Why we like Google Scholar: postgraduate students' perceptions of factors influencing their intention to use

This study examines the use of the search engine, Google Scholar from perspective of a specific study group, that of international postgraduate students. Based on theory of task perceived performance and effort expectancy influencing intention to use, further factors of system, individual, social and organisational in the postgraduate student context are explored. The questionnaire for the measurement of eleven factors was developed from related studies of e-library use, and data was collected from 200 international postgraduate students studying in the UK. Analysis using Confirmatory Factor Analysis established the contextual influencing factors and Structural Equation Modelling examined the predicted model. Influence of the task based factors of performance and expectancy were confirmed and revealed that these were based on the perception of the visibility, accessibility and relevance of the system, and on perceived self-efficacy. The perception postgraduates held of themselves as competent users of Google Scholar was further borne out in the participants’ own words when asked for the reason for their preference. The approach taken enables research into use of search tools to go beyond ease of use as a main driver and to explore the relationship held among the internal and external influences of use. Recommendations for further user research are suggested as well as possible impact on the university library provision and support of services for students

Perceptions of University Digital Libraries as information source by international postgraduate student

University digital libraries (UDLs) have taken the place of the traditional library in the present day. In the university context, in particular, they are the obvious solution to the library needs of students. However, they encounter considerable competition from web-based search engines on the internet, which limits effective usage of the library resources by students. This research set out to identify factors that affect international postgraduate students’ choice to use Google Scholar over their UDL to create an information driven framework that can positively influence and be responsive to dynamic needs and search strategies of the end-user (student). This research utilises two theoretical models: the unified theory of acceptance and use of technology (UTAUT) model (Venkatesh et al., 2003), and Wilson’s (1999) model of information-seeking behaviour, in the process of achieving its aim of identifying factors influencing information search strategy by postgraduate students. The research used an extended version of UTAUT to evaluate the factors influencing the adoption and acceptance of UDLs and Google Scholar. The research was designed to use a mixed methodological approach, with a sample-frame of 400 international postgraduate students in two groups: both groups based in a large city in the United Kingdom. The study utilised a questionnaire to survey 400 respondents; it contained questions relating to the UTAUT model, as well as students’ intent to use their UDLs or Google Scholar. The collected data were quantitatively analysed using various statistical tests including regression and Structural Equation Modelling (SEM). Open-ended questions were also conducted to obtain further information examining six aspects of their intention to use– namely spectrum, search and functionality, availability, accessibility, accuracy, and references. The research found that international students preferred to use Google Scholar over UDLs because it was perceived to be faster and easier to use. It was also found that there were myriad factors that influenced the behavioural intent of the information seeker, such as social influence, domain knowledge, perceived outcome, and perceived effort. The research found that international students were not only using Google Scholar on its own, but also found the use of UDLs as the most valuable source of quality information that they could rely on. Based on the above stated findings, the research has contributed to knowledge by proposing a step-wise framework that can be used in UDLs as a means of harnessing the strength in digital libraries and amalgamate it with the technological iv platforms used by students. The framework takes into consideration systems features of information search platforms, behavioural intentions of each individual student as well as the social contextual environment that international students find themselves. Adoption of the proposed framework is recommended for university libraries to establish the ideal intervention point for educating and training students on the use of their digital library

Effect of serialized messaging on web services performance

Message serialization is a format of messaging leveraging Web services to exchange data over the network. Serialized messages are processed at the server and sent as objects over the network to the client to be consumed. While, serialization process minimizes network bandwidth requirement but then incurs overhead at the communicating ends. This research contributes to the study of message exchange using HTTP across communication systems. The research identified the fundamental effect of serializing high-volume messages across network and the sources for the effects at the communication endpoints. The study utilized server - client SOAP Web services to identify the fundamental effect of serialization in the communication endpoints. SOAP messages were exchanged as XML messages over HTTP. Payload sizes (1MB-22MB) for serialized and normal messages were exchanged through the services. The message payload, overhead, and response time were monitored and measured. The overall result indicated that is more beneficial to serialized large payload than smaller one. Generally, the serialization and deserialization cost incurred at individual ends are slightly constant irrespective of the payload size. Also, the serialization and deserialization process is insignificant to the overall transaction as it delay is below 3% of the total overhead

