In recent years, e-learning has become pivotal in higher education sectors. Researchers are correlating novel approaches with e-learning to facilitate education. However, despite the increase in ...e-learning research, there is still a lack of comprehensive literature analysis of e-learning in the higher education sector. Thus, this study aims to conduct a systematic literature review of the literature on e-learning in higher education. This study classifies the selected studies according to the focus of the study, utilizes a theoretical model and framework, and research methods. Also, it presents limitations and future research directions of e-learning in the higher education sector. A systematic approach is conducted, and a total of 47 relevant articles published between the year 2011 and 2019 were selected based on the inclusion and exclusion criteria. The findings on selected studies focus on the adoption, acceptance, readiness, and user insight, as well as e-learning expansion and challenges in the higher education sector. This study also classified theoretical models and frameworks based on their usage in the pre-adoption, adoption, and post-adoption stages. The findings revealed that most of the theoretical models and frameworks were used at the post-adoption stage. Nevertheless, this study revealed that most of the current studies in this domain were conducted using a quantitative research approach. Finally, this study highlighted limitations and presented possible future research directions as a guide for further enhancement in e-learning and higher education studies.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised ...machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models.
Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death according to internal classification of disease version 10 (ICD-10) classification system through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Personal and business users prefer to use e-mail as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, ...such as electronic messages, mobile applications, and social networks. As the volume of business-critical e-mails continues to grow, the need to automate the management of e-mails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. This paper comprehensively reviews articles on e-mail classification published in 2006-2016 by exploiting the methodological decision analysis in five aspects, namely, e-mail classification application areas, data sets used in each application area, feature space utilized in each application area, e-mail classification techniques, and the use of performance measures. A total of 98 articles (56 articles from Web of Science core collection databases and 42 articles from Scopus database) are selected. To achieve the objective of the study, a comprehensive review and analysis is conducted to explore the various areas where e-mail classification was applied. Moreover, various public data sets, features sets, classification techniques, and performance measures are examined and used in each identified application area. This review identifies five application areas of e-mail classification. The most widely used data sets, features sets, classification techniques, and performance measures are found in the identified application areas. The extensive use of these popular data sets, features sets, classification techniques, and performance measures is discussed and justified. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.
In this study the environmental impact of consolidated rice farms (CF) – farms which have been integrated to increase the mechanization index – and traditional farms (TF) – small farms with lower ...mechanization index – in Guilan Province, Iran, were evaluated and compared using Life cycle assessment (LCA) methodology and adaptive neuro-fuzzy inference system (ANFIS). Foreground data were collected from farmers using face-to-face questionnaires and background information about production process and inventory data was taken from the EcoInvent®2.0 database. The system boundary was confined to within the farm gate (cradle to farm gate) and two functional units (land and mass based) were chosen. The study also included a comparison of the input–output energy flows of the farms. The results revealed that the average amount of energy consumed by the CFs was 57GJ compared to 74.2GJ for the TFs. The energy ratios for CFs and TFs were 1.6 and 0.9, respectively. The LCA results indicated that CFs produced fewer environmental burdens per ton of produced rice. When compared according to the land-based FU the same results were obtained. This indicates that the differences between the two types of farms were not caused by a difference in their production level, but rather by improved management on the CFs. The analysis also showed that electricity accounted for the greatest share of the impact for both types of farms, followed by P-based and N-based chemical fertilizers. These findings suggest that the CFs had superior overall environmental performance compared to the TFs in the study area. The performance metrics of the model based on ANFIS show that it can be used to predict the environmental burdens of rice production with high accuracy and minimal error.
•Integrated (IF) and conventional (CF) rice were compared using LCA methodology.•Electricity and chemical fertilizers were the major energy inputs in rice cultivation.•IF had better environmental performance due to better agricultural management.•Electricity dominated most impact categories in both cultivation systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In recent years, different types of review systems have been developed with the recommender system (RS). RSs are developed based on user textual reviews, ratings, and comparative opinions. RSs for ...social media resources, such as blogs, forums, social network websites, social bookmarking websites, video portals, and chat portals help users to collaborate effectively. Social media resources are used in the RS for recommending contents, articles, news, e-commerce products, and users. Although research on social media in RSs has increased annually, comprehensive literature review and classification of these RS studies are limited and must, therefore, be improved. This paper aims to provide a comprehensive review of the social media RS on research articles published from 2011 to 2015 by exploiting a methodological decision analysis in six aspects, including recommendation approaches, research domains, and data sets used in each domain, data mining techniques, recommendation type, and the use of performance measures. A total of 61 articles are reviewed among the initial 434 articles on RS research published in Web of Science and Scopus between 2011 and 2015. To accomplish the aim of this paper, a comprehensive review and analysis was performed on extracted articles to explore various recommendation approaches which are used in the RS. In addition, various social media domains are identified, where RSs have been employed. In each identified domain, publicly available data sets are also reported. Furthermore, various data mining techniques, recommendation types, and performance measures are also analyzed and reviewed in technical aspects. Finally, potential open research directions are also presented for future researchers intended to work in social media RS domain.
