this sense, there are works related to the following topics: the use of AI with the IoT, campaign management, topic models and fusion methods, sales forecasting, price forecasting for electricity market, NLP techniques in computational medicine, evaluation of patient triage in hospital emergency settings, algorithms for solving the assignment problem, scheduling strategy for scientific workflow, driver fatigue detection mechanisms, virtual reality and specialized training, image segmentation, web service selection, multimedia documents adaptation, 3D navigation in virtual environments, multi-criteria decision-making methods and emotional states classification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T11:02:12Z No. of bitstreams: 1 ijimai_5_4_0_pdf_20163.pdf: 192131 bytes, checksum: af9e89cafe5375787740df1711528ae5 (MD5); Made available in DSpace on 2022-02-21T11:02:13Z (GMT). No. of bitstreams: 1 ijimai_5_4_0_pdf_20163.pdf: 192131 bytes, checksum: af9e89cafe5375787740df1711528ae5 (MD5) https://reunir.unir.net/handle/123456789/12480 https://reunir.unir.net/handle/123456789/12479 Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling Pourvali, Mohsen; Orlando, Salvatore; Omidvarborna, Hosna Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus. In this paper, we represent an enriching document approach, using state-of-the-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling. We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model. Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents’ vectors can significantly improve the quality of the results in clustering the documents. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T10:58:19Z No. of bitstreams: 1 ijimai_5_4_3_pdf_49140.pdf: 2145322 bytes, checksum: 573ec5c868f5dcbe3137036e445222f6 (MD5); Made available in DSpace on 2022-02-21T10:58:19Z (GMT). No. of bitstreams: 1 ijimai_5_4_3_pdf_49140.pdf: 2145322 bytes, checksum: 573ec5c868f5dcbe3137036e445222f6 (MD5) https://reunir.unir.net/handle/123456789/12479 https://reunir.unir.net/handle/123456789/12478 Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm Hemeida, Ashraf; Mansour, Radwa; Hussein, M E Image segmentation is considered one of the most important tasks in image processing, which has several applications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop the electromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMO performance for determining the optimal multi-level thresholding of image segmentation. In general, EMO simulates the mechanism of attraction and repulsion between charges to develop the individuals of a population. EMO takes random samples from search space within the histogram of image, where, each sample represents each particle in EMO. The quality of each particle is assessed based on Otsu’s or Kapur objective function value. The solutions are updated using EMO operators until determine the optimal objective functions. Finally, this approach produces segmented images with optimal values for the threshold and a few number of iterations. The proposed technique is validated using different standard test images. Experimental results prove the effectiveness and superiority of the proposed algorithm for image segmentation compared with well-known optimization methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T10:14:40Z No. of bitstreams: 1 ijimai_5_4_12_pdf_62616.pdf: 1296186 bytes, checksum: eeedeaecf12dec987042458aa965850d (MD5); Made available in DSpace on 2022-02-21T10:14:40Z (GMT). No. of bitstreams: 1 ijimai_5_4_12_pdf_62616.pdf: 1296186 bytes, checksum: eeedeaecf12dec987042458aa965850d (MD5) https://reunir.unir.net/handle/123456789/12478 https://reunir.unir.net/handle/123456789/12477 Data-Aware Scheduling Strategy for Scientific Workflow Applications in IaaS Cloud Computing Makhlouf, Sid Ahmed; Yagoubi, Belabbas Scientific workflows benefit from the cloud computing paradigm, which offers access to virtual resources provisioned on pay-as-you-go and on-demand basis. Minimizing resources costs to meet user’s budget is very important in a cloud environment. Several optimization approaches have been proposed to improve the performance and the cost of data-intensive scientific Workflow Scheduling (DiSWS) in cloud computing. However, in the literature, the majority of the DiSWS approaches focused on the use of heuristic and metaheuristic as an optimization method. Furthermore, the tasks hierarchy