Jeng-Shyang Pan

Bio: Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.

A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

Abstract: To this day, the prevention of coronavirus disease is still an arduous battle. Medical imaging technology has played an important role in the fight against the epidemic. This paper is to perform feature selection on the CT image feature sets used for COVID-19 detection to improve the speed and accuracy of detection. In this work, the population-based intelligent optimization algorithm Aquila optimizer is used for feature selection. This feature selection method uses an S-shaped transfer function to process continuous values and convert them into binary form. And when the performance of the updated solution is not good, a new mutation strategy is proposed to enhance the convergence effect of the solution. Through the verification of two CT image sets, the experimental results show that the use of the S-shaped transfer function and the proposed mutation strategy can effectively improve the effect of feature selection. The prediction accuracy of the features selected by this method on the two open datasets is 99.67% and 99.28%, respectively.

2D(PC)2 A for Face Recognition with One Training Image per Person

Abstract: In the real-world application of face recognition system, owing to the difficulties of collecting samples or storage space of systems, only one sample image per person is stored in the system, which is so-called one sample per person problem. In this paper, we propose a novel algorithm, called 2D(PC)2A, to solve this problem. The procedure of 2D(PC)2A can be divided into the three stages: 1) creating the combined image from the original image 2) performing 2DPCA on the combined images; 3) classifying a new face based on assembled matrix distance (AMD). Experiments implemented on two real datasets show that 2D(PC)2A method is an efficient and practical approach for face recognition.

A new method for constrained optimization problems to produce initial values

Abstract: To solve the hard problem that it is difficult to produce initial values satisfied with the constrained conditions in constrained optimization problems, according to the good ability of particle swarm optimization in finding good values, the paper presents a new method for producing stochastic values satisfied with constrained conditions using particle swarm optimization, which can be used in the all kinds of algorithms to produce initial values. The examples show that the algorithm this paper presents can get good stochastic values satisfied with constrained conditions.

Completely hide sensitive association rules using EMO by deleting transactions

Abstract: Data mining techniques enable efficient extraction of useful knowledge from a large data repository. However, it also can disclose sensitive information if used inappropriately. A feasible way to address this problem is to sanitize the database to conceal sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the tradeoff between hiding sensitive rules and disclosing non-sensitive ones during the hiding process, a novel association rule hiding approach is proposed based on evolutionary multi-objective optimization (EMO). It modifies the original database by deleting identified transactions/tuples to hide sensitive rules. Experiment results are reported to show the effectiveness of the proposed approach.

An Improving Data Compression Capability in Sensor Node to Support SensorML-Compatible for Internet-of-Things.

Abstract: Limited bandwidth and lacked compatibility information of the sensor nodes in Wireless Sensor Network (WSN) are critical issues in implementing WSN applications. This paper proposes a method of improvement to data compression capability to support SensorML interface for information exchange in the sensor nodes. The delivery of the packets with XML format of all nodes in WSN could cause the traffic load increases. This paper proposes a method of improvement of data compression capability for exchanging data in the sensorML for the Internet of Things (IoT). The delivery of XML formatted packets of all nodes in Wireless sensor networks (WSN) could cause the traffic load increases. The proposed method suggests data compression condensing the packets in the node and analyzes the relationship between the parameters and the performance. The settings of sliding window size and comparing length are factors to affect the performance of network traffic load significantly. The experimental results show that, with a format of 256 bytes and LZSS compression, the transmission performance is improved up to 48.8%, in comparison with the original approach. Besides, the proposed method can deliver up to 204% of information than the other works.

Mean shift: a robust approach toward feature space analysis

Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Digital Watermarking

Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

PORs: Proofs of Retrievability for Large Files

Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.