100 results on '"Eid, Marwa M."'
Search Results
2. Optimizing Potato Disease Classification Using a Metaheuristics Algorithm for Deep Learning: A Novel Approach for Sustainable Agriculture
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El-Kenawy, El-Sayed M., Alhussan, Amel Ali, Khafaga, Doaa Sami, Abotaleb, Mostafa, Mishra, Pradeep, Arnous, Reham, and Eid, Marwa M.
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- 2024
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3. Forecasting Production of Potato for a Sustainable Future: Global Market Analysis
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Mishra, Pradeep, Alhussan, Amel Ali, Khafaga, Doaa Sami, Lal, Priyanka, Ray, Soumik, Abotaleb, Mostafa, Alakkari, Khder, Eid, Marwa M., and El-kenawy, El-Sayed M.
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- 2024
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4. The beneficial effects of low intensity laser acupuncture therapy in chronic tonsillitis
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Eid, Marwa M., Waked, Intsar S., and Wahid, Amany R. Abdel
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- 2012
5. Fresh versus frozen embryo transfer in women with polycystic ovaries syndrome undergoing in vitro fertilisation
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Hussein, Mohamed, Sayed, Abdullah, Eldaly, Ashraf, AlSawaf, Ahmed H., Eid, Marwa M., Abdel-Rasheed, Mazen, and Rashwan, Ahmed S.
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- 2023
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6. Greylag Goose Optimization: Nature-inspired optimization algorithm
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El-kenawy, El-Sayed M., Khodadadi, Nima, Mirjalili, Seyedali, Abdelhamid, Abdelaziz A., Eid, Marwa M., and Ibrahim, Abdelhameed
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- 2024
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7. Delayed versus early umbilical cord clamping for near-term infants born to preeclamptic mothers; a randomized controlled trial
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Rashwan, Ahmed, Eldaly, Ashraf, El-Harty, Ahmed, Elsherbini, Moutaz, Abdel-Rasheed, Mazen, and Eid, Marwa M.
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- 2022
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8. Optimized LSTM for Accurate Smart Grid Stability Prediction Using a Novel Optimization Algorithm.
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Karim, Faten Khalid, Khafaga, Doaa Sami, El-kenawy, El-Sayed M., Eid, Marwa M., Ibrahim, Abdelhameed, Abualigah, Laith, Khodadadi, Nima, Abdelhamid, Abdelaziz A., Baptista, José, and Li, Yushuai
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OPTIMIZATION algorithms ,LOAD forecasting (Electric power systems) ,ENERGY management ,ENERGY development ,MATHEMATICAL optimization ,MACHINE learning - Abstract
The stability of smart grids is crucial for ensuring reliable and efficient power distribution in modern energy systems. This paper presents an optimized Long Short-Term Memory model for predicting smart grid stability, leveraging the Novel Guide-Waterwheel Plant Algorithm (Guide-WWPA) for enhanced performance. Traditional methods often struggle with the complexity and dynamic nature of smart grids, necessitating advanced approaches for accurate predictions. The proposed LSTM model, optimized using Guide-WWPA, addresses these challenges by effectively capturing temporal dependencies and nonlinear relationships in the data. The proposed approach involves a comprehensive preprocessing pipeline to handle data heterogeneity and noise, followed by the implementation of the LSTM model optimized through Guide-WWPA. The Guide-WWPA combines the strength of the WWPA with a novel guidance mechanism, ensuring efficient exploration and exploitation of the search space. The optimized LSTM is evaluated on a real-world smart grid dataset, demonstrating superior performance compared to traditional optimization techniques. Experimental Results indicate significant improvements in prediction accuracy and computational efficiency, highlighting the potential of the Guide-WWPA optimized LSTM for real-time smart grid stability prediction. This work contributes to the development of intelligent energy management systems, offering a robust tool for maintaining grid stability and enhancing overall energy reliability. On the other hand, statistical evaluations were carried out to prove the stability and difference of the proposed methodology. The results of the experiments demonstrate that the Guide-WWPA + LSTM strategy is superior to the other machine learning approaches. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Air pollution prediction using blind source separation with Greylag Goose Optimization algorithm.
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Ben Ghorbal, Anis, Grine, Azedine, Elbatal, Ibrahim, Almetwally, Ehab M., Eid, Marwa M., and El-Kenawy, El-Sayed M.
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BLIND source separation ,OPTIMIZATION algorithms ,AIR pollution ,AIR quality management ,AIR quality monitoring ,GEESE ,INDEPENDENT component analysis - Abstract
Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Optimizing electric vehicle paths to charging stations using parallel greylag goose algorithm and Restricted Boltzmann Machines.
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Alharbi, Amal H., Khafaga, Doaa Sami, El-kenawy, El-Sayed M., Eid, Marwa M., Ibrahim, Abdelhameed, Abualigah, Laith, Khodadadi, Nima, Abdelhamid, Abdelaziz A., Kotb, Hossam, and Abdel Aleem, Shady H. E.
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BOLTZMANN machine ,ELECTRIC vehicle charging stations ,ELECTRIC vehicles ,OPTIMIZATION algorithms ,GEESE ,INFRASTRUCTURE (Economics) - Abstract
As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user's present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm.
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Elkenawy, El-Sayed M., Alhussan, Amel Ali, Eid, Marwa M., and Ibrahim, Abdelhameed
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OPTIMIZATION algorithms ,METAHEURISTIC algorithms ,EXTREME weather ,RAINFALL ,MACHINE learning ,NATURAL resources management - Abstract
Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOALSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier.
