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.

Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales

Abstract: This paper proposes a novel meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA) to select optimal feature subset for classification purposes of Wisconsin Breast Cancer Database (WBCD). WOA is considered one of the recent bio-inspired optimization algorithms presented in 2016. A set of measurements are used to evaluate the different algorithm over WBCD from the UCI repository. These measurements are precision, accuracy, recall and f-measure. The obtained results are analyzed and compared with those from other algorithms published in breast cancer diagnosis. The experimental results show that WOA algorithm is very competitive for breast cancer diagnosis. Also it has been compared with seven well known features selection algorithms; genetic algorithm (GA), principle component analysis (PCA), mutual information (MI), statistical dependency (SD), random subset feature selection (RSFS), sequential floating forward selection (SFFS) and Sequential Forward Selection (SFS). It obtains overall 98.77 % accuracy, 99.15 % precision, 98.64 % recall and 98.9 % f-score.

Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm

Abstract: Historic manuscript image binarization is considered an important step due to the different kinds of degradation effects on optical character recognition (OCR) or word spotting systems. Previous methods failed on to find the optimal threshold for binarization. In this paper, we investigate the effects of sine cosine algorithm (SCA) on reducing the compactness K-means Clustering as the objective function. The SCA searches for the optimal clustering of the given handwritten manuscript image into compact clusters under some constraints. The proposed approach is evaluated and assessed on a set of selected handwritten Arabic manuscript images. The Experimental result shows that the proposed approach provides the highest value than the famous binarization methods such as; Otsu’s and Niblack’s in terms of F-measure, Pseudo- F-measure, PSNR, Geometric accuracy and the low value on DRD, NRM, MPM.

A Hybrid Krill-ANFIS Model for Wind Speed Forecasting

Abstract: Finding an alternative renewable energy source instead of using traditional energy such as electricity or gas is an important research trend and challenge. This paper presents a new hybrid algorithm that uses Krill Herd (KH) optimization algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) to be able to fit for wind speed forecasting, which is an essential step to generate wind power. ANFIS’s parameters are optimized using KH. The proposed model called (Krill-ANFIS). This model is compared with three models basic ANFIS, PSO-ANFIS, and GA-ANFIS. Krill-ANFIS proved that it can be used as an efficient predictor for the wind speed as well as it can achieve high results and performance measures of root mean square error (RMSE), Coefficient of determination \(R^{2}\) and average absolute percent relative error (AAPRE).

Improving Accessing Efficiency of Cloud Storage Using De-Duplication and Feedback Schemes

Abstract: File distribution and storage in a cloud storage environment is usually handled by storage device providers or physical storage devices rented from third parties. Files can be integrated into useful resources that users are then able to access via centralized management and virtualization. Nevertheless, when the number of files continues to increase, the condition of every storage node cannot be guaranteed by the manager. High volumes of files will result in wasted hardware resources, increased control complexity of the data center, and a less efficient cloud storage system. Therefore, in order to reduce workloads due to duplicate files, we propose the index name servers (INS) to manage not only file storage, data de-duplication, optimized node selection, and server load balancing, but also file compression, chunk matching, real-time feedback control, IP information, and busy level index monitoring. To manage and optimize the storage nodes based on the client-side transmission status by our proposed INS, all nodes must elicit optimal performance and offer suitable resources to clients. In this way, not only can the performance of the storage system be improved, but the files can also be reasonably distributed, decreasing the workload of the storage nodes.

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.