Hmood Al-Dossari

A Cloud-Assisted Internet of Things Framework for Pervasive Healthcare in Smart City Environment

Recently, cloud computing and Internet of Things (IoT) have made their entrance in the pervasive healthcare field in smart city environment. However, the integration of IoTs and cloud computing in healthcare domain impose several technical challenges that have not yet received enough attention from the research community. Some of these challenges are reliable transmission of vital sign data to cloud, dynamic resource allocation to facilitate seamless access and processing of IoT data, and effective data mining techniques. In this paper, we propose a framework to address above challenging issues. In addition, we discuss the possible solutions to tackle some of these challenges in smart city environment.

Arabic Sentiment Analysis: A Survey

Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.

QoS Assessment over Multiple Attributes

In an open service oriented computing environment, multiple providers may offer functionally identical services but with varying qualities. It is desirable therefore that we are able to assess the quality of a service (QoS), so that service consumers can be given additional guidance in selecting their preferred services. Various methods have been proposed to assess QoS using the data collected from monitoring tools, but they do not deal with multiple QoS attributes adequately. Typically these methods assume that the quality of a service may be assessed by first assessing the quality level delivered by each QoS attribute individually, and then aggregating them in some way to give an overall verdict for the service. In this paper, we show that this may lead to incorrect assessment, and suggest how existing methods may be improved to deal with multiple attributes more effectively.

A Lexicon-based Approach to Build Service Provider Reputation from Arabic Tweets in Twitter

Nowadays Social media has become a popular com-munication tool among Internet users. Many users share opinions and experiences on different service providers everyday through the social media platforms. Thus, these platforms become valuable sources of data which can be exploited and used efficiently to support decision-making. However, finding and monitoring customers’ opinions on the social media is difficult task due to the fast growth of the content. This work focus on using Twitter for the task of building service providers’ reputation. Particularly, service provider’s reputation is calculated from the collected Saudi tweets in Twitter. To do so, a Saudi dialect lexicon has been developed as a basic component for sentiment polarity to classify words extracted from Twitter into either a positive or negative word. Then, beta probability density functions have been used to combine feedback from the lexicon to derive reputation scores. Experimental evaluations show that the proposed approach were consistent with the results of Qaym, a website that calculates restaurants’ rankings based on consumer ratings and comments.

Resource Provisioning for Cloud-Assisted Body Area Network in a Smart Home Environment

In recent years, cloud-assisted body area network (CABAN) technologies have made their entrance in the Smart healthcare field, such as Smart home environment, and play a significant role for healthcare data storage, processing, and efficient decision making. However, currently, the CABAN paradigm in the healthcare domain is facing increasing difficulty in handling the huge amount of sensor data that the body sensor devices generate from diverse Smart home applications. Therefore, the challenging is now timely storing, processing, and analyzing of the sensor data in real time to maintain the Quality of Service (QoS) requirements of the caregivers or Smart home applications. QoS, here, is the capacity to support diverse Smart home applications in healthcare with different priorities, performance, and resource requirements. Therefore, in this paper, we present a fast and robust cloud resource allocation model for body sensor devices to ensure QoS for Smart home healthcare applications. We develop the proposed resource allocation algorithm using agent-based modeling (ABM) and ontology. There are few works, which consider ABM and ontology for resource allocation in CABAN platform. Moreover, we used an ABM tool called NetLogo to implement the proposed resource allocation model. The results from the implementation were compared with the results of existing algorithms and found to be promising.