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Alhussan, Amel Ali, Khafaga, Doaa Sami, El-kenawy, El-Sayed M., Eid, Marwa M., and Ibrahim, Abdelhameed
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METAHEURISTIC algorithms ,OPTIMIZATION algorithms ,INFRASTRUCTURE (Economics) ,ELECTRIC vehicle charging stations ,ELECTRIC charge ,PARTICLE swarm optimization - Abstract
To reduce the negative effects that conventional modes of transportation have on the environment, researchers are working to increase the use of electric vehicles. The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge. The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue. Nevertheless, the powering of these terminals presents challenges because of the high energy requirements, which may influence the quality of service. Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure. It is vital to do an analysis of specific regional traffic patterns, such as road networks, route details, junction density, and economic zones, rather than making arbitrary conclusions about traffic patterns. When vehicle traffic is simulated using this data and other variables, it is possible to detect limits in the design of the current traffic engineering system. Initially, the binary graylag goose optimization (bGGO) algorithm is utilized for the purpose of feature selection. Subsequently, the graylag goose optimization (GGO) algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion. Based on the results of the analysis of variance (ANOVA), a comprehensive summary of the components that contribute to the observed variability in the dataset is provided. The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms, such as the voting GGO algorithm, the voting grey wolf optimization algorithm (GWO), the voting whale optimization algorithm (WOA), the voting particle swarm optimization (PSO), the voting firefly algorithm (FA), and the voting genetic algorithm (GA), to the theoretical median that would be expected that there is no difference. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Hybrid waterwheel plant and stochastic fractal search optimization for robust diabetes classification.
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Alhussan, Amel Ali, Khafaga, Doaa Sami, El-kenawy, El-Sayed M., Eid, Marwa M., and Abdelhamid, Abdelaziz A.
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MACHINE learning ,ARTIFICIAL pancreases ,ROBUST optimization ,METAHEURISTIC algorithms ,DIABETES ,SUPPORT vector machines ,PANCREAS - Abstract
Diabetes is a chronic disease that is usually caused when the pancreas fails to produce sufficient insulin or when the body is unable to effectively utilize the insulin produced by the pancreas. Early detection of diabetes enables the implementation of a suitable treatment method, which can lead to a healthy lifestyle. A necessity arises for an automated system capable of diagnosing diabetes using clinical and physical data in cases when the conventional approach to detecting diabetes proves to be arduous. In this paper, a new diabetes classification model based on optimized long short-term memory (LSTM) is presented and evaluated on the Pima Indians Diabetes Database (PIDD). To improve the LSTM model, a novel hybrid waterwheel plant and stochastic fractal search (WWPASFS) is proposed for optimizing its parameters. To confirm the performance superiority of the proposed WWPASFS + LSTM model, it is compared to various machine learning models and metaheuristic optimization methods. In addition, the binary WWPASFS is proposed to extract the relevant features in the PIDD dataset, with the aim of improving the accurate classification of diabetes patients. The WWPASFS + LSTM model attained the highest accuracy of 98.2% in classifying diabetes patients on the dataset in hand. The WWPASFS + LSTM model exhibited superior performance compared to the other five models, namely decision tree, K-nearest neighbors, neural networks, random forest, and support vector machines. On the other hand, the statistical analysis of the proposed approach is studied and the results prove its difference and significance. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization.
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Alkanhel, Reem Ibrahim, Khafaga, Doaa Sami, Zaki, Ahmed Mohamed, Eid, Marwa M., Al-Mooneam, Abdyalaziz A., Ibrahim, Abdelhameed, and Towfek, S. K.
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WIRELESS sensor networks ,PARTICLE swarm optimization ,SENSOR networks ,GENETIC algorithms ,BOX-Jenkins forecasting ,COMMUNICATION infrastructure - Abstract
The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor's specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter.
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Alharbi, Amal H., Khafaga, Doaa Sami, Zaki, Ahmed Mohamed, El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, Abdelhamid, Abdelaziz A., Eid, Marwa M., El-Said, M., Khodadadi, Nima, Abualigah, Laith, Saeed, Mohammed A., Piras, Giuseppe, and Pierantozzi, Mariano
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COOLING loads (Mechanical engineering) ,HEATING load ,ENERGY consumption ,FORECASTING ,ENERGY consumption of buildings ,ALGORITHMS - Abstract
Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA- MLPRegressor performed well in terms of different metrices variables and fitted time. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Machine learning-powered lead-free piezoelectric nanoparticle-based deep brain stimulation: A paradigm shift in Parkinson's disease diagnosis and evaluation.
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Eid, Marwa M., Chinnaperumal, Seelammal, Raju, Sekar Kidambi, Kannan, Subhash, Alharbi, Amal H., Natarajan, Sivaramakrishnan, Khafaga, Doaa Sami, and Tawfeek, Sayed M.
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DEEP brain stimulation , *PARKINSON'S disease , *DIAGNOSIS , *MACHINE learning , *ELECTRIC stimulation - Abstract
Lead-based deep brain stimulation (DBS) electrodes have been employed to treat Parkinson's disease (PD), but their limitations have led to the development of lead-free piezoelectric nanoparticle-based DBS (LF-PND-DBS). This novel approach utilizes non-invasive biocompatible piezoelectric nanoparticles to generate electrical stimulation, offering a promising alternative to traditional DBS. In this study, an innovative machine learning (ML)-optimized LF-PND-DBS system for diagnosing and evaluating PD is proposed. By leveraging ML algorithms, the optimized design of LF-PND electrodes and stimulation parameters is derived, ensuring precise and personalized treatment delivery. The ML-optimized LF-PND-DBS system was evaluated in a cohort of PD patients, demonstrating an exceptional diagnostic accuracy with a sensitivity of 99.1% and a specificity of 98.2%. It effectively assessed PD severity and response to DBS treatment, providing valuable guidance for treatment monitoring. The findings highlight the immense potential of the ML-optimized LF-PND-DBS system as a transformative tool for PD diagnosis and evaluation. This novel approach has the potential to enhance DBS efficacy, safety, and personalization, paving the way for improved patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]
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- 2024
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17. Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areas.
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Shams, Mahmoud Y., Tarek, Zahraa, El-kenawy, El-Sayed M., Eid, Marwa M., and Elshewey, Ahmed M.
