IJIMAI 2020 - Special Issue on Artificial Intelligence and Blockchain - Vol. 6 Issue 3

  • Year: 2020
  • Vol: 6
  • Number: 3

In this special issue, we want to gather some innovative applications that are currently pushing forward the research on Blockchain technologies. In particular, we are interested also in those applications that put the focus on the data, enabling new processes that are able to leverage relevant knowledge from the data. This special issue will be successful if readers gain a better understanding on how Blockchain can be applied to very diverse areas, and might even be interested in designing, implementing and deploying an innovative solution to a completely different field of knowledge. We hope this Special Issue can provide a better understanding and key insights to readers on how Blockchain and artificial intelligence are cross-fertilizing to revolutionize many aspects in our societies.

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IJIMAI 2020 - Regular Issue - Vol. 6 Issue 2

  • Year: 2020
  • Vol: 6
  • Number: 2

The present regular issue starts with two articles related to one of the most relevant problems nowadays, which is the COVID-19 pandemic. Over the past years, there have been great advancements in health, but the evidence is that it remains a challenge to deal with pandemics and to achieve global health. IJIMAI has always reserved a space for heath topics and in the last years, a Special Issue on Big Data and e-health or a Special Issue on 3D Medicine and Artificial Intelligence were published. As Mochón and Baldominos state, “from a global perspective, a clear statement can be made: Artificial Intelligence can have an immense positive impact on societies... AI is turning into a key player at the time of diagnosing diseases at an early stage or developing new medicines and specialized treatment”. Being aware of this, a great number of researchers and scientific entities are focusing the efforts in this field and, specifically on the current world pandemic, as the researchers involved in the first two articles of this regular issue.

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. Already indexed in the Science Citation Index Expanded by Clarivate Analytics, within the categories “Computer Science, Artificial Intelligence” and “Computer Science, Interdisciplinary Applications”, during the next month the journal will be listed in the 2019 Journal Citation Reports. Again, given this great milestone, the IJIMAI Editorial Board reiterates its appreciation for their support to authors, reviewers and readers.

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IJIMAI 2020 - Special Issue on Soft Computing - Vol. 6 Issue 1

  • Year: 2020
  • Vol: 6
  • Number: 1

The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) covers all types of Artificial Intelligence (AI) research. The effort of reviewers, editors and authors have made the journal’s quality to go up every year. Last year, the goal of being included in the Journal Citation Reports index was achieved. It is totally clear to me that the journal could not have grown up that much without all the people that supports the journal. These include the abovementioned editors, reviewers and, specially, the authors, who trust and support the journal with their high-quality work. Let’s continue this trail and keep growing in order to make IJIMAI the influential AI journal that it deserves to be.

Last year, a set of very interesting special issues were published in this journal. Some of them are, for instance, the Special Issue on Uses Cases of Artificial Intelligence, Digital Marketing and Neuroscience, the Special Issue on Artificial Intelligence Applications or the Special Issue on Big Data and Open Education. This time, for the first issue of the year, we are glad to present a Special Issue on Soft Computing. Soft Computing is an AI branch that focuses on solving problems that have incomplete, inexact or fuzzy information. In other words, Soft Computing area includes algorithms and methods that are typically used when the imprecision or lack of the dealt data make other type of methods to become useless. Deep Learning, Machine learning and Fuzzy Systems related methods have achieved really good results even when the available data is not as good as desired. This success has converted the Soft Computing area in one of the most important ones inside the AI field.

This special issue’s goal is to reunite some of the most recent research on the Soft Computing area. The selected research covers different aspects and problems on the AI area in an effort to provide a clear overview of the state of the art on the topic.


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IJIMAI 2019 - Regular Issue - Vol. 5 Issue 7

  • Year: 2019
  • Vol: 5
  • Number: 7

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 will be 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.


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IJIMAI 2019 - Special Issue on Uses Cases of Artificial Intelligence, Digital Marketing and Neuroscience - Vol. 5 Issue 6

  • Year: 2019
  • Vol: 5
  • Number: 6

Artificial Intelligence (AI) is becoming the main character of what is now being called the fourth industrial revolution. Its role is gaining importance in everyday life, and the reason is straightforward: it offers a wide range of possibilities to make life easier in many different aspects.

