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The biomedical technology application of ultraviolet light device was reviewed in the current research in a manner to improve public healthcare safety. The research adopted ultraviolet light irradiation to enable elimination of hospital acquired infections caused by bacteria, viruses, and other pathogens within the healthcare facilities. The paper reviewed 12 related biomedical literature that discussed the topic of hospital disinfection using ultraviolet device technology. The paper observed that installation of autonomous internet of things 5G medical disinfecting device for continuous sterilisation of high-touch areas is important in the ongoing COVID-19 pandemic. The research concluded that installation of autonomous internet of things 5G ultraviolet device within the hospital facilities will provide a means for infectious surveillance that will effectively control the menace of hospital acquired infections through ultraviolet light irradiation as the susceptibility of hospital acquired infections are exceedingly high in the overcrowded healthcare centres.

In the customer-oriented enterprise environment, data mining business intelligence (BI) performs key functions that enable business organizations to determine investment risks and estimate returns on investment. It aids in evaluating the gathered information and presenting them in an understandable and practical design that promote data mining analytics. In the current study, banking and retail services were viewed among the rapidly developing technological industry that maintains significant amounts of electronic data. On the account that fraudsters constantly come up with new schemes and refine their existing schemes so they may avoid being easily identified, it is challenging to identify fraud and prevent it from happening. In order to monitor activities relating to fraud prevention, fraud detection, customer retention, business risk management, investment performance, and improving customer satisfaction, the chapter analyzed an artificial neural network machine learning algorithm in data mining techniques and its applications in the retail sector towards service reliability.

Many software projects utilize Bug Tracking System (BTS) to manage and process bug reports. Over the years, the number of bug report submissions has increased exponentially with some projects receiving as many as about a hundred submissions daily. Bug report processing (BRP) consists of five key processes: duplicate detection, severity prediction, fix-time prediction, bug triage, and bug localization. Previously, traditional machine learning (ML) algorithms -based models were proposed to automate BRP tasks. However, in recent years deep BRP (DBRP) models were proposed to exploit the ever-increasing bug repositories for automatic extraction of semantic and contextual features of bug reports. Although, some papers reviewed related literature from many perspectives of software maintenance, no existing work comprehensively reviewed the state of DBRP. In this paper, we review the state of DBRP models in the five key BRP tasks. For this, we collect papers from four international databases published between 2015 and 2021. We evaluate the papers with regard to the deep neural networks, text representation models, and bug report features utilized. Finally, we present findings and prospects for future research works in DBRP. Our review analyzes how word embedding, CNNs, LSTMs, attention mechanism are used to represent bug report and source code, model performance and challenges such as data imbalance, feature utilization and complexity.

With the advent of digital games and its rapid evolution, it is almost impossible for a lot of people especially the young children to go a day without coming into contact with them. One of the impacts of these digital games is that it is changing the way these young children think and learn. It is therefore important to carefully examine the influence of digital games on children’s education. The purpose of this research is to identify and examine the factors that motivate children to play digital games and to determine the effect of such games to the children’s learning abilities. Responses from 172 students of ages between 11 and 16 are analysed in this research. A questionnaire is used to capture the children’s motivation towards digital gaming. Also, an intellectual test was carried out to determine the effect of digital games on the children’s learning abilities. The findings have revealed that competitive spirit is the major factor that influences children to play digital games because of the challenge and the competition that comes along with it. Furthermore, a critical view from the results of the intellectual test has shown that the children that play digital games score higher results and were able to finish within a short period of time as against the children that do not play. The outcome of this research could be used to explore the possibilities of using digital games as tools for learning, especially to the young ages.

The parents of children with dyslexia often experience more parenting stress and depressive symptoms compared to other parents. Studies have shown that supporting and encouraging such parents help in reducing their frustrations, fear, anger, and low self-esteem towards their children. The purpose of this study was to identify and examine the different types of support needed by the parents of children with dyslexia and to explore the relationships between the required support with the parents’ marital status. Fifty questionnaires were distributed to the parents of children with dyslexia and analyzed. The findings showed that the type of support needed for parents of children with dyslexia could be grouped into social, peer-to-peer, financial, and government support. Furthermore, the analysis indicated that there were no significant differences between the social (p = 0.4014), peer-to-peer (p = 0.5020), and government (p = 0.7121) support with parents’ marital status. However, based on one-way ANOVA analysis, there was a significant difference found between the parents’ marital status and financial support (p = 0.0241). Accordingly, it is anticipated that the implication of this research could be used as a guide and a reference for supporting and encouraging parents of children with dyslexia and other learning disabilities.

Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods.

COVID‐19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health‐protective behaviors such as social distancing. For this purpose, a questionnaire‐based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases., Highlights Reducing mortality caused by COVID‐19 can be achieved by awareness.Situation awareness can be increased by formal information sources.Increased situational awareness lead to adoption of health protective behavior.

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

Accurate prediction of crude oil price can successfully be used to deviate from the negative impact of the crude oil price hike. Limitations of previous studies prompt the proposal of an alternative approach for the hybridization of Cuckoo Search Algorithm with lévy flight and Back-propagation neural network (CSBP) for the estimation of crude oil price to enhance convergence speed accuracy. To evaluate the effectiveness of the proposed CSBP, we used ABC to train BP neural network (ABCNN) and Levenberg-Marquardt neural network (ABCLM) to develop an approach for crude oil price prediction. The outcomes of simulated comparative indicate that the proposed CSBP outperforms the ABCLM and ABCNN in both accuracy and convergence speed. Analysis of variance including post hoc multiple comparative test shows that the performance of the proposed CSBP is statistically significant than the comparison algorithms. The negative side of the crude oil prices in the global market can be defeated with proper planning based on the crude oil prices predicted by the CSBP. The CSBP proposed in this paper could provide a new strategy for risk managers and analysts to create an efficient risk management framework to formulate policies related to risk issues in the global crude oil market.

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user's stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquiry.

Conflict analysis plays an important role in the fields of politics, military operations, economics, business management, games, urban planning, management negotiations and etcetera. Computational intelligence model such as rough set theory has been used in managing conflict situations which have the ability to handle uncertainties. However, there is a great concern in the computational time of the rough set approach in determining strength, certainty and coverage of conflicts. Motivated by the fact that every rough set approach can be represented using soft set theory, we derived an alternative method based on the concept of co-occurrence from multi-soft sets to handle conflict situations. We first used an illustrative example of a movie selection problem to demonstrate the proposed approach and provide an extensive elaboration using a publicly available dataset. Our motivation is to provide a new measure based on support, strength, certainty and coverage of soft set on movie selection problem. Our findings have revealed that the proposed approach achieved less computational time when compared with the rough set-based approach of up to 8.05%. One potential application of the proposed approach is the domain of recommendation systems. The proposed approach can be used to easily identify users/items nearest neighbours based on support, strength, certainty and coverage, which is crucial for the success of recommendation systems.

Massive open online courses (MOOCs) have revolutionized todays education by offering a global accessible form of online learning. Over the years, MOOCs have been an attractive research area and have yielded an ample amount of research publications. However, the existing review studies in MOOCs are characterized by short year coverage or focusing on a specific theme. As such, a systematic mapping methodology was adopted to provide a fine-grain overview of MOOC research domain by identifying the quantity, types of research, available results and publication trends in educational aspects of MOOCs from 2009 to 2018. Key findings show that I) MOOC research have been on the rise since MOOCs became mainstream in 2011. II) MOOC research largely resides in the United States and few European countries. II) Most of MOOC studies focused on addressing learners' completion/dropout/retention. In addition, we proposed some recommendations for future research on MOOCs.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

This work investigates the use of distance learning in saving students' academic year amid COVID-19 lockdown. It assesses the adoption of distance learning using various online application tools that have gained widespread attention during the coronavirus infectious disease 2019 (COVID-19) pandemic. Distance learning thrives as a legitimate alternative to classroom instructions, as major cities around the globe are locked down amid the COVID-19 pandemic. To save the academic year, educational institutions have reacted to the situation impulsively and adopted distance learning platforms using online resources. This study surveyed random undergraduate students to identify the impact of trust in formal and informal information sources, awareness and the readiness to adopt distance learning. In this study, we have hypothesized that adopting distance learning is an outcome of situational awareness and readiness, which is achieved by the trust in the information sources related to distance learning. The findings indicate that trust in information sources such as institute and media information or interpersonal communication related to distance learning programs is correlated with awareness (β = 0.423, t = 12.296, p = 0.000) and contribute to readiness (β = 0.593, t = 28.762, p = 0.001). The structural model path coefficient indicates that readiness strongly influences the adoption of distance learning (β = 0.660, t = 12.798, p = 0.000) amid the COVID-19 pandemic. Our proposed model recorded a predictive relevance (Q2) of 0.377 for awareness, 0.559 for readiness, and 0.309 for the adoption of distance learning, which explains how well the model and its parameter estimates reconstruct the values. This study concludes with implications for further research in this area.

