Knowledge Management Toolkit

Cloud computing (CC) is a recently developed computing paradigm that can be utilized to deliver everything-as-a-service to various businesses. In higher education institutions (HEIs), CC is rapidly being deployed and becoming an integral part of institution experience. CC adoption in HEIs is accompanied by numerous scientific contributions that address the topic from different perspectives. A systematic review of these heterogeneous contributions, which provide a coherent taxonomy, can be considered interesting for HEIs to identify opportunities to use CC in its own context. Therefore, this systematic literature review aims to analyze existing research on adopting and using CC in HEIs, review background research to develop a coherent taxonomy and provide a landscape for future research on CC in HEIs. The outcomes of this paper include a coherent taxonomy and an overview of the basic characteristics of this emerging field in terms of motivation and barriers of adopting CC in HEIs, existing individual and organizational theoretical models to understand the future requirements for extensively adopting and using CC in HEIs, and factors that influence the adoption of CC in HEIs at individual and organizational levels. Considerable information is available in relation to adopting and using CC in HEIs. This review will enhance this information by offering an in-depth analysis of the existing data to bridge any gap and expand on existing literature.

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Cloud Computing Adoption in Higher Education Institutions: A Systematic Review

Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and ...

Data Analytics and Modeling for Appointment No-show in Community Health Centers

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Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

For the past few years, huge interest and dramatic development have been shown for the Internet of Things (IoT) based constrained Wireless sensor network (WSN) to achieve efficient resource utilization and better service delivery. IoT requires a better communication network for data transmission between heterogeneous devices and an optimally deployed energy-efficient WSN. The clustering technique applied for WSN node deployment needs to be efficient; therefore, the entire architecture can obtain a better network lifetime. While clustering, the entire network is partitioned into various clusters. Moreover, the cluster head (CH) selection process also needs proper attention for achieving efficient data communication towards the sink node via selected CH and also for increasing the node reachability within the cluster. In this proposed framework, an energy efficient deep belief network (DBN) based routing protocol is developed, which achieves better data transmission through the selected path. Due to this the packet delivery ratio (PDR) gets improved. In this framework, initially, the nodes in the whole network is grouped as clusters using a reinforcement learning (RL) algorithm, which assigns a reward for the nodes that belong to the particular cluster. Then, the CH required for efficient data communication is selected using a Mantaray Foraging Optimization (MRFO) algorithm. The data is transmitted to the sink node via the selected CH using an efficient deep learning approach. At last, the performance of proposed deep network based routing protocol is evaluated using different evaluation metrics they are network lifetime, energy consumption, number of alive nodes, and packet delivery rate. Finally, the evaluated results are compared with few existing algorithms. Among all these algorithms, the proposed DBN routing protocol has achieved better network lifetime. 

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Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Energy efficiency is considered the major design issue in wireless sensor networks (WSN), which can be addressed using clustering and routing techniques. They are treated as Non-deterministic Polynomial (NP)-hard optimization problems and are solved using metaheuristic algorithms to identify the optimal or near-optimal solutions. With this motivation, this paper develops a hybridization of the metaheuristic cluster-based routing (HMBCR) technique for WSN. The HMBCR technique initially involves a brainstorm optimization with levy distribution (BSO-LD) based clustering process using a fitness function incorporating four parameters such as energy, distance to neighbors, distance to the base station, and network load. Besides, a water wave optimization with a hill-climbing (WWO-HC) based routing process is carried out for optimal route selection. Extensive experimentation analysis is performed to ensure the energy efficiency and network lifetime performance of the HMBCR technique. The experimental outcome ensured the superior results of the HMBCR technique over the compared methods under different aspects.

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Hybridization of Metaheuristic Algorithm for Dynamic Cluster-Based Routing Protocol in Wireless Sensor Networksx

Prediction of financial and economic markets is very challenging but valuable for economists, business owners, and traders. Forecasting stock market prices depends on many factors, such as other markets’ performance, economic state of a country, and others. In behavioral finance, people’s emotions and opinions influence their transactional decisions and therefore the financial markets. The focus of this research is to predict the Saudi Stock Market Index by utilizing its previous values and the impact of people’s sentiments on their financial decisions. Human emotions and opinions are directly influenced by media and news, which we incorporated by utilizing the Global Data on Events, Location, and Tone (GDELT) dataset by Google. GDELT is a collection of news from all over the world from different types of media such as TV, broadcasts, radio, newspapers, and websites. We extracted two time series from GDELT, filtered for Saudi Arabian news. The two time series represent daily values of tone and social media attention. We studied the characteristics of the generated multivariate time series, then deployed and compared multiple multivariate models to predict the daily index of the Saudi stock market.

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Predicting Saudi Stock Market Index by Incorporating GDELT Using Multivariate Time Series Modelling

The widespread use of computer technologies in education has reshaped the roles of instructors, who are encouraged to innovate interactive forms of technology‐supported instruction that promote participation and collaboration. This in turn engenders new experiences of teaching that need to be gathered and capitalized as teaching assets to be shared among communities of instructors. Among these experiences, best teaching practices (BTPs) are instructional practices accumulated in teaching that have been proven to work well, give good results, and can therefore be recommended as a model. Identifying and sharing best practices means duplicating successes which help instructors learn from each other and deliver better quality teaching. This paper presents a knowledge management framework for acquiring, coding, sharing, and reusing BTPs. To encourage instructors’ participation, the framework is based on peer scoring of BTPs, which stimulates contribution and interaction. The framework has been implemented as a knowledge portal that allows instructors to create, store, search, and share BTPs and to receive feedback and comments from other users, providing many useful functionalities and services to users as individuals and communities. The paper presents also a real‐life case study, lessons learned from using the system within a community of instructors, and a system evaluation of the effectiveness of reusing BTPs using the reuse effort and impact metrics. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:163–178, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21776

Effective reuse and sharing of best teaching practices

The latest COVID-19 pandemic is a specific and unusual event. It forced universities to close their doors and move fully to distance education. The sudden shift from traditional education to full distance education created many challenges and difficulties for universities, faculty members, and students. This study aims to investigate the challenges and obstacles faced by undergraduate women in Saudi Arabia universities while using online-only learning during the COVID-19 pandemic outbreak. Moreover, this study provides some recommendations to address these challenges from undergraduate women’s perspectives. The study used a qualitative research methodology to investigate the challenges and difficulties. The participants were undergraduate women selected using random purposive sampling technique from the population of College of Computer and Information Sciences (CCIS) at Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia. The final sample consisted of 68 undergraduate women who responded to a predesigned open-ended questionnaire that was sent via e-mail to targeted respondents. The data gathered from the questionnaire were analyzed using qualitative content analysis. Results of the research revealed that the most obvious challenges identified by the participants were technical issues, lack of in-person interaction, distractions and time management, lack of a systematic schedule, stress and psychological pressure, missing the traditional university environment, limited availability of digital devices, and lack of access to external learning resources.

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Amal Al-Rasheed

Papers

Hybridization of Metaheuristic Algorithm for Dynamic Cluster-Based Routing Protocol in Wireless Sensor Networksx

Predicting Saudi Stock Market Index by Incorporating GDELT Using Multivariate Time Series Modelling

Effective reuse and sharing of best teaching practices

The Challenges Faced by Undergraduate Women during the COVID-19 Pandemic in Saudi Arabia

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data