However, AI is also at the center of a debate since some studies anticipate a radical transformation of the future industry, where some scenarios involve the destruction of millions of jobs. However, it is generally acknowledged that many new, more specialized, job positions will be created, as it has happened throughout history with other technological revolutions (see The Future of Work special issue in Nature). Nevertheless, uncertainties remain on the net impact on employment. While this debate is certainly useful, at this point we find it better to focus on a general objective assessment of how AI is impacting the world and the society.

From a global perspective, a clear statement can be made: Artificial Intelligence can have an immense positive impact on societies. Some of this impact is already unveiling in recent years and is particularly observable in the fields of health and medicine, where AI is turning into a key player at the time of diagnosing diseases at an early stage or developing new medicines and specialized treatment. Personalized medicine is probably the biggest breakthrough of the coming years, and AI is taking an active role to push this field forward.

However, medicine is not the only field where AI can enhance the process of personalization and customization. Marketing is certainly another good scope of application, where intelligent software can help knowing the target audience and offering them what they need in response. In this sense, we can already find intelligent devices that are able to make predictions about our behaviors. From a more practical perspective, AI can facilitate the sales process, dealing with most routinely procedures such as information tasks or documentation handling, which become streamlined and turn cheaper.

Additionally, AI enable brands to offer a commitment to their customers. This commitment will trigger some kind of emotional response in the customer depending on the perceived quality and the surrounding circumstances. In fact, AI can provide a software with means to detect the feelings arising during an interaction or engagement with customers, and determining whether these feelings are positive, negative or neutral.

In summary, with tools such as those outlined, AI would be capable to allow:

  • Knowing your tastes, desires and expectations as a consumer, as well as predicting your needs.
  • Analyzing your behavior and consumer habits when browsing the Internet.
  • Studying the emotional response during an interaction.
  • Anticipating trends.
  • Offering to selected customers the products or services they demand in a timely manner.
  • Using the most effective channels to enhance the consumer experience to the best possible.
  • Customizing communications to enhance the customers feelings.

All of these achievements are easy to reach when virtual assistants are able to retrieve a complete picture of a customer’s behavior, tastes and way of interacting. By these means, customers are not offered a generic experience but rather a unique result fitting their needs.

Since all data is not available in digital formats, the speed in which they are generated, processed and analyzed is dizzying. This velocity is one of the key aspects when it comes to applying AI to marketing, and especially when monetizing publications or services in social networks or the Internet.

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.

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IJIMAI 2019 - Regular Issue - Vol. 5 Issue 5

  • Year: 2019
  • Vol: 5
  • Number: 5

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.

The issue starts with a review article, written by A. Ghazvini, S. N. H. S. Abdullah and M. Ayob, which gives a view on current individual counting approaches based on clustering, detection, regression or density based methods. 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.

The issue starts with a review article, written by A. Ghazvini, S. N. H. S. Abdullah and M. Ayob, which gives a view on current individual counting approaches based on clustering, detection, regression or density based methods. They describe their advantages and limitations, concluding that the use of convolutional neural networks with a density map approach will contribute in scheming more precise counting techniques, focusing not only on counting but also on localization of individuals in crowded scenes.

The second article describes a work of F. López Hernández, L. de- la-Fuente Valentín and I. Sarría Martínez de Mendivil about a quick and simple method to detect brush editing in images, which can be used in image-tampering detection tools. Their two main contributions are: the design of a new approach to detect brush editing and the algorithm of the filter that detects this editing; and the introduction of intentions as subjective metric, in contrast to other classical objective forgery metrics.

N. Bouchra, A. Aouatif, N. Mohammed and H. Nabil investigate deep learning models for face classification tasks. They propose using deep belief networks and stacked auto-encoder besides back propagation neural network to capture various latent facial features. The proposed approach shows better performance on two facial databases compared to other published methods.

A deep learning model is proposed by S. Jha, A. Dey, R. Kumar and V. Kumar-Solanki for visual question answering, a process in which a machine answers to a natural language question related to an image. Specifically the model involves faster Region based Convolutional Neural Network (R-CNN) for extracting image features with an extra fully connected layer whose weights are dynamically obtained by Long Short Time Memory (LSTM) cell according to the question. Questions can be open ended or multiple choice questions and authors show that the visual question answering problem can be solved by a single R-CNN model.