Effectiveness of interventions to improve the anticholinergic prescribing practice in older adults: a systematic review

Background: Pharmacotherapy in older adults is one of the most challenging aspects of patient care. Older people are prone to drug-related problems such as adverse effects, ineffectiveness, underdosage, overdosage, and drug interactions. Anticholinergic medications are associated with poor outcomes in older patients, and there is no specific intervention strategy for reducing drug burden from anticholinergic activity medications. Little is known about the effectiveness of current interventions that may likely improve the anticholinergic prescribing practice in older adults. Aims: This review seeks to document all types of interventions aiming to reduce anticholinergic prescribing among older adults and assess the current evidence and quality of existing single and combined interventions. Methods: We systematically searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO from January 1990 to August 2021. Only studies that examined the effect of interventions in older people focused on improving compliance with anticholinergic prescribing guidelines with quantifiable data were included. The primary outcome of interest was to find the effectiveness of interventions that enhance the anticholinergic prescribing practice in older adults. Results: We screened 3168 records and ended up in 23 studies that met the inclusion criteria. We found only single-component interventions to reduce anticholinergic prescribing errors in older people. Pharmacists implemented interventions without collaboration in nearly half of the studies (n = 11). Medication review (43%) and education provision (26%) to healthcare practitioners were the most common interventions. Sixteen studies (70%) reported significant reductions in anticholinergic prescribing errors, whereas seven studies (30%) showed no significant effect. Conclusion: This systematic review suggests that healthcare practitioner-oriented interventions have the potential to reduce the occurrence of anticholinergic prescribing errors in older people. Interventions were primarily effective in reducing the burden of anticholinergic medications and assisting with deprescribing anticholinergic medications in older adults

Accelerating biomedical image segmentation using equilibrium optimization with a deep learning approach

Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches

Prioritising Organisational Factors Impacting Cloud ERP Adoption and the Critical Issues Related to Security, Usability, and Vendors: A Systematic Literature Review

Abstract: Cloud ERP is a type of enterprise resource planning (ERP) system that runs on the vendor’s cloud platform instead of an on-premises network, enabling companies to connect through the Internet. The goal of this study was to rank and prioritise the factors driving cloud ERP adoption by organisations and to identify the critical issues in terms of security, usability, and vendors that impact adoption of cloud ERP systems. The assessment of critical success factors (CSFs) in on-premises ERP adoption and implementation has been well documented; however, no previous research has been carried out on CSFs in cloud ERP adoption. Therefore, the contribution of this research is to provide research and practice with the identification and analysis of 16 CSFs through a systematic literature review, where 73 publications on cloud ERP adoption were assessed from a range of different conferences and journals, using inclusion and exclusion criteria. Drawing from the literature, we found security, usability, and vendors were the top three most widely cited critical issues for the adoption of cloud-based ERP; hence, the second contribution of this study was an integrative model constructed with 12 drivers based on the security, usability, and vendor characteristics that may have greater influence as the top critical issues in the adoption of cloud ERP systems. We also identified critical gaps in current research, such as the inconclusiveness of findings related to security critical issues, usability critical issues, and vendor critical issues, by highlighting the most important drivers influencing those issues in cloud ERP adoption and the lack of discussion on the nature of the criticality of those CSFs. This research will aid in the development of new strategies or the revision of existing strategies and polices aimed at effectively integrating cloud ERP into cloud computing infrastructure. It will also allow cloud ERP suppliers to determine organisations’ and business owners’ expectations and implement appropriate tactics. A better understanding of the CSFs will narrow the field of failure and assist practitioners and managers in increasing their chances of success

