Citations based relevant research paper recommendations can be generated primarily with the assistance of three citation models: (1) Bibliographic Coupling, (2) Co-Citation, and (3) Direct Citations. Millions of new scholarly articles are published every year. This flux of scientific information has made it a challenging task to devise techniques that could help researchers to find the most relevant research papers for the paper at hand. In this study, we have deployed an in-text citation analysis that extends the Direct Citation Model to discover the nature of the relationship degree-ofrelevancy among scientific papers. For this purpose, the relationship between citing and cited articles is categorized into three categories: weak, medium, and strong. As an experiment, around 5,000 research papers were crawled from the CiteSeerX. These research papers were parsed for the identification of in-text citation frequencies. Subsequently, 0.1 million references of those articles were extracted, and their in-text citation frequencies were computed. A comprehensive benchmark dataset was established based on the user study. Afterwards, the results were validated with the help of Least Square Approximation by Quadratic Polynomial method. It was found that degreeof-relevancy between scientific papers is a quadratic increasing/decreasing polynomial with respect to-increase/decrease in the in-text citation frequencies of a cited article. Furthermore, the results of the proposed model were compared with state-of-the-art techniques by utilizing a well-known measure, known as the normalized Discount Cumulative Gain (nDCG). The proposed method received an nDCG score of 0.89, whereas the state-of-the-art models such as the Content, Bibliographic-coupling, and Metadata-based Models were able to acquire the nDCG values of 0.65, 0.54, and 0.51 respectively. These results indicate that the proposed mechanism may be applied in future information retrieval systems for better results.
The exponential growth in the volume of data and information lead to problems in management, controlling effective and high costs of storage operation, where organizations are having problems: data retrieval and preparation and backups, and other acts of data. Therefore seeking companies and business organizations at the present time to achieve the highest return on their investments in technology through the planning and implementation of virtualization technologies and cloud computing, in order to protect data and manage more effectively and efficiently. We find that the government funding for higher education is decreasing continuously in third world countries, and the education management stand for a set of challenges. Cloud computing can help to provide solutions for these challenges, they bring multiple solutions cannot be applied to regular IT models. This paper aims to discuss and analyzing: concepts of cloud computing, cloud computing models, cloud computing services, cloud computing Architecture and the main objective of this paper is to how to use and applied cloud computing Architecture in higher education, in third world countries, the republic of Sudan as a model.
The Capability Maturity Model Integration (CMMI) is a renowned Software Process Improvement (SPI) framework. Research studies have revealed that CMMI adoption needs a lot of resources in terms of training, funds, and professional workers. However, the software SMEs (SSMEs) have few resources and cannot adopt CMMI. One of the challenges of adopting CMMI is that CMMI tells "What to do?" as requirements to be met, and leaves "How to do?" to the implementers. The software industry especially SSMEs faces difficulties in successfully implementing various process areas (PAs) particularly Configuration Management Process Area (CM-PA). SG-2 (Track and control changes) is one of the important Specific Goals (SGs) required by CMMI to successfully implement CM-PA. As a starting point, we have achieved this SG by implementing its two contributing Specific Practices (SPs). The proposed WFMs were validated through an Expert Panel Review (EPR) process. In addition, a case study approach was used for the evaluation. The results showed that the models are useful, easy to use, supportive in the achievement of SG-2, and applicable to SSMEs. It is worth mentioning that this research work has not only contributed to the implementation studies but also added to the empirical software engineering body of knowledge.
One of the most common causes of death is Ischaemic heart disease (IHD). Clinical decisions are often made based on doctors' intuition and experience rather than on the knowledge-rich data hidden in the database, which leads to unwanted errors and excessive medical costs that affects the quality of service provided to patients. On the other hand, there is lack of cardiologist and IHD specialist in developing countries. Therefore, the development of an expert system that improves the diagnostic and therapeutic decision model of IHD creates a universal need. The expert system is developed based on the cardiologist expertises in diagnosing IHD symtomps and the given prescriptions. This work attempts to increase the accuracy and the effectiveness of the expert system to treat IHD patient by leveraging deep neural networks and adopting deep learning strategy for Retristic Boltzman Machine (RBM). The deep neural network in this work has 152 neurons in the input layer, 52 neurons in the output layer, and 4 hidden layer. Experimental results show that the proposed system achieves up to 0.00974 error level in the training sessions and average improvement of 0.7322% in term of accuracy compared to expert system with standard machine learning in the testing phase. Some results that have discrepancies are consulted to the cardiologist to confirm the results.
Chronic diseases for children such as DIABETES is a challenge for parents in all third world countries. Especially that percentage of giving birth to children with chronic diseases is increasing dramatically in North African countries. Thus the official task of parents to help their children in how to live and deal the condition with this chronic disease. In this paper we propose a child DIABETES monitoring system which is integrated sensors and smart phone to facilitate the management of chronic disease -DIABETES. The system automatically collects physical signs, such as Blood Glucose level. It allows users, especially children with diabetes to conveniently record daily test results and track long term health condition changes regardless of their locations. All the procedures are performed automatically without entering the user (Patient) any data on the system manually. Of the most important objectives of the application is help the parents to monitoring the level of sugar in the blood for their child at any time and alert them automatically to any risk.
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