Chaoyang University of Technology

Abstract Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.

We propose a new remote user authentication scheme using smart cards. The scheme is based on the ElGamal's (1985) public key cryptosystem. Our scheme does not require a system to maintain a password table for verifying the legitimacy of the login users. In addition, our scheme can withstand message replaying attack.

In order to improve the capacity of the hidden secret data and to provide an imperceptible stego-image quality, a novel steganographic method based on least-significant-bit (LSB) replacement and pixel-value differencing (PVD) method is presented. First, a different value from two consecutive pixels by utilising the PVD method is obtained. A small difference value can be located on a smooth area and the large one is located on an edged area. In the smooth areas, the secret data is hidden into the cover image by LSB method while using the PVD method in the edged areas. Because the range width is variable, and the area in which the secret data is concealed by LSB or PVD method are hard to guess, the security level is the same as that of a single using the PVD method of the proposed method. From the experimental results, compared with the PVD method being used alone, the proposed method can hide a much larger information and maintains a good visual quality of stego-image.

Abstract The main purpose of this note is to modify the assumption of the trade credit policy in previously published results to reflect the real-life situations. All previously published models implicitly assumed that the supplier would offer the retailer a delay period, but the retailer would not offer the trade credit period to his/her customer. In most business transactions, this assumption is debatable. In this note, we assume that the retailer also adopts the trade credit policy to stimulate his/her customer demand to develop the retailer's replenishment model. Furthermore, we assume that the retailer's trade credit period offered by supplier M is not shorter than the customer's trade credit period offered by retailer N(M⩾N). Under these conditions, we model the retailer's inventory system as a cost minimization problem to determine the retailer's optimal ordering policies. Then a theorem is developed to determine efficiently the optimal ordering policies for the retailer. We deduce some previously published results of other researchers as special cases. Finally, numerical examples are given to illustrate the theorem obtained in this note.

Abstract Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including M any-dimensions, M any-changes, M any-optima, M any-constraints, and M any-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty , increasing algorithm diversity , accelerating convergence speed , reducing running time , and extending application field . Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

Antrodia camphorata is a unique mushroom of Taiwan, which has been used as a traditional medicine for protection of diverse health-related conditions. In an effort to translate this Eastern medicine into Western-accepted therapy, a great deal of work has been carried out on A. camphorata. This review discusses the biological activities of the crude extracts and the main bioactive compounds of A. camphorata. The list of bioactivities of crude extracts is huge, ranging from anti-cancer to vasorelaxation and others. Over 78 compounds consisting of terpenoids, benzenoids, lignans, benzoquinone derivatives, succinic and maleic derivatives, in addition to polysaccharides have been identified. Many of these compounds were evaluated for biological activity. Many activities of crude extracts and pure compounds of A. camphorata against some major diseases of our time, and thus, a current review is of great importance. It is concluded that A. camphorata can be considered as an efficient alternative phytotherapeutic agent or a synergizer in the treatment of cancer and other immune-related diseases. However, clinical trails of human on A. camphorata extracts are limited and those of pure compounds are absent. The next step is to produce some medicines from A. camphorata, however, the production may be hampered by problems related to mass production.

Medicinal plants constitute an important component of flora and are widely distrib- uted in India. The pharmacological evaluation of substances from plants is an established method for the identification of lead compounds which can leads to the development of novel and safe medicinal agents. Based on the ethnopharmacological literature, several species of medicinal plants used in traditional medicine in India were collected. In the present study, aqueous extracts of these medicinal plants were screened for their cytotoxicity using brine shrimp lethality test. Out of the 120 plants tested, Pistacia lentiscus exhibited potent brine shrimp lethality with LC50 2.5μg . Aristolochia indica (Aristolochiaceae), Boswellia serrata (Burseraceae), Ginkgo biloba (Ginkgoaceae), Garcinia cambogia (Clusiaceae), and Semecarpus anacardium (Anacardiaceae) have also showed significant cytotoxicity with LC50 13, 18, 21, 22, and 29.5μg respectively. The present study supports that brine shrimp bioassay is simple reliable and convenient method for assessment of bioactivity of medicinal plants and lends support for their use in traditional medi- cine.

Abstract: Plant cell culture systems represent a potential renewable source of valuable medici-nal compounds, flavors, fragrances, and colorants, which cannot be produced by microbial cells or chemical synthesis. Biotechnological applications of plant cell cultures presents the most up-dated reviews on current techniques in plant culture in the field. The evolving commercial im-portance of the secondary metabolites has in recent years resulted in a great interest, in secon-dary metabolism, and particularly in the possibility to alter the production of bioactive plant me-tabolites by means of cell culture technology. The principle advantage of this technology is that it may provide continuous, reliable source of plant pharmaceuticals and could be used for the large-scale culture of plant cells from which these metabolites can be extracted. In addition to its importance in the discovery of new medicines, plant cell culture technology plays an even more significant role in solving world hunger by developing agricultural crops that provide both higher yield and more resistance to pathogens and adverse environmental and climatic conditions. This paper describes the callus and suspension culture methods that we have established in our labo-ratory for the production of bioactive secondary metabolites from medicinal plants.

