Y.-C. Lee

Bio: Y.-C. Lee is an academic researcher from WuFeng University. The author has contributed to research in topics: Authentication & Radio-frequency identification. The author has an hindex of 6, co-authored 12 publications receiving 134 citations.

A New Ultralightweight RFID Protocol with Mutual Authentication

Abstract: Due to the well-developed technology and its variety of applications, the Radio Frequency Identifications (RFIDs) become more and more popular. In many applications such as authentication, the RFID systems need security mechanism to resist all possible attacks and threats. However, most of the security mechanisms always too complex on computation or need large memory space such that they are not suit for low-cost RFIDs. In this paper, we propose a new ultralightweight RFID authentication protocol with mutual authentication. The protocol requires only simple bit-wise operations and can resist various attacks.

An Improvement on RFID Authentication Protocol with Privacy Protection

Abstract: Due to the well-developed technology, the radio frequency identification (RFID) is widely used in all areas. For some applications, the RFID system simply worked as a memory card without security mechanism. However, in many applications, the RFID systems need security mechanism for authentication. In scenario such as e-passport, the RFID systems even need mechanism to protect userpsilas privacy. In 2004, Gao et al. proposed a RFID system for supply chain, and their system has security drawbacks. In 2007, Chen et al. proposed an improved mechanism to enhance the security. However, in this paper, we will show that Chen et al.'s protocol is not so secure as they claimed. Any adversary can masquerade as a legal user if he/she eavesdrops the transmitted message. We propose an improvement protocol to fix the flaws. The improvement protocol is secure with merits of privacy protection, resisting counterfeit attack and obtain mutual authentication.

Method for permitting and blocking use of internet by detecting plural terminals on network

Abstract: The present invention is related to a technology for grasping the number of a plurality of terminals of a client using a Cookie in a private network in which plural terminals are shared by redirecting a session which is to be connected to a Web by analyzing a TCP/IP packet, detecting the accurate number of a plurality of terminals of a client using an Internet, and making the accurate number as a DB, and selectively permitting or blocking a connection to Internet according to TCP/IP by using the Cookie pool information of a DB type and JOB when the users configuring and using a private network connect to the Internet at the same time.

Feature selection and classification by using grid computing based evolutionary approach for the microarray data

Abstract: The cancer classification through gene expression patterns becomes one of the most promising applications of the microarray technology. It is also a significant procedure in bioinformatics. In this study a grid computing based evolutionary mining approach is proposed as discriminant function for gene selection and tumor classification. The proposed approach is based on the grid computing infrastructure for establishing the best attributes set. The discriminant analysis based on vector distant of median method as the evaluation function of genetic algorithm which lays stress on find the key attributes set of the data set to establish the best attributes set for constructing a classification response model with highest accuracy. We show experimentally that the proposed approach for several benchmarking cancer microarray data sets can work effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures.

An immune based two-phase approach for the multiple-type surveillance camera location problem

Abstract: For the recent years, there are more and more surveillance cameras set on lanes, train/bus stations, hospitals, schools, banks, supermarkets, shopping malls, etc., to improve the safety of people around them. In Taiwan, the so-called ''E-Patrol'' system is used to support policemen for providing patrol services and several criminal cases have been solved with the use of surveillance cameras of ''E-Patrol'' system. It is well known that inappropriate settings of surveillance cameras will result in some dead angles and oversetting of surveillance cameras will waste limited resources. The setting of surveillance cameras is an important issue and a complicated NP problem. In this paper, we consider the design problem of multiple-type surveillance cameras on various lanes in which two kinds of variables have to be decided simultaneously, namely, discrete variables for types of surveillance cameras and continuous variables for locations of surveillance cameras. The main purposes of this paper are (i) to propose an immune based two-phase approach for solving the optimal design of multiple-type surveillance camera problem, and (ii) to show the excellent performance of the proposed two-phase approach.

A Survey on Evolutionary Computation Approaches to Feature Selection

Abstract: Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

A Hybrid Genetic Algorithm With Wrapper-Embedded Approaches for Feature Selection

Abstract: Feature selection is an important research area for big data analysis. In recent years, various feature selection approaches have been developed, which can be divided into four categories: filter, wrapper, embedded, and combined methods. In the combined category, many hybrid genetic approaches from evolutionary computations combine filter and wrapper measures of feature evaluation to implement a population-based global optimization with efficient local search. However, there are limitations to existing combined methods, such as the two-stage and inconsistent feature evaluation measures, difficulties in analyzing data with high feature interaction, and challenges in handling large-scale features and instances. Focusing on these three limitations, we proposed a hybrid genetic algorithm with wrapper−embedded feature approach for selection approach (HGAWE), which combines genetic algorithm (global search) with embedded regularization approaches (local search) together. We also proposed a novel chromosome representation (intron+exon) for global and local optimization procedures in HGAWE. Based on this “intron+exon” encoding, the regularization method can select the relevant features and construct the learning model simultaneously, and genetic operations aim to globally optimize the control parameters in the above non-convex regularization. We mention that any efficient regularization approach can serve as the embedded method in HGAWE, and a hybrid $L_{1/2}+L_{2}$ regularization approach is investigated as an example in this paper. Empirical study of the HGAWE approach on some simulation data and five gene microarray data sets indicates that it outperforms the existing combined methods in terms of feature selection and classification accuracy.

Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects

Abstract: This study aims to systematically review prior research on the evaluation and benchmarking of automated acute leukaemia classification tasks The review depends on three reliable search engines: ScienceDirect, Web of Science and IEEE Xplore A research taxonomy developed for the review considers a wide perspective for automated detection and classification of acute leukaemia research and reflects the usage trends in the evaluation criteria in this field The developed taxonomy consists of three main research directions in this domain The taxonomy involves two phases The first phase includes all three research directions The second one demonstrates all the criteria used for evaluating acute leukaemia classification The final set of studies includes 83 investigations, most of which focused on enhancing the accuracy and performance of detection and classification through proposed methods or systems Few efforts were made to undertake the evaluation issues According to the final set of articles, three groups of articles represented the main research directions in this domain: 56 articles highlighted the proposed methods, 22 articles involved proposals for system development and 5 papers centred on evaluation and comparison The other taxonomy side included 16 main and sub-evaluation and benchmarking criteria This review highlights three serious issues in the evaluation and benchmarking of multiclass classification of acute leukaemia, namely, conflicting criteria, evaluation criteria and criteria importance It also determines the weakness of benchmarking tools To solve these issues, multicriteria decision-making (MCDM) analysis techniques were proposed as effective recommended solutions in the methodological aspect This methodological aspect involves a proposed decision support system based on MCDM for evaluation and benchmarking to select suitable multiclass classification models for acute leukaemia The said support system is examined and has three sequential phases Phase One presents the identification procedure and process for establishing a decision matrix based on a crossover of evaluation criteria and acute leukaemia multiclass classification models Phase Two describes the decision matrix development for the selection of acute leukaemia classification models based on the integrated Best and worst method (BWM) and VIKOR Phase Three entails the validation of the proposed system