品質學報/Journal of Quality
中華民國品質學會 & Ainosco Press,正常發行
選擇卷期
- 期刊
Medical waste is a waste derived from healthcare and other such medical activities. If there is no medical waste management or if that management is inadequate, there might be higher risks of infections and hazards because some of the waste can be infectious, containing toxic chemicals and posing contamination risks to both people and the environment. In order to have a good medical waste treatment, most medical institutions outsource their waste treatment to waste disposal firms. Selecting the right logistic firm is a critical issue to the medical institutions. To a medical institution, there are several different criteria to be considered when selecting a logistic firm. In addition, some of these multiple criteria usually have conflicting decision objectives. Therefore, the selection of the medical waste logistic firms is a multi-criteria decision-making (MCDM) problem. Several MCDM methods have been proposed in the literature. This study proposes an evaluation model to rank the priorities of potential logistic firms based on their properties using vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) and analytic hierarchy process (AHP). This approach should increase the level of satisfaction after selecting the logistic firm. The working of the proposed model is illustrated with a numerical example.
- 期刊
An authentication and key agreement scheme enables participants to agree a common secret key and to establish a secure channel. A secure authentication and key agreement scheme for telecare medicine information systems provides doctors, nurses, patients, etc. with mutual authentication and secure communication. Recently, Z. Wang et al. (2015) proposed an efficient dynamic identity based authentication scheme using chaotic maps for telecare medicine information systems, and also claimed that their scheme can resist possible attacks. However, this investigation shows that Z. Wang et al.'s scheme fails to provide session key security and user anonymity, and suffers from password guessing and impersonation attacks. To overcome the weaknesses, this investigation proposes an improved authentication scheme by using extended chaotic map-based Diffie-Hellman key change. The proposed scheme avoids the weaknesses in previous schemes, and retains low computational cost.
- 期刊
Detection of cancer recurrence for events of asymptomatic is highly related to the survival. In this study, we considered the variable screening mechanisms and four data mining techniques. The pathological data were obtained from Cancer Center of Chung Shan Medical University Hospital. Results show that primary site and pathologic stage are important independent risk factors. Before variable screenings showed that the highest of average accuracy and area under the curve (AUC) were: C5.0. Screening results of the colon site, the accuracy of ＜ IIb stage was the highest with support vector machine (SVM) (0.91), and that of ≥ IIb stage was the highest with extreme learning machine (ELM) (0.86). In the rectum site, the accuracy of ＜ IIb stage was the best with ELM (0.96), and that of ≥ IIb stage was the highest with multivariate adaptive regression splines (MARS) (0.89) and ELM (0.89). The results of this study provide that for recurrence detection in colorectal cancer patients can be used by clinicians to recommend adjuvant treatment.
- 期刊
Protein citrullination is catalyzed by peptidylarginine deiminase (PAD), during which the positive charge of arginine is changed to the neutral charge of citrulline. Some human diseases such as rheumatoid arthritis, autoimmune diseases, and Alzheimer's disease are known to be associated with PAD enzymes and citrullinated proteins. However, none of the existing prediction tools for citrullination have resulted in a good outcome. This study was conducted to evaluate the performance of the catalyzing rules of PADs, which have been described in previous studies. Machine-learning approaches were used to construct a prediction model for citrullination sites with eight features, i.e., catalyzing rules, sequence similarity, evolutionary information, physicochemical and biochemical properties of amino acids, secondary structure, and disorder and surface accessibility, which were derived from previous studies. We then designed small data modeling and proposed a feature model selection to construct the evaluation of feature model selection (FMS) model that could predict unknown citrullination candidates. Finally, our prediction model was able to achieve an accuracy of up to 0.90 and a Matthews correlation coefficient (MCC) of 0.80, while the selected features were almost similar to those in previous biological analyses.
- 期刊
THE PERFORMANCE OF THE KERNEL DISTANCE CONTROL CHART IN MULTIVARIATE NON-NORMAL PROCESSES MONITORING
Most statistical process control (SPC) methods are designed for normal process data. Statistical learning techniques have been applied to process monitoring for non-normal data in last two decades due to their versatility. The kernel distance-based control chart (k-chart) began to the stream research on the support vector machine methods as an one-class classification to classify observations as either in-control or out-of-control. This study investigates the effectiveness of k-chart in detecting bivariate t-distribution (symmetric data) and bivariate gamma (skewed data). The properties and limitations of the k-chart are also studied. The results show that the k-chart is appropriate for moderate to large mean shifts and for symmetric distributions.