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 low evaluation accuracy of the effectiveness of cross-border e-commerce supply chain innovation model and the low correlation coefficient of innovation model influencing factors, the evaluation method of cross-border e-commerce supply chain innovation model based on blockchain technology is studied. First, analyse the operation mode of cross-border e-commerce supply chain, and determine the key factors affecting the innovation mode; Then, the comprehensive integration weighting method is used to analyse the factors affecting innovation and calculate the weight value; Finally, the blockchain technology is introduced to build an evaluation model for the supply chain innovation model and realise the evaluation of the cross-border e-commerce supply chain innovation model. The experimental results show that the evaluation accuracy of the proposed method is high, and the highest correlation coefficient of the influencing factors of innovation mode is about 0.99, which is feasible.

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.

In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.

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.

In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1.

In order to overcome the problems of low efficiency, low user satisfaction and poor customer growth rate under the traditional marketing strategy, this paper studies the innovative strategy of mobile payment business marketing strategy under the internet environment. First of all, study the status quo of mobile payment business marketing in the internet environment, obtain mobile payment business data through questionnaire survey, and analyse the problems in mobile payment business marketing. Secondly, build a user profile of mobile payment business marketing, and classify user attributes, consumption characteristics and user activity through K-means clustering method; Finally, the marketing strategy is innovated from three aspects: product marketing, pricing marketing and channel marketing. The results show that the marketing benefit after the application of this strategy is 19.52 million yuan, the user satisfaction can reach 98.9%, and the customer growth rate can reach 21.3%, improving the marketing benefit of mobile payment business.

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 European Union Rights Management Directive 2014/26/EU, provides regulatory oversight of European Union (EU) Collective Management Organizations (CMOs). However, the Directive has no provision indicating how members of EU CMOs may conduct non-financial audits of their CMO income and reporting. This paper addresses the problem of a lack of an audit method through a case study of the five writer members of the music group Duran Duran, who have been members of the UK's CMO for performing rights - the Performing Rights Society (PRS) for over 35 years. The paper argues a new audit CMO member method that can address the lacunae regarding the absence of CMO member right to audit a CMO and an applicable CMO audit method.

The present paper offers a critique of the General Data Protection Regulation in the realm of access to information. Even though the GDPR supports the constitutionally obvious position that the right to data protection does not outweigh other equally important rights, the enhanced protection of the right to the protection of personal data leads to the potential neglect of other constitutional rights, such as that of access to information. Data protection and access to information authorities should be established both on an EU, as well as at national level as a single authority. Scientific research must be facilitated through access to a multitude of information. The present article explores the question of data ownership and aims to propose a new system that will enhance access to information. A key tool of our research will be the comparative overview of existing legislative systems and a review of the different approaches in the case-law of independent authorities.

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.

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.

This research was carried out with an objective of studying the existing mushroom value chain, identifying demand-supply gap, carrying out SWOT analysis to explore challenges, proposing action plan and presenting finally standard operating procedure for enhancing value chain effectiveness. Data was collected from 71 actors identified in the oyster mushroom value chain in Tumakuru Taluk, Karnataka State, India and analysed. Analysis showed that there were five different models of value chain, and the shortest value chain was the most profitable one. Based on the respondents' perceptions, mushroom cultivation offers many opportunities such as creating employment, improving economic condition and diet. Meanwhile they face challenges like, pest attack, hike in input materials' prices, lack of technical guidance during farming, finance support, inefficient marketing system. There is a need to address demand-supply gap, invest more in facilities and related research, integrate all the actors in value chain to enhance productivity.

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.

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 an environment for the “sheer curation” of the experimental data of a group of researchers in the fields of biophysics and structural biology. The approach involves embedding data capture and interpretation within researchers' working practices, so that it is automatic and invisible to the researcher. The environment does not capture just the individual datasets generated by an experiment, but the entire workflow that represent the “story” of the experiment, including intermediate files and provenance metadata, so as to support the verification and reproduction of published results. As the curation environment is decoupled from the researchers’ processing environment, the provenance is inferred from a variety of domain-specific contextual information, using software that implements the knowledge and expertise of the researchers. We also present an approach to publishing the data files and their provenance according to linked data principles by using OAI-ORE (Open Archives Initiative Object Reuse and Exchange) and OPMV.

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.

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.

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.

International universities are changing with their style of operation, mode of teaching and learning operations. This change is noticeable rapidly in India and also in international contexts due to healthy and innovative methods, educational strategies, and nomenclature throughout the world. Technologies are changing rapidly, including ICT. Different subjects are developed in the fields of IT and computing with the interaction or applications to other fields, viz. health informatics, bio informatics, agriculture informatics, and so on. Agricultural informatics is an interdisciplinary subject dedicated to combining information technology and information science utilisation in agricultural sciences. The digital agriculture is powered by agriculture informatics practice. For teaching, research and development of any subject educational methods is considered as important and various educational programs are there in this regard viz. Bachelor of Education, Master of Education, PhD in Education, etc. Degrees are also available to deal with the subjects and agricultural informatics should not be an exception of this. In this context, Doctor of Education (EdD or DEd) is an emerging degree having features of skill sets, courses and research work. This paper proposed on EdD program with agricultural informatics specialisation for improving healthy agriculture system. Here, a proposed model core curriculum is also presented.

In this article, we have attempted to study the ways in which the COVID-19 pandemic has gradually increased and impacted the world. The authors integrate the knowledge from cognitive psychology literature to illustrate how the limitations of the human mind might have a critical role in the decisions taken during the COVID-19 pandemic. The authors show the correlation between different biases in various contexts involved in the COVID-19 pandemic and highlight the ways in which we can nudge ourselves and various stakeholders involved in the decision-making process. This study uses a typology of biases to examine how different patterns of biases affect the decision-making behaviour of people during the pandemic. The presented model investigates the potential interrelations among environmental transformations, cognitive biases, and strategic decisions. By referring to cognitive biases, our model also helps to understand why the same performance improvement practices might incite different opinions among decision-makers.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

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.