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Research on the topic of social media for teaching and learning in the higher education have received much attention from academia and practitioners. Social media technology was acknowledged not only as an important communication tool between faculty members and students but also shows great potential as the next social learning platform that better serves the teaching, learning and administration of a higher education institute. Its informal setting allows more flexibility for both students and faculty members to teach and learn anywhere and everywhere. Despite its important, little is known about how this research topic is evolving within the literature. Hence, using a bibliometric analysis technique this study examines the trends, topics, and challenges addressed by previous research for the past ten years (2008-2018). The paper ends by discussing future research directions related to this topic.

We consider the problem of scheduling the transmission of packets in an input-queued switch. In order to achieve maximum throughput, scheduling algorithms usually employ the queue length as a parameter for determining the priority to serve a given queue. In this work we propose a novel scheme to optimize the performance of a preexisting scheduler. Our main idea is to assist the scheduling decision, considering "messages" rather than queue lengths. Such messages are obtained by running an iterative parallel algorithm, inspired by a rigorous Belief-Propagation approach. We demonstrate that Belief-Propagation-assisted scheduling is able to boost the performance of a given scheduler, reaching almost optimal throughput, even under critical traffic scenarios.

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Nowadays, the evolution in transportation technologies makes the necessity for increasing road safety. In this context, we propose the implementation of a smart onboard GPS/GPRS system to be attached to vehicles for monitoring and controlling their speed. In case of traffic speed violation, a GPRS message containing information about the vehicle such as location and maximum speed is sent to a hosting server located in an authorized office so that the violated vehicle is ticketed. Moreover, this system can also track the vehicle's current location on a Google Map, which is mostly beneficial when vehicles should follow a specific road and in case of robbery. Also geo-casting can have a major role in this model. Some sensors, such as shock/vibration sensor usually attached to the air-bags in vehicles, are attached to the system that in case of accident, it will send notifications to the nearest hospital, police station and civil defense. Our proposed model can be utilized for different implementations, both in public and private sectors. While similar existing systems in Palestine have focalized just on the tracking aspect of vehicles' monitoring, it would be the first system supporting both ticketing and tracking.

Recently, remote monitoring systems have evolved to respond for particular needs in healthcare sector, which is an essential pillar in the modern concept of smart city, we propose a smart system to monitor patient current health conditions, as a smart healthcare system based on the widely spread available technologies; namely, GSM and GPS. Statistics shows that hypertensive heart disease and blood pressure are risk factors for high death rate to decrease it a preventive measures should be applied providing a real-time health monitoring system, to save patients life at acceptable time. The objectives of this paper is to provide an effective system model, that will track, trace, and monitor patient vital readings in order to provide efficient medical services in time. By using sensors, the data will be captured and compared with a predefined threshold. The study focuses on heartbeat rate, and body temperature, thus in case of emergency an SMS will be sent to the Doctors mobile containing measured values and position. Moreover, the paper demonstrates the possibility of building a complete end-to-end smart healthcare monitoring system by using wide range of available sensors for more vital human health parameters to connect patient with doctors in cases of emergency.

Information technology (IT) has transformed many industries, from education to health care to government, and is now in the early stages of transforming transportation systems. Transportation faces many issues like high accidents rate in general, and much more rate in developing countries due to the lack of proper infrastructure for roads, is one of the reasons for these crashes. In this project we focus on public transportation vehicles-such as buses, and mini-buses-, where the goal of the project is to design and deploy a smart/intelligent unit attached to public vehicles by using embedded microcontroller and sensors and empowering them to communicate with each other through wireless technologies. The proposed Offline Intelligent Public Transportation Management System will play a major role in reducing risks and high accidents rate, whereas it can increase the traveler satisfactions and convenience. Here, we propose a method, software as well as a framework as enabling technologies to for evaluation, planning and future improvement the public transportation system. Our system even though can be as whole or parts can be applied all over the world we mostly target developing countries. This limitation mostly appear by consider off-shelf technologies such as WiFi, GPS and Open Street Maps (OSM).

With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.