Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

In order to improve the mental health level of college students, a data mining based mental health education strategy for college students is proposed. Firstly, analyse the characteristics of data mining and its potential value in mental health education. Secondly, after denoising the mental health data of college students using wavelet transform, data mining methods are used to identify the psychological crisis status of college students. Finally, based on the psychological crisis status of college students, measures for mental health education are proposed from the following aspects: building a psychological counselling platform, launching psychological health promotion activities, establishing a psychological support network, strengthening academic guidance and stress management. The example analysis results show that after the application of the strategy in this article, the psychological health scores of college students have been effectively improved, with an average score of 93.5 points.

Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

Due to the problems of low clustering accuracy and efficiency in traditional similar resource clustering methods, this paper studies an English MOOC similar resource clustering method based on grey correlation. Principal component analysis was used to extract similar resource features of English MOOC, and feature selection methods was used to pre-process similar resource features of English MOOC. On this basis, based on the grey correlation method, the pre-processed English MOOC similar resource features are standardised, and the correlation degree between different English MOOC similar resource features is calculated. The English MOOC similar resource correlation matrix is constructed to achieve English MOOC similar resource clustering. The experimental results show that the contour coefficient of the proposed method is closer to one, and the clustering accuracy of similar resources in English MOOC is as high as 94.2%, with a clustering time of only 22.3 ms.

The conventional methods for identifying English online course learning behaviours have the problems of low recognition accuracy and high time cost. Therefore, a multimodal data fusion-based method for identifying English online course learning behaviours is proposed. Firstly, the analytic hierarchy process is used for decision fusion of multimodal data of learning behaviour. Secondly, based on the fusion results of multimodal data, weight coefficients are set to minimise losses and extract learning behaviour features. Finally, based on the extracted learning behaviour characteristics, the optimal classification function is constructed to classify the learning behaviour of English online courses. Based on the transfer information of learning behaviour status, the identification of online course learning behaviour is completed. The experimental results show that the recognition accuracy of the proposed method is above 90%, and its recognition accuracy is and can shorten the recognition time of learning behaviour, with high practical application reliability.

In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.

In order to shorten the integration and sharing time of English teaching resources, a MOOC English online and offline mixed teaching resource integration model based on convolutional neural networks is proposed. The intelligent integration model of MOOC English online and offline hybrid teaching resources based on convolutional neural network is constructed. The intelligent integration unit of teaching resources uses the Arduino device recognition program based on convolutional neural network to complete the classification of hybrid teaching resources. Based on the classification results, an English online and offline mixed teaching resource library for Arduino device MOOC is constructed, to achieve intelligent integration of teaching resources. The experimental results show that when the regularisation coefficient is 0.00002, the convolutional neural network model has the best training effect and the fastest convergence speed. And the resource integration time of the method in this article should not exceed 2 s at most.

This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.

The value and utility of traditional knowledge in conserving and commercialising genetic resources are increasingly becoming apparent due to advances in biotechnology and bioprospecting. However, the absence of an international legally binding instrument within the WIPO system means that traditional knowledge associated with genetic resources is not sufficiently protected like other forms of intellectual property. This means that indigenous peoples and local communities (IPLCs) do not benefit from the commercial exploitation of these resources. The efficacy of domestic tools to protect traditional knowledge and in balancing the rights of IPLCs and intellectual property rights (IPRs) is still debated. This paper employs a doctrinal research methodology based on desktop research of international and regional law instruments and the Constitution of the Republic of South Africa, 1996, to determine the basis for balancing the protection of genetic resources and associated traditional knowledge with competing interests of IPLCs and IPRs in South Africa.

Presently, there are many existing academic studies on the development, protection and operation of intellectual property management (IPM). Therefore, provides a comprehensive econometric analysis in order to provide scholars, with a clearer understanding of the evolution and development of IP management research during 2000 to 2022. The study is aiming to help scholars to better discern the expanding IPM research field from a multidimensional perspective. The database used for this analysis is the Web of Science Core Collection database. After retrieval through keywords and using a variety of tools such as CiteSpace, VOSviewer, Bibliometrix and HistCite, 1033 documents were refined to conduct the econometric analysis, and produce graphs. The findings indicate that the US is a highly active country/region in the field IP management research, and its expanding IP management research is branching out into other disciplines. The study also presents the future directions and possible challenges for IPM in technology management.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

