A Linear General Type-2 Fuzzy Logic Based Computing With Words Approach for Realising an Ambient Intelligent Platform for Cooking Recipes Recommendation

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social scienc

Twenty Years of Mobile Banking Services Development and Sustainability: A Bibliometric Analysis Overview (2000–2020)

The current paper aims to analyze the keywords related to mobile banking (otherwise known as m-banking) issues by focusing on its development from 2000 to 2020, of which the first publication about this issue appeared in the Scopus database. This paper explored and analyzed 1206 research papers using the Scopus database. Bibliometric analysis and content analysis had been conducted through Excel and VOS viewer software to obtain the results. In addition, the findings of this paper reveal that the universal trends and increased production at a global level led to many changes, and the most rampant topic associated with m-banking in most periods is mobile telecommunication systems. By showcasing the creation of the key terms in m-banking, it was possible to identify significant changes in the development of the field\u27s key terminologies. Therefore, it is important to follow up on the development in future decades, particularly how the recent universal occurrences have influenced the changes in m-banking use at a global level. Moreover, the present study makes a significant contribution to the literature by providing a framework for future research. The framework provides opportunities for researchers to explore the research streams in future research. Finally, the current paper is the first of its kind in its method of contribution, ad according to the research databases (Scopus, Google Scholar, etc.), no work was witnessed in the published literature covering m-banking in a detailed and comprehensive multi-period manner and in such an applied method. In addition, the current paper fills this gap by conducting a bibliometric analysis and content analysis

Coronary Artery Bypass Grafting (CABG) versus Percutaneous Coronary Intervention (PCI) in treatment of left main coronary artery disease

BackgroundCoronary artery bypass graft surgery (CABG) has been widely used for left main coronary artery disease (LMCAD). Percutaneous coronary intervention (PCI) has become an option for this condition.AimsTo summarize the current evidence that compare between CABG vs. PCI in regards to ‎cardiac death, stroke, and myocardial infarction.‎Methods We searched randomized trials of treatment of LMCAD with PubMed, Google Scholar, and EBSCO.Results Five randomized studies were retrieved, which compared the efficacy between CABG vs. PCI in treatment of LMCAD.ConclusionPCI may be reasonable management of patients with LM stenosis involving distal bifurcation or with coexisting multivessel disease

A cloud computing based many objective type-2 fuzzy logic system for mobile field workforce area optimization

Large scale optimization problems in the real world are often very complex and require multiple objectives to be satisfied. This applies to industries that employ a large mobile field workforce. Sub-optimal allocation of tasks to engineers in this workforce can lead to poor customer service, higher travel costs and higher CO2 emissions. One solution is to create optimal working areas, which are geographical regions containing many task locations, where the engineers can work. Finding the optimal design of these working areas as well as assigning the correct engineers to them is known as workforce optimization and is a very complex problem, especially when scaled up over large areas. As a result of the vast search space, given by this problem, meta heuristics like genetic algorithms and multi-objective genetic algorithms, are used to find solutions to the problem in reasonable time. However, the hardware these algorithms run on can play a big part in their day-to-day use. This is because the environment in which the engineers are working within is changing on a daily bases. This means that there are severe time-restrictions on the optimization process if the working areas were to be optimized every day. One way to tackle this is to move the optimization system to the cloud where the computing resources available are often far greater than personal desktops or laptops. This paper presents our proposed cloud based many objective type-2 fuzzy logic system for mobile field workforce area optimization. The proposed system showed that utilizing cloud computing with multi-threading capabilities significantly reduce the optimization time allowing greater population sizes, which led to improved working area designs to satisfy the faced objectives

Designing Interactive Experiences for Children with Cochlear Implant

Information and Communication Technologies (ICTs) have grown exponentially in the education context and the use of digital products by children is increasing. As a result, teachers are taking advantage of ICTs to include mobile devices such as Tablets or Smartphones inside the classroom as playful support material to motivate children during their learning. Designing an interactive experience for a child with a special need such as a hearing impairment is a great challenge. In this article, two interactive systems are depicted, using a non-traditional interaction, by the following stages: analysis, design and implementation, with the participation of children with cochlear implant in the Institute of Blind and Deaf Children of Valle del Cauca, Colombia and the ASPAS Institute, Mallorca, Spain, who evaluated both interactive systems, PHONOMAGIC and CASETO. Positive results were obtained, showing that the use of real objects can greatly influence the environment in which children interact with the game, allowing them to explore and manipulate the objects supporting their teaching-learning processes

