https://reunir.unir.net/handle/123456789/12420 2024-11-04T17:50:22Z 2024-11-04T17:50:22Z Mochón, Francisco https://reunir.unir.net/handle/123456789/12480 2022-02-21T11:02:13Z

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)

Pourvali, Mohsen Orlando, Salvatore Omidvarborna, Hosna https://reunir.unir.net/handle/123456789/12479 2022-02-21T10:58:19Z

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)

Hemeida, Ashraf Mansour, Radwa Hussein, M E https://reunir.unir.net/handle/123456789/12478 2022-02-21T10:14:40Z

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)

Makhlouf, Sid Ahmed Yagoubi, Belabbas https://reunir.unir.net/handle/123456789/12477 2022-02-21T10:00:59Z

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)

Rehman, I Ullah, S Raees, M https://reunir.unir.net/handle/123456789/12476 2022-02-21T09:54:32Z

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)

Díaz, Julia Romero, Álvaro Dorronsoro, José Ramón https://reunir.unir.net/handle/123456789/12475 2022-02-21T09:43:11Z

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)

Lombardo, Juan Manuel López, Miguel Ángel Velasco, Susana García, Vicente López, Mabel Cañadas, Rubén León, Mónica https://reunir.unir.net/handle/123456789/12474 2022-02-21T09:16:45Z

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)

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 https://reunir.unir.net/handle/123456789/12473 2022-02-21T08:43:47Z

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)

Cantón Croda, Rosa María Gibaja Romero, Damián Emilio Caballero Morales, Santiago Omar https://reunir.unir.net/handle/123456789/12441 2022-02-14T12:04:11Z

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)

Redondo, Teófilo Díaz, Julia Moreno Sandoval, Antonio Campillos Llanos, Leonardo https://reunir.unir.net/handle/123456789/12440 2022-02-14T11:15:51Z

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)

González García, Cristian Núñez-Valdez, Edward García-Díaz, Vicente Pelayo García-Bustelo, B. Cristina Cueva-Lovelle, Juan Manuel https://reunir.unir.net/handle/123456789/12439 2023-11-02T12:23:17Z

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)

Herrera-Viedma, Enrique Carrasco, Ramón Alberto Blasco, María Francisca García-Madariaga, Jesús https://reunir.unir.net/handle/123456789/12438 2022-02-14T10:37:54Z

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)

Magdin, Martin Prikler, F https://reunir.unir.net/handle/123456789/12437 2022-02-14T10:32:31Z

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)

Bahaj, Mohamed Khallouki, Hajar https://reunir.unir.net/handle/123456789/12435 2022-02-14T10:03:53Z

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)

Bagga, Pallavi Hans, Rahul Joshi, Aarchit https://reunir.unir.net/handle/123456789/12434 2022-02-14T09:58:20Z

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)

Sayoti, Fatima Riffi, Mohammed Essaid https://reunir.unir.net/handle/123456789/12433 2022-02-14T08:43:12Z

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)

Naz, Saima Ziauddin, Sheikh Shahid, Ahmad https://reunir.unir.net/handle/123456789/12432 2022-02-14T08:24:18Z

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)