Under the current framework of police online teaching, in order to effectively solve the lack of high-quality resources of the traditional platform, backward sharing technology, poor performance of the digital platform and the privacy problems faced by each subject in sharing. This paper designs and implements the online teaching platform based on blockchain and interplanetary file system (IPFS). Based on the architecture of a 'decentralised' police online teaching platform, the platform uses blockchain to store hashes of encrypted private information and user-set access control policies, while the real private information is stored in IPFS after encryption. In the realisation of privacy information security storage at the same time to ensure the effective control of the user's own information. In order to achieve flexible rights management, the system classifies private information. In addition, the difficulties of police online teaching and training reform in the era of big data are solved one by one from the aspects of communication mode, storage facilities, incentive mechanism and confidentiality system, which effectively improves the stability and security of police online teaching.

The research goal is to solve the problems of low accuracy and long time existing in traditional teaching reform performance evaluation methods, a performance evaluation method of teaching reform based on hierarchical multi-task deep learning is proposed. Under the principle of constructing the evaluation index system, the evaluation indicator system should be constructed. The weight of the evaluation index is calculated through the analytic hierarchy process, and the calculation result of the evaluation weight is taken as the model input sample. A hierarchical multi-task deep learning model for teaching reform performance evaluation is built, and the final teaching reform performance score is obtained. Through relevant experiments, it is proved that compared with the experimental comparison method, this method has the advantages of high evaluation accuracy and short time, and can be further applied in relevant fields.

In order to improve the identification accuracy, accuracy and time-consuming of traditional financial risk identification methods, this paper proposes a risk identification method for financial abnormal data based on weighted random forest. Firstly, SMOTE algorithm is used to collect abnormal financial data; secondly, the original accounting data is decomposed into features, and the features of abnormal data are extracted through random forests; then, the index weight is calculated according to the entropy weight method; finally, the negative gradient fitting is used to determine the loss function, and the weighted random forest method is used to solve the loss function value, and the recognition result is obtained. The results show that the identification accuracy of this method can reach 99.9%, the accuracy rate can reach 96.06%, and the time consumption is only 6.8 seconds, indicating that the risk identification effect of this method is good.

In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes.

In order to improve the classification accuracy and online allocation accuracy of teaching resources and shorten the allocation time, this paper proposes a new online allocation method of college ideological and political curriculum teaching resources based on differential search algorithm. Firstly, the feedback parameter model of teaching resources cleaning is constructed to complete the cleaning of teaching resources. Secondly, according to the results of anti-interference consideration, the linear feature extraction of ideological and political curriculum teaching resources is carried out. Finally, the online allocation objective function of teaching resources for ideological and political courses is constructed, and the differential search algorithm is used to optimise the objective function to complete the online allocation of resources. The experimental results show that this method can accurately classify the teaching resources of ideological and political courses, and can shorten the allocation time, with the highest allocation accuracy of 97%.

In view of the problems of low accuracy, long time consuming and low efficiency of the existing engineering investment risk assessment method; this paper puts forward the investment risk assessment method of power grid construction project based on grey correlation analysis. Firstly, classify the risks of power grid construction project; secondly, determine the primary index and secondary index of investment risk assessment of power grid construction project; then construct the correlation coefficient matrix of power grid project investment risk to calculate the correlation degree and weight of investment risk index; finally, adopt the grey correlation analysis method to construct investment risk assessment function to realise investment risk assessment. The experimental results show that the average accuracy of evaluating the investment risk of power grid construction projects using the method is 95.08%, and the maximum time consuming is 49 s, which proves that the method has high accuracy, short time consuming and high evaluation efficiency.

In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student's classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students' classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students' classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students' classroom behaviour more effectively and reliably.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

The EU 'General Data Protection Regulation' (GDPR) marked the most important step towards reforming data privacy regulation in recent years, as it has brought about significant changes in data process in various sectors, ranging from healthcare to banking and beyond. Various concerns have been raised, and as a consequence of these, certain parts of the text of the GDPR itself have already started to become questionable due to rapid technological progress, including, for example, the use of information technology, automatisation processes and advanced algorithms in individual decision-making activities. The road to GDPR compliance by all European Union members may prove to be a long one and it is clear that only time will tell how GDPR matters will evolve and unfold. In this paper, we aim to offer a review of the practical, ethical and constitutional aspects of the new regulation and examine all the controversies that the new technology has given rise to in the course of the regulation's application.

