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Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

This research attempts to reveal ways of addressing challenges in open universities related to empowering lifelong learning; establishing policies and strategies in dropouts, student portfolio and support services for students with special needs; and implementing online instructional design and strategies. Two institutions were investigated, namely National Open University (NOU) Taiwan and Universitas Terbuka (UT) Indonesia, both founded in the 1980s to serve lifelong learners with diverse backgrounds and needs. This study was aimed at understanding good practices and challenges for improvement for the two open universities in those areas being investigated.

This research was qualitative using document analysis along with focus group discussions and interviews with administrators, academic staff, students and alumni to collect data for analysis.

Lifelong learning is the necessity of individual in societies for continuing professional development through enabling access to quality university education. Open universities have been tasked to cater for lifelong learners using non-traditional approaches, new technology and adapting to online learning and teaching in digital age. This research was exploratory, and the findings were expected to improve understanding of lifelong learning in open universities, particularly in NOU and UT.

Findings of this research are relevant to open universities to enhance its missions and define its possible new roles to serve lifelong learners.

This research reveals the roles of open universities in lifelong learning and enhances understanding of open universities that have a wide range of responsibilities in offering programs and courses to accommodate lifelong learners.

The purpose of this paper is to reveal how support services for open and distance students are designed, developed and implemented to ensure successful learning to take place, with specific references to the Open University of Sri Lanka (OUSL) and Universitas Terbuka (UT) Indonesia. Success in distance learning is one major challenge for open universities to respond to expectations of students and stakeholders. This study focuses on the strategies of student support services in OUSL and UT, investigating related factors including instructional design and development, learning engagement and motivation, policy and strategy in reducing dropouts, use of OER/MOOCs, and quality assurance.

A qualitative study was employed involving analyses of documents; interviews and focus group discussion with senior administrators, academic staff, students; and on-site observation in locations of teaching and learning.

This research is exploratory in nature. Findings of the study are expected to improve our understanding of student support in distance learning, in which analysis is based on good practices, challenges and rooms for improvement of both OUSL and UT.

Findings of this study reveal practices and lessons learnt that may be useful as reference to open universities, taking into considerations the fact that each open university has been established to address specific challenges in its own unique circumstances.

This research may be adopted as baseline framework for analysis of student support for open universities. Further in-depth study is needed to understand how various aspects of student support contribute to success in open and distance learning.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

As online course enrollments increase, it is important to understand how common course features influence students' behaviors and performance. Asynchronous online courses often include a discussion forum to promote community through interaction between students and instructors. Students interact both socially and cognitively; instructors' engagement often demonstrates social or teaching presence. Students' engagement in the discussions introduces both intrinsic and extraneous cognitive load. The purpose of this study is to validate an instrument for measuring cognitive load in asynchronous online discussions.

This study presents the validation of the NASA-TLX instrument for measuring cognitive load in asynchronous online discussions in an introductory physics course.

The instrument demonstrated reliability for a model with four subscales for all five discrete tasks. This study is foundational for future work that aims at testing the efficacy of interventions, and reducing extraneous cognitive load in asynchronous online discussions.

Nonresponse error due to the unincentivized, voluntary nature of the survey introduces a sample-related limitation.

This study provides a strong foundation for future research focused on testing the effects of interventions aimed at reducing extraneous cognitive load in asynchronous online discussions.

This is a novel application of the NASA-TLX instrument for measuring cognitive load in asynchronous online discussions.

How to obtain a list of the 100 largest scientific publishers sorted by journal count? Existing databases are unhelpful as each of them inhere biased omissions and data quality flaws. This paper tries to fill this gap with an alternative approach.

The content coverages of Scopus, Publons, DOAJ and SherpaRomeo were first used to extract a preliminary list of publishers that supposedly possess at least 15 journals. Second, the publishers' websites were scraped to fetch their portfolios and, thus, their “true” journal counts.

The outcome is a list of the 100 largest publishers comprising 28.060 scholarly journals, with the largest publishing 3.763 journals, and the smallest carrying 76 titles. The usual “oligopoly” of major publishing companies leads the list, but it also contains 17 university presses from the Global South, and, surprisingly, 30 predatory publishers that together publish 4.517 journals.

Additional data sources could be used to mitigate remaining biases; it is difficult to disambiguate publisher names and their imprints; and the dataset carries a non-uniform distribution, thus risking the omission of data points in the lower range.

The dataset can serve as a useful basis for comprehensive meta-scientific surveys on the publisher-level.

The catalogue can be deemed more inclusive and diverse than other ones because many of the publishers would have been overlooked if one had drawn from merely one or two sources. The list is freely accessible and invites regular updates. The approach used here (webscraping) has seldomly been used in meta-scientific surveys.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.