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Anjum V, Bagale U, Kadi A, Malinin A, Potoroko I, Alharbi AH, Khafaga DS, AlMetwally M, Qenawy AST, Anjum A, Ali F. Process Optimization of Tinospora cordifolia Extract-Loaded Water in Oil Nanoemulsion Developed by Ultrasound-Assisted Homogenization. Molecules 2024; 29:1797. [PMID: 38675617 PMCID: PMC11052499 DOI: 10.3390/molecules29081797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Nanoemulsions are gaining interest in a variety of products as a means of integrating easily degradable bioactive compounds, preserving them from oxidation, and increasing their bioavailability. However, preparing stable emulsion compositions with the desired characteristics is a difficult task. The aim of this study was to encapsulate the Tinospora cordifolia aqueous extract (TCAE) into a water in oil (W/O) nanoemulsion and identify its critical process and formulation variables, like oil (27-29.4 mL), the surfactant concentration (0.6-3 mL), and sonication amplitude (40% to 100%), using response surface methodology (RSM). The responses of this formulation were studied with an analysis of the particle size (PS), free fatty acids (FFAs), and encapsulation efficiency (EE). In between, we have studied a fishbone diagram that was used to measure risk and preliminary research. The optimized condition for the formation of a stable nanoemulsion using quality by design was surfactant (2.43 mL), oil concentration (27.61 mL), and sonication amplitude (88.6%), providing a PS of 171.62 nm, FFA content of 0.86 meq/kg oil and viscosity of 0.597 Pa.s for the blank sample compared to the enriched TCAE nanoemulsion with a PS of 243.60 nm, FFA content of 0.27 meq/kg oil and viscosity of 0.22 Pa.s. The EE increases with increasing concentrations of TCAE, from 56.88% to 85.45%. The RSM response demonstrated that both composition variables had a considerable impact on the properties of the W/O nanoemulsion. Furthermore, after the storage time, the enriched TCAE nanoemulsion showed better stability over the blank nanoemulsion, specially the FFAs, and the blank increased from 0.142 to 1.22 meq/kg oil, while TCAE showed 0.266 to 0.82 meq/kg.
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Affiliation(s)
- Varisha Anjum
- Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk, Russia; (U.B.); (A.M.); (I.P.)
| | - Uday Bagale
- Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk, Russia; (U.B.); (A.M.); (I.P.)
| | - Ammar Kadi
- Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk, Russia; (U.B.); (A.M.); (I.P.)
| | - Artem Malinin
- Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk, Russia; (U.B.); (A.M.); (I.P.)
| | - Irina Potoroko
- Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk, Russia; (U.B.); (A.M.); (I.P.)
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (A.H.A.); (D.S.K.)
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (A.H.A.); (D.S.K.)
| | - Marawa AlMetwally
- Intelligent Systems and Machine Learning Lab, Shenzhen 518000, China; (M.A.); (A.-S.T.Q.)
| | - Al-Seyday T. Qenawy
- Intelligent Systems and Machine Learning Lab, Shenzhen 518000, China; (M.A.); (A.-S.T.Q.)
| | - Areefa Anjum
- Department of Ilmul Advia, School of Unani Medical Education and Research, Jamia Hamdard, New Delhi 110062, India;
| | - Faraat Ali
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203, 50005 Hradec Králové, Czech Republic;
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Elshewey AM, Shams MY, Tawfeek SM, Alharbi AH, Ibrahim A, Abdelhamid AA, Eid MM, Khodadadi N, Abualigah L, Khafaga DS, Tarek Z. Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework. Diagnostics (Basel) 2023; 13:3439. [PMID: 37998575 PMCID: PMC10670002 DOI: 10.3390/diagnostics13223439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/04/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
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.
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Affiliation(s)
- Ahmed M. Elshewey
- Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43533, Egypt
| | - Mahmoud Y. Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
| | - Sayed M. Tawfeek
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
| | - Marwa M. Eid
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA;
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Zahraa Tarek
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt
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Ang KM, Lim WH, Tiang SS, Sharma A, Eid MM, Tawfeek SM, Khafaga DS, Alharbi AH, Abdelhamid AA. Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning. Biomimetics (Basel) 2023; 8:525. [PMID: 37999166 PMCID: PMC10669013 DOI: 10.3390/biomimetics8070525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
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.