The major technological developments have changed the traditional way of doing business. These developments have facilitated whistle-blowing. Access to data is easier and faster and communicating with the public can be done in seconds. Another development is the artificial intelligence (AI) which enters the business workplace in different forms challenging the traditional working relations. The combination of these concepts gives the idea of artificial whistle-blowing or robot whistle-blowing. The concept is that a machine should conceive and report relevant wrongdoing avoiding the traditional model of whistle-blowing where the employee is the person who should report. This concept, yet unexplored, presents interesting positive and negative aspects. The purpose of this contribution is to present the idea of artificial whistle-blowing and its advantages and disadvantages for the business sector. As a conclusion, this paper suggests that the concept of artificial whistle-blowing needs still to be researched and an optimal solution, for the time being, is to permit artificial whistle-blowing as a helping tool for the employees to detect wrongdoings but report them themselves.

This paper presents a review of the literature on the challenges of national information and communication technology (ICT) policies in the context of African countries. National ICT policies have been aligned with socio-development agendas of African countries. However, the policies have not delivered the expected outcomes due to many challenges. Studies have been conducted in isolation to highlight the challenges in the policy process. The study used grounded theory informed literature review to holistically analyse the problems in the context of African countries. The results were categorised in the typology of the policy process to understand the challenges from a broad perspective. The problems were categorised into agenda setting, policy formulation, legal frameworks, implementation and evaluation. In addition, there were constraints related to policy monitoring in the policy phases and imbalance of power among the policy stakeholders. The review suggests areas of further research.

This paper analyses the adoption of learning analytics to predict at-risk students. A total of 233 research articles between 2004 and 2023 were collected from Scopus for this study. They were analysed in terms of the relevant types and sources of data, targets of prediction, learning analytics methods, and performance metrics. The results show that data related to students' academic performance, socio-demographics, and learning behaviours have been commonly collected. Most studies have addressed the identification of students who have a higher chance of poor academic performance or dropping out of their courses. Decision trees, random forests, and artificial neural networks are the most frequently used techniques for prediction, with ensemble methods gaining popularity in recent years. Classification accuracy, recall, sensitivity, and true positive rate are commonly used as performance metrics for evaluation. The results reveal the potential of learning analytics for informing timely and evidence-based support for at-risk students.

For decades, data analytics has been instrumental in helping companies enhance their performance and achieve growth. By leveraging data analytics and visualisation, businesses have reaped numerous benefits, including the ability to identify emerging trends, analyse relationships and patterns within data, conduct in-depth analysis, and gain valuable insights from these patterns. Given the current demands of the industry, it is crucial to thoroughly explore these concepts to capitalise on the advantages they offer. This research specifically focuses on examining a dataset from Capital Bikes in Washington DC, providing a comprehensive understanding of data analytics and visualisation.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

This research delves into the wealth effect of shareholders from bidding firms created by mergers and acquisitions (M&A) in China and India, two of the world's most populous nations. The study reveals that on average, M&A deals create wealth for shareholders of the acquiring firms, as determined by abnormal percentage returns in a five-day event window. Regarding the further classification of acquiring firms based on industry, the abnormal percentage returns vary in different sectors in both countries. In China, shareholders benefit in seven out of ten industries, while in India, they gain in five out of nine industries. Moreover, the stock markets' responses vary depending on the type of M&A in each country. Cross-industry M&A deals in China generate higher gains for shareholders than within-industry deals, whereas, in India, within-industry M&A deals generate higher gains.

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

As academia in general, and research funders in particular, place ever greater importance on data as an output of research, so the value of good research data management practices becomes ever more apparent. In response to this, the Innovative Design and Manufacturing Research Centre (IdMRC) at the University of Bath, UK, with funding from the JISC, ran a project to draw up a data management planning regime. In carrying out this task, the ERIM (Engineering Research Information Management) Project devised a visual method of mapping out the data records produced in the course of research, along with the associations between them. This method, called Research Activity Information Development (RAID) Modelling, is based on the Unified Modelling Language (UML) for portability. It is offered to the wider research community as an intuitive way for researchers both to keep track of their own data and to communicate this understanding to others who may wish to validate the findings or re-use the data.