Conventional remote password authentication schemes allow a serviceable server to authenticate the legitimacy of a remote login user. However, these schemes are not used for multiserver architecture environments. We present a remote password authentication scheme for multiserver environments. The password authentication system is a pattern classification system based on an artificial neural network. In this scheme, the users only remember user identity and password numbers to log in to various servers. Users can freely choose their password. Furthermore, the system is not required to maintain a verification table and can withstand the replay attack.

A standard assumption when using a control chart to monitor a process is that the observations from the process output are independent. However, for many processes the observations are autocorrelated, and this autocorrelation can have a significant effect on the performance of the control chart. This paper considers the problem of monitoring the mean of a process in which the observations can be modeled as an AR(1) process plus a random error. An exponentially weighted moving average (EWMA) control chart based on the residuals from the forecast values of the model is evaluated using an integral equation method. This control chart's performance is compared to the performance of an EWMA control chart based on the original observations, and the effect of process parameter estimation on the control charts is investigated. When the level of autocorrelation is low or moderate, the two EWMA charts require about the same amount of time to detect various shifts; but for high levels of autocorrelation and large shifts, the EWMA chart of the residuals is a little faster.

Network bandwidth and hardware technology are developing rapidly, resulting in the vigorous development of the Internet. A new concept, cloud computing, uses low-power hosts to achieve high reliability. The cloud computing, an Internet-based development in which dynamically scalable and often virtualized resources are provided as a service over the Internet has become a significant issue. The cloud computing refers to a class of systems and applications that employ distributed resources to perform a function in a decentralized manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. However, in this study, a two-phase scheduling algorithm under a three-level cloud computing network is advanced. The proposed scheduling algorithm combines OLB (Opportunistic Load Balancing) and LBMM (Load Balance Min-Min) scheduling algorithms that can utilize more better executing efficiency and maintain the load balancing of system.

Based on the technology acceptance model (TAM), this study uses the framework of the extended TAM to examine the antecedents and consequences for employees' acceptance of the e-learning system within financial services organizations. The total of 328 useable responses collected from eight international or domestic financial services companies in Taiwan were tested against the model using structural equation modelling (SEM). The main research results are summarized as follows in terms of the antecedents of e-learning acceptance and its impact on employees' perceived performance. Four types of determinants are demonstrated: individual factors, system factors, social factors and network externality factor. Finally, this study proposes relevant suggestions for practitioners and future researchers.

Hwang and Li (see IEEE Trans. Consumer Electron., vol.46, no.1, p.28-30, 2000) proposed a new remote authentication scheme using smart cards. Their scheme is based on the ElGamal's public key cryptosystem. However, Chan and Cheng (see IEEE Trans. Consumer Electron., vol.46, p.992-993, 2000) pointed out that the scheme is vulnerable to the masquerade attack. In this article, we show a different attack on Hwang-Li scheme which is easier and simpler. Furthermore, we present an enhanced scheme for repairing the above attacks.

Plants are a tremendous source for the discovery of new products of medicinal value for drug development. Today several distinct chemicals derived from plants are important drugs currently used in one or more countries in the world. Many of the drugs sold today are simple synthetic modifications or copies of the naturally obtained substances. The evolving commercial importance of secondary metabolites has in recent years resulted in a great interest in secondary metabolism, particularly in the possibility of altering the production of bioactive plant metabolites by means of tissue culture technology. Plant cell culture technologies were introduced at the end of the 1960's as a possible tool for both studying and producing plant secondary metabolites. Different strategies, using an in vitro system, have been extensively studied to improve the production of plant chemicals. The focus of the present review is the application of tissue culture technology for the production of some important plant pharmaceuticals. Also, we describe the results of in vitro cultures and production of some important secondary metabolites obtained in our laboratory.

Purpose The main purpose of this study is to examine whether quality factors as the antecedents to learner beliefs can affect learners' intention to use an e‐learning system. Design/methodology/approach This study gathered sample data from eight high‐tech companies in Taiwan. A total of 680 questionnaires were randomly distributed, 522 questionnaires were returned for a response rate of 76.76 percent, and 483 usable questionnaires were analyzed, with a usable response rate of 71.03 percent. Data were analyzed by using structural equation modeling. Findings Information quality, service quality, system quality, and instructor quality, as the antecedents of e‐learning acceptance can provide detailed accounts of the key forces underpinning employees' perception with regard to their beliefs (i.e. perceived usefulness, perceived ease of use, and perceived enjoyment), and this situation can further enhance employees' usage intention of the e‐learning system. Originality/value Based on the extended technology acceptance model and the updated DeLone and McLean information systems success model, this study integrates related e‐learning quality factors including information quality, service quality, and system quality into the research model and further contributes additionally to the identification of instructor quality that may lead to e‐learning acceptance. Also, it should be noted that the empirical evidence on capturing both extrinsic and intrinsic motivators for completely explaining quality antecedents of e‐learning acceptance is well documented in this study. Hence, this study contributes significantly to the body of research on evaluating the quality antecedents of e‐learning acceptance.

