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The primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.

The authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.

The results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.

This study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

Despite the wide availability of internet banking, levels of intention to use such facilities remain variable between countries. The purpose of this paper is to focus on e-banking in a country with low intention to use e-banking – Jordan – and to explain the slow uptake.

A quantitative method employing a cross-sectional survey was used as an appropriate way of meeting the research objectives. The survey was distributed to bank customers in Amman, Jordan, collecting a total of 328 completed questionnaires. SPSS and AMOS software were used, and multiple regression and artificial neural networks were applied to determine the relative impact and importance of e-banking predictors.

The statistical techniques revealed that several major factors, including perceived ease of use, perceived usefulness, security and reasonable price, stand out as the barriers to intention to use e-banking services in Jordan.

This study theorizes a series of implications on intention to use e-banking. It draws the attention of Jordanian banks to the full functionality of their e-banking systems, emphasizing positive safety features, which could contribute to changing negative customer perceptions. It also contributes to eliciting the theory of customer value among banks by focusing on how they should properly enhance their use of shared value. Moreover, it will present to managers how e-banking predictors can send meaningful and timely information to customers.

Divestitures and other forms of organizational separation are not commonly associated with continuity and ongoing collaboration in inter-organizational relationships. Instead, separation is often equated with terminating relationships and gaining independence. Here, the authors argue that achieving separation does not require terminating relationships and that ongoing collaboration between separating entities may actually contribute to successful separation. The authors base this argument on the assertion that the objective of organizational separation is to achieve organizational autonomy for all entities involved and that separating entities can enable each other’s development of autonomy while remaining interdependent. The authors also discuss how collaborative separation may contribute to a range of benefits, as well as why it may nevertheless fail to emerge in practice. In this respect, the authors consider the relevance of ethical perspectives and emotional dynamics related to feelings of (dis)respect, (dis)trust, pride and shame. The authors conclude by discussing activities that may contribute to, and undermine, effective collaborative separation.

Today, low-income people seeking resources from the federal government must often work through non-profit organizations. The purpose of this paper is to examine the constraints that the poor must face today to secure resources through non-profit organizations.

This is a conceptual paper. The authors review cases of non-profit organizations providing federally supported resources to the poor across multiple sectors.

The authors find that to accept government contracts serving the poor, nonprofit organizations must often engage in one or several practices: reject clients normally consistent with their mission, select clients based on likely outcomes, ignore problems in clients’ lives relevant to their predicament, or undermine client progress to manage funding requirements. To secure government-supported resources from nonprofits, the poor must often acquiesce to intrusions into one or more of the following: their privacy (disclosing sensitive information), their self-protection (renouncing legal rights), their identity (avowing a particular self-understanding) or their self-mastery (relinquishing authority over daily routines).

The authors show that the nonprofits’ dual role as brokers, both liaisons transferring resources and representatives of the state, can complicate their relation to their clients and the predicament of the poor themselves; the authors suggest that two larger trends, toward increasing administrative accountability and demonstrating deservingness, are having both intended and unintended consequences for the ability of low-income individuals to gain access to publicly funded resources.

The mass exodus of the professional healthcare workforce has become a cankerworm for a developing nation like Nigeria, and this worsens the already depleted healthcare systems in underdeveloped nation. This study investigated the rationale behind medical workers' brain-drain syndrome and the quality healthcare delivery in the Nigerian public healthcare sector.

To stimulate an understanding of the effect of the phenomenon called brain drain, the study adopted a diagnostic research design to survey the public healthcare personnel in government hospitals. The study administered a battery of adapted research scales of different measures to confirm the variables of interest of this study on a probability sampling strategy. The study surveyed 450 public healthcare sector employees from four government hospitals to gather pertinent data. The study used a structural equation model (SEM) and artificial neural networks (ANNs) to analyse the collected data from the medical personnel of government hospitals.

The findings of this study are significant as postulated. The study discovered that poor quality worklife experienced by Nigerian medical personnel was attributed to the brain-drain effect and poor healthcare delivery. The study further demonstrated that job dissatisfaction suffered among the public healthcare workforce forced the workforce to migrate to the international labour market, and this same factor is a reason for poor healthcare delivery. Lastly, the study discovered that inadequate remuneration and pay discouraged Nigerian professionals and allied healthcare workers from being productive and ultimately pushed them to the global market.

Practically, this study has shown three major elements that caused the mass movement of Nigerian healthcare personnel to other countries of the world and that seems novel given the peculiarity of the Nigerian labour market. The study is original and novel as much study has not been put forward in the public healthcare sector in Nigeria concerning this phenomenon.

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 paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.