The article describes the integrated adoption of Fedora Commons and MediaMosa for managing a digital repository. The integration was experimented along with the development of a cooperative project, Sapienza Digital Library (SDL). The functionalities of the two applications were exploited to built a weaving factory, useful for archiving, preserving and disseminating of multi-format and multi-protocol audio video contents, in different fruition contexts. The integration was unleashed by means of both repository-to-repository interaction, and mapping of video Content Model's disseminators to MediaMosa's Restful services. The outcomes of this integration will lead to a more flexible management of the dissemination services, as well as to economize the overproduction of different dissemination formats.

This paper describes the FISHNet project, which developed a repository environment for the curation and sharing of data relating to freshwater science, a discipline whose research community is distributed thinly across a variety of institutions, and usually works in relative isolation as individual researchers or within small groups. As in other “small sciences”, these datasets tend to be small and “hand-crafted”, created to address particular research questions rather than with a view to reuse, so they are rarely curated effectively, and the potential for sharing and reusing them is limited. The paper addresses a variety of issues and concerns raised by freshwater researchers as regards data sharing, describes our approach to developing a repository environment that addresses these concerns, and identifies the potential impact within the research community of the system.

In this paper, we describe our recent work in using cloud computing to provision digital library services. We consider our original and current motivations, technical details of our implementation, the path we took, and our future work and lessons learned. We also compare our work with other digital library cloud efforts.

In 2008 the University of Hull, Stanford University and University of Virginia decided to collaborate with Fedora Commons (now DuraSpace) on the Hydra project. This project has sought to define and develop repository-enabled solutions for the management of multiple digital content management needs that are multi-purpose and multi-functional in such a way as to allow their use across multiple institutions. This article describes the evolution of Hydra as a project, but most importantly as a community that can sustain the outcomes from Hydra and develop them further. The data modelling and technical implementation are touched on in this context, and examples of the Hydra heads in development or production are highlighted. Finally, the benefits of working together, and having worked together, are explored as a key element in establishing a sustainable open source solution.

The paper describes the investigations and outcomes of the JISC-funded Kindura project, which is piloting the use of hybrid cloud infrastructure to provide repository-focused services to researchers. The hybrid cloud services integrate external commercial cloud services with internal IT infrastructure, which has been adapted to provide cloud-like interfaces. The system provides services to manage and process research outputs, primarily focusing on research data. These services include both repository services, based on use of the Fedora Commons repository, as well as common services such as preservation operations that are provided by cloud compute services. Kindura is piloting the use of the DuraCloud2, open source software developed by DuraSpace, to provide a common interface to interact with cloud storage and compute providers. A storage broker integrates with DuraCloud to optimise the usage of available resources, taking into account such factors as cost, reliability, security and performance. The development is focused on the requirements of target groups of researchers.

The UK JISC-funded Content Lifecycle Integration Framework (CLIF) project has explored the management of digital content throughout its lifecycle from creation through to preservation or disposal. Whilst many individual systems offer the capability of carrying out lifecycle stages to varying degrees, CLIF recognised that only by facilitating the movement of content between systems could the full lifecycle take advantage of systems specifically geared towards different stages of the digital lifecycle. The project has also placed the digital repository at the heart of this movement and has explored this through carrying out integrations between Fedora and Sakai, and Fedora and SharePoint. This article will describe these integrations in the context of lifecycle management and highlight the issues discovered in enabling the smooth movement of content as required.

In the realm of digital data, vendor-supplied cloud systems will still leave the user with responsibility for curation of digital data. Some of the very tasks users thought they were delegating to the cloud vendor may be a requirement for users after all. For example, cloud vendors most often require that users maintain archival copies. Beyond the better known vendor cloud model, we examine curation in two other models: inhouse clouds, and what we call "open" clouds—which are neither inhouse nor vendor. In open clouds, users come aboard as participants or partners—for example, by invitation. In open cloud systems users can develop their own software and data management, control access, and purchase their own hardware while running securely in the cloud environment. To do so will still require working within the rules of the cloud system, but in some open cloud systems those restrictions and limitations can be walked around easily with surprisingly little loss of freedom. It is in this context that REDDnet (Research and Education Data Depot network) is presented as the place where the Texas Tech University (TTU)) Libraries have been conducting research on long-term digital archival storage. The REDDnet network by year's end will be at 1.2 petabytes (PB) with an additional 1.4 PB for a related project (Compact Muon Soleniod Heavy Ion [CMS-HI]); additionally there are over 200 TB of tape storage. These numbers exclude any disk space which TTU will be purchasing during the year. National Science Foundation (NSF) funding covering REDDnet and CMS-HI was in excess of $850,000 with $850,000 earmarked toward REDDnet. In the terminology we used above, REDDnet is an open cloud system that invited TTU Libraries to participate. This means that we run software which fits the REDDnet structure. We are beginning to complete the final design of our system, and starting to move into the first stages of construction. And we have made a decision to move forward and purchase one-half petabyte of disk storage in the initial phase. The concerns, deliberations and testing are presented here along with our initial approach.