Next work, described by F. Z. Benhacine, B. Atmani and F. Z. Abdelouhab, has the objective of improving the visualization of large sets of association rules to ease doctors’ activity when using clinical decision support systems. The authors propose to use the CASI (Cellular Automata for Symbolic Induction) cellular machine together with the colored 2D matrices to improve the visualization of association rules. Effective interactivity between the human expert and the visualization matrix is a focus of the work to facilitate clinical decision.

In the field of renewable energy, A. H. A. Elkasem, S. Kamel, A. Rashad and F. Jurado aim to allow doubly fed induction generator wind farms (DFIG), which are connected to the power system, to effectively participate in feeding electrical loads. To achieve this they propose a multiobjective optimization algorithm that is applied to a model of doubly fed induction generator wind farm (DFIG) with the  aim  of determining the  optimal values of the gain soft he DFIGcontrol system. The oscillation in power system is one of the problems of the interconnection of wind farms to the grid, and this proposal achieves lower oscillation in electrical power, so that DFIG wind turbines are more reliable.

In the following article, L. E. George and H. A. Hadi propose the use of Electroencephalogram (EEG) signals for user identification and verification processes, which have shown effectiveness against forgery and theft. However, to make EEG applicable, the acquisition process should be easy and a minimum number of mental tasks must be asked to be performed by the user. Specifically, the authors propose methods using only two EEG channels when the user is performing one mental task, in order to reduce system complexity while maintaining high system accuracy.

Next article by S. Taleb Zouggar and A. Adla presents a work on ensemble methods. Although these methods improve the performance of classifiers, they deteriorate the readability of the models. Therefore, some researchers propose to synthesize the structure of a tree from a set of classifiers, but prediction gets worse in this case. S. Taleb Zouggar and A. Adla propose an evaluation function combining performance and diversity for selection in a homogeneous ensemble used in a process of hill climbing. The method was evaluated on several benchmarks and compared to pruning homogeneous ensembles in literature, achieving better results.

In the field of activity recognition, A. Jalal and S. Kamal propose a system that classifies the nature of 3D body postures obtained by Kinect. They present novel features suitable for depth data, which are robust to noise, invariant to translation and scaling, and capable of monitoring fast human body parts movements. Besides, an advanced hidden Markov model is used to recognize different activities. The system outperforms other methods when applied to three depth- based behavior datasets, in both posture classification and behavior recognition.

M. M. G. Ribeiro and A. J. P. Gomes face the challenge of improving visual perception of deutan and protan dichromats, by introducing the first algorithm mainly focused on the enhancement of object contours in images. Typically the problem is mitigated by remapping colors to other colors but this does not help individuals to learn the naturalness of colors from past experience. Their algorithm increases the image contrast while keeping the naturalness of image colors, so the image perception improves but perceptual learning about the world is not disturbed.

Going back to recognition tasks but, this time, of handwritten characters, M. Daldali and A. Souhar propose an Arabic text line segmentation approach using seam carving. The technique offers satisfactory results at extracting handwritten text lines without the need for the binary representation of the document image, even with documents presenting low text-to-background contrast such as degraded historical manuscripts. Although this work focuses on Arabic language, the method is language independent.

Next paper presents another work in the field of medicine, specifically a tool to help radiologists in breast cancer detection tasks. L. Belkhodja and D. Hamdadou describe an automatic computer aided detection system, which combines medical image processing, bioinspired pattern recognition areas and others methods in computer vision. The system succeeded in automatic detection of abnormal regions.

H. M. Keerthi Kumar, B. S. Harish and H. K. Darshan present a work on sentiment analysis. They intend to identify sentiments analyzing short texts, specifically movie reviews. They propose the use of hybrid features obtained by concatenating machine learning features with lexicon features. Experiments show that the results obtained are highly promising both in terms of space complexity and classification accuracy.

Next article by M. Raees and S. Ullah presents an approach on Three Dimensional (3D) interaction inside a Virtual Environment (VE). They describe an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers, thumb and index. Experimental results show that the proposed approach has reliable recognition and accuracy rates. Moreover, the system neither needs training of images nor use any feature extraction, hence providing fast preprocessing.

W. Waheeb and R. Ghazali describe a new application of ridge polynomial based neural network models in multivariate time series forecasting. Their main objective is to investigate and compare the forecasting efficiency of neural network models in forecasting the well-known multivariate time series called Box-Jenkins gas furnace time series.