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Affiliation(s)
- Koon Meng Ang
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; (K.M.A.); (S.S.T.)
| | - Wei Hong Lim
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; (K.M.A.); (S.S.T.)
| | - Sew Sun Tiang
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; (K.M.A.); (S.S.T.)
| | - Abhishek Sharma
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India;
| | - Marwa M. Eid
- Delta Higher Institute for Engineering and Technology, Mansoura 35511, Egypt;
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt
| | - Sayed M. Tawfeek
- Delta Higher Institute for Engineering and Technology, Mansoura 35511, Egypt;
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (D.S.K.); (A.H.A.)
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (D.S.K.); (A.H.A.)
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt;
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Sahqra 11961, Saudi Arabia
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Alharbi AH, Towfek SK, Abdelhamid AA, Ibrahim A, Eid MM, Khafaga DS, Khodadadi N, Abualigah L, Saber M. Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm. Biomimetics (Basel) 2023; 8:313. [PMID: 37504202 PMCID: PMC10807651 DOI: 10.3390/biomimetics8030313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/03/2023] [Accepted: 07/12/2023] [Indexed: 07/29/2023] Open
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.
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Affiliation(s)
- Amal H Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - S K Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
| | - Abdelaziz A Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Marwa M Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
| | - Mohamed Saber
- Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt
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Xing P, Zhang H, Derbali M, Sefat SM, Alharbi AH, Khafaga DS, Sani NS. An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence. Heliyon 2023; 9:e17622. [PMID: 37424589 PMCID: PMC10328847 DOI: 10.1016/j.heliyon.2023.e17622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/20/2023] [Accepted: 06/22/2023] [Indexed: 07/11/2023] Open
Abstract
The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms.
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Affiliation(s)
- Peizhen Xing
- Henan Vocational College of Water Conservancy and Environment, Zhengzhou, 450008, Henan, China
| | - Hui Zhang
- College of Information Engineering, Zhengzhou University of Technology, Zhengzhou, 450044, China
| | - Morched Derbali
- Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia
| | - Shebnam M. Sefat
- Department of Computer Science, Independent University, Bangladesh
- Islamic university Centre for scientific research, The Islamic University, Najaf, Iraq
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Nor Samsiah Sani
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
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Alharbi AH, Abdelhamid AA, Ibrahim A, Towfek SK, Khodadadi N, Abualigah L, Khafaga DS, Ahmed AE. Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications. Biomimetics (Basel) 2023; 8:241. [PMID: 37366836 DOI: 10.3390/biomimetics8020241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023] Open
Abstract
Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials' exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm's regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon's rank-sum and ANOVA tests.
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Affiliation(s)
- Amal H Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelaziz A Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - S K Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 1800, Penang, Malaysia
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ayman Em Ahmed
- Faculty of Engineering, King Salman International University, El-Tor 11341, Egypt
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ZainEldin H, Gamel SA, El-Kenawy ESM, Alharbi AH, Khafaga DS, Ibrahim A, Talaat FM. Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization. Bioengineering (Basel) 2022; 10:bioengineering10010018. [PMID: 36671591 PMCID: PMC9854739 DOI: 10.3390/bioengineering10010018] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/12/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Diagnosing a brain tumor takes a long time and relies heavily on the radiologist's abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifying brain tumors that were both accurate and fast. Deep Learning (DL) approaches have recently been popular in developing automated systems capable of accurately diagnosing or segmenting brain tumors in less time. DL enables a pre-trained Convolutional Neural Network (CNN) model for medical images, specifically for classifying brain cancers. The proposed Brain Tumor Classification Model based on CNN (BCM-CNN) is a CNN hyperparameters optimization using an adaptive dynamic sine-cosine fitness grey wolf optimizer (ADSCFGWO) algorithm. There is an optimization of hyperparameters followed by a training model built with Inception-ResnetV2. The model employs commonly used pre-trained models (Inception-ResnetV2) to improve brain tumor diagnosis, and its output is a binary 0 or 1 (0: Normal, 1: Tumor). There are primarily two types of hyperparameters: (i) hyperparameters that determine the underlying network structure; (ii) a hyperparameter that is responsible for training the network. The ADSCFGWO algorithm draws from both the sine cosine and grey wolf algorithms in an adaptable framework that uses both algorithms' strengths. The experimental results show that the BCM-CNN as a classifier achieved the best results due to the enhancement of the CNN's performance by the CNN optimization's hyperparameters. The BCM-CNN has achieved 99.98% accuracy with the BRaTS 2021 Task 1 dataset.
