Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

In the construction sector, moulding machines are crucial in producing concrete products, yet changing their mould can pose challenges for some businesses. This paper presents a case study aimed at reducing the setup time of HESS RH 600 moulding machine. Four alternatives are proposed and evaluated to achieve this goal. The first alternative involves converting internal to external activities, while the subsequent alternatives aim to improve the basic solution. These include building a canopy near the machine (alternative 2), installing an air reservoir (alternative 3), and a comprehensive approach involving building the canopy, installing the air reservoir, and adding a new forklift to facilitate the machine setup process (alternative 4). The analytic hierarchy process (AHP) heuristic method is used to select the best alternative solution based on prespecified criteria. It is found that the application of the single-minute exchange of die (SMED) solution without any further improvement is the most favourable.

This research aims to establish a valid and accurate measurement scale and identify consumer-driven characteristics for minimalism. The study has employed a hybrid approach to produce items for minimalism. Expert interviews were conducted to identify the items for minimalism in the first phase followed by consumer survey to obtain their response in second phase. A five-point Likert scale was used to collect the data. Further, data was subjected to reliability and validity check. Structural equation modelling was used to test the model. The findings demonstrated that there are five dimensions by which consumers perceive minimalism: decluttering, mindful consumption, aesthetic choices, financial freedom, and sustainable lifestyle. The outcome also revealed a high correlation between simplicity and well-being. This study is the first to provide a reliable and valid instrument for minimalism. The results will have several theoretical and practical ramifications for society and policymakers. It will support policymakers in gauging and encouraging minimalistic practices, which enhance environmental performance and lower carbon footprint.

Numerous scandals due to unethical decisions occur despite the growing use of decision support systems (DSS). Several scholars recommend incorporating ethical attributes along with business requirements in DSS design. However, little guidance exists to indicate which ethical attributes to include and the importance ethical attributes should be given in comparison to business requirements. This study addresses this deficiency by identifying ethical requirements to integrate in DSS design drawn from the business ethics literature. This study conducted a large-scale empirical survey with information technology decision-makers to examine the relative importance of DSS fit with ethical and business requirements as well as the appropriate balance of those requirements on perceived DSS performance. The results show that decision makers perceive better DSS performance when the ethical and business requirements align with its organisation's beliefs than from ethical or business requirements alone.

This research investigates the correlation between risk management and supply chain performance (SCP) along with moderation of AI-embedded technologies such as big data analytics, Internet of Things (IoT), virtual reality, and blockchain technologies. To calculate the results, this study utilised 644 questionnaires through the structural equation modelling (SEM) method. It is revealed using SmartPls that financial risk management (FRM) is positively linked with SCP. Second, it was observed that AI significantly moderates the connection between FRM and SCP. In addition, the study presents certain insights into supply chain and AI-enabled technologies and how these capabilities can beneficially advance SCP. Besides, certain implications, both managerial and theoretical are described for the supply chain managers along with limitations for future scholars of the world.

The phenomenon of cloud computing and related innovations such as Big Data have given rise to many fundamental changes that are evident in information and data. Managing, measuring and developing business value from the plethora of this new data has significant impact on many corporate agendas, particularly in relation to the successful implementation of business intelligence and analytics (BI&A). However, although the influence of Big Data has fundamentally changed the IT application landscape, the metrics for measuring success and in particular, the quality of information, have not evolved. The measurement of information quality and the antecedent factors that influence information has also been identified as an area that has suffered from a lack of research in recent decades. Given the rapid increase in data volume and the growth and ubiquitous use of BI&A systems in organisations, there is an urgent need for accurate metrics to identify information quality.

To assess a country's economic outlook and achieve higher economic growth, econometric models and prediction techniques are significant tools. Policymakers are always concerned with the correct future estimates of economic variables to take the right economic decisions, design better policies and effectively implement them. Therefore, there is a need to improve the predictive accuracy of the existing models and to use more sophisticated and superior algorithms for accurate forecasting. Deep learning models like recurrent neural networks are considered superior for forecasting as they provide better predictive results as compared to many of the econometric models. Against this backdrop, this paper presents the feasibility of using different deep-learning neural network architectures for trade forecasting. It predicts export trade using different recurrent neural architectures such as 'vanilla recurrent neural network (VRNN)', 'bi-directional long short-term memory network (Bi-LSTM)', 'bi-directional gated recurrent unit (Bi-GRU)' and a hybrid 'bi-directional LSTM and GRU neural network'. The performances of these models are evaluated and compared using different performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE) Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE) and coefficient of determination <em>R</em>-squared (<em>R</em>²). The results validated the effective export prediction for India.

The traditional numerical simulation method of financial fluctuation cycle does not focus on the study of non-linear financial fluctuation but has problems such as high numerical simulation error and long time. To solve this problem, this paper introduces the non-linear equation of motion to optimise the numerical simulation method of financial fluctuation cycle. A comprehensive analysis of the components of the financial market, the establishment of a financial market network model and the acquisition of relevant financial data under the support of the model. Based on the collection of financial data, set up financial volatility index, measuring cycle, the financial wobbles, to establish the non-linear equations of motion, the financial wobbles, the influence factors of the financial volatility cycle as variables in the equation of motion, through the analysis of different influence factors under the action of financial volatility cycle change rule, it is concluded that the final financial fluctuation cycle, the results of numerical simulation. The simulation results show that, compared with the traditional method, the numerical simulation of the proposed method has high precision, low error and short time, which provides relatively accurate reference data for the stable development of regional economy.

