Asian Journal of Research in Computer Science

With the advancement of information and communication technology (ICT) nowadays education is transforming throughout the world. Cloud computing has added a new dimension to this transformation. Cloud computing is providing education as a service (EaaS) and is delivering education to the remote students. This service is making education available to the students at any place and at any time. However, the matter of disgrace is that the bottom of the pyramid (BoP) people is far behind enjoying the facilities of EaaS. Most of BoP people live in rural villages, or urban slums and shantytowns, and they have little or no formal education. Traditional education system failed to provide the proper education. Hence EaaS can be used to remove their illiteracy. However, they do not have any necessary equipment or money that is necessary for accessing EaaS. A portable box will design that will consist of all equipment necessary for having access to EaaS. This box will carry by a representative from door to door to provide education to the bottom of the pyramid (BoP) people which will help them to turn over their luck with better life without their internet facility and to spend a significant amount of money.

Image fusion (IF) is integrating more than one image into a single image. It accepts multiple images as input and produces a single image as an output. Image w needs an image with high spectral and spatial information. It has wide varieties of application in medical diagnostics and treatment. It is more reliable and compact, easily combined with other methods. Different methods were proposed for remote sensing image and medical image fusion. The aim of the proposed technique is to present an image fusion technique using Intutionistic fuzzy logic (IFL).Mis – registration is the major issue of IF and the research work found solution for the problem. Image features were filtered and integrated with IFL and compute pixels. The proposed method produced better results compared to the existing methods.

Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud.

Diabetes is caused due to an inability of a body to produce or respond to hormone insulin causing abnormal metabolism of carbohydrate which can lead to rising in sugar level in the blood. This work proposed a fuzzy -  neuro hybrid control model to diagnose diabetes in terms of seven symptoms such as an increase in urination, increase in thirst, increase in fatigue, tingling in hands/feet, blurred vision, sores slow to heal and significant loss of weight. 15 patients were diagnosed with sugar levels as followed 9.6 mmol/l, 6.8 mmol/l, 9.1 mmol/l, 11.2 mmol/l, 6.5 mmol/l, 5.7 mmol/l, 11.8mmol/l, 8.9 mmol/l, 7.0 mmol/l, 11.0 mmol/l, 8.5 mmol/l, 9.0mmol/l, 12.4 mmol/l, 9.5 mmol/l and 10.4 mmol/l. The average diagnosis error is obtained as 0.05%, which is acceptable in medical diagnosis. In this regards, it is recommended that fuzzy- neuro hybrid control model is a good soft computing tool for diagnosing diabetes.

In recent times, there is a paradigm shift from the use of paper based systems to the use of software systems in all spheres of life. However, the development of high quality, cost effective and useable software systems is a major challenge. One of the major obstacles confronting the successful implementation of software systems is the inability to implement all stakeholders' requirements in software development projects. This constraint is usually due to limited human resources, budget and time. Thus, most software systems have failed. It, therefore, becomes pertinent to prioritize software requirements. Requirement prioritization involves the selection of requirements that are considered more important from an accumulated list of stakeholders' requirements. There are two techniques that are used for categorizing software requirements. These techniques include the requirement prioritization methods and the negotiation methods. Requirement prioritization methods are based on different scales which include nominal scale, ordinal scale and ratio scale. The accuracy of these methods, however, is a challenge especially when prioritizing large number of requirements.

Aims: Hence, this paper reviews different techniques for prioritizing requirements by highlighting their strengths and weaknesses. Techniques such as binary search tree, AHP, hierarchy AHP, priority group/Numerical Analysis, bubble sort, MoSoW, simple ranking and Planning Game were analyzed and compared in this study.

Methodology: The study is based on previous literature on requirement prioritization.

Results: The study showed that numerical assignment and simple ranking methods require less time in the prioritization process and they also have low scalability and reliability. The study also showed that the analytic hierarchy process requires more time for requirement prioritization; it is reliable but it is not scalable. The study also revealed that it is difficult to prioritize requirements with the existing prioritization techniques when multiple stakeholders are involved.

Conclusion: The study suggests that future researches should be based on the design of requirement prioritization techniques that will have the ability to accommodate large stakeholders and requirements.