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GROSS domestic product ,STANDARD deviations ,PEARSON correlation (Statistics) ,EVIDENCE gaps - Abstract
Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy's health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R
2 ) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2 , with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates. [ABSTRACT FROM AUTHOR]- Published
- 2024
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18. A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm.
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Saeed, Mohammed A., El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, Abdelhamid, Abdelaziz A., Eid, Marwa M., El-Said, M., Abualigah, Laith, Alharbi, Amal H., Khafaga, Doaa Sami, Sirisumrannukul, Somporn, and Tan, Hong
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OPTIMIZATION algorithms ,MAJORITIES ,INFRASTRUCTURE (Economics) ,VOTING ,PLURALITY voting - Abstract
The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EVtype for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization.
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Zaki, Ahmed Mohamed, Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Eid, Marwa M., and El-Kenawy, El-Sayed M.
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WIRELESS sensor networks ,PARTICLE swarm optimization ,K-nearest neighbor classification ,STANDARD deviations ,ALGORITHMS - Abstract
The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusion process. Through an iterative process of incremental adjustments and evaluations, the algorithm fine-tunes its parameters, resulting in a refined KNN regression model. The enhanced model exhibits substantial improvements, as indicated by the notable reduction in root mean square error (RMSE) and mean absolute error (MAE), accompanied by a strengthened correlation between variables. The favorable outcomes underscore the efficacy of the SFS-PSO optimization technique in augmenting the KNN algorithm's performance within wireless sensor networks. In simpler terms, the application of SFS-PSO in conjunction with KNN leads to a significant decrease in RMSE, reaching a value as low as 0.00894, demonstrating the notable effectiveness of this optimization approach in refining the predictive capabilities of the KNN algorithm in the context of WSNs. [ABSTRACT FROM AUTHOR]
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- 2024
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20. Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.
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Elshewey, Ahmed M., Shams, Mahmoud Y., Tawfeek, Sayed M., Alharbi, Amal H., Ibrahim, Abdelhameed, Abdelhamid, Abdelaziz A., Eid, Marwa M., Khodadadi, Nima, Abualigah, Laith, Khafaga, Doaa Sami, and Tarek, Zahraa
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MACHINE learning ,HEPATITIS C virus ,DRUG abuse ,SUPPORT vector machines ,HEPATITIS C ,HEALTH facilities - Abstract
The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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21. An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.
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Tarek, Zahraa, Shams, Mahmoud Y., Towfek, S. K., Alkahtani, Hend K., Ibrahim, Abdelhameed, Abdelhamid, Abdelaziz A., Eid, Marwa M., Khodadadi, Nima, Abualigah, Laith, Khafaga, Doaa Sami, and Elshewey, Ahmed M.
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DEEP learning ,DEATH forecasting ,CONVOLUTIONAL neural networks ,COVID-19 ,STANDARD deviations ,COVID-19 pandemic - Abstract
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R
2 ). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction. [ABSTRACT FROM AUTHOR]- Published
- 2023
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22. Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning.
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Ang, Koon Meng, Lim, Wei Hong, Tiang, Sew Sun, Sharma, Abhishek, Eid, Marwa M., Tawfeek, Sayed M., Khafaga, Doaa Sami, Alharbi, Amal H., and Abdelhamid, Abdelaziz A.
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IMAGE recognition (Computer vision) ,DEEP learning ,CONVOLUTIONAL neural networks ,SMART devices ,TEACHERS ,CONCEPT learning - Abstract
This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner's search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners' improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development. [ABSTRACT FROM AUTHOR]
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- 2023
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23. Enhancing Cyclone Intensity Prediction for Smart Cities Using a Deep-Learning Approach for Accurate Prediction.
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Jayaraman, Senthil Kumar, Venkatachalam, Venkataraman, Eid, Marwa M., Krithivasan, Kannan, Raju, Sekar Kidambi, Khafaga, Doaa Sami, Karim, Faten Khalid, and Ahmed, Ayman Em
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CYCLONE forecasting ,SMART cities ,METEOROLOGICAL research ,EXTREME weather ,ATMOSPHERIC models ,CYCLONES ,TROPICAL cyclones - Abstract
Accurate cyclone intensity prediction is crucial for smart cities to effectively prepare and mitigate the potential devastation caused by these extreme weather events. Traditional meteorological models often face challenges in accurately forecasting cyclone intensity due to cyclonic systems' complex and dynamic nature. Predicting the intensity of cyclones is a challenging task in meteorological research, as it requires expertise in extracting spatio-temporal features. To address this challenge, a new technique, called linear support vector regressive gradient descent Jaccardized deep multilayer perceptive classifier (LEGEMP), has been proposed to improve the accuracy of cyclone intensity prediction. This technique utilizes a dataset that contains various attributes. It employs the Herfindahl correlative linear support vector regression feature selection to identify the most important characteristics for enhancing cyclone intensity forecasting accuracy. The selected features are then used in conjunction with the Nesterov gradient descent jeopardized deep multilayer perceptive classifier to predict the intensity classes of cyclones, including depression, deep depression, cyclone, severe cyclone, very severe cyclone, and extremely severe cyclone. Experimental results have demonstrated that LEGEMP outperforms conventional methods in terms of cyclone intensity prediction accuracy, requiring minimum time, error rate, and memory consumption. By leveraging advanced techniques and feature selection, LEGEMP provides more reliable and precise predictions for cyclone intensity, enabling better preparedness and response strategies to mitigate the impact of these destructive storms. The LEGEMP technique offers an improved approach to cyclone intensity prediction, leveraging advanced classifiers and feature selection methods to enhance accuracy and reduce error rates. We demonstrate the effectiveness of our approach through rigorous evaluation and comparison with conventional prediction methods, showcasing significant improvements in prediction accuracy. Integrating our enhanced prediction model into smart city disaster management systems can substantially enhance preparedness and response strategies, ultimately contributing to the safety and resilience of communities in cyclone-prone regions. [ABSTRACT FROM AUTHOR]
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- 2023
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24. A randomized placebo-controlled trial of preoperative tranexamic acid among women undergoing elective cesarean delivery
- Author
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Maged, Ahmed M., Helal, Omneya M., Elsherbini, Moutaz M., Eid, Marwa M., Elkomy, Rasha O., Dahab, Sherif, and Elsissy, Maha H.