Voice Activity Detection (VAD) distinguishes speech segments from noise or silence areas. An efficient and noise-robust VAD system can be widely used for emerging speech technologies such as wireless communication and speech recognition. In this paper, we propose two versions of an unsupervised Arabic VAD method based on the combination of the Short-Time Energy (STE) and the Spectral Centroid (SC) features for formulating a typical threshold to detect speech areas. The first version compares only the STE feature to the threshold (STE-VAD). In contrast, the second compares the SC vector and the threshold (SC-VAD). The two versions of our VAD method were tested on 770 sentences of the Arabphone corpus, which were recorded in clean and noisy environments and evaluated under different values of Signal-to-Noise-Ratio. The experiments demonstrated the robustness of the STE-VAD in terms of accuracy and Mean Square Error.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

This article presents a case study that outlines the potential of DigiBot technology, an interactive automated response program, in mobile open learning (MOL) for business subjects. The study, which draws on a project implemented in Sub-Saharan Africa, demonstrates the applications of DigiBots delivered via WhatsApp to over 650,000 learners. Employing a mixed-methods approach, the article reports on live event tracking, qualitative observations from facilitators and learning technologists, and a learner survey (<i>N</i> = 304,000). The research offers practical recommendations and proposes a model for scalable DigiBot learning. Findings reveal that in this case, DigiBot MOL had the potential to effectively address two key obstacles in open learning: accessibility and scalability. Leveraging mobile platforms such as WhatsApp mitigates accessibility restrictions, particularly in resource-constrained contexts, while tailored micro-learning enhances scalability.

Bright Lights has a history of success, but is at a pivotal point, facing the pains of strategic change. One salesperson has found a way to maintain sales and increase profit margin, but it requires operating between the lines of ethical boundaries. Ethics provides a choice between right and right as opposed to moral temptation of right and wrong (Kidder, 1996). As the case unfolds, Jim receives a mandate of which customers he can call on, reducing sales, profit margin, and customer satisfaction. A top performer, Jim finds a solution within company policy and the law, but although not hidden, is not entirely transparent. This creates two ethical decisions: 1) Should he be reprimanded or praised? 2) Should the company update policies to ban his actions, or promote his actions among other salespeople? This case clearly strikes the dilemma found in navigating the boundaries of a questionable business strategy.

This study inquires about the COVID-19-generated momentum and how it resulted in transformative opportunities for the hard-hit tourism industry in Hawai'i. It also investigates the type of sustainability-based management strategies that were favoured by actors from the industry to help navigate uncertain times and capture transformative opportunities. Findings indicate that actors from the tourism industry in Hawai'i perceived the COVID-19 pandemic as a huliau, or a point of transformation, to reflect and re-evaluate the tourism industry's responsibility and shift toward a recovery focused on sustainability. This research confirms that the pandemic-driven momentum accelerated opportunities for changing and transforming traditional business models and indicators of progress within the tourism industry in Hawai'i. Further research may explore additional Pacific Island countries to gain a deeper understanding of the problem within the region's context.

In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as <i>ResNet18</i> and <i>DenseNet121</i> to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

In order to improve the quality of higher education, student satisfaction, and employment rate, a data mining based high-quality management method for higher education is proposed. Firstly, construct a high-quality evaluation system for higher education based on the principles of education quality evaluation. Secondly, the association rule mining method is used to construct a university education quality management model and determine the weight of the impact indicators for high-quality management of university education. Finally, the fuzzy evaluation method is used to determine the high-quality evaluation function of higher education, and the results of high-quality evaluation of higher education are obtained. High-quality management strategies are developed based on the evaluation results to improve the quality of education. The experimental results show that the student satisfaction rate of this method can reach 99.3%, and the student employment rate can reach 99.9%.

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.

Intellectual property rights face new obstacles and possibilities as a result of the emergence of the Metaverse, a simulation of the actual world. This paper explores the current status of intellectual property rights in the Metaverse and examines the challenges and opportunities for enforcement. The article describes virtual assets and investigates their copyright and trademark protection. It also examines the protection of user-generated content in the Metaverse and the potential liability for copyright infringement. The article concludes with a consideration of the technological and jurisdictional obstacles to enforcing intellectual property rights in the Metaverse, as well as possible solutions for stakeholders. This paper will appeal to lawyers, policymakers, developers of virtual assets, platform owners, and anyone interested in the convergence of technology and intellectual property rights.

Dynamic nature of the FMCG sector perpetually provides a tricky challenge for organisational leaders to nurture their employees. High demand for products, less shelf life and tough competitors always challenge the leaders to uphold their products in the market. Due to technology and e-commerce, many new competitors have joined the market, vying with the industry's veterans. Due to their unique business models that match client needs, these firms are expected to boost FMCG industry income in the future. Managers' leadership styles depend primarily on emotional intelligence. This quantitative study examines how emotional intelligence influences West Bengal FMCG senior managers' leadership styles. 500 FMCG managers were selected. PLS-SEM is used to study. Emotionally competent leaders choose transactional and transformational leadership styles depending on the occasion. Managers' transactional leadership style is strongly influenced by their sympathetic awareness, as shown by a path coefficient of 0.755. Transformational leadership style has a path coefficient of 0.693, indicating that managers' empathy affects their organisational management. Thus, sympathetic awareness and emotion regulation predict good management leadership.

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.

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.

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 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.

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.

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.

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.

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.

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.

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.