in data-intensive scientific workflows has not been extensively explored in the current literature. Specifically, in this paper, a data-intensive scientific workflow is represented as a hierarchy, which specifies hierarchical relations between workflow tasks, and an approach for data-intensive workflow scheduling applications is proposed. In this approach, first, the datasets and workflow tasks are modeled as a conditional probability matrix (CPM). Second, several data transformation and hierarchical clustering are applied to the CPM structure to determine the minimum number of virtual machines needed for the workflow execution. In this approach, the hierarchical clustering is done with respect to the budget imposed by the user. After data transformation and hierarchical clustering, the amount of data transmitted between clusters can be reduced, which can improve cost and makespan of the workflow by optimizing the use of virtual resources and network bandwidth. The performance and cost are analyzed using an extension of Cloudsim simulation tool and compared with existing multi-objective approaches. The results demonstrate that our approach reduces resources cost with respect to the user budgets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T10:00:59Z No. of bitstreams: 1 ijimai_5_4_9_pdf_20202.pdf: 807325 bytes, checksum: 722b5222347095131cae7b5cc55b94ff (MD5); Made available in DSpace on 2022-02-21T10:00:59Z (GMT). No. of bitstreams: 1 ijimai_5_4_9_pdf_20202.pdf: 807325 bytes, checksum: 722b5222347095131cae7b5cc55b94ff (MD5) https://reunir.unir.net/handle/123456789/12477 https://reunir.unir.net/handle/123456789/12476 Two Hand Gesture Based 3D Navigation in Virtual Environments Rehman, I; Ullah, S; Raees, M Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T09:54:32Z No. of bitstreams: 1 ijimai_5_4_15_pdf_17617.pdf: 1445539 bytes, checksum: 01851524e9e0dd1e592a28dcadf4fdd5 (MD5); Made available in DSpace on 2022-02-21T09:54:32Z (GMT). No. of bitstreams: 1 ijimai_5_4_15_pdf_17617.pdf: 1445539 bytes, checksum: 01851524e9e0dd1e592a28dcadf4fdd5 (MD5) https://reunir.unir.net/handle/123456789/12476 https://reunir.unir.net/handle/123456789/12475 Day-Ahead Price Forecasting for the Spanish Electricity Market Díaz, Julia; Romero, Álvaro; Dorronsoro, José Ramón During the last years, electrical systems around the world and in particular the Spanish electric sector have undergone great changes with the focus of turning them into more liberalized and competitive markets. For this reason, in many countries like Spain have appeared electric markets where producers sell and electricity retailers buy the power we consume. All agents involved in this market need predictions of generation, demand and especially prices to be able to participate in them in a more efficient way, obtaining a greater profit. The present work is focused on the context of development of a tool that allows to predict the price of electricity for the next day in the most precise way possible. For such target, this document analyzes the electric market to understand how prices are calculated and who are the agents that can make prices vary. Traditional proposals in the literature range from the use of Game Theory to the use of Machine Learning, Time Series Analysis or Simulation Models. In this work we analyze a normalization of the target variable due to a strong seasonal component in an hourly and daily way to later benchmark several models of Machine Learning: Ridge Regression, K-Nearest Neighbors, Support Vector Machines, Neural Networks and Random Forest. After observing that the best model is Random Forest, a discussion has been carried out on the appropriateness of the normalization for this algorithm. From this analysis it is obtained that the model that gives the best results has been Random Forest without applying the normalization function. This is due to the loss of the close relationship between the objective variable and the electric demand, obtaining an Average Absolute Error of 3.92€ for the whole period of 2016. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T09:43:11Z No. of bitstreams: 1 ijimai_5_4_5_pdf_14997.pdf: 737444 bytes, checksum: 29b9319e28eee412d8a16e3c73027b44 (MD5); Made available in DSpace on 2022-02-21T09:43:11Z (GMT). No. of bitstreams: 1 ijimai_5_4_5_pdf_14997.pdf: 737444 bytes, checksum: 29b9319e28eee412d8a16e3c73027b44 (MD5) https://reunir.unir.net/handle/123456789/12475 https://reunir.unir.net/handle/123456789/12474 PRACTICA. A Virtual Reality Platform for Specialized Training Oriented to Improve the Productivity Lombardo, Juan Manuel; López, Miguel Ángel; Velasco, Susana; García, Vicente; López, Mabel; Cañadas, Rubén; León, Mónica With the proliferation of Virtual reality headset that are emerging into a consumer-oriented market for video games, it will open new possibilities for exploiting the virtual reality (VR). Therefore, the PRACTICA project is defined as a new service aimed to offering a system for creating courses based on a VR simulator for specialized training companies that allows offering to the students an experience close to reality. The general problem of creating these virtual courses derives from the need to have programmers that can generate them. Therefore, the PRACTICA project allows the creation of courses without the need to program source code. In addition, elements of virtual interaction have been incorporated that cannot be used in a real environment due to risks for the staff, such as the introduction of fictional characters or obstacles that interact with the environment. So to do this, artificial intelligence techniques have been incorporated so these elements can interact with the user, as it may be, the movement of these fictional characters on stage with a certain behavior. This feature offers the opportunity to create situations and scenarios that are even more complex and realistic.This project aims to create a service to bring virtual reality technologies closer and artificial intelligence for non-technological companies, so that they can generate (or acquire) their own content and give it the desired shape for their purposes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T09:16:45Z No. of bitstreams: 1 ijimai_5_4_11_pdf_83808.pdf: 632376 bytes, checksum: efb27c6187a8d60ef1ab8f73f00fdaf5 (MD5); Made available in DSpace on 2022-02-21T09:16:45Z (GMT). No. of bitstreams: 1 ijimai_5_4_11_pdf_83808.pdf: 632376 bytes, checksum: efb27c6187a8d60ef1ab8f73f00fdaf5 (MD5) https://reunir.unir.net/handle/123456789/12474 https://reunir.unir.net/handle/123456789/12473 Evaluation of a Diagnostic Decision Support System for the Triage of Patients in a Hospital Emergency Department Seara, Germán; Mayol, Julio; Nazario Arancibia, JC; Martín Sanchez, FJ; Del rey Mejías, AL; del Gonzalez Castillo, J; Chafer Vilaplana, J; García Briñon, MA; Suárez-Cadenas, MM One of the biggest challenges for the management of the emergency department (ED) is to expedite the management of patients since their arrival for those with low priority pathologies selected by the classification systems, generating unnecessary saturation of the ED. Diagnostic decision support systems (DDSS) can be a powerful tool to guide diagnosis, facilitate correct classification and improve patient safety. Patients who attended the ED of a tertiary hospital with the preconditions of Manchester Triage system level of low priority (levels 3, 4 and 5), and with one of the five most frequent causes for consultation: dyspnea, chest pain, gastrointestinal bleeding, general discomfort and abdominal pain, were interviewed by an independent researcher with a DDSS, the Mediktor system. After the interview, we compare the Manchester triage and the final diagnoses made by the ED with the triage and diagnostic possibilities ordered by probability obtained by the Mediktor system, respectively. In a final sample of 214 patients, the urgency assignment made by both systems does not match exactly, which could indicate a different classification model, but there were no statistically significant differences between the assigned levels (S = 0.059, p = 0.442). The diagnostic accuracy between the final diagnosis and any of the first 10 Mediktor diagnoses was of 76.5%, for the first five diagnoses was 65.4%, for the first three diagnoses was 58%, and the exact match with the first diagnosis was 37.9%. The classification of Mediktor in this segment of patients shows that a higher level of severity corresponds to a greater number of hospital admissions, hospital readmissions and emergency screenings at 30 days, although without statistical significance. It is expected that this type of applications may be useful as a complement to the triage, to accelerate the diagnostic approach, to improve the request for appropriate complementary tests in a protocolized action model and to reduce waiting times in the ED. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T08:43:47Z No. of bitstreams: 1 ijimai_5_4_7_pdf_43667.pdf: 526584 bytes, checksum: 4040beea73b719a1ee00a981e5a8a6de (MD5); Made available in DSpace on 2022-02-21T08:43:47Z (GMT). No. of bitstreams: 1 ijimai_5_4_7_pdf_43667.pdf: 526584 bytes, checksum: 4040beea73b719a1ee00a981e5a8a6de (MD5) https://reunir.unir.net/handle/123456789/12473 https://reunir.unir.net/handle/123456789/12441 Sales Prediction through Neural Networks for a Small Dataset Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio; Caballero Morales, Santiago Omar Sales forecasting allows firms to plan their production outputs, which contributes to optimizing firms' inventory management via a cost reduction. However, not all firms have the same capacity to store all the necessary information through time. So, time-series with a short length are common within industries, and problems arise due to small time series does not fully capture sales' behavior. In this paper, we show the applicability of neural networks in a case where a company reports a short time-series given the changes in its warehouse structure. Given the neural networks independence form statistical assumptions, we use a multilayer-perceptron to get the sales forecasting of this enterprise. We find that learning rates variations do not significantly increase the computing time, and the validation fails with an error minor to five percent. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T12:04:11Z No. of bitstreams: 1 ijimai_5_4_4_pdf_17317.pdf: 559885 bytes, checksum: 2179d8c307eff4a29cfaa1b1f4482f18 (MD5); Made available in DSpace on 2022-02-14T12:04:11Z (GMT). No. of bitstreams: 1 ijimai_5_4_4_pdf_17317.pdf: 559885 bytes, checksum: 2179d8c307eff4a29cfaa1b1f4482f18 (MD5) https://reunir.unir.net/handle/123456789/12441 https://reunir.unir.net/handle/123456789/12440 Biomedical Term Extraction: NLP Techniques in Computational Medicine Redondo, Teófilo; Díaz, Julia; Moreno Sandoval, Antonio; Campillos Llanos, Leonardo Artificial Intelligence (AI) and its branch Natural Language Processing (NLP) in particular are main contributors to recent advances in classifying documentation and extracting information from assorted fields, Medicine being one that has gathered a lot of attention due to the amount of information generated in public professional journals and other means of communication within the medical profession. The typical information extraction task from technical texts is performed via an automatic term recognition extractor. Automatic Term Recognition (ATR) from technical texts is applied for the identification of key concepts for information retrieval and, secondarily, for machine translation. Term recognition depends on the subject domain and the lexical patterns of a given language, in our case, Spanish, Arabic and Japanese. In this article, we present the methods and techniques for creating a biomedical corpus of validated terms, with several tools for optimal exploitation of the information therewith contained in said corpus. This paper also shows how these techniques and tools have been used in a prototype. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T11:15:51Z No. of bitstreams: 1 ijimai_5_4_6_pdf_46381.pdf: 515240 bytes, checksum: be9566029f2b44cefc55c806875b64c7 (MD5); Made available in DSpace on 2022-02-14T11:15:51Z (GMT). No. of bitstreams: 1 ijimai_5_4_6_pdf_46381.pdf: 515240 bytes, checksum: be9566029f2b44cefc55c806875b64c7 (MD5) https://reunir.unir.net/handle/123456789/12440 https://reunir.unir.net/handle/123456789/12439 A Review of Artificial Intelligence in the Internet of Things González García, Cristian; Núñez-Valdez, Edward; García-Díaz, Vicente; Pelayo García-Bustelo, B. Cristina; Cueva-Lovelle, Juan Manuel Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T11:04:32Z No. of bitstreams: 1 ijimai_5_4_1_pdf_27055.pdf: 579397 bytes, checksum: 8e97461f7808e8189d461fdfbf3b6ca0 (MD5); Made available in DSpace on 2022-02-14T11:04:32Z (GMT). No. of bitstreams: 1 ijimai_5_4_1_pdf_27055.pdf: 579397 bytes, checksum: 8e97461f7808e8189d461fdfbf3b6ca0 (MD5) https://reunir.unir.net/handle/123456789/12439 https://reunir.unir.net/handle/123456789/12438 A Fuzzy Linguistic RFM Model Applied to Campaign Management Herrera-Viedma, Enrique; Carrasco, Ramón Alberto; Blasco, María Francisca; García-Madariaga, Jesús