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- 2015
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25. Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm.
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Khafaga, Doaa Sami, El-kenawy, El-Sayed M., Alhussan, Amel Ali, and Eid, Marwa M.
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ENERGY consumption forecasting ,OPTIMIZATION algorithms ,METAHEURISTIC algorithms ,MACHINE learning ,SEARCH algorithms ,ENERGY consumption ,FORECASTING - Abstract
The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long shortterm memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic fractal search (SFS) algorithm and is referred to as dynamic DTOSFS. To prove the effectiveness and superiority of the proposed approach, five standard benchmark algorithms, namely, stochastic fractal search (SFS), dipper throated optimization (DTO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimization (GWO), are used to optimize the parameters of the LSTM-based model, and the results are compared with that of the proposed approach. Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013, which is the best among the recorded results of the other methods. In addition, statistical experiments are conducted to prove the statistical difference of the proposed model. The results of these tests confirmed the expected outcomes. [ABSTRACT FROM AUTHOR]
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- 2023
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26. Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0.
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Kidambi Raju, Sekar, Ramaswamy, Seethalakshmi, Eid, Marwa M., Gopalan, Sathiamoorthy, Alhussan, Amel Ali, Sukumar, Arunkumar, and Khafaga, Doaa Sami
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CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,NATURAL languages ,ORAL communication ,FUZZY logic - Abstract
Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method's success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component's benefits to enhance the predictive model's overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy. [ABSTRACT FROM AUTHOR]
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- 2023
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27. A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting.
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Karim, Faten Khalid, Khafaga, Doaa Sami, Eid, Marwa M., Towfek, S. K., and Alkahtani, Hend K.
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ALGORITHMS ,WIND power ,CLIMATE change ,STORMS ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence - Abstract
Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results. [ABSTRACT FROM AUTHOR]
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- 2023
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28. Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.
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Alharbi, Amal H., Towfek, S. K., Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami, Khodadadi, Nima, Abualigah, Laith, and Saber, Mohamed
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MONKEYPOX ,COVID-19 pandemic ,CONVOLUTIONAL neural networks ,ARTIFICIAL neural networks ,ARTIFICIAL intelligence - Abstract
The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection. [ABSTRACT FROM AUTHOR]
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- 2023
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29. Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection.
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Kidambi Raju, Sekar, Ramaswamy, Seethalakshmi, Eid, Marwa M., Gopalan, Sathiamoorthy, Karim, Faten Khalid, Marappan, Raja, and Khafaga, Doaa Sami
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FEATURE selection ,RESPIRATORY infections ,SARS-CoV-2 ,COVID-19 pandemic ,MACHINE learning - Abstract
This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission's stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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30. Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization.
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Alhussan, Amel Ali, Abdelhamid, Abdelaziz A., Towfek, S. K., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami, and Saraya, Mohamed S.
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FEATURE selection ,MACHINE learning ,METAHEURISTIC algorithms ,OPTIMIZATION algorithms ,STATISTICAL significance ,FIBROMYALGIA - Abstract
Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
31. Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.
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Alhussan, Amel Ali, Eid, Marwa M., Towfek, S. K., and Khafaga, Doaa Sami
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- *
BREAST cancer diagnosis , *CONVOLUTIONAL neural networks , *DEEP learning , *FEATURE selection , *COMPUTER algorithms - Abstract
According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women's death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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32. The antibacterial and cytocompatibility of the polyurethane nanofibrous scaffold containing curcumin for wound healing applications.
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Abdelbasset, Walid Kamal, Jasim, Saade Abdalkareem, Abed, Azher M., Altimari, Usama S., Eid, Marwa M., Karim, Yasir Salam, Elkholi, Safaa M., Mustafa, Yasser Fakri, and Jalil, Abduladheem Turki
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- 2023
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33. Waterwheel Plant Algorithm: A Novel Metaheuristic Optimization Method.
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Abdelhamid, Abdelaziz A., Towfek, S. K., Khodadadi, Nima, Alhussan, Amel Ali, Khafaga, Doaa Sami, Eid, Marwa M., and Ibrahim, Abdelhameed
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METAHEURISTIC algorithms ,SEARCH engines ,MATHEMATICAL models ,ENGINEERING design ,MATHEMATICAL optimization ,ALGORITHMS - Abstract
Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), a novel stochastic optimization technique motivated by natural systems. The proposed WWPA's basic concept is based on modeling the waterwheel plant's natural behavior while on a hunting expedition. To find prey, WWPA uses plants as search agents. We present WWPA's mathematical model for use in addressing optimization problems. Twenty-three objective functions of varying unimodal and multimodal types were used to assess WWPA's performance. The results of optimizing unimodal functions demonstrate WWPA's strong exploitation ability to get close to the optimal solution, while the results of optimizing multimodal functions show WWPA's strong exploration ability to zero in on the major optimal region of the search space. Three engineering design problems were also used to gauge WWPA's potential for improving practical programs. The effectiveness of WWPA in optimization was evaluated by comparing its results with those of seven widely used metaheuristic algorithms. When compared with eight competing algorithms, the simulation results and analyses demonstrate that WWPA outperformed them by finding a more proportionate balance between exploration and exploitation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Al-Biruni Earth Radius Optimization Based Algorithm for Improving Prediction of Hybrid Solar Desalination System.
