Naif Radi Aljohani

Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination

Abstract Social media has an impact on many aspects of human life ranging from sharing personal information to revolutionizing political systems of entire countries. One not so well studied aspect of social media is analyzing its usage and efficacy in healthcare, particularly in developing countries which lack state-of-the-art healthcare systems and processes. In such countries, social media may be used to facilitate patient-centric healthcare by involving the patient for fulfilling personal healthcare needs. This article provides an in-depth analysis of one such need, that is, how people use social media to request for blood donations. We study the request and dissemination behavior of people using social media to fulfill blood donation requests. We focus on twitter, and blood donation accounts in India. Our study reveals that each of the seven twitter accounts we studied have a large followership of more than 35,000 users on an average and receive a substantial number (more than 900) of donation requests in a day on an average. We analyze the requests in various ways to present an outlook for healthcare providers to make their systems more patient-centric through a better understanding of the needs of people requesting for blood donations. Our study also identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients. These systems can provide support for saving more lives by reducing the gap between blood donors and the people in need.

A comparison between mobile and ubiquitous learning from the perspective of human-computer interaction

In this paper, the notions of mobile learning and ubiquitous learning are compared from the viewpoint of the nature of interaction between learners and computers. This comparison leads to better understanding of their potential and the differences between these notions.

Saudi-KAU Coupled Global Climate Model: Description and Performance

BackgroundA new coupled global climate model (CGCM) has been developed at the Center of Excellence for Climate Change Research (CECCR), King Abdulaziz University (KAU), known as Saudi-KAU CGCM.PurposeThe main aim of the model development is to generate seasonal to subseasonal forecasting and long-term climate simulations.MethodsThe Saudi-KAU CGCM currently includes two atmospheric dynamical cores, two land components, three ocean components, and multiple physical parameterization options. The component modules and parameterization schemes have been adopted from different sources, and some have undergone modifications at CECCR. The model is characterized by its versatility, ease of use, and the physical fidelity of its climate simulations, in both idealized and realistic configurations. A description of the model, its component packages, and parameterizations is provided.ResultsResults from selected configurations demonstrate the model’s ability to reasonably simulate the climate on different time scales. The coupled model simulates El Niño-Southern Oscillation (ENSO) variability, which is fundamental for seasonal forecasting. It also simulates Madden-Julian Oscillation (MJO)-like disturbances with features similar to observations, although slightly weaker.ConclusionsThe Saudi-KAU CGCM ability to simulate the ENSO and the MJO suggests that it is capable of making useful predictions on subseasonal to seasonal timescales.

CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks

Abstract A community structure is an integral part of a social network. Detecting such communities plays an important role in a wide range of applications, including but not limited to cluster analysis, recommendation systems and understanding the behaviour of complex systems. Researchers have derived many algorithms to discover the community structures of networks. Discovering communities is a challenging task, and there is no single algorithm that produces the best results for all networks. Therefore, despite many elegant solutions, discovering communities remains an active area of research. In this paper, we propose a novel algorithm, the Clustering Coefficient-based Genetic Algorithm (CC-GA), for detecting them in social and complex networks. Researchers have used several genetic algorithms to detect communities, but the proposed algorithm is novel in terms of both the generation of the initial population and the mutation method, and these improve its efficiency and accuracy. Experiments on a variety of real-world datasets and a comparison to state-of-the-art genetic and non-genetic-based algorithms show improved results.

2 nd cross-LAK: learning analytics across physical and digital spaces

It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.

CommuniMents: A Framework for Detecting Community Based Sentiments for Events

Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms

AbstractIn supervised learning, some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. Hyperrectangles can be viewed as storing objects in Rn which can be used to learn concepts combining instance-based classification with the axis-parallel rectangle mainly used in rule induction systems. This hybrid paradigm is known as nested generalized exemplar learning. In this paper, we propose the selection of the most effective hyperrectangles by means of evolutionary algorithms to tackle monotonic classification. The model proposed is compared through an exhaustive experimental analysis involving a large number of data sets coming from real classification and regression problems. The results reported show that our evolutionary proposal outperforms other instance-based and rule learning models, such as OLM, OSDL, k-NN and MID; in accuracy and mean absolute error...