Chempound is a new generation repository architecture based on RDF, semantic dictionaries and linked data. It has been developed to hold any type of chemical object expressible in CML and is exemplified by crystallographic experiments and computational chemistry calculations. In both examples, the repository can hold >50k entries which can be searched by SPARQL endpoints and pre-indexing of key fields. The Chempound architecture is general and adaptable to other fields of data-rich science.

In his oft-quoted seminal paper ‘Institutional Repositories: Essential Infrastructure For Scholarship In The Digital Age’ Clifford Lynch (2003) described the Institutional Repository as “a set of services that a university offers to the members of its community for the management and dissemination of digital materials created by the institution and its community members.” This paper seeks instead to define the repository service at a more primitive level, without the specialism of being an ‘Institutional Repository’, and looks at how it can viewed as providing a service within appropriate boundaries, and what that could mean for the future development of repositories, our expectations of what repositories should be, and how they could fit into the set of services required to deliver an Institutional Repository service as describe by Lynch.

The Digital Assets Repository (DAR) is an Institutional Repository developed at the Bibliotheca Alexandrina to manage the full lifecycle of a digital asset: its creation and ingestion, its metadata management, storage and archival in addition to the necessary mechanisms for publishing and dissemination. DAR was designed with a focus on integrating DAR with different sources of digital objects and metadata in addition to integration with applications built on top of the repository. As a modern repository, the system architecture demonstrates a modular design relying on components that are best of the breed, a flexible content model for digital objects based on current standards and heavily relying on RDF triples to define relations. In this paper we will demonstrate the building blocks of DAR as an example of a modern repository, discussing how the system addresses the challenges that face an institution in consolidating its assets and a focus on solving scalability issues.

The study aims to review the literature on mobile wallet payment and align research trends using a systematic literature review with bibliometric and network visualisation analysis over two decades. It uses bibliometric analysis of the literature research retrieved from the Web of Science database. The study period was from 2001 to 2021, with 1,134 research papers. It also provides the indicators like citation trends, cited reference patterns, authorship patterns, subject areas published on the mobile wallet, top contributing authors, and highly cited research articles using the database. Furthermore, network visualisation analysis, like the co-occurrence of author keywords and keywords plus terms, has also been examined using VOSviewer software. The bibliometric analysis shows that the Republic of China dominates mobile wallet payment, and India is a significant contributor. Furthermore, the constructions of the network map using a co-citation analysis and bibliographic coupling shows an interesting pattern of mobile wallet payment.

The surge of learner enrolment in massive open online courses (MOOCs) has led to a wealth of learner-generated data, such as online course reviews that document learner experience. To unveil learner experience with MOOCs, this research uses machine learning methods to extract prominent topics from MOOC reviews and assess the sentiments expressed by learners within them. Furthermore, this research investigates the cooccurrence of the topics using association rule mining. The findings reveal six central topics discussed in MOOC reviews, such as "instructor", "design", "material", "assignment", "platform", and "experience". Notably, most learners express positive sentiments in their reviews. The sentiment indicated in reviews of skill-seeking MOOCs is higher than that in reviews of knowledge-seeking MOOCs. Furthermore, the association rule mining identifies four meaningful association rules. The findings offer valuable insights for MOOC instructors to enhance course design and for platform operators to ensure the long-term viability and success of MOOC platforms.

A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%.

The study aims to analyse the impact of shopping applications on the shopping behaviour of the working women community in Bangalore, a city known as the IT hub. The research uses a quantitative analysis with SPSS version 23 software and a structured questionnaire survey technique to gather data from the working women community. The study uses descriptive statistics, ANOVA, regression, and Pearson correlation analysis to evaluate the perception of working women regarding the significance of online shopping applications. The results show that digital shopping applications are more prevalent among the working women community in Bangalore. The study also evaluates the socio-economic and psychological factors that influence their purchasing behaviour. The findings suggest that online marketers should enhance their strategies to improve their business on digital platforms. The research provides valuable insights into the shopping habits of the working women community in Bangalore.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.