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Hesham Arafat

Bio: Hesham Arafat is an academic researcher from Mansoura University. The author has contributed to research in topics: Cluster analysis & Intelligent word recognition. The author has an hindex of 7, co-authored 28 publications receiving 144 citations.

Papers
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Journal ArticleDOI
TL;DR: This study presents an Improved Dynamic Deployment Technique based-on Genetic Algorithm (IDDT-GA) to maximize the area coverage with the lowest number of nodes as well as minimizing overlapping area between neighboring nodes.
Abstract: Recently, many researchers have paid attention to wireless sensor networks (WSNs) due to their ability to encourage the innovation of the IT industry. Although WSN provides dynamically scalable solutions with various smart applications, the growing need to maximize the area coverage with decreasing the percentage of deployed sensor nodes is still required. Random deployment is preferable for large areas that require a maximal number of nodes but result in coverage holes. As a result, mobile nodes are used to reduce coverage holes and maximize area coverage. The main objective of this study is to present an Improved Dynamic Deployment Technique based-on Genetic Algorithm (IDDT-GA) to maximize the area coverage with the lowest number of nodes as well as minimizing overlapping area between neighboring nodes. A two-point crossover novel is introduced to demonstrate the notation of variable-length encoding. Simulation results reveal that the superiority of the proposed IDDT-GA compared with other state-of-the-art techniques. IDDT-GA has better coverage rates with 9.69% and a minimum overlapping ratio with 35.43% compared to deployment based on Harmony Search (HS). Also, IDDT-GA has minimized the network cost by 13% and 7.44% than Immune Algorithm (IA) and Whale Optimization Algorithm (WOA) respectively. Besides, it confirms its stability with 83.04% compared to maximizing coverage with WOA.

46 citations

Journal ArticleDOI
TL;DR: A Spark Based Mining Framework (SBMF) is proposed to address the imbalanced data problem and shows better performance for the different datasets and classifiers.
Abstract: Classification of imbalanced big data has assembled an extensive consideration by many researchers during the last decade Standard classification methods poorly diagnosis the minority class samples Several approaches have been introduced for solving the problem of class imbalance in big data to enhance the generalization in classification However, most of these approaches neglect the effect of border samples on classification performance; the high impact border samples might expose to misclassification In this paper, a Spark Based Mining Framework (SBMF) is proposed to address the imbalanced data problem Two main modules are designed for this purpose The first is the Border Handling Module (BHM) which under samples the low impact majority border instances and oversamples the minority class instances The second module is the Selective Border Instances sampling (SBI) Module, which enhances the output of the BHM module The performance of the SBMF framework is evaluated and compared with other recent systems A number of experiments were performed using moderate and big datasets with different imbalanced ratio The results obtained from SBMF framework, when compared to the recent works, show better performance for the different datasets and classifiers

26 citations

Journal ArticleDOI
TL;DR: An efficient fast-response content-based image retrieval framework based on Hadoop MapReduce is proposed to operate stably with high performance targeting big data and a novel bag of visual words technique based on a proposed chain-clustering binary search-tree algorithm to build the visual statements for representing the image.

19 citations

Proceedings Article
16 Sep 1991
TL;DR: A technique for real-time recognition of unconstrained Arabic characters is presented and can be extended to cursive words after introducing the additional segmentation stage.
Abstract: A technique for real-time recognition of unconstrained Arabic characters is presented. The proposed technique does not require any constraints of the character forms other than limiting them to a reasonable size and orientation. Structural features, which are more suitable for handwritten character recognition, are selected. Structural features that are independent of the writer style, which are called stable features, use a list of integer values (vector) to describe the character. On the other hand CHAIN CODE is used for other structural features (decisive) that are suitable for more variation of the writer style. A suitable clustering technique is chosen to accomplish the classifier procedure. The algorithm can be extended to cursive words after introducing the additional segmentation stage. >

15 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: This paper is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed.
Abstract: The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

503 citations

Journal ArticleDOI
TL;DR: This paper introduces the general topic of optical character recognition (OCR), and introduces a five stage model for AOTR systems and classify research work according to this model, and presents an historical review of the Arabic text recognition systems.

260 citations

Journal ArticleDOI
TL;DR: This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.
Abstract: Off-line recognition requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text. Other complexities that an off-line recognition system has to deal with are the lower resolution of the document and the poor binarisation, which can contribute to readability when essential features of the characters are deleted or obscured. Recognising Arabic script presents two additional challenges: orthography is cursive and letter shape is context sensitive. Certain character combinations form new ligature shapes, which are often font-dependent. Some ligatures involve vertical stacking of characters. Since not all letters connect, word boundary location becomes an interesting problem, as spacing may separate not only words, but also certain characters within a word. Various techniques have been implemented to achieve high recognition rates. These techniques have tackled different aspects of the recognition system. This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.

207 citations

Journal ArticleDOI
TL;DR: This survey conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics, and aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimediabig data analytics.
Abstract: With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

168 citations