A parallel framework for software defect detection and metric selection on cloud computing

With the continued growth of Internet of Things (IoT) and its convergence with the cloud, numerous interoperable software are being developed for cloud. Therefore, there is a growing demand to maintain a better quality of software in the cloud for improved service. This is more crucial as the cloud environment is growing fast towards a hybrid model; a combination of public and private cloud model. Considering the high volume of the available software as a service (SaaS) in the cloud, identification of non-standard software and measuring their quality in the SaaS is an urgent issue. Manual testing and determination of the quality of the software is very expensive and impossible to accomplish it to some extent. An automated software defect detection model that is capable to measure the relative quality of software and identify their faulty components can significantly reduce both the software development effort and can improve the cloud service. In this paper, we propose a software defect detection model that can be used to identify faulty components in big software metric data. The novelty of our proposed approach is that it can identify significant metrics using a combination of different filters and wrapper techniques. One of the important contributions of the proposed approach is that we designed and evaluated a parallel framework of a hybrid software defect predictor in order to deal with big software metric data in a computationally efficient way for cloud environment. Two different hybrids have been developed using Fisher and Maximum Relevance (MR) filters with a Artificial Neural Network (ANN) based wrapper in the parallel framework. The evaluations are performed with real defect-prone software datasets for all parallel versions. Experimental results show that the proposed parallel hybrid framework achieves a significant computational speedup on a computer cluster with a higher defect prediction accuracy and smaller number of software metrics compared to the independent filter or wrapper approaches.

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.

Handling asynchronous data in assessing QoS over multiple attributes

The ability to assess the quality of a service (QoS) is important to the emerging SOC paradigm. When multiple providers offer functionally identical services in a SOC environment, it is only natural that consumers should ask how their qualities would compare. While various methods have been proposed to help assess QoS using monitored quality data, they do not handle multiple QoS attributes adequately, especially when the qualities of these attributes are monitored asynchronously. In this paper, we proposed a method that takes both accuracy and confidence into account when assessing QoS over multiple attributes, and employs a kNN based technique to deal with asynchronous data. Our experiments show that the new method can give a more accurate QoS assessment over multiple attributes than existing methods do.

A Framework for Software Defect Prediction and Metric Selection

Automated software defect prediction is an important and fundamental activity in the domain of software development. However, modern software systems are inherently large and complex with numerous correlated metrics that capture different aspects of the software components. This large number of correlated metrics makes building a software defect prediction model very complex. Thus, identifying and selecting a subset of metrics that enhance the software defect prediction method’s performance are an important but challenging problem that has received little attention in the literature. The main objective of this paper is to identify significant software metrics, to build and evaluate an automated software defect prediction model. We propose two novel hybrid software defect prediction models to identify the significant attributes (metrics) using a combination of wrapper and filter techniques. The novelty of our approach is that it embeds the metric selection and training processes of software defect prediction as a single process while reducing the measurement overhead significantly. Different wrapper approaches were combined, including SVM and ANN, with a maximum relevance filter approach to find the significant metrics. A filter score was injected into the wrapper selection process in the proposed approaches to direct the search process efficiently to identify significant metrics. Experimental results with real defect-prone software data sets show that the proposed hybrid approaches achieve significantly compact metrics (i.e., selecting the most significant metrics) with high prediction accuracy compared with conventional wrapper or filter approaches. The performance of the proposed framework has also been verified using a statistical multivariate quality control process using multivariate exponentially weighted moving average. The proposed framework demonstrates that the hybrid heuristic can guide the metric selection process in a computationally efficient way by integrating the intrinsic characteristics from the filters into the wrapper and using the advantages of both the filter and wrapper approaches.

Research area classification using wikipedia and information retrieval

The research areas1 of researchers can be identified using their publication that reflects their current interest. However, the full text of these publications is not always available. Other data regarding these publications, such as titles, are available in many bibliographic databases, but these data are usually short, sparse, and multidimensional. In this paper, we propose an approach for identifying the research areas of faculty members using some of their publication, information retrieval and Wikipedia content. Wikipedia is one of the largest encyclopedias to date. It is collaboratively written and kept up to date. A prototype system was implemented and evaluated using real data. Accuracy of 89.3% was achieved. The results are encouraging and show that keywords achieve the highest accuracy in identifying research areas.