Tealab A, Hefney H, Badr A. Forecasting of Nonlinear Time Series using Artificial Neural Networks. Future Computing and Informatics Journal. 2017.Abstract

<p><span>When forecasting time series, it is important to classify them according to linearity behavior; the linear time series remains at the forefront of academic and applied research. It has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life, with its autoregressive and inherited moving average terms, pose the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.</span></p>

Tealab A, Hefny H, Badr A. A Short-Term Fuzzy Inference System for Stock Market Prediction. International Arab Journal of Information Technology (IAJIT). 2017.Abstract

<p><strong>This study describes a short-term stock fuzzy inference system for predicting and trading stock market indices. The proposed system trading strategy is based on technical analysis by using a mixture of technical indicators. The selected technical indicators for designing trading rules consist of commonly used indicators and new developed one which is based on daily candlestick information produces short and long entry signals. Fuzzy logic is applied for both technical indicators and trading rules definition. The purpose of this study to develop a fuzzy trading system for predicting market trends by using fuzzy rules. The system is aiming to maximize profit and minimize loss using a portfolio management technique. The proposed system is tested using the </strong><strong>gold prices&nbsp;</strong><strong>data. The results are compared to classical non-fuzzy system. The proposal performance produced less losses and better profits. The results demonstrate that the fuzzy logic is promising in stock market prediction with steady upward profit and low losses.&nbsp;</strong></p>

Tealab A, Henefy H, Badr A. Short-Term Stock Fuzzy Decision System with Fuzzy Capital Management. ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH. 2017.Abstract

<p>Forecasting financial market indices became a necessary operation for investors’ decisions in order to get the maximum return of investments. The stock market usually has fluctuations and sometimes perturbation due to political, psychological and even environmental factors that may affect market behavior. That results in nonlinear market characteristics with vagueness, incompleteness, and uncertainty of the used information. Therefore the process of predicting stock prices is complex and risky. This work proposes a short-term stock fuzzy decision system using a novel trading strategy based on mixture of technical indicators. Fuzzy logic is applied for both trading rules definition and portfolio management. The selected stock market technical indicators for designing trading rules consist of commonly used indicators and new developed one. That is based on daily candlestick information produces short and long entry signals. The proposed system is tested using the Athens Stock Exchange data. The results are compared to classical non-fuzzy systems in addition to latest fuzzy approaches. The proposals performance produced less losses and better profits. The results demonstrate that the fuzzy logic is promising in portfolio management with steady upward profit and low losses. However, that deserves more study.&nbsp;</p>

Abdelrahman A, El-Bastawissy AH, Kholief M. Inconsistency Resolution In The Virtual Database Environment Using Fuzzy Logic. International Journal of New Computer Architectures and their Applications (IJNCAA). 2016;6(2):65-72.Abstract

<p><strong> Data inconsistencies due to different representation of the same objects at the data source. Many researchers have tried to solve this problem manually or using source features. None of them took the user’s preferences to source features into account. This paper proposes using fuzzy logic with multiple constraints, in accordance with user preference, to resolve inconsistencies. This approach uses token-based cleaner, a content based inconsistency detection algorithm, to detect inconsistencies. Then, uses fuzzy logic to resolve inconsistencies. An experiment was conducted using our fuzzy algorithm on a trained dataset that reflects our designated point of view. The result indicates that multiple constraints decision making is a suitable technique for resolving inconsistencies.</strong></p>
<p><strong>KEYWORDS Data warehouses; Data analysis; Data integration; Fuzzy logic; Data processing. </strong></p>

Wanas GS, EL-BASTAWISSY ALIH, Kadry MA. Decreasing ERP Implementation Failure in Egypt. In: The 25th International Conference on Computer Theory and Applications (ICCTA 2015) . The 25th International Conference on Computer Theory and Applications (ICCTA 2015) . Alexandria, Egypt; 2015. p. .
MS Abdel-Moneim, Ali H. El-Bastawissy MHK. Quality Driven Approach for Data Integration Systems. In: The 7th International Conference on Information Technology Amman-Jordan. The 7th International Conference on Information Technology Amman-Jordan.; 2015. p. 409–420.
Gouda A, Alu M, Nasr E. Requirements Engineering Techniques in Security of Embedded Systems: A Systematic Review. 2015.Abstract

<p>Security is an important aspect of embedded systems design. The characteristics of embedded systems give rise to a number of novel vulnerabilities. A variety of different solutions are being developed to address these security problems and hence why and how security requirements engineering must be adapted or developed for different approaches have to be studied.This paper presents a systematic review of the current use of requirements engineering activities for the security of embedded systems.8 papers from the last decade have been reviewed from an initial set of 51 papers. The results show that although Security Domain Models techniques are used to a great extent with partitioning methodologies, at the requirements level most Security Domain Models approaches for embedded systems use only partially defined requirements models or even natural language. We additionally identify several research gaps such as a need for more efforts to explicitly deal with requirements traceability and providing a better tool support.</p>
<p>Key words: model-driven engineering, security requirements engineering, embedded systems, computer security, design methodologies, component architecture, and systematic review. </p>

Gouda A, Hefny H, Badr A. Time Series Forecasting using Artificial Neural Networks Methodologies. 2015.Abstract

<p>Abstract— Objective: The aim of this paper is to analyze the development of new forecasting models based on neural networks using the guidelines of the synthesis method, systematic literature review. Method: A systematic literature review method with a manual search of papers published in the last 15 years (2000 to 2014) on new neural networks models for time series forecasting is presented. Results: Only 19 studies meet all the requirements of the inclusion criteria. Of these, only three proposals considered a neural networks model using a process different to the Autoregressive. Conclusion: Although studies relating to the application of neural network models were frequently present, with few studies that proposing new neural networks models for forecasting with theoretical support systematic procedure for the construction of model in the period of study.</p>
<p>Keywords — Nonlinear models, Neural networks models, Forecasts, Innovation, Search methods.</p>

Seddik K, Farghaly A. "Anaphora/Coreference Resolution". In: Zitouni I "Natural Language Processing Approaches to Semitic Languages". 1st ed. "Natural Language Processing Approaches to Semitic Languages". Berlin, Heidelberg: Springer; 2014. p. 247-277. Available from:

<p><a class="reference-link webtrekk-track" href=";facet-content-type... (AR) has attracted the attention of many researchers because of its relevance to Machine Translation, Information Retrieval,&nbsp;</span><a class="reference-link webtrekk-track" href=";facet-co... Summarization</a><span>&nbsp;and many other applications. AR is a complicated problem in NLP especially in Semitic languages because of their complex morphological structure.&nbsp;</span><a class="reference-link webtrekk-track" href=";facet-content-type... be defined as a linguistic relation between two textual entities which is determined when a textual entity (the anaphor) refers to another entity of the text which usually occurs before it (the antecedent). The process of determining the antecedent of an anaphor is referred to as anaphora resolution. In this chapter, we present an account of the anaphora resolution task. The chapter consists of ten sections. The first section is an introduction to the problem. In the second section, we present different types of anaphora. Section 3 discusses the determinants and factors to anaphora resolution and its effect on increasing system performance. In section 4, we discuss the process of anaphora resolution. In section 5 we present different approaches to resolving anaphora and we discuss previous work in the field. Section 6 discusses the recent work in anaphora resolution, and section 7 discusses an important aspect in the anaphora resolution process which is the evaluation of AR systems. In sections 8 and 9, we focus on the anaphora resolution in Semitic languages in particular and the difficulties and challenges facing researchers. Finally, section 10 presents a summary of the chapter.</span></p>