Search results

Parkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.

To provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.

The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.

A novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

This study aims to examine the mediating role of innovation capability in the relationship between digital transformation strategy and innovation performance of microfinance institutions in Ethiopia.

Survey data were collected from 12 microfinance institutions in Ethiopia through self-administered questionnaires. Statistical analysis was conducted using structural equation modeling with AMOS and SPSS. Covariance-based structural equation modeling was used to test the study hypotheses.

Digital transformation strategy indicators such as (digitization vision, information technology integration, information technology agility and flexibility of information technology) directly affect innovation performance. The innovation capability mediates the relationship between digital transformation strategy indicator (information technology agility) and innovation performance. However, innovation capability does not have mediation effect in the relationship between digital transformation strategy remaining indicators (digitization vision, information technology flexibility and information technology integration) and innovation performance.

The study affirmed the importance of dynamic capability theory and presents noteworthy conclusions applicable to managers, stakeholders, and policymakers. It illuminates how innovation capability serves as a crucial link between digital transformation strategies and innovation performance within microfinance institutions in Ethiopia. This research enhances the current understanding of innovation capability, digital transformation strategy and innovation performance in the literature.

The role of personal value systems as antecedents to risk has been largely ignored. Following Gigerenzer's view of ecological rationality, the authors argue an individual's personal value system serves as concrete motivations that guide risky choices and facilitate adaptation to one's environment.

The authors elicit risk attitudes using a satisficing-based risk elicitation method that exploits the idea of worst-case aspiration or minimum portfolio returns given a portfolio comprising a safe and risky prospect. The elicited worst-case aspiration allows for more descriptive and natural ways of characterizing attitudes to risk (i.e. satisficing measures of risk). Using the Schwartz Value Survey, the authors assess the relative importance individuals place on value systems, such as personal focus versus social focus. The authors argue that preference to value systems has linkages with the worst-case aspiration setting emphasized in the satisficing task.

This study’s findings suggest that individuals who are willing to give up higher potential returns to protect their downside risk (by setting higher worst-case aspiration) are positively associated with personal focus—concern about own outcomes than social focus—concern about the outcomes for others or established institutions.

Currently, the study’s setting is in the domain of financial decision-making. Going forward, milestones could be set for studying risky real-world choices by simply changing the risk measure in different contexts, such as job choices, education, health and social interactions.

This study contributes to the discussion on the psychometric structure of risk. Prescriptive benefits of satisficing as a positive heuristic, which is interpreted as setting achievable goals or aspiration levels, are extensive and recognized in various industries ranging from agriculture, airlines, insurance to financial advising. More recently, cognitive processes, such as emotions and personal value systems, are recognized as a type of social cognition that subserve heuristic functions that can guide behavior quickly and accurately.