The Shapley value for a fair division of group discounts for coordinating cooling loads

We consider a demand response program in which a block of apartments receive a discount from their electricity supplier if they ensure that their aggregate load from air conditioning does not exceed a predetermined threshold. The goal of the participants is to obtain the discount, while ensuring that their individual temperature preferences are also satisfied. As such, the apartments need to collectively optimise their use of air conditioning so as to satisfy these constraints and minimise their costs. Given an optimal cooling profile that secures the discount, the problem that the apartments face then is to divide the total discounted cost in a fair way. To achieve this, we take a coalitional game approach and propose the use of the Shapley value from cooperative game theory, which is the normative payoff division mechanism that offers a unique set of desirable fairness properties. However, applying the Shapley value in this setting presents a novel computational challenge. This is because its calculation requires, as input, the cost of every subset of apartments, which means solving an exponential number of collective optimisations, each of which is a computationally intensive problem. To address this, we propose solving the optimisation problem of each subset suboptimally, to allow for acceptable solutions that require less computation. We show that, due to the linearity property of the Shapley value, if suboptimal costs are used rather than optimal ones, the division of the discount will be fair in the following sense: each apartment is fairly “rewarded” for its contribution to the optimal cost and, at the same time, is fairly “penalised” for its contribution to the discrepancy between the suboptimal and the optimal costs. Importantly, this is achieved without requiring the optimal solutions

The Shapley value for a fair division of group discounts for coordinating cooling loads

We consider a demand response program in which a block of apartments receive a discount from their electricity supplier if they ensure that their aggregate load from air conditioning does not exceed a predetermined threshold. The goal of the participants is to obtain the discount, while ensuring that their individual temperature preferences are also satisfied. As such, the apartments need to collectively optimise their use of air conditioning so as to satisfy these constraints and minimise their costs. Given an optimal cooling profile that secures the discount, the problem that the apartments face then is to divide the total discounted cost in a fair way. To achieve this, we take a coalitional game approach and propose the use of the Shapley value from cooperative game theory, which is the normative payoff division mechanism that offers a unique set of desirable fairness properties. However, applying the Shapley value in this setting presents a novel computational challenge. This is because its calculation requires, as input, the cost of every subset of apartments, which means solving an exponential number of collective optimisations, each of which is a computationally intensive problem. To address this, we propose solving the optimisation problem of each subset suboptimally, to allow for acceptable solutions that require less computation. We show that, due to the linearity property of the Shapley value, if suboptimal costs are used rather than optimal ones, the division of the discount will be fair in the following sense: each apartment is fairly "rewarded" for its contribution to the optimal cost and, at the same time, is fairly "penalised" for its contribution to the discrepancy between the suboptimal and the optimal costs. Importantly, this is achieved without requiring the optimal solutions

A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification

Soccer video summarization and classification is becoming a very important topic due to the world wide importance and popularity of soccer games which drives the need to automatically classify video scenes thus enabling better sport analysis, refereeing, training, advertisement, etc. Machine learning has been applied to the task of sports video classification. However, for some specific image and video problems (like sports video scenes classification), the learning task becomes convoluted and difficult due to the dynamic nature of the video sequence and the associated uncertainties relating to changes in light conditions, background, camera angle, occlusions and indistinguishable scene features, etc. The majority of previous techniques (such as SVM, neural network, decision tree, etc.) applied to sports video classifications did not provide a consummate solution, and such models could not be easily understood by human users; meanwhile, they increased the complexity and time of computation and the associated costs of the involved standalone machines. Hence, there is a need to develop a system which is able to address these drawbacks and handle the high levels of uncertainty in video scenes classification and undertake the heavy video processing securely and efficiently on a cloud computing based instance. Hence, in this paper we present a cloud computing based multi classifier systems which aggregates three classifiers based on neural networks and two fuzzy logic classifiers based on type-1 fuzzy logic and type-2 fuzzy logic classification systems which were optimized by a Big-Bang Big crunch optimization to maximize the system performance. We will present several real world experiments which shows the proposed classification system operating in real-time to produce high classification accuracies for soccer videos which outperforms the standalone classification systems based on neural networks, type-1 and type-2 fuzzy logic systems