Evaluating Weapon System using Fuzzy Analytical Hierarchy Process

We propose a nw algorithm for evaluating a weapon system by fuzzy analytical hierarchy process. using the symmetric triangular fuzzy number we built a judgement matrix through pair-wise comparison technique. To derive fuzzy eigenvectors, we utilized interval arithmetic, a-cuts together with index of optimism B(Bita) to estimate the degree of satisfaction. Thus, the required weights of the final evaluation could be obtained. Finally by selecting a weapon system as an example we demonstrated the new algorithm

A web-based architecture for implementing electronic procurement in military organisations."

Abstract In recent years, the vital development of the Internet offers increasing opportunity for electronic commerce. E-commerce attracts much attention from enterprises, not only to get connection with others and make a profit from their product/service, but also to reduce the costs of internal and external operational procedures. Procurement is a very critical task because it is a matter not only of making a profit, but also of staying in business in a highly competitive environment. In the government sector, procurement is sometimes the source of corruption, scandal and abuse of public resources. Besides inadequately qualified personnel, "transparency" of the procurement environment becomes another source of problems in procurement procedure. This paper investigates a case study of e-commerce in the Taiwanese military organization by diagnosing and preventing procurement faults, constructing a transparent procurement environment, and enhancing military procurement efficiency, and is an attempt to establish an e-market environment via web-based architecture on e-procurement procedure. The design of a relational database is introduced and system implementation is presented. Also, efficiency and benefits of the proposed system are discussed

A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits

A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector machines (linear and radial basis functions), gene expression programming, multilayer perceptron regression, and generalized regression neural networks to calculate the importance of indicators. We then integrated the five indicator selection methods to find the key indicators. Next, we used long short-term memory (LSTM) and gated recurrent units (GRU) to build time series models for forecasting stock prices and compare them with the listing models. To evaluate the effectiveness of the proposed model, we collected six different stock market data from 2011 to 2019 to evaluate their forecast performance based on RMSE and MAPE metrics. It is worth mentioning that this study proposes two trading policies to evaluate trading profits and compare them with the listing methods, and their profits are pretty good to investors. After the experiments, the proposed time series model (GRU/LSTM combined with the selected key indicators) exhibits better forecast ability in fluctuating and non-fluctuating environments than the listing models, thus presenting an effective reference for stakeholders

Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method

Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans. From the literature, although the automatic segmentation method is less laborious and time-consuming, it is restricted in several specific types of images. In addition, hybrid techniques segmentation improves the shortcomings of the single segmentation method. Therefore, this study proposed a hybrid segmentation combined with rough set classifier and wavelet packet method to identify degenerative brain disease. The proposed method is a three-stage image process method to enhance accuracy of brain disease classification. In the first stage, this study used the proposed hybrid segmentation algorithms to segment the brain ROI (region of interest). In the second stage, wavelet packet was used to conduct the image decomposition and calculate the feature values. In the final stage, the rough set classifier was utilized to identify the degenerative brain disease. In verification and comparison, two experiments were employed to verify the effectiveness of the proposed method and compare with the TV-seg (total variation segmentation) algorithm, Discrete Cosine Transform, and the listing classifiers. Overall, the results indicated that the proposed method outperforms the listing methods

Rough Classifier Based on Region Growth Algorithm for Identifying Liver CT Image

[[abstract]]Over decades, liver cancer is a rising cause of death in Taiwan, and more and more researchers are concerned about detecting hepatic tumors in computed tomography (CT) images. For clinical applications in terms of diagnosis and treatment planning, image segmentation on abdominal CT is indispensable. Patients with a large number of CT images need specialist physicians to identify, and detecting tumor location correctly from many CT images has been a major challenge subsequently. Therefore, this paper proposed a novel computer-aided detection (CAD) method that had high classification accuracy for identifying tumors. The proposed method used a region growing algorithm to segment liver CT images, employed REDUCT sets to reduce attributes, and then utilized a rough set algorithm to enhance classification performance. To evaluate the classification performances, the proposed method was compared with five different classification methods: decision tree (C4.5 and REP (reduced error pruning)), multilayer perceptron, Naïve Bayes, and support vector machine (SVM). The results indicate that the proposed method is superior to the listing methods in terms of classification accuracy

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations

Developing a Mobile APP-Supported Learning System for Evaluating Health-Related Physical Fitness Achievements of Students

This study developed a mobile APP support learning system to compare the effects of two different learning approaches based on students’ health-related physical fitness (HRPF) achievements, self-efficacy, and system usability. There were 90 participants from four physical education classes in an elementary school of Taiwan who were assigned to the experimental and control groups. An 8-week experiment was conducted to evaluate the two different learning approaches. The experimental results showed that a mobile APP support learning approach could improve the students’ HRPF achievements. Furthermore, this study found that the self-efficacy and system operations affect the students’ HRPF achievements. To sum up, the combination of traditional and a mobile APP support learning system is an effective approach that would help students to improve their HRPF achievements. The findings can provide the key factors of assisted learning design and students’ HRPF achievements for the teachers and the related educators as references

An Appraisal Model Based on a Synthetic Feature Selection Approach for Students’ Academic Achievement

Obtaining necessary information (and even extracting hidden messages) from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years because it can be used to find historical patterns and employ machine learning to aid in decision-making. When we find unexpected rules or patterns from the data, they are likely to be of high value. This paper proposes a synthetic feature selection approach (SFSA), which is combined with a support vector machine (SVM) to extract patterns and find the key features that influence students’ academic achievement. For verifying the proposed model, two databases, namely, “Student Profile” and “Tutorship Record”, were collected from an elementary school in Taiwan, and were concatenated into an integrated dataset based on students’ names as a research dataset. The results indicate the following: (1) the accuracy of the proposed feature selection approach is better than that of the Minimum-Redundancy-Maximum-Relevance (mRMR) approach; (2) the proposed model is better than the listing methods when the six least influential features have been deleted; and (3) the proposed model can enhance the accuracy and facilitate the interpretation of the pattern from a hybrid-type dataset of students’ academic achievement