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Affiliation(s)
- Hanaa ZainEldin
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Samah A. Gamel
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - El-Sayed M. El-Kenawy
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
- Correspondence: (E.-S.M.E.-K.); (D.S.K.); (A.I.)
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (E.-S.M.E.-K.); (D.S.K.); (A.I.)
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
- Correspondence: (E.-S.M.E.-K.); (D.S.K.); (A.I.)
| | - Fatma M. Talaat
- Machine Learning & Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
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Khafaga DS, Alharbi AH, Mohamed I, Hosny KM. An Integrated Classification and Association Rule Technique for Early-Stage Diabetes Risk Prediction. Healthcare (Basel) 2022; 10:healthcare10102070. [PMID: 36292517 PMCID: PMC9602561 DOI: 10.3390/healthcare10102070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
The number of diabetic patients is increasing yearly worldwide, requiring the need for a quick intervention to help these people. Mortality rates are higher for diabetic patients with other serious health complications. Thus, early prediction for such diseases positively impacts healthcare quality and can prevent serious health complications later. This paper constructs an efficient prediction system for predicting diabetes in its early stage. The proposed system starts with a Local Outlier Factor (LOF)-based outlier detection technique to detect outlier data. A Balanced Bagging Classifier (BBC) technique is used to balance data distribution. Finally, integration between association rules and classification algorithms is used to develop a prediction model based on real data. Four classification algorithms were utilized in addition to an a priori algorithm that discovered relationships between various factors. The named algorithms are Artificial Neural Network (ANN), Decision Trees (DT), Support Vector Machines (SVM), and K Nearest Neighbor (KNN) for data classification. Results revealed that KNN provided the highest accuracy of 97.36% compared to the other applied algorithms. An a priori algorithm extracted association rules based on the Lift matrix. Four association rules from 12 attributes with the highest correlation and information gain scores relative to the class attribute were produced.
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Affiliation(s)
- Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence:
| | - Israa Mohamed
- Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
- Faculty of Engineering and Computer Sciences, King Salman International University, Tor Sinai 46512, Egypt
| | - Khalid M. Hosny
- Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
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Alslamah T, Altuwaijri EA, Abalkhail A, Alwashmi ASS, Alannas SM, Alharbi AH, Al-Salamah YA, Alhumaydhi FA. Motivational factors influencing undergraduate medical students' willingness to volunteer during an infectious disease pandemic in Saudi Arabia, a cross-sectional study. Eur Rev Med Pharmacol Sci 2022; 26:6084-6089. [PMID: 36111908 DOI: 10.26355/eurrev_202209_29624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Healthcare outbreaks, especially infectious disease pandemics, often stretch the healthcare systems to its limits. Healthcare systems have no option other than being supported by the participation of young and motivated healthcare providers (HCPs) in their undergraduate medical studies during their prevention and control internship program during the outbreak. Understanding key motivation factors influencing HCPs are vital to ensure their effective participation in such situations. SUBJECTS AND METHODS A cross-sectional study was conducted on 410 undergraduate medical students at Qassim University in Saudi Arabia with the aim to describe the motivation factors that affect their willingness to volunteer during a pandemic. An online survey questionnaire was conducted. RESULTS 410 participants of which 239 (58.29%) were female, 108 (26.34%) were in their third academic year and 129 (31.46%) were between 21-22 years of age. More than 70% of participants showed willingness to volunteer during a pandemic. Their willingness to volunteer was motivated by distance of workplace to home, availability of transportation, being vaccinated, access to health care for self and family if affected, and provision of specialized training. CONCLUSIONS Healthcare administrators and policy makers need to address these factors effectively to ensure the availability of skilled and motivated healthcare providers during a pandemic.
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Affiliation(s)
- T Alslamah
- Department of Public Health, College of Public Health and Health Informatics, Qassim University, Al Bukairiyah, Saudi Arabia.
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Alharbi AH, V AC, Lin M, Ashwini B, Jabarulla MY, Shah MA. Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models. Comput Intell Neurosci 2022; 2022:3922763. [PMID: 35655511 PMCID: PMC9155968 DOI: 10.1155/2022/3922763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/17/2022] [Accepted: 04/25/2022] [Indexed: 11/28/2022]
Abstract