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Ibrahim, Abdelhameed, El-kenawy, El-Sayed M., Kabeel, A. E., Karim, Faten Khalid, Eid, Marwa M., Abdelhamid, Abdelaziz A., Ward, Sayed A., El-Said, Emad M. S., El-Said, M., and Khafaga, Doaa Sami
- Subjects
HYBRID systems ,MATHEMATICAL optimization ,PARTICLE swarm optimization ,PRESSURE drop (Fluid dynamics) ,FLUID pressure ,AIR flow - Abstract
The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification–dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER–PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms' inputs, and their outputs were the hot and cold fluids' outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER–PSO method to identify the nonlinear link between operating circumstances and process responses. In addition, compared to the other analyzed models, it offers better statistical performance measures for the prediction of the outlet temperature of hot and cold fluids and pressure drop values. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
35. Evaluation Of Middle Cerebral/Umbilical Artery Resistance And Pulsatility Indices Ratio As Predictors For Fetal Well-Being And Neonatal Outcome In Preeclampsia With Or Without Intrauterine Growth Restriction.
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Nasr, Lamiaa E. A., Mohamed, Ahmed H., Abd El Maksoud, Khaled A., Eid, Marwa M., and Abd El Razek, Abd Alrahman A.
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UMBILICAL arteries ,FETAL growth retardation ,PREECLAMPSIA ,VASCULAR resistance ,ECLAMPSIA ,HIGH-risk pregnancy ,WELL-being ,DOPPLER ultrasonography - Abstract
Background Pre-eclampsia is linked to abnormal uteroplacental circulation, which could have a negative perinatal result by affecting foetal growth and oxygenation. Doppler ultrasound is a good tool for examining the aberrant vascular resistance to blood flow in the uteroplacental and fetoplacental circulation. Objective Detection of the middle cerebral to umbilical arteries with accuracy Prediction of foetal health and newborn outcome using the Doppler indices ratio in pregnancies complicated by pre-eclampsia with or without signs of intrauterine growth restriction. Methods This study included 90 pregnant patients attending to Kasr El-Ainy Hospital high risk pregnancy unit from 2014 to 2016. Patients were divided into two groups, first group included 30 cases with normal uncomplicated pregnancies. The second group included 60 cases with pregnancies complicated by pre-eclampsia Patients of the preeclamptic group were subdivided further into two subgroups first is mild pre-eclampsia and second is sever pre-eclampsia and all subgroups were assessed for the presence of IUGR. Results IUGR and adverse neonatal outcome were significantly associated with the severity of preeclampsia. The ability of CPR and R.I ratio to predict IUGR in mild group was evaluated; the sensitivity, specificity, positive predictive value, negative predictive value and accuracy for the CPR were 100%, 96%, 83.3%, 96% and 96.7% respectively. The corresponding figures for the RI ratio were 80%, 92%, 66.7%, 95.8% and 90%, respectively. Furthermore, in the sever group, the ability of CPR and R.I ratio to predict IUGR was evaluated ;the sensitivity, specificity, positive predictive value, negative predictive value and accuracy for the CPR were 94.4%, 100%, 94.4%, 92.3% and 96.7%. For detection of adverse neonatal outcome in the mild group, both CPR and R.I ratio showed similar values of sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CPR and R.I ratio in detection of adverse neonatal outcome were 100%, 85.7%, 33.3%, 85, 7% and 86.7% respectively. Where as in the sever group the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CPR and R.I ratio in detection of adverse outcome were 91.7%, 66.7%, 64.7%, 92.3%, 76.7% and 91.7%, 61.1%, 61.1%, 91.7% and 73.3% respectively. Conclusion When it comes to IUGR and poor neonatal outcomes in the foetuses of preeclamptic and gestational hypertensive women, CPR is a very good predictor. When it comes to detecting IUGR and a poor newborn outcome in pre-eclampsia, CPR is more accurate than R.I ratio. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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36. Classification of Diabetic Foot Thermal Images Using Deep Convolutional Neural Network.
- Author
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Yousef, Reem N., Eid, Marwa M., and Mohamed, Mohamed A.
- Subjects
TREATMENT of diabetic foot ,CONVOLUTIONAL neural networks ,DIABETES complications ,ULCERS - Abstract
Diabetic foot (DF) is one of the most common chronic complications of poorly controlled diabetes mellitus (DM). Early diagnosis of DF and effective treatment is usually difficult by traditional approaches. Lately, it has been found a strong relationship between temperature variation and diabetic foot ulcer emergence. Thus, the current study focused on monitoring the temperature of feet using thermal images and its analysis techniques. The proposed system was based on employing a deep convolutional neural network (CNN) on thermal foot images. Experimental results showed that the proposed CNN has a maximum accuracy of 99.3% with minimum losses. When comparing the proposed system to other relevant systems, the proposed system approved greater accuracy, lower elapsed and testing time, which offers an automatic diagnostic tool for the diabetic foot and differentiates between its types. Thus, a simple, cost-effective, and accurate computer aided design (CAD) system could be presented to get a valuable system for the clinicians in hospitals. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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37. Watermarking System for Medical Images Using Optimization Algorithm.
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Saber, Mohamed, El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, and Eid, Marwa M.
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DIGITAL watermarking ,DIAGNOSTIC imaging ,COMPUTER algorithms ,DISCRETE wavelet transforms ,SIGNAL-to-noise ratio - Abstract
One of the main methods used to provide security for medical records when exchanging these records through open networks is digital watermarking. In order to preserve the privacy of patients, this system also requires a means to secure images. In this paper, a watermarking based on discrete wavelet transform (DWT), and discrete and discrete cosine transform (DCT) in cascade provides more robustness and security. DCT divides the image into low and high-frequency regions, watermarking message can be embedded into low-frequency regions to prevent distortion of the original image. DWT splits the image into four frequency coefficients; horizontal, vertical, approximation, and detailed frequency component. The judgment factors for the strength of the watermark system are robustness, invisibility, and embedded message capacity. Invisibility means transparency of the watermark logo or data in the original or host image without any distortion. Capacity data payload means the size of the embedded image which is related to the amount of data or logo size that will be embedded in the host image. Robustness refers to the capability of the watermark to stand with the host image operations. In this paper, we propose an optimizer to tradeoff between robustness, invisibility, and message capacity. Three metrics were employed to assess the results achieved by the proposed approach, namely, Peak Signal-to-Noise Ratio (PSNR), Normalized Cross Correlation (NCC), and Image Fidelity (IF). The achieved results confirmed the effectiveness and superiority of the proposed approach for real-world digital watermarking applications. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones.
- Author
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El-Kenawy, El-Sayed M., Khodadadi, Nima, Mirjalili, Seyedali, Makarovskikh, Tatiana, Abotaleb, Mostafa, Karim, Faten Khalid, Alkahtani, Hend K., Abdelhamid, Abdelaziz A., Eid, Marwa M., Horiuchi, Takahiko, Ibrahim, Abdelhameed, and Khafaga, Doaa Sami
- Subjects
METAHEURISTIC algorithms ,WILCOXON signed-rank test ,ONE-way analysis of variance ,FEATURE selection ,MACHINE learning ,SUPPORT vector machines ,K-nearest neighbor classification - Abstract
Background and aim: Machine learning methods are examined by many researchers to identify weeds in crop images captured by drones. However, metaheuristic optimization is rarely used in optimizing the machine learning models used in weed classification. Therefore, this research targets developing a new optimization algorithm that can be used to optimize machine learning models and ensemble models to boost the classification accuracy of weed images. Methodology: This work proposes a new approach for classifying weed and wheat images captured by a sprayer drone. The proposed approach is based on a voting classifier that consists of three base models, namely, neural networks (NNs), support vector machines (SVMs), and K-nearest neighbors (KNN). This voting classifier is optimized using a new optimization algorithm composed of a hybrid of sine cosine and grey wolf optimizers. The features used in training the voting classifier are extracted based on AlexNet through transfer learning. The significant features are selected from the extracted features using a new feature selection algorithm. Results: The accuracy, precision, recall, false positive rate, and kappa coefficient were employed to assess the performance of the proposed voting classifier. In addition, a statistical analysis is performed using the one-way analysis of variance (ANOVA), and Wilcoxon signed-rank tests to measure the stability and significance of the proposed approach. On the other hand, a sensitivity analysis is performed to study the behavior of the parameters of the proposed approach in achieving the recorded results. Experimental results confirmed the effectiveness and superiority of the proposed approach when compared to the other competing optimization methods. The achieved detection accuracy using the proposed optimized voting classifier is 97.70%, F-score is 98.60%, specificity is 95.20%, and sensitivity is 98.40%. Conclusion: The proposed approach is confirmed to achieve better classification accuracy and outperforms other competing approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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39. Ensemble of Machine Learning Fusion Models for Breast Cancer Detection Based on the Regression Model.
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Alsayadi, Hamzah A., Abdelhamid, Abdelaziz A., El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, and Eid, Marwa M.
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MACHINE learning ,BREAST cancer ,CANCER detection dogs ,REGRESSION analysis ,CANCER-related mortality - Abstract
Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. The various machine learning (ML) algorithms applied include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 real-valued feature information. Among all five machine learning methods, K-Neighbors regressor had the best performance and ensemble using K-Neighbors regressor gave the best accuracy. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases.
- Author
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Eid, Marwa M., El-Kenawy, El-Sayed M., Khodadadi, Nima, Mirjalili, Seyedali, Khodadadi, Ehsaneh, Abotaleb, Mostafa, Alharbi, Amal H., Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Amer, Ghada M., Kadi, Ammar, and Khafaga, Doaa Sami
- Subjects
- *
MONKEYPOX , *BIG data , *ONE-way analysis of variance , *ARTIFICIAL intelligence , *MATHEMATICAL optimization , *MACHINE learning - Abstract
Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
41. Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm.
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Abdelhamid, Abdelaziz A., El-Kenawy, El-Sayed M., Khodadadi, Nima, Mirjalili, Seyedali, Khafaga, Doaa Sami, Alharbi, Amal H., Ibrahim, Abdelhameed, Eid, Marwa M., and Saber, Mohamed
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MONKEYPOX ,FEATURE selection ,MATHEMATICAL optimization ,CLASSIFICATION algorithms ,PARTICLE swarm optimization ,METAHEURISTIC algorithms ,FEATURE extraction ,MACHINE learning - Abstract
The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%. [ABSTRACT FROM AUTHOR]
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- 2022
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42. Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm.
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El-kenawy, El-Sayed M., Albalawi, Fahad, Ward, Sayed A., Ghoneim, Sherif S. M., Eid, Marwa M., Abdelhamid, Abdelaziz A., Bailek, Nadjem, and Ibrahim, Abdelhameed
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FEATURE selection ,ARTIFICIAL intelligence ,ALGORITHMS ,GAS analysis ,MATHEMATICAL optimization ,CLASSIFICATION - Abstract
Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers' ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults' diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon's rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases. [ABSTRACT FROM AUTHOR]
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- 2022
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43. Meta-Heuristic Optimization and Keystroke Dynamics for Authentication of Smartphone Users.
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El-Kenawy, El-Sayed M., Mirjalili, Seyedali, Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Khodadadi, Nima, and Eid, Marwa M.
