Towards Fractal Approach in Healthcare Information Systems: A Review

Recently, traditional information systems need adaption capabilities in order to overcome modifications and maintains of external environment. For that, researchers proposed many solutions from the Fractal method to improve the flexibility and quick adaptive of the system. Computer Information System, as widely used systems, needs modifications and adaptations to real changes. The most important action is to circulate and updating new data and information among the hosts in agent-based information systems. This paper presents the review of using features of fractal method to solve many problems in different fields. The  paper is also suggesting employing fractal features for improving the flexibility and adaption of Healthcare Information System (HIS)

Building the Bridge between Higher Learning Institution and Social Media Technologies through Mobile Learning in Malaysia

The Malaysian government sees education as one of the important factors in the country’s advancement. In order to achieve this, Malaysia needs to have an up-to-date technology such as Social Media Technologies (SMTs) to support the learning system in Higher learning institutions (HLIs). There is a great argument that 21st Century students learn differently. SMTs help to form a student-centered environments among the digital generation. It bridges a gap in knowledge in relation between SMTs and its impact in HLIs. Mobile learning and SMTs deliver the ability to communicate with academicians and students. HLIs should take this opportunity to connect SMTs to design a modern educational setting that will advance their learning experiences. It shows that SMTs have a great value for academic associated, particularly as a teaching and learning tool. HLIs and students have identified numerous challenges related to the adoption process. This study reviews and evaluates the SMTs related research articles published in academic journals between the year 2010 to 2018, mainly in HLIs fields. It identifies the impacts of SMTs on both students and academicianss in HLIs and associate how SMTs can be effectively connected to Mobile learning to support and enhance the students’ learning in Malaysia. © 2019, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved

Cultural Issues in Offshore Teams: A Categorization based on Existing Studies

Cultural and personal issues resulting from dispersed teams are considered to be serious barriers to form trust and organize effective agile teams. However, apart from separate, reported evidence of such issues from work experience, there has been no theoretical classification in literature. This paper provides a list and analysis of common challenges mainly resulting from cultural differences and barriers in Agile Software Development (ASD) offshore teams. The data source comprise Articles published in IEEE, mostly of conferences related to ASD. Among the Articles, papers with concrete evidence of Agile Methods (AM) implementation were selected. The results show that despite the relative significance of such issues, ASD adopters typically still rely on their own experience, and creativity rather than using well-defined methods. Moreover, this study reveals that the notion of trust, as discussed in the literature, mainly refers to maintaining the pace of communication, which is the focal point in ASD. © 2019 KSII

Data warehouse design on the basis of Hierarchical Degenerate Snowflake (HDS)

Two of the most data model in Data Warehouse (DW) and advanced database includes star and snowflake schema, which play pivotal roles in the underlying performance. Today, DW queries comprise a group of aggregations and joining operations. As a result, snowflake schema does not seem to be an adequate option since several relations must combine to provide answers for queries that involve aggregation. In spite of its widespread application and undeniable advantages, snowflaking technique has certain theoretical and practical demerits. This paper proposes Hierarchical Degenerate Snowflake (HDS) as an alternative logical data model to achieve DW structure to improve the query performance. Copyright © 2011 Inderscience Enterprises Ltd

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets