Person: ELMASRY, WİSAM
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Dr. Öğr. Üyesi
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ELMASRY
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WİSAM
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Publication AT-ODTSA: A Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis(University of Bahrain, 2022) Sahmoud, Shaaban; Abudalfa, Shadi; ELMASRY, WİSAMIn the field of sentiment analysis, most of research has conducted experiments on datasets collected from Twitter for manipulating a specific language. Little number of datasets has been collected for detecting sentiments expressed in Arabic tweets. Moreover, very limited number of such datasets is suitable for conducting recent research directions such as target dependent sentiment analysis and open-domain targeted sentiment analysis. Thereby, there is a dire need for reliable datasets that are specifically acquired for open-domain targeted sentiment analysis with Arabic language. Therefore, in this paper, we introduce AT-ODTSA, a dataset of Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets (topics) and sentiments (opinions) expressed in the collected tweets. To the best of our knowledge, our work presents the first dataset that manually annotated for applying Arabic open-domain targeted sentiment analysis. We also present a detailed statistical analysis of the dataset. The AT-ODTSA dataset is suitable for train numerous machine learning models such as a deep learning-based model. © 2022 University of Bahrain. All rights reserved.Publication A Comparative Assessment of Five Different Distributions Based on Five Different Optimization Methods for Modeling Wind Speed Distribution(Gazi University, 2023) Wadi, Mohammed; ELMASRY, WİSAMDetermining wind regime distribution patterns is essential for many reasons; modelling wind power potential is one of the most crucial. In that regard, Weibull, Gamma, and Rayleigh functions are the most widely used distributions for describing wind speed distribution. However, they could not be the best for describing all wind systems. Also, estimation methods play a significant role in deciding which distribution can achieve the best matching. Consequently, alternative distributions and estimation methods are required to be studied. An extensive analysis of five different distributions to describe the wind speeds distribution, namely Rayleigh, Weibull, Inverse Gaussian, Burr Type XII, and Generalized Pareto, are introduced in this study. Further, five metaheuristic optimization methods, Grasshopper Optimization Algorithm, Grey Wolf Optimization, Moth-Flame Optimization, Salp Swarm Algorithm, and Whale Optimization Algorithm, are employed to specify the optimum parameters per distribution. Five error criteria and seven statistical descriptors are utilized to compare the good-of-fitness of the introduced distributions. Therefore, this paper provides different important methods to estimate the wind potential at any site..Publication Two-Tier Cascaded Classifiers to Improve Electrical Power Quality(Institute of Electrical and Electronics Engineers Inc., 2022) ELMASRY, WİSAM; Wadi, Mohammed; Shahinzadeh, HosseinAlthough the manifestation of faults in electrical distribution networks is likely scarce, they deemed to be one of the most severe threats which encounter the reliability and stability of power systems. Thereby, most of research efforts in the last decade have been directed to characterize the faults and propose detection systems. However, the state-of-art of these systems still undergo serious shortcomings such as automation, integration and validation. To surpass these shortcomings, a two-tier system is proposed for both fault detection and fault classification in electrical distribution networks. Prior to deal with faults, the captured voltage signals pass through a preparatory unit to process the raw voltage signals. Afterwards, the optimized classifiers detect and classify the faults in the first and second tiers, respectively. Furthermore, the performance of the classifiers in both tiers is validated using the VSB dataset (real-time data for fault detection). Finally, the proposed two-tier system proved its efficiency in detecting electrical faults and their type as well. © 2022 IEEE.Publication Sensitivity Reliability Analysis of Power Distribution Networks Using Fuzzy Logic(Institute of Electrical and Electronics Engineers Inc., 2022) Wadi, Mohammed; ELMASRY, WİSAM; Küçük, İsmail; Shahinzadeh, HosseinThis paper proposes a combined method utilizing both the reliability block diagram analytical technique and the Monte Carlo simulation method to estimate the reliability of power systems. Since the reliability of collected data is associated with noise and erroneous data, performing the sensitivity analysis is indispensable. Sensitivity analysis utilizing fuzzy logic specifies these uncertainties and their effects on the reliability calculations. The proposed method is applied to the Roy Billiton Test System Bus-2 to confirm its applicability. The obtained results have verified the sensitivity analysis's importance in drawing an accurate picture of reliability evaluation and a crucial tool for distribution power utilities to identify the susceptible parameters that seriously erode the system's complete reliability. © 2022 IEEE.Publication Utilizing Metaheuristics to Estimate Wind Energy Integration in Smart Grids With A Comparative Analysis of Ten Distributions(Taylor & Francis Inc., 2024) Wadi, Mohammed; ELMASRY, WİSAM; Çolak, İlhami; Jouda, Muhammed; Küçük, İsmailRenewable energy presents the most favorable approach to address the escalating challenge of greenhouse gas emissions while simultaneously guaranteeing the safeguarding of the environment. This article utilizes ten different distributions to approximate the wind energy integration in smart grids. The employed distributions are Rayleigh, Poisson, Weibull, Normal, Gamma, Laplace, LogNormal, Nakagami, Birnbaum Saunders, and Burr. The parameters of each distribution are calculated based on metaheuristic methods such as particle swarm optimization and genetic algorithms. Six error criteria have been employed to evaluate the precision of introduced distributions and metaheuristic methods. The approximation is performed by utilizing the wind data collected over three years hourly in the Marmara region of Turkiye. The empirical findings indicate that Gamma, Burr, and Weibull distributions exhibit more significant superiority than the remaining distributions across all datasets.Publication Rüzgâr Enerjisi Potansiyelini Değerlendirirken Önemli Hususlar(Oğuzhan Yılmaz, 2023) Wadi, Mohammed; ELMASRY, WİSAM; Tamyiğit, Furkan AhmetRüzgâr rejimi dağılım modelinin belirlenmesi birkaç nedenden dolayı gereklidir, rüzgâr gücü çıktısını tahmin etmek en önemli konulardan biridir. Bu açıdan rüzgâr hızı dağılımını modellemek için Weibull, Gamma ve Rayleigh dağılımları en yaygın olarak kullanılan dağılımlardır. Ancak, tüm rüzgâr modellerini modellemede üstün olmayabilirler. Sonuç olarak, yerine geçecek dağılım fonksiyonlarının çalışılması gerekmektedir. Bu makale, rüzgâr hızı dağılımını tanımlamak için Weibull, Uç Değer, Ters Gauss, Lojistik, Log-Lojistik, Yarı-Normal, Burr Tipi XII, Genelleştirilmiş Uç Değer, Genelleştirilmiş Pareto ve T Konum-Ölçeği adlı on farklı dağılım fonksiyonlarını kapsamlı bir şekilde sunar. Ayrıca, her dağılımın parametre değerlerini optimize etmek için iki metasezgisel optimizasyon yöntemi olan Genetik Algoritması ve Parçacık Sürü Optimizasyonu kullanılmaktadır. Sunulan dağılımların iyi durumlarını (good-of-fitness) karşılaştırmak için yedi istatistiksel tanımlayıcı ile birlikte altı hata kriteri kullanılmıştır.