This study provides a systematic review of technology-assisted language learning. This study provides a summary content of the reviewed articles in the aspects of technology usage, language, and ...learning skills, and the benefits offered by technology in language learning. The study focused on the published articles between 2012 and 2022. Out of 5719 articles initially retrieved from five academic databases and reviewed, twenty-seven (27) research articles were selected. Based on the review findings, the most used technology is the intelligent system (n=7). The study also revealed that the most common target language is English (n=22), whereas skills such as vocabulary, writing, and grammar gained the most attention in the selected studies. The review also identified and analyzed the empirical evidence on the benefits of technology in language learning, such as language performance development, motivation, metacognitive skills, positive attitudes towards learning, enhancement of students' learning retention, collaborative learning model, and extensive learning opportunity. Barriers to the implementation of the technology, such as learning anxiety, insufficient technology literacy, and technical limitations, were also recognized, and some suggestions were provided to overcome those barriers. Thus, this review can be used as a guide for educators and researchers who intend to design technology-assisted language learning and teaching in the future.
Currently, the power to master the English language has become prominent in academia, research, and business. Therefore, many non-English speaking countries, including Arab countries, are striving to ...improve their education systems in teaching English as a foreign language, and English vocabulary is an important factor to boost English proficiency among Arab students. Moreover, in Arab countries, there is a lack of motivation among learners of English as a foreign language which affects the learning process of students. As such, digital gaming technologies, especially mobile games, are emerging as the best way to create enthusiasm for learning new languages. Therefore, a mobile application named VocabGame was developed based on a set of persuasive guidelines, and it was launched in the Google Play Store. This paper investigated whether the developed VocabGame can motivate native Arab students learning the English language to achieve better performance. Sixty-four students were divided equally into two groups: the control group (high-performance group) and the experimental group (low-performance group). Students in the experimental group improved their motivation level significantly after the mobile learning intervention. Our findings showed that mobile game application is helpful for those students who had poor performance initially while studying English and improves their confidence. There was also an association between the pre-test and the post-test scores according to the motivation to learn based on the analysis of the covariate analysis with <inline-formula> <tex-math notation="LaTeX">\eta </tex-math></inline-formula>_p^2 being 0.148. A mobile application game was successfully developed to motivate Arab native students to learn English as a secondary language.
Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopathology (Hp) biopsy images are generally recommended for early diagnosis of BrC because Hp image-based ...diagnosis enables doctors to make cancer diagnostic decisions more confidently than with mammograms. Several studies have used Hp images to classify BrC. However, the performance of classification models is compromised due to the higher misclassification rate. Therefore, this study aimed to develop a reliable, accurate, and computationally cost-effective ensembled BrC classification network (EBrC-Net) model with three misclassification algorithms to diagnose breast malignancy in early stages using Hp images. The proposed EBrC-Net model is based on the deep convolutional neural network approach. For experiments, the publicly available BreakHis dataset was used and split into training, validation, and testing sets. In addition, image augmentation was adopted for the training set only, and features were extracted through the well-trained EBrC-Net. Thereafter, the extracted features were further evaluated by six machine learning classifiers, of which two best performing classifiers (i.e., softmax and k-nearest neighbour kNN) were selected on the basis of five performance metric evaluation results. Furthermore, three misclassification reduction (McR) algorithms were developed and implemented in cascaded manner to reduce the false predictions of the softmax and kNN classifiers. After the implementation of the McR algorithms, experiments showed that the kNN results were much better and reliable than the softmax. The proposed BrC classification model achieved accuracy, specificity, and sensitivity rates of 97.74%, 100%, and 97.01%, respectively. Moreover, the performance of proposed BrC classification model was compared with that of state-of-the-art baseline models. Findings showed that the proposed EBrC-Net classification model, coupled with the proposed McR algorithms, achieved the best results in comparison with the baseline classification models. The proposed EBrC-Net model and the McR algorithms are a reliable source for doctors aiming for second opinion in making early diagnostic decisions for BrC using Hp images.
Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media ...sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain.
Despite numerous potential benefits of cloud computing usage, there are still some users reluctant to adopt this technology. This study aims to investigate the factors that influence student adoption ...of cloud computing in higher education settings and to generate a set of decision rules to guide through a series of critical decisions needed in this adoption process. Accordingly, a two-stage Structural Equation Modelling (SEM)-Classification and Regression Trees (CART) methodology is applied in order to test the overall research model and related hypotheses as well as to generate decision rules to predict behavioural intention towards adoption. Using survey questionnaire method, a total of 418 valid questionnaires are collected from students of top-ranked Malaysian universities. The results show that task-technology fit, performance expectancy, effort expectancy, social influence, self-efficacy, collaboration technology experience, peer and superior influence and familiarity with group members are significant predictors of intention to adopt cloud computing. The findings of this study can serve as a guideline for the ministry of education, university administrators, and cloud service providers to manage the successful adoption of cloud computing in the education sector.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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