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RECURRENT neural networks ,BIOMETRIC identification ,SMARTPHONES ,HUMAN fingerprints ,FEATURE selection ,PERSONAL identification numbers ,STATISTICS ,MATHEMATICAL optimization - Abstract
Personal Identification Numbers (PIN) and unlock patterns are two of the most often used smartphone authentication mechanisms. Because PINs have just four or six characters, they are subject to shoulder-surfing attacks and are not as secure as other authentication techniques. Biometric authentication methods, such as fingerprint, face, or iris, are now being studied in a variety of ways. The security of such biometric authentication is based on PIN-based authentication as a backup when the maximum defined number of authentication failures is surpassed during the authentication process. Keystroke-dynamics-based authentication has been studied to circumvent this limitation, in which users were categorized by evaluating their typing patterns as they input their PIN. A broad variety of approaches have been proposed to improve the capacity of PIN entry systems to discriminate between normal and abnormal users based on a user's typing pattern. To improve the accuracy of user discrimination using keystroke dynamics, we propose a novel approach for improving the parameters of a Bidirectional Recurrent Neural Network (BRNN) used in classifying users' keystrokes. The proposed approach is based on a significant modification to the Dipper Throated Optimization (DTO) algorithm by employing three search leaders to improve the exploration process of the optimization algorithm. To assess the effectiveness of the proposed approach, two datasets containing keystroke dynamics were included in the conducted experiments. In addition, we propose a feature selection algorithm for selecting the proper features that enable better user classification. The proposed algorithms are compared to other optimization methods in the literature, and the results showed the superiority of the proposed algorithms. Moreover, a statistical analysis is performed to measure the stability and significance of the proposed methods, and the results confirmed the expected findings. The best classification accuracy achieved by the proposed optimized BRNN is 99.02% and 99.32% for the two datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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44. Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation.
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Eid, Marwa M., Alassery, Fawaz, Ibrahim, Abdelhameed, Aloyaydi, Bandar Abdullah, Ali, Hesham Arafat, and El-Mashad, Shady Y.
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HEMOGLOBINS ,HUMAN body ,JOB analysis ,POLYCYTHEMIA vera ,RANDOM forest algorithms ,PHYSIOLOGICAL transport of oxygen ,PLETHYSMOGRAPHY - Abstract
The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. TheHGBlevels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved model. The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels. An optimization method is utilized to get the ensemble's optimum weights. The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search (SCSFS). The proposed SCSFS ensemble is compared toDecision Tree, Multilayer perceptron (MLP), Support Vector Regression (SVR) and Random Forest Regressors as model-based approaches and the average ensemble model. The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate. [ABSTRACT FROM AUTHOR]
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- 2022
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45. Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels.
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Eid, Marwa M., Alassery, Fawaz, Ibrahim, Abdelhameed, and Saber, Mohamed
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SIGNAL classification ,METAHEURISTIC algorithms ,CLASSIFICATION algorithms ,FEATURE selection ,DIGITAL signal processing ,ELECTROENCEPHALOGRAPHY ,MATHEMATICAL optimization ,BRAIN-computer interfaces - Abstract
Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFSGuided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this. [ABSTRACT FROM AUTHOR]
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- 2022
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46. Comparative effectiveness study of low versus high-intensity aerobic training with resistance training in community-dwelling older men with post-COVID 19 sarcopenia: A randomized controlled trial.
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Nambi, Gopal, Abdelbasset, Walid Kamal, Alrawaili, Saud M., Elsayed, Shereen H., Verma, Anju, Vellaiyan, Arul, Eid, Marwa M., Aldhafian, Osama R., Nwihadh, Naif Bin, and Saleh, Ayman K.
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MUSCLE physiology ,RESISTANCE training ,PATIENT aftercare ,GRIP strength ,EXERCISE tests ,STATISTICS ,AEROBIC exercises ,PHOBIAS ,ANALYSIS of variance ,MUSCLE contraction ,PHYSICAL therapy ,TIME ,MULTIVARIATE analysis ,SARCOPENIA ,EXERCISE physiology ,CLINICS ,FEAR ,TREADMILLS ,TREATMENT effectiveness ,RANDOMIZED controlled trials ,T-test (Statistics) ,INDEPENDENT living ,EXERCISE intensity ,MUSCLE strength ,BODY movement ,QUALITY of life ,BLIND experiment ,HEART rate monitoring ,QUESTIONNAIRES ,DESCRIPTIVE statistics ,REPEATED measures design ,RESEARCH funding ,STATISTICAL sampling ,DATA analysis ,STATISTICAL correlation ,LONGITUDINAL method - Abstract
Objective: To find and compare the clinical and psychological effects of low and high-intensity aerobic training combined with resistance training in community-dwelling older men with post-COVID-19 sarcopenia symptoms. Design: Randomized control trial. Setting: University physiotherapy clinic. Participants: Men in the age range of 60–80 years with post-COVID-19 Sarcopenia. Intervention: All participants received resistance training for whatever time of the day that they received it, and that in addition they were randomized into two groups like low-intensity aerobic training group (n = 38) and high-intensity aerobic training group (n = 38) for 30 minutes/session, 1 session/day, 4 days/week for 8 weeks. Outcomes: Clinical (muscle strength and muscle mass) and psychological (kinesiophobia and quality of life scales) measures were measured at the baseline, fourth week, the eighth week, and at six months follow-up. Results: The 2 × 4 group by time repeated measures MANOVA with corrected post-hoc tests for six dependent variables shows a significant difference between the groups (P < 0.001). At the end of six months follow up, the handgrip strength, −3.9 (95% CI −4.26 to −3.53), kinesiophobia level 4.7 (95% CI 4.24 to 5.15), and quality of life −10.4 (95% CI −10.81 to −9.9) shows more improvement (P < 0.001) in low-intensity aerobic training group than high-intensity aerobic training group, but in muscle mass both groups did not show any significant difference (P > 0.05). Conclusion: Low-intensity aerobic training exercises are more effective in improving the clinical (muscle strength) and psychological (kinesiophobia and quality of life) measures than high-intensity aerobic training in post-COVID 19 Sarcopenia. [ABSTRACT FROM AUTHOR]
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- 2022
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47. Falls and potential therapeutic interventions among elderly and older adult patients with cancer: a systematic review.
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Abdelbasset, Walid Kamal, Nambi, Gopal, Elsayed, Shereen H., Osailan, Ahmad M., and Eid, Marwa M.
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- 2021
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48. Comparison of Extracorporeal Shock Wave Therapy versus Manual Lymphatic Drainage on Cellulite after Liposuction: A Randomized Clinical Trial.
