52 research outputs found

    Improvement of authorship invarianceness for individuality representation in writer identification

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    Writer Identification (WI) is one of the areas in pattern recognition that have created a center of attention for many researchers to work in. Recently, its main focus is in forensics and biometric application, e.g. writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on feature extraction and classi?cation task in order to identify the handwritten authorship. However, additional steps need to be per- formed in order to have a better representation of input prior to the classi?cation task. Features extracted from the feature extraction task for a writer are in vari- ous representations, which degrades the classi?cation performance. This paper will discuss this additional process that can transform the various representations into a better representation of individual features for Individuality of Handwriting, in order to improve the performance of identification in WI

    Digital Paleography: Using the Digital Representation of Jawi Manuscripts to Support Paleographic Analysis

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    Palaeography is the study of ancient handwritten manuscripts to date the age and to localize ancient and medieval scripts. It also deals with analysing the development of the letters shape. Ancient Jawi manuscripts are one of the least studiedarea. Nowadays, over 7789 known Jawi manuscripts are kept in custody of various libraries in Malaysia. Most of these manuscripts were undated with unknown authors and location of origin. Analysing the different types of writing styles and recognizing the manuscript illuminations can discover this important information. In this paper, we discuss the palaeographical analysis from the perspective of computer science and propose a general framework for that. This process involves investigation of Arabic influence on the Jawi manuscript writings, establishing the palaeographical type of the script, and classification of writing styles based on local and global Jawi image features

    Arabic Calligraphy Classification using Triangle Model for Digital Jawi Paleography Analysis

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    Calligraphy classification of the ancient manuscripts gives useful information to paleographers. Researches on digital paleography using calligraphy are done on the manuscripts to identify unidentified place of origin, number of writers, and the date of ancient manuscripts. Information that are used are features from characters, tangent value and features known as Grey-Level Co-occurrence Matrix (GLCM). For Digital Jawi Paleography, a novel technique is proposed based on the triangle. This technique defines three important coordinates in the image of each character and translates it into triangle geometry form. The features are extracted from the triangle to represent the Jawi (Arabic writing in Malay language) characters. Experiments have been conducted using seven Unsupervised Machine Learning (UML) algorithms and one Supervised Machine Learning (SML). This stage focuses on the accuracy of Arabic calligraphy classification. Hence, the model and test data are Arabic calligraphy letters taken from calligraphy books. The number of model is 711 for the UML and 1019 for the SML. Twelve features are extracted from the formed triangles used

    An approach to estimate the saving from negotiation based on cost- benefit anaylsis model

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    This paper presents an approach to estimate the savings from implementing negotiation in requirements elicitation process. The aim of implementing negotiation is to minimize the possibility of introducing defects during the creation of requirements and to decrease later effort required to fix requirements defects. An empirical evaluation study is adopted through a role play experiment to evaluate the benefit of exercising negotiation. The net-gain and the return-oninvestment show positive value which suggest that negotiation activities worth an investment. Based on the return-oninvestment of 197 percent in average, this paper suggests that negotiation is a useful prevention activity to inhibit defects from occurring during the creation of requirements

    Intelligent Biometric Detection System for Disabled People

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    This paper presents an intelligent system for disabled people to have a full control of computers using biometric detection so that they can still operate computers like normal people do. An idea behind this project is a fact that the emergence of difficulties for disabled people to interact with computers. For instance, paralyzed people are unable to control computer mouse. Hence this project was conducted. This project only uses a webcam as an input which captures the user’s head and then tracks their head movement and iris in order to control the mouse actions. It is done by using image processing techniques such as Component Localization, Haar Cascade classifier and Hough transform

    Computational Intelligence In Digital Forensics: Forensic Investigation And Applications

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    The Series "Studies in Computational Intelligence" publishes new development and advances in the various areas of computational intelligence - quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output

    Arbitrarily Substantial Number Representation For Complex Number

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    Researchers are often perplexed when their machine learning algorithms are required to deal with complex number. Various strategies are commonly employed to project complex number into real number, although it is frequently sacrificing the information contained in the complex number. This paper proposes a new method and four techniques to represent complex number as real number, without having to sacrifice the information contained. The proposed techniques are also capable of retrieving the original complex number from the representing real number, with little to none of information loss. The promising applicability of the proposed techniques has been demonstrated and worth to receive further exploration in representing the complex number
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