scispace - formally typeset
Search or ask a question
Author

Noura Metawa

Bio: Noura Metawa is an academic researcher from Mansoura University. The author has contributed to research in topics: Computer science & Information system. The author has an hindex of 9, co-authored 23 publications receiving 421 citations. Previous affiliations of Noura Metawa include College of Business Administration & American University in the Emirates.

Papers
More filters
Journal ArticleDOI
TL;DR: An intelligent model based on the Genetic Algorithm to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC), which provides a framework to optimize bank objectives when constructing the loan portfolio.
Abstract: The bank lending decisions in credit crunch environments are big challenge.This NP-hard optimization problem is solved using a proposed GA based model.The proposed model is tested using two scenarios with simulated and real data.The real data is collected from Southern Louisiana Credit Union.The proposed model increased the bank profit and improved the system performance. To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%50%. Moreover, it greatly increases the bank profit by a range of 3.9%8.1%.

210 citations

Journal ArticleDOI
TL;DR: Experimental analysis shows that the ACO-FCP ensemble model is superior and more robust than its counterparts, and this study strongly recommends that the proposed ACO -FCP model is highly competitive than traditional and other artificial intelligence techniques.

111 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the relationship between investors' demographic characteristics (age, gender, education level, and experience) and their investment decisions through behavioral factors (sentiment, overconfidence, overreaction and underreaction) as mediator variables in the Egyptian stock market.
Abstract: This paper aims to investigate the relationship between investors’ demographic characteristics (age, gender, education level and experience) and their investment decisions through behavioral factors (sentiment, overconfidence, overreaction and underreaction and herd behavior) as mediator variables in the Egyptian stock market.,This paper collects data from a structured questionnaire survey carried out among 384 local Egyptian, foreign, institutional and individual investors. This paper used a partial multiple regression method to analyze the effect of investors’ demographic characteristics on investment decisions through behavioral factors as the mediator variable.,Investor sentiment, overreaction and underreaction, overconfidence and herd behavior significantly affect investment decisions. Also, age, gender and the level of education have significant positive effects on investment decisions by investors. Experience does not play a significant role in investment decisions, but as investors gain experience, they tend to overlook the emotional factors.,The findings of this paper would help to understand common behavioral patterns of investors and indicate a path toward the growth of the Egyptian stock market.,There is a lack of research in behavioral finance covering Middle East and North African markets. This paper attempts to fulfill the gap by analyzing behavioral factors in the Egyptian market.

74 citations

Journal ArticleDOI
TL;DR: A plithogenic multi-criteria decision-making (MCDM) model based on neutrosophic analytic hierarchy process (AHP), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, and Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) method is presented in this article.
Abstract: Financial performance evaluation is very significant for manufacturing industries in a competitive environment to achieve investment goals, especially increasing revenue. Financial performance measures must be identified accurately, because the evaluation process reflects the effectiveness of a company. The purpose of this article is to present a plithogenic multi-criteria decision-making (MCDM) model based on neutrosophic analytic hierarchy process (AHP), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, and Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The financial performance in this study is measured by a set of financial ratios. To examine the proposed model, the top 10 steel companies in Egypt are evaluated based on specified financial ratios. According to steel manufacturing experts, the weight of the criteria is determined using AHP method. The company ranking is determined using VIKOR and TOPSIS comparatively. The results show that the obtained ranks of the companies by these methods are almost the same.

66 citations

Journal ArticleDOI
TL;DR: A cluster based classification model, comprises of two stages: improved K-means clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA) based Classification model, is presented.
Abstract: Financial crisis prediction (FCP) plays a vital role in the economic phenomenon. The precise prediction of the number and possibility of failing firms acts as an index of the growth and strength of a nation’s economy. Traditionally, several methods have been presented for effective FCP. On the other hand, the classification performance and prediction accuracy and data legality is not good enough for practical applications. In addition, many of the developed methods perform well for some of the particular dataset but not adaptable to different dataset. Hence, there is a requirement to develop an efficient prediction model for better classification performance and adaptable to diverse dataset. This paper presents a cluster based classification model, comprises of two stages: improved K-means clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA) based classification model. In the first stage, an improved K-means algorithm is devised to eliminate the wrongly clustered data. Then, a rule-based model is selected to design to fit the given dataset. At the end, FSCGACA is employed for seeking the optimal parameters of the rule-based model. The proposed algorithm is employed to a collection of three benchmark dataset which include qualitative bankruptcy dataset, Weislaw dataset and Polish dataset. A detailed statistical analysis of the dataset is also given. The results analysis ensured that the presented FCP model is superior to other classification model based on the different measures and also found to be more appropriate for diverse dataset.

51 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: A new model to optimize virtual machines selection in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0 applications is proposed and outperforms on the state-of-the-art models in total execution time and the system efficiency.

249 citations

01 Jan 1997
TL;DR: In this paper, the authors examined one-three-year performance of common stocks following 5,596 stock split and 76 reverse split announcements made during the period 1976-91, and found that the announcement period and the long-run abnormal returns are both positively associated with an increase in dividends.
Abstract: The authors examine one-three-year performance of common stocks following 5,596 stock split and 76 reverse split announcements made during the period 1976-91. For stock splits, on average, the one- and three-year buy-and-hold abnormal returns after the announcement month are 7.05 percent and 11.87 percent, respectively. For reverse splits, the corresponding abnormal returns are -10.76 percent and -33.90 percent. The results suggest that the market underreacts to both the stock split and the reverse split announcements. The authors also find that the announcement period and the long-run abnormal returns are both positively associated with an increase in dividends. Copyright 1997 by University of Chicago Press.

244 citations

Journal ArticleDOI
TL;DR: An intelligent model based on the Genetic Algorithm to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC), which provides a framework to optimize bank objectives when constructing the loan portfolio.
Abstract: The bank lending decisions in credit crunch environments are big challenge.This NP-hard optimization problem is solved using a proposed GA based model.The proposed model is tested using two scenarios with simulated and real data.The real data is collected from Southern Louisiana Credit Union.The proposed model increased the bank profit and improved the system performance. To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%50%. Moreover, it greatly increases the bank profit by a range of 3.9%8.1%.

210 citations

Journal ArticleDOI
TL;DR: The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.
Abstract: Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

195 citations

Journal ArticleDOI
TL;DR: An efficient, Bezier curve based approach for the path planning in a dynamic field using a Modified Genetic Algorithm (MGA), which aims to boost the diversity of the generated solutions of the standard GA which increases the exploration capabilities of the MGA.

178 citations