In the past few years, the Subjectivity and Sentiment Analysis (SSA) has garnered a lot of attention, and many unsuccessful attempts were made for developing an SSA model for the morphologically-rich languages like Arabic. In this study, we aimed to fill the gap by designing a new manual and auto-annotated corpus of the Sudanese-Dialect Arabic (SDA) along with a novel polarity lexicon. The corpus is a collection of political tweets, which were annotated by Twitter on a sentence-level. We have also described some automated, online SSA-tagging tools, which could explore all annotated data. Furthermore, we have investigated the effect of various pre-processing techniques on the Sudanese SSA process. Here, we presented a novel SSA technique for the Sudanese dialect, which displayed a good subjectivity classification result and showed an 83.5% accuracy for the Decision Tree performance.

The use of renewable energy resources, such as solar, wind, and biomass will not diminish their availability. Sunlight being a constant source of energy is used to meet the ever-increasing energy need. This review discusses the world's energy needs, renewable energy technologies for domestic use, and highlights public opinions on renewable energy. A systematic review of the literature was conducted from 2009 to 2018. During this process, more than 300 articles were classified and 42 papers were filtered for critical review. The literature analysis showed that despite serious efforts at all levels to reduce reliance on fossil fuels by promoting renewable energy as its alternative, fossil fuels continue to contribute 73.5% to the worldwide electricity production in 2017. Conversely, renewable sources contributed only 26.5%. Furthermore, this study highlights that the lack of public awareness is a major barrier to the acceptance of renewable energy technologies. The results of this study show that worldwide energy crises can be managed by integrating renewable energy sources in the power generation. Moreover, in order to facilitate the development of renewable energy technologies, this systematic review has highlighted the importance of public opinion and performed a real-time analysis of public tweets. This example of tweet analysis is a relatively novel initiative in a review study that will seek to direct the attention of future researchers and policymakers toward public opinion and recommend the implications to both academia and industries. 2013 IEEE. This work was supported in part by the UM Power Energy Dedicated Advanced Centre (UMPEDAC) and the Higher Institution Centre of Excellence (HICoE) Program Research under Grant UMPEDAC-2016 (MOHE HICOE-UMPEDAC), and in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grand no. RG-6-611-39. Scopus

Several approaches have been proposed to help researchers in acquiring relevant and useful scholarly papers from the enormous amount of information (information overload) that is available over the internet. The significant challenge for those approaches is their assumption of the availability of the whole contents of each of the candidate recommending papers to be freely accessible, which is not always the case considering the copyright restrictions. Also, they immensely depend on priori user profiles, which required a significant number of registered users for the systems to work effectively, and a stumbling block for the creation of a new recommendation system. This paper proposes a citation-based recommender system based on the latent relations connecting research papers for the scholarly paper recommendation. The novelty of the proposed approach is that unlike the existing works, the latent associations that exist between a scholarly paper and its various citations are utilised. The proposed approach aimed to personalise scholarly recommendations regardless of the user expertise and research fields based on paper-citation relations. Experimental results have shown significant improvement over other baseline methods.

Several approaches have been proposed to help researchers in retrieving relevant and useful information from the cyber-ocean of information. However, the approaches assumed the academic content to be easily accessible, which is not always the case considering the copyright restrictions. Different from the existing works, we proposed a collaborative approach that leverages public contextual metadata to personalize scholarly recommendations. Our proposed approach has the ability to recommend research papers to the individual researchers regardless of the researcher expertise and research field. As demonstrated using a freely accessible dataset, our proposed approach have shown significant improvements over another baseline method.

In this paper, we present the concept of context and the three-contextualization paradigms for incorporating contextual information in the recommendation process. We provide a comprehensive overview on the several novel approaches of contextual pre-filtering, contextual post-filtering and contextual modelling approaches. We then present state-of-the-art comparison across these three paradigms and raised some key concerns that are not fully addressed in the literature. This will help academicians and practitioners in comparing these three approaches to choose the best option according to their market strategy.

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.