This regular issue starts and ends with review works, specifically the last article is about marketing intelligence and big data. Jan Lies describes how marketing intelligence has started to impact marketing practice with an important scope of social engineering techniques, concluding that marketing intelligence has led to a paradigm shift in marketing, from digital marketing to social engineering. Digital marketing includes data driven marketing, search engine marketing, recommender marketing, etc., while social engineering relates to content marketing, influencer marketing, social media marketing or creative marketing. Marketing intelligence is, without doubt, a current relevant issue. In fact, this is an advance of a next special issue of this journal that will focus on use cases of artificial intelligence, digital marketing and neuroscience.


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IJIMAI 2019 - Special Issue on Artificial Intelligence Applications - Vol. 5 Issue 4

  • Year: 2019
  • Vol: 5
  • Number: 4

The term artificial inteligence (AI) basically refers to using machines to do things that we consider to be “intelligent”, that is, being able to either simulate or do things that we describe people as doing with their cognitive faculties. A more complete definition presents artificial intelligence as the ability of a machine to perform cognitive functions we associate with the human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. The concept is related to machines that process huge amounts of information, learn from the results of their actions and never rest. The concept of AI makes us think that human genius has managed to create something that seems to exceed its own capacity.

The term was introduced by Alan Turing in 1950, so it is actually a pretty well-known field. However, we have seen an acceleration lately in the use of the AI due to two main factors. First, computational power is rising with exponential growth, and second, the amount of available data has grown at an impressive rate in recent years. To a large extent, then, the exponential growth in data and in computational power has led to the hype of AI.

One of the remarkable aspects of AI is the degree to which it is an extension of the features that we have seen in data and analytics. One of the enabling factors for machine learning to take hold is the large amounts of data. We have seen more and more data collected by companies and all kinds of organizations, be it transactional data, voice data, or data from the Internet of Things in the physical world. When you have all that data, you can extend the work you have done in analytics with AI techniques. Therefore, you will see methodologies about machine learning and deep learning with new neural networks that are applied to those vast amounts of data. In this sense, there are four technology systems of which machine learning is just part of and where some of the recent advancements and developments have been happening:

  • Physical AI, i.e., robotics and autonomous vehicles.
  • Computer vision, i.e., image processing or video processing.
  • Natural-language processing, be it spoken language particularly, or written language. We are seeing a lot of natural-language work being done.
  • Virtual agents or conversational interfaces; this is the ability of systems to roughly converse with you whether by voice or online through chats.

To some extent, artificial intelligence is going through a bit of a hype cycle. There are a lot of applications, a lot of industries and activities, and a lot of value is at stake. But it can be said that today we are in a phase where we have applications which we would call narrow AI. Those are very specific tasks that machines today can do better than human beings. But there is that question about a general AI, where you have a broader spectrum of capabilities that can be managed by a machine.

We are beginning to enter that phase. And we should not forget that the speed of development is exponential in those key technologies. It is coming much, much faster than we can imagine. As Peter Diamandis says, cumulative “intelligence” (both artificial and human) is the single greatest predictor of success for both a company or a nation.

Therefore, the suggestion for leaders of all types of organizations (companies, hospitals, government agencies, etc.) would be to start an analytics transformation now if have not done so already. This will require them to build capabilities, build technology and start the change in the organization, which will also be necessary to ultimately go into AI-enabled processes.

We can ask ourselves whether there is a case for a portfolio-of- initiatives approach, where one considers what can be done here and now.

In this sense, one possible suggestion would be to take the right use cases at the right point in time. Getting started now with the easier and simpler use cases also prepares us to take the more advanced use cases in the future. Empowering organizations to become analytics- or AI- driven is key to success in the future.

With these ideas in mind, we have prepared this special issue. It 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.

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IJIMAI 2018 - Regular Issue - Vol. 5 Issue 3 - 10th Anniversary

  • Year: 2018
  • Vol: 5
  • Number: 3

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 AI tools or tools that use AI with interactive multimedia techniques. This was the first phrase that appeared into the website of the journal, whose launching had several motivations. First, IJIMAI was established on December 2008 in response to several agents, such as students, teachers, researchers, primarily in Spain and Colombia, who wanted to increase the impact of science in their environment. Second, IJIMAI was established to increase the number of scientific journals developed in Spain into the scope of Artificial Intelligence and Interactive Multimedia; there are very few journals about these topics in our country. Third, since the beginning we believed into an open access project, open for the whole stakeholders. Currently no money is needed to public a contribution in IJIMAI, and no money is needed to read all papers in IJIMAI as well; science should be open to achieve the maximum dissemination of knowledge. Finally, IJIMAI was established with the hope of being a long-term project; this anniversary allows us to affirm that this goal is getting closer.

