The Current State of Senior Library Staff and the Particularities of Their Professional Training in the Republic of Cameroon
Issue:
Volume 4, Issue 3, September 2019
Pages:
46-51
Received:
23 May 2019
Accepted:
2 July 2019
Published:
31 July 2019
Abstract: In Cameroon a policy for the management of human resources in the field of Librarianship andInformation Scienceis lacking. A brief history of the development of professional training in the field of Library and Information Science (LIS) education and the results of the analysis of the current state of librarians and information workers of the Republic of Cameroon reveal that, the system of continuous training of library Executives and staff lack some complementary levels of training. The main principles of the content of the training of such a system, which must be the basis of the corresponding training programs, are not defined. There is no mechanism for cooperation between the Ministry of Higher Education and the Ministry of Arts and Culture in the process of training of library staff. The normative and legal documents regulating the career of the library specialist and their training are lacking. The training programs of the main departments offering training in Documentation and Information, at the Advanced School of Mass Communication (ASMAC), University of Yaounde II and the Department of Library and Information Science of the Protestant University of Central Africa (PUCA), are not standardized nor have the required experience to provide high-level continuing training. Equally, there is no professional publishing house in the field of library science in Cameroon, which is necessary for discussions and debates on issues related to the application of technology in the field of LIS. Based on the above results, this study concludes that training of librarian specialistsis not yet well developed and the current state of affairs is not satisfactory for the development of the national culture.
Abstract: In Cameroon a policy for the management of human resources in the field of Librarianship andInformation Scienceis lacking. A brief history of the development of professional training in the field of Library and Information Science (LIS) education and the results of the analysis of the current state of librarians and information workers of the Repub...
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A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method
Xiaoxia Wu,
Dongqi Yang,
Wenyu Zhang,
Shuai Zhang
Issue:
Volume 4, Issue 3, September 2019
Pages:
52-58
Received:
5 August 2019
Published:
27 September 2019
Abstract: The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).
Abstract: The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustnes...
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