Abstract: The major purpose of this study was to investigate the existence of distinct motivational groups within a population of Taiwan English as a Foreign Language (EFL) learners. Based on previous English as a Second Language (ESL) research, this study assumed the existence of both an integrative and an instrumental motivation. A hypothesized motivation, labeled “required,” was also tested for. A survey instrument was developed and completed by over 2000 non‐English majors at two educational institutions in Taiwan. This paper reports preliminary results from the first educational institution and includes the first wave of 500 responses. Exploratory factor analysis was employed to confirm the existence the motivational groups and to determine their temporal orientations (past, present, or future). Results did not support the existence of an integrative motivational group, but did find a strong required motivational group as well as an instrumental group. Lack of integrative motivation among Taiwan EFL learners has significance for language education in Taiwan, since most EFL classroom techniques are derived directly from Western ESL theory that assumes integration as one of the main motivations. Cultural influences on EFL settings are discussed.

Abstract The circular economy (CE) is a more holistic approach that advocates towards extracting the value from the waste and reaching sustainability goals. The objective of the present study is to highlight the prospects, impediments, and prerequisites while transiting from the linear economy (LE) to CE of SMEs. The study gathers information on prospects, impediments, and prerequisites for the transition of LE to a CE from recent studies . A semi‐structured interview questionnaire was prepared, and a survey was conducted on representatives of six SMEs . Further, six caselets were developed to understand the prospects, impediments, and prerequisites based on the findings of the interview and previous information gained from existing literature . The major prospects favoring transition from LE to CE found in the study are significance of 3R (reduce and reuse and recycling) approach, CE leads to competitive advantage, recycling attracts consumers in few cases, CE helps in achieving sustainability goals and reuse of materials are significant in resource conservation. There are certain impediments found such as issues associated with awareness, recyclability issues, financial challenges, and weak management vision of SMEs towards CE implementation. Other resource‐based impediments were found related to trained employees, lack of experience. Whereas, consumer acceptability is also a major concern towards implementing CE. The findings of the study suggest major prerequisites towards CE implementations such as strong “management will,” innovation, technology up‐gradation, training to employees, motivation, and appropriate guidelines. Government pressure to implement CE cannot be an effective step towards the transition of LE to CE.

Many parallelization techniques have been proposed to enhance the performance of the Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce functions, MapReduce has emerged and excels in the mining of datasets of terabyte scale or larger in either homogeneous or heterogeneous clusters. Minimizing the scheduling overhead of each map-reduce phase and maximizing the utilization of nodes in each phase are keys to successful MapReduce implementations. In this paper, we propose three algorithms, named SPC, FPC, and DPC, to investigate effective implementations of the Apriori algorithm in the MapReduce framework. DPC features in dynamically combining candidates of various lengths and outperforms both the straight-forward algorithm SPC and the fixed passes combined counting algorithm FPC. Extensive experimental results also show that all the three algorithms scale up linearly with respect to dataset sizes and cluster sizes.

Over the past few decades, there has been a significant increase in population and unsustainable urbanization, resulting in the exploitation of water and energy resources on a large scale. This has led to a rise in the demand for freshwater, which, in turn, has caused water pollution due to the improper disposal of wastewater from various sources such as industries, agriculture, and households. The wastewater from industries often contains toxic heavy metals and harmful emerging contaminants (ECs), which can harm living beings and cannot biodegrade. Hence, it is a severe issue that needs to be addressed. To combat this problem, wastewater treatments are necessary for two main reasons: firstly, to recycle and reuse wastewater to meet future human demand and reduce water scarcity, and secondly, to ensure compliance with wastewater discharge standards for environmental sustainability while minimizing groundwater and soil contamination. In this review, several wastewater treatment technologies comprising physical, chemical, and biological methods have been discussed critically. The state-of-the-art discussion on different existing wastewater treatment techniques and their limitations has been deliberated. Finally, the limitations of the various existing wastewater treatment techniques and contemporary trends in wastewater treatment have been highlighted.

This paper considers the effects of the reworking of defective items on the economic production quantity (EPQ) model with backlogging allowed. The classic EPQ model assumes that manufacturing facility functions perfectly during a production run. However, due to process deterioration or other factors, the production process may shift and produce imperfect quality items. In this study, a random defective rate is considered, and when regular production ends, the reworking of defective items starts immediately. Not all of the defective items are reworked, a portion of them are scrap and are discarded. Repairing and holding cost per reworked item and disposal cost per scrap item are included in the proposed mathematical modelling and analysis. The renewal reward theorem is utilized to deal with the variable cycle length, and the optimal lot size that minimizes the overall costs for the imperfect quality EPQ model is derived where backorders are permitted.