In the literature there are some proposals for integrated schemes for campaign management based on segmentation from the results of the RFM model. RFM is a technique used to analyze customer behavior by means of three variables: Recency, Frequency and Monetary value. It is s very much in use in the business world due to its simplicity of use, implementation and interpretability of its results. However, RFM applications to campaign management present known limitations like the lack of precision because the scores of these variables are expressed by an ordinal scale. In this paper, we propose to link customer segmentation methods with campaign activities in a more effective way incorporating the 2–tuple model both to the RFM calculation process and to its subsequent exploitation by means of segmentation algorithms, specifically, k-means. This yields a greater interpretability of these results and also allows computing these values without loss of information. Therefore, marketers can effectively develop more effective marketing strategy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T10:37:54Z No. of bitstreams: 1 ijimai_5_4_2_pdf_37693.pdf: 785885 bytes, checksum: 28aa5dd80e319abd1fbe056954d1bc2d (MD5); Made available in DSpace on 2022-02-14T10:37:54Z (GMT). No. of bitstreams: 1 ijimai_5_4_2_pdf_37693.pdf: 785885 bytes, checksum: 28aa5dd80e319abd1fbe056954d1bc2d (MD5) https://reunir.unir.net/handle/123456789/12438 https://reunir.unir.net/handle/123456789/12437 Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System Magdin, Martin; Prikler, F At the present time, various freely available or commercial solutions are used to classify the subject's emotional state. Classification of the emotional state helps us to understand how the subject feels and what he is experiencing in a particular situation. Classification of the emotional state can thus be used in various areas of our life from neuromarketing, through the automotive industry (determining how emotions affect driving), to implementing such a system into the learning process. The learning process, which is the (mutual) interaction between the teacher and the learner, is an interesting area in which individual emotional states can be explored. In this pedagogical-psychological area several research studies were realized. These studies in some cases demonstrated the important impact of the emotional state on the results of the students. However, for comparison and unambiguous classification of the emotional state most of these studies used the instructed (even constructed) stereotypical facial expressions of the most well-known test databases (Jaffe is a typical example). Such facial expressions are highly standardized, and the software can recognize them with a fairly big percentage, but this does not necessarily point to the actual success rate of the subject's emotional classification in such a test because the similarity to real emotional expression remains unknown. Therefore, we examined facial expressions in real situations. We have subsequently compared these examined facial expressions with the instructed expressions of the same emotions (the Jaffe database). The overall average classification score in real facial expressions was 94.58%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T10:32:31Z No. of bitstreams: 1 ijimai_5_4_16_pdf_15374.pdf: 655849 bytes, checksum: 6022b34cc54c84517ded043763d9162e (MD5); Made available in DSpace on 2022-02-14T10:32:31Z (GMT). No. of bitstreams: 1 ijimai_5_4_16_pdf_15374.pdf: 655849 bytes, checksum: 6022b34cc54c84517ded043763d9162e (MD5) https://reunir.unir.net/handle/123456789/12437 https://reunir.unir.net/handle/123456789/12435 Multimodal Generic Framework for Multimedia Documents Adaptation Bahaj, Mohamed; Khallouki, Hajar Today, people are increasingly capable of creating and sharing documents (which generally are multimedia oriented) via the internet. These multimedia documents can be accessed at anytime and anywhere (city, home, etc.) on a wide variety of devices, such as laptops, tablets and smartphones. The heterogeneity of devices and user preferences has raised a serious issue for multimedia contents adaptation. Our research focuses on multimedia documents adaptation with a strong focus on interaction with users and exploration of multimodality. We propose a multimodal framework for adapting multimedia documents based on a distributed implementation