Content-Aware Power Saving Multimedia Adaptation for Mobile Learning

Due to the tremendous enhancements in the capabilities of mobile devices in recent years and accessibility to higher bandwidth mobile internet, the use of online multimedia learning resources on mobile devices is increasingly becoming popular. Improvements in battery capacity have not matched the same advancements compared to other features of mobile devices. Limited Battery power is introducing a significant challenge in making better use of online educational multimedia resources. Online Multimedia Resources drains more battery power as a result of higher amount of wireless data transfer and therefore limiting learning opportunities on the move. Many power saving multimedia adaptation techniques have been suggested. Majority of these techniques achieve battery efficiency while reducing multimedia quality. So far, however, to the best of our knowledge no previous effort has considered the factor of learning efficacy in multimedia adaptation process. Existing adaptation techniques are susceptible to information loss as a result of quality of reduction. Such loss affects the learning content efficacy and jeopardizes the learning process. In this paper, we recommend a novel power save educational multimedia adaptation approach that considers the learning aspect of multimedia in the adaptation process. Our technique enables learning for extended duration by battery power saving without putting the learning process at risk. Efficacy of entire learning resources is managed by not allowing any part of the learning multimedia to be delivered in a quality that will negatively affect the learning outcome. We also present a framework that guides the implementation of our approach followed by description of our prototype application that uses educational multimedia metadata implemented in semantic web technologies.

Energy-Aware Adaptation of Educational Multimedia in Mobile Learning

As a result of tremendous enhancements in the capabilities of mobile devices and availability of higher data rate mobile internet, the use of online multimedia learning resources on mobile devices is increasingly becoming popular. Limited Battery Power of mobile devices, however, is still one big challenge in Mobile Learning. High Quality multimedia learning resources are power hungry and if used on mobile devices drain battery power rapidly limiting learning opportunities on the move. Lack of significant improvements in battery capacities has resulted in significant interest in battery power saving techniques. Existing power-saving streaming multimedia adaptation techniques tend to extend battery life by reducing quality of multimedia making them susceptible to information loss. This loss may affect the learning content efficacy and jeopardizes the learning process. To the best of our knowledge, no previous work has considered the learning content efficacy in multimedia streaming adaptation mechanism. In this paper, we present MoBELearn system, which is a prototype implementation of our proposed Content Aware Power Saving Educational Multimedia Adaptation (CAPS-EMA) approach. We demonstrate battery efficiency in educational multimedia streaming while keeping the adapted resource suitable for learning. We also describe our semantic metamodel for educational multimedia resource that support our energy efficient adaptation technique.

Prototype selection to improve monotonic nearest neighbor

Abstract Student surveys occupy a central place in the evaluation of courses at teaching institutions. At the end of each course, students are requested to evaluate various aspects such as activities, methodology, coordination or resources used. In addition, a final qualification is given to summarize the quality of the course. The prediction of this final qualification can be accomplished by using monotonic classification techniques. The outcome offered by these surveys is particularly significant for faculty and teaching staff associated with the course. The monotonic nearest neighbor classifier is one of the most relevant algorithms in monotonic classification. However, it does suffer from two drawbacks, (a) inefficient execution time in classification and (b) sensitivity to no monotonic examples. Prototype selection is a data reduction process for classification based on nearest neighbor that can be used to alleviate these problems. This paper proposes a prototype selection algorithm called Monotonic Iterative Prototype Selection (MONIPS) algorithm. Our objective is two-fold. The first one is to introduce MONIPS as a method for obtaining monotonic solutions. MONIPS has proved to be competitive with classical prototype selection solutions adapted to monotonic domain. Besides, to further demonstrate the good performance of MONIPS in the context of a student survey about taught courses.