An Interactive Mobile Application to Request the Help of the Nearest First Aider by the Injured

Saudi Arabia is interested in providing health care and ambulatory for all citizens, residents and tourists of the pilgrims and pilgrims, and it is cooperating with the Saudi Red Crescent Authority (SRCA) to provide emergency health care especially for the pilgrims, Ammar - an independent body dealing with this. The efforts of SRCA can be highly noticed during on the Hajj season and public events such as the national day celebrations. The main issue lies in the fact that despite their efforts, the Ambulance Response Time (ART) remains higher than the global standard. Moreover, the reasons behind the high ART are circumstantial and thus hard to maneuver or manipulate. Therefore, to benefit from the Red Crescent's efforts and the first aid courses they offer, a system where credible first aiders can be summoned to provide proper and faster first aid to the injured is suggested. This study aims to develop an application to request the help of the nearest first aider by the injured or bystanders close to the injured. Also, to develop an interface that shows the route to get to the victim. The result has shown a positive indication of the importance of a system where credible first aiders can be summoned to provide proper and faster first aid to the injured. This study contributes to increase bystanders' awareness because they have the ability to connect with the nearest registered first aider

A Neural-CBR System for Real Property Valuation

In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

Persuasion and Recommendation System Applied to a Cognitive Assistant

In this paper, we present a persuasive recommendation module included in the iGenda framework. iGenda is a cognitive assistant that helps care-receivers and caregivers in the management of their activities of daily living, by resolving scheduling conflicts and promoting active aging activities. The proposed new module will allow the system to select and recommend to the users an event that potentially best suits to his/her interests (likes or medical condition). The multi-agent approach followed by the iGenda framework facilitates an easy integration of these new features. The social objective is to promote social activities and engaging the users in physical or psychological activities that improve their medical condition

Decentralized Coalition Formation with Agent-based Combinatorial Heuristics

A steadily growing pervasion of the energy distribution grid with communication technology is widely seen as an enabler for new computational coordination techniques for renewable, distributed generation as well as for bundling with controllable consumers. Smart markets will foster a decentralized grid management. One important task as prerequisite to decentralized management is the ability to group together in order to jointly gain enough suitable flexibility and capacity to assume responsibility for a specific control task in the grid. In self-organized smart grid scenarios, grouping or coalition formation has to be achieved in a decentralized and situation aware way based on individual capabilities. We present a fully decentralized coalition formation approach based on an established agent-based heuristics for predictive scheduling with the additional advantage of keeping all information about local decision base and local operational constraints private. Two closely interlocked optimization processes orchestrate an overall procedure that adapts a coalition structure to best suit a given set of energy products. The approach is evaluated in several simulation scenarios with different type of established models for integrating distributed energy resources and is also extended to the induced use case of surplus distribution using basically the same algorithm

Intelligent Dolls and robots for the treatment of elderly people with dementia

Doll and robot are effective and beneficial non-pharmacological therapies applied in different clinical settings. Doll therapy (DT), principally based in Bowlby's attachment theory, uses an empathy or lifelike baby doll to conduct caring behaviors of it. Robot therapies (RT) use care robots with a friendly attitude and appearance that create emotions and movements that lead to different verbal, motor and emotional reactions. Both DT and RT are person-centred therapies that aim to improve wellbeing of people that suffer from different neurological, psychological and mental health disorders, such as Alzheimer's Disease, autism spectrum disorder, stress or depression, by providing a realistic experience. In this paper, the characteristics of both therapies, their benefits and the possibilities of innovation in the therapeutic field are presented

An Agent-based Environment for Dynamic Positioning of the Fogg Behavior Model Threshold Line

In this paper, it is presented a mathematical modeling for the action line, or threshold line, of the Fogg Behavior Model (FBM) as well as an analysis of its positioning in relation to the dataset. According to the mathematical modeling formation process for both Motivation and Ability axes, the action line evaluation was performed by simulations via agents. This behavioral model is mainly used as an empirical evaluation method applied to processes based on persuasive technologies. The results showed that the threshold line should not be fixed, as originally proposed in the model, but dynamically allocated based on the Kolmogorov mean. This dynamic allocation ensures its use as a visual feature towards greater efficiency in triggers implementations. This work aims to contribute with an approach that transits between theoretical and practical when related to applications that requires the FBM, thus allowing the use of this behavioral model with higher degree of certainty and thus maximizing efficiency in the evaluation and implementation processes based on persuasive technologies

Adding real data to detect emotions by means of smart resource artifacts in MAS

[EN] This article proposes an application of a social emotional model, which allows to extract, analyse, represent and manage the social emotion of a group of entities. Specifically, the application is based on how music can influence in a positive or negative way over emotional states. The proposed approach employs the JaCalIVE framework, which facilitates the development of this kind of environments. A physical device called smart resource offers to agents processed sensor data as a service. So that, agents obtain real data from a smart resource. MAS uses the smart resource as an artifact by means of a specific communications protocol. The framework includes a design method and a physical simulator. In this way, the social emotional model allows the creation of simulations over JaCalIVE, in which the emotional states are used in the decision-making of the agents.This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon.Ricon, JA.; Poza-Lujan, J.; Posadas-Yagüe, J.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2016). Adding real data to detect emotions by means of smart resource artifacts in MAS. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 5(4):85-92. https://doi.org/10.14201/ADCAIJ2016548592S85925

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented