Abstract In this paper, an integrated knowledge base system for mobile phone failure diagnosis will be designed. ‘The Knowledge Extraction’ and ‘The System Development’ are the main two challengesfaced during the knowledge based system development.The mobile phone hardware faults detection and maintenance is a critical process which needs a high level of expertise.In this paper the mobile phone issues are clearly... In this paper, an integrated knowledge base system for mobile phone failure diagnosis will be designed. ‘The Knowledge Extraction’ and ‘The System Development’ are the main two challengesfaced during the knowledge based system development.The mobile phone hardware faults detection and maintenance is a critical process which needs a high level of expertise.In this paper the mobile phone issues are clearly described and analyzed. As well as the proposed knowledge base system structure, components and functions are presented and described.The CLIPS is used as an expert system tool for designing the proposed system. The proposedsystem consists of 125 rules for the differentmobile phone hardware types of failures and reasons. It can detect all the different types of hardware failures. The developed system has been evaluated and tested by many users and mobile phone experts, positive feedback was received. Read More Read Less

Abstract Patient length of stay is the most commonly employed outcome measure for hospital resource consumption and to monitor the performance of the hospital. Predicting the patient’s length of stay in a hospital is an important aspect for effective planning at various levels. It helps in efficient utilization of resources and facilities. So, there exist a strong demand to make accurate... Patient length of stay is the most commonly employed outcome measure for hospital resource consumption and to monitor the performance of the hospital. Predicting the patient’s length of stay in a hospital is an important aspect for effective planning at various levels. It helps in efficient utilization of resources and facilities. So, there exist a strong demand to make accurate and robust models to predict length of stay. This paper analyzes various methods for length of stay prediction, its advantages and disadvantages and proposes a novel approach for predicting whether the length of stay of the patient is greater than one week. The approach uses DBSCAN clustering to create the training set for classification. The prediction models are compared using accuracy, precision and recall and found that using DBSCAN as a precursor to classification gives better results. Read More Read Less

Abstract Classification of the test samples using positive selection is computationally expensive, as it requires comparisons with large number of detectors. In this paper, we propose an enhanced positive selection algorithm with variable-size detectors to reduce the number of detectors required to cover the training sample space while resolving the issues of ambiguity of the test samples (test samples covered by... Classification of the test samples using positive selection is computationally expensive, as it requires comparisons with large number of detectors. In this paper, we propose an enhanced positive selection algorithm with variable-size detectors to reduce the number of detectors required to cover the training sample space while resolving the issues of ambiguity of the test samples (test samples covered by more than one detector from different classes) and exclusion (test samples not covered by any detectors). We apply clustering in the preprocessing phase to reduce the number of detectors. We then perform overlap checking to adjust the radiuses of the variable-size detectors aiming at reducing overlap while covering the sample space. Furthermore, a weighted voting scheme is employed to resolve the ambiguity, and a distance-based method is devised to resolve the issue of exclusion. We evaluate the performance of our proposed algorithm on five benchmark datasets. The experimental results confirm the superiority of the proposed method in terms of number of detectors and accuracy rate. Read More Read Less