Since 2008 many changes have happened, the main ones are as follows:

  • From 2008 to 2011 we only published one issue per year. These 4 years were very difficult. We had to work hard to find good quality papers.
  • From 2011 to 2015 we increased our capacity of publication. We extended our issues from 1 to 4 per year. Also we achieved several indexations in different indexes and databases such as, DOAJ, INSPEC, DBLP, LATINDEX, among others. Many colleagues decided to work with us and they enrolled into the project. They helped us offering IJIMAI to several congresses such as IEEE-DCAI, IBERAMIA, WORLDCIST, etc.; they also helped us working as reviewers, editors and authors. IJIMAI was transformed into a robust project.
  • From 2015 to 2018 IJIMAI was indexed at Web of Science through Emerging Science Citation Index. The visibility increased a lot and the number of received papers also increased notably. There are some data that reflects this status as you can read below.
    • Considering the years 2013-2018 the average number of articles per year is 45, while if we only consider the last two years, the average number is 52.5.
    • All research articles are peer-reviewed through a blind process in which identities of authors and reviewers are hidden.
    • The Editorial Board of IJIMAI is composed of important researchers in Computer Science. With the goal to provide a wide geographic coverage of the journal and its impact, the Editorial Board has members of 23 countries: Argentina, Australia, Bolivia, Canada, China, Colombia, Finland, France, Germany, Greece, India, Ireland, Italy, Japan, Malaysia, Morocco, Norway, Peru, Portugal, Spain, Slovakia, United Kingdom and USA.
    • I should highlight the high internationality of IJIMAI. There is a 66% of international institutions that publish papers in IJIMAI.
    • The current acceptance rate is 0.30.
    • Publication of special issues collaborating with authors of prestigious entities such as Hospital Universitario La Paz of Madrid (Spain) and technology-related companies such as Telefónica, BBVA or Banco Santander (Special Issue on 3D Medicine and Artificial Intelligence, Special Issue on Advances and Applications in the IoT and Cloud Computing, Special Issue on Big Data and AI, etc.).
    • Since the start of the journal in 2008 until December 2018, 355 articles have been published in the journal.
    • Taking into account last studies done, calculated with the same parameters than Clarivate Analytics, our impact factor is 1.05.
    • Finally, I would like to remark that IJIMAI has been indexed in the annual Ibero-American Journal Ranking, launched by the "Red Iberoamericana de Innovación y Conocimiento Científico" (REDIB) and Clarivate Analytics, appearing in position 27 of this ranking in which 748 journals are listed.

IJIMAI has to thank many colleagues who have helped the journal in many times. Thanks to all of them. However, there are proper names that have to be mentioned because they have been key in this project: Jesús Soto, Oscar Sanjuán, Carlos Montenegro, Juan Manuel Cueva, Enrique Herrera-Viedma, Francisco Mochón, Daniel Burgos, Ainhoa Puente, Elena Verdú and of course Miguel Arrufat.

Also, IJIMAI has to thank all authors for all the papers sent and all the papers published. Without authors there is no journal.

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IJIMAI 2018 - Special Issue on Big Data and Open Education - Vol. 5 Issue 2

  • Year: 2018
  • Vol: 5
  • Number: 2

One of the most well-known requirements in educational settings is the need to know what happens in a course, lesson plan or full academic programme. That is true for any type of education but in particular for Open Education with the multiple dimensions of openness. On the one hand, educators (i.e. teachers, professors, tutors, etc.) and practitioners in Open Education need to reshape the course plan according to the actual features of the learners (e.g., learning styles, motivation, performance, et cetera) and therefore they require real-time analytical information to supervise, assess, adapt and offer feedback to the learners. On the other hand, Open Education offers specific opportunities through online learning using Open Educational Resources (OER) and providing Massive Open Online Courses (MOOCs). The online environments and platforms provide huge amount of data on all activities (a huge Excel sheet, labelled as Big Data). More importantly, Open Education with open teaching and learning is now commonly shaped by a learner- centred approach that pushes the learners to be the driver of their own learning. That is, learners require awareness to self-assess their progress along the course and make decisions for their next steps. In short, Open Education is now an always changing process that requires effective support for the decision making process by the educators and the learners.