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Allam, Nesma M., Elshorbagy, Radwa T., Eid, Marwa M., Abdelbasset, Walid Kamal, Elkholi, Safaa Mostafa, and Eladl, Hadaya Mosaad
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BUTTOCKS ,RANDOMIZED controlled trials ,LIPECTOMY ,BLIND experiment ,VITAMIN A ,DESCRIPTIVE statistics ,ULTRASONIC therapy ,LYMPHATIC massage ,STATISTICAL sampling ,CUTANEOUS therapeutics ,ADIPOSE tissues - Abstract
Introduction. Cellulite is associated with variations in the skin appearance with cottage cheese, mattress-like, or orange peel. The most common areas for these lesions are the posterior or upper thighs and buttocks and mainly affect females after puberty. The objective of the study was to determine whether extracorporeal shock wave therapy (ESWT) or manual lymphatic drainage (MLD) is more effective for the reduction of the grade of cellulite after liposuction. Methods. This study is a single-blinded randomized controlled clinical trial. Thirty females with grade 3 cellulite were randomly distributed into two groups equal in number (n = 15), group A was equipped to ESWT and group B was equipped to MLD. The cellulite grading scale was used to assess cellulite grade, and the skinfold caliper was used to assess the thickness of subcutaneous fat. The assessment was carried out before and four weeks after starting the treatment. Both groups received topical retinol twice daily for four weeks; in addition, group A received ESWT, while group B received MLD, two times/week for 4 weeks. Results. The mean values of the skinfold caliper in group A decreased by 24.4% and in group B by 15.38% with a significant difference between the two groups p < 0.001 . Also, the mean values of the cellulite grading scale decreased significantly after treatment in group A compared with the mean values of group B p < 0.001 . Conclusions. There was more reduction in the grade of cellulite and thickness of subcutaneous fat in the ESWT group than the MLD group after liposuction. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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49. Effect of physical therapy rehabilitation program combined with music on children with lower limb burns: A twelve-week randomized controlled study.
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Eid, Marwa M., Abdelbasset, Walid Kamal, Abdelaty, Fatma Moustafa, and Ali, Zeinab A.
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TREATMENT effectiveness , *TREATMENT programs , *CHILDREN'S music , *PHYSICAL therapy , *MUSIC therapy , *VISUAL analog scale , *MEDICAL care - Abstract
Background: Burn injuries cause significant physical impairments that need comprehensive rehabilitation and coordination with the acute burn care team. Music had been shown to increase positive mood during exercise, which may result in motivation for participation in exercise programs. The current study aimed to evaluate the effect of physical therapy rehabilitation program combined with music therapy on children with lower limb burns.Methods: A twelve-week randomized controlled study including thirty pediatrics with lower limb burns. They were randomly assigned into two equal groups, 15 children per each. Group A received a physical therapy rehabilitation program combined with music therapy in addition to routine medical care. However, Group B received a physical therapy rehabilitation program without music therapy. Assessment of pain was by visual analogue scale (VAS), assessment of the range of motion (ROM) by goniometer, and gait assessed by GAIT Rite. The evaluation was carried before and after the interventions.Results: Before starting the study, no significant differences were detected between the two study groups (p < 0.05). The study results reported statistically significant improvement in VAS, ROM, and GAIT Rite in both groups after the intervention (p < 0.05). Group A showed greater improvement than group B in all outcome measures (p < 0.05).Conclusion: Physical therapy program combined with music therapy is an effective and safe modality for improving pain, range of motion, and gait parameters in pediatrics with lower limb burn. Also, physical therapy combined with music therapy is more effective than physical therapy alone in the treatment of pediatrics with lower limb burns. [ABSTRACT FROM AUTHOR]- Published
- 2021
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50. Effectiveness of transcutaneous electrical nerve stimulation as an adjunct to selected physical therapy exercise program on male patients with pudendal neuralgia: A randomized controlled trial.
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Eid, Marwa M, Rawash, Mohamed F, Sharaf, Moussa A, and Eladl, Hadaya Mosaad
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STATISTICS , *CONFIDENCE intervals , *ANALYSIS of variance , *NEURALGIA , *PHYSICAL therapy , *MULTIVARIATE analysis , *ETODOLAC , *TREATMENT effectiveness , *RANDOMIZED controlled trials , *PRE-tests & post-tests , *T-test (Statistics) , *COMPARATIVE studies , *BLIND experiment , *DESCRIPTIVE statistics , *REPEATED measures design , *MALE reproductive organ diseases , *STATISTICAL sampling , *DATA analysis software , *DATA analysis , *TRANSCUTANEOUS electrical nerve stimulation , *EXERCISE therapy - Abstract
Objective: To assess the effectiveness of Transcutaneous Electrical Nerve Stimulation (TENS) combined with selected physical therapy exercise program on male patients with pudendal neuralgia. Design: A double-blinded randomized controlled study. Setting: Out-patient setting. Participants: Fifty-two male participants with pudendal neuralgia (30–50 years) were allocated randomly into two groups; study and control. The same physical therapy exercises were applied to all participants, plus the same prescribed analgesic medication (Etodolac). Participants in the study group received additional TENS and sham TENS were given to those in control group. Intervention: Intervention lasted for 12 weeks, three sessions per week (60 minutes/session). Outcome measures: Numerical pain rating scale and daily Etodolac intake dose were measured before and after intervention. Results: Statistically significant differences were detected in numerical pain rating scale and daily Etodolac intake in favor of the study group (P < 0.05). After 12 weeks of intervention, the mean ± SD for numerical pain rating scale and daily Etodolac intake were 4.25 ± 1.9 and 259.25 ± 84.4 mg, in the study group, and 6.22 ± 2.22 and 355.55 ± 93.36 mg in the control group, respectively. The mean difference (95% CI) for numerical pain rating scale and daily Etodolac intake was −1.97 (−3.09: −0.83) and −96.3 (−144.9: −47.69), between groups post treatment, respectively. Conclusion: Adding TENS to physical therapy exercise program is more effective than physical therapy program alone in improving pain in male patients with pudendal neuralgia as measured by numerical pain rating scale and daily analgesic intake dose. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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