Quranic diacritic and character segmentation and recognition using flood fill and k-nearest neighbors algorithm

The detection, recognition and conversion of the characters in an image into a text are called optical character recognition (OCR). A distinctive type of OCR is used to process Arabic characters, namely, Arabic Optical Character Recognition (AOCR). OCR is increasingly used in many applications, where this process is preferred to automatically perform a process without human intervention. The Quranic handwriting text contains two elements, namely, diacritics and characters. However, the current Arabic handwritten OCR system produces low levels of accuracy and no research focused on Quran image recognition. The current AOCR inaccurately recognizes diacritic and characters, and the research and efforts in the area of AOCR are insufficient. Many studies have been carried out so far, but for Quran handwriting has not been researched as thoroughly as Arabic, Latin or Chinese handwritten systems. The current research is focused on solving the mentioned problems through improving the accuracy of recognition rate of AOCR by proposing a new segmentation, feature extraction methods and finding a suitable classification. In this thesis, a new techniques, methods and algorithms are proposed to check the similarities and originalities of the Quranic handwriting content. The diacritic detections are performed using a region-based algorithm with 89% accuracy and 95% improved by using flood fill segmentations method. 2DMED feature extraction accuracy was 90% for diacritics and 96% improved by applied CNN. Character recognition is performed based on the projection method with 86% accuracy, and 92% improved by using flood fill. 2DMED in characters was 88% and 91 % after improved by applied CNN. For classification, KNN used before and after enhancement technique based on essential vector with our dataset, the diacritic accuracy was 96.4286% after enhancement, which is better than the 87.5020% in detecting. For characters was at 92.3077% improvement, which is better that normal KNN algorithm which exhibited an 86.1429% accuracy in detecting

Google Scholar or University Digital Libraries: A comparison of student perceptions and intention to use

Google Scholar has become an important search platform for students in higher education, and, as such, can be regarded as a competitor to university libraries. Previous research has explored students’ intention to use Google Scholar (GS) and University Digital Libraries (UDLs), but there is a lack of comparative studies that explore students’ preferences between these two platforms. Therefore, this study seeks to explore the search behaviour of a select group of users, international postgraduate students, and more specifically compares the factors that influence their use of Google Scholar and University Digital Libraries (UDLs). A questionnaire-based survey, based on the factors in the UTAUT model (unified theory of acceptance and use of technology) was conducted to collect data on acceptance and use of technology of GS and UDL’s respectively. Data was collected from 400 international postgraduate students studying in the United Kingdom. Confirmatory factor analysis was used to establish the contextual influencing factors, whilst structural equation modelling examined the predicted model. The results suggest some differences between the influence of various factors between the UDL dataset and the GS datasets. They suggest that social influence (SI) did not affect behavioural intention (BI) for either data set, but that for the UDL dataset, effort expectancy did not affect BI, whereas for the GS dataset facilitating conditions did not influence BI. The approach taken in this study further facilitates research into the use of search tools to progress beyond ease of use as a main driver and to explore the relationship between internal and external influences of use. Recommendations for further research are suggested and the value of the insights gained for UDLs and their provision and support for all students is discussed

Credit card default prediction using machine learning techniques

Credit risk plays a major role in the banking industry business. Banks' main activities involve granting loan, credit card, investment, mortgage, and others. Credit card has been one of the most booming financial services by banks over the past years. However, with the growing number of credit card users, banks have been facing an escalating credit card default rate. As such data analytics can provide solutions to tackle the current phenomenon and management credit risks. This paper provides a performance evaluation of credit card default prediction. Thus, logistic regression, rpart decision tree, and random forest are used to test the variable in predicting credit default and random forest proved to have the higher accuracy and area under the curve. This result shows that random forest best describe which factors should be considered with an accuracy of 82 % and an Area under Curve of 77 % when assessing the credit risk of credit card customers