Open Education can benefit from Big Data and Learning Analytics used and analysed in the right way as well as Open Education can be the enabler for a broader implementation and acceptance of Big Data and Learning Analytics, again if realized in the correct way. Recent research shows the upcoming relevance of Big Data in Open Education through decision support systems aiming at learners and educators, usually representing the information with information visualization techniques and dashboards. There are a lot of on-going research projects related Learning Analytics and Open Education that are producing interesting results to improve learning quality and to achieve societal impact. In this special issue, we present just a selection of fine papers focused on analytics (Moreno et al.), an intelligent assistant, higher education, vocational education, employment, gamification, augmented reality and Open Educational Resources. We hope that the reader enjoys as much as we did, as editors of this double-blind, peer reviewed compilation.

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IJIMAI 2018 - Regular Issue - Vol. 5 Issue 1

  • Year: 2018
  • Vol: 5
  • Number: 1

The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) provides an interdisciplinary forum in which scientists and professionals share their research results and report new advances on tools that use AI with interactive multimedia techniques. A brief of selected papers of IJIMAI are mentioned below:

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva (Slovakia) by Magdin and Prikler: the proposed system uses neural network algorithm for classification and recognizes 6 (respectively 7) facial expressions, namely anger, disgust, fear, happiness, sadness, surprise and neutral.

Self-Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment (India) by Nighot et al. This research paper proposes a solution for searching randomly moving target in unknown area using Mobile sensor nodes and combination of both Static and Mobile sensor nodes. Using algorithms like MSNs Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), Leader’s Movement Prediction Algorithm (LMPA) and follower.

Novel Clustering Method Based on K-Medoids and Mobility Metric (Morocco) authors (Hamzaoui et al.) propose a new algorithm of clustering based on new mobility metric and K-Medoid to distribute nodes into several clusters, to avoid the problem of negative influence of MANETS on the performance of QoS.

Spectral Restoration Based Speech Enhancement for Robust Speaker Identification (Pakistan) By Saleem and Tareen employed and evaluated Minimum Mean-Square-Error Short-Time Spectral Amplitude to improve performance of the speaker identification systems in presence of background noise. The identification rates are found to be higher after employing speech enhancement algorithms.

Hybrid Model for Passive Locomotion Control of a Biped Humanoid: The Artificial Neural Network Approach (India) by Raj et al. led to the observation that base model with learning-based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors.

Users Integrity Constraints in SOLAP Systems. Application in Agroforestry (Algeria) by Charef and Djamila, proposed a system for the implementation of user integrity constraints in SOLAP, namely “UIC-SOLAP”.

A Study on Persuasive Technologies: The Relationship between User Emotions, Trust and Persuasion (Malaysia) by Ahmad and Ali, concluded that emotions have significant effect on trust, and effect of emotions on persuasion using the persuasive technology was mediated by trust.

EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls (Australia) authors (Perera et al.) study revealed that the extra difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns.

Applying Bayesian Regularization for Acceleration of Levenberg- Marquardt based Neural Network Training (Malasia y Kazakhstan) by Suliman and Omarov: describe a method of applying Bayesian regularization to improve Levenberg-Marquardt (LM) algorithm and make it better usable in training neural networks. Results showed 98.8% correct classification when run on test samples.

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm (India), Samdurkar et al. observed that the MDS (modified diamond search pattern) performs better than DS (diamond search) and CDS (cross diamond search algorithms) on average search point and average computation time.

Spatial and Textural Aspects for Arabic Handwritten Characters Recognition (Morocco), Boulid, et al. purpose was the recognition of handwritten Arabic characters in their isolated form, and had 2.82% error rate.

A Novel Smart Grid State Estimation Method Based on Neural Networks (Egypt) Abdel-Nasseret al. presents a novel method called SE-NN (state estimation using neural network) for smart grid state estimation using artificial neural networks (ANNs) and is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.

Conceptual model development of big data (Malaysia) Adrian et al. aims to identify and analyze the affecting factors of BDA implementation and to propose a conceptual model for effective decision-making through BDA implementation assessment.

MSA for Optimal Reconfiguration and capacitor Allocation in Radial/Ring Distribution Networks (Egypt y Japan) Mohamed et al. presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN). Results state that MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms.

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