https://reunir.unir.net/handle/123456789/12652 2026-03-31T00:28:41Z 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/123456789/12667 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/123456789/12666 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/123456789/12665 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/123456789/12664 "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/123456789/12663 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/123456789/12662 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/123456789/12661 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/123456789/12660 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/123456789/12659 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/123456789/12658 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/123456789/12657 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/123456789/12656 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/123456789/12655 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/123456789/12654 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/123456789/12653 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)