of W3C’s Multimodal Architecture and Interfaces applied to ubiquitous computing. The core of our proposed architecture is the presence of a smart interaction manager that accepts context related information from sensors in the environment as well as from other sources, including information available on the web and multimodal user inputs. The interaction manager integrates and reasons over this information to predict the user’s situation and service use. A key to realizing this framework is the use of an ontology that undergirds the communication and representation, and the use of the cloud to insure the service continuity on heterogeneous mobile devices. Smart city is assumed as the reference scenario. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T10:03:53Z No. of bitstreams: 1 ijimai_5_4_14_pdf_17167.pdf: 606586 bytes, checksum: 01d64e19657f9c8b4ec47e294f475cb6 (MD5); Made available in DSpace on 2022-02-14T10:03:53Z (GMT). No. of bitstreams: 1 ijimai_5_4_14_pdf_17167.pdf: 606586 bytes, checksum: 01d64e19657f9c8b4ec47e294f475cb6 (MD5) https://reunir.unir.net/handle/123456789/12435 https://reunir.unir.net/handle/123456789/12434 QoS based Web Service Selection and Multi-Criteria Decision Making Methods Bagga, Pallavi; Hans, Rahul; Joshi, Aarchit With the continuing proliferation of web services offering similar efficacies, around the globe, it has become a challenge for a user to select the best web service. In literature, this challenge is exhibited as a 0-1 knapsack problem of multiple dimensions and multiple choices, known as an NP-hard problem. Multi-Criteria Decision Making (MCDM) method is one of the ways which suits this problem and helps the users to select the best service based on his/her preferences. In this regard, this paper assists the researchers in two conducts: Firstly, to witness the performance of different MCDM methods for large number of alternatives and attributes. Secondly, to perceive the possible deviation in the ranking obtained from these methods. For carrying out the experimental evaluation, in this paper, five different well-known MCDM methods have been implemented and compared over two different scenarios of 50 as well as 100 web services, where their ranking is defined on an account of several Quality of Service (QoS) parameters. Additionally, a Spearman’s Rank Correlation Coefficient has been calculated for different pairs of MCDM methods in order to provide a clear depiction of MCDM methods showing the least deviation in their ranking. The experimental results comfort web service users in conquering an appropriate decision on the selection of suitable service. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T09:58:20Z No. of bitstreams: 1 ijimai_5_4_13_pdf_11750.pdf: 552495 bytes, checksum: c23853ef8ddd26a53c1c71ea8df858fb (MD5); Made available in DSpace on 2022-02-14T09:58:20Z (GMT). No. of bitstreams: 1 ijimai_5_4_13_pdf_11750.pdf: 552495 bytes, checksum: c23853ef8ddd26a53c1c71ea8df858fb (MD5) https://reunir.unir.net/handle/123456789/12434 https://reunir.unir.net/handle/123456789/12433 Hybrid Algorithm for Solving the Quadratic Assignment Problem Sayoti, Fatima; Riffi, Mohammed Essaid The Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literature of QAP. The obtained numerical results show that the GBSA produces optimal solutions in reasonable time; it has the better computational time. This work demonstrates that our proposed adaptation is effective in solving the quadratic assignment problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T08:43:12Z No. of bitstreams: 1 ijimai_5_4_8_pdf_86744.pdf: 495397 bytes, checksum: 774b6eead3deafdd23d9dd2d5b46a3bd (MD5); Made available in DSpace on 2022-02-14T08:43:12Z (GMT). No. of bitstreams: 1 ijimai_5_4_8_pdf_86744.pdf: 495397 bytes, checksum: 774b6eead3deafdd23d9dd2d5b46a3bd (MD5) https://reunir.unir.net/handle/123456789/12433 https://reunir.unir.net/handle/123456789/12432 Driver Fatigue Detection using Mean Intensity, SVM, and SIFT Naz, Saima; Ziauddin, Sheikh; Shahid, Ahmad Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T08:24:18Z No. of bitstreams: 1 ijimai_5_4_10_pdf_16089.pdf: 620274 bytes, checksum: 3f68d042a3f7f7adadf0833cc8c7f09a (MD5); Made available in DSpace on 2022-02-14T08:24:18Z (GMT). No. of bitstreams: 1 ijimai_5_4_10_pdf_16089.pdf: 620274 bytes, checksum: 3f68d042a3f7f7adadf0833cc8c7f09a (MD5) https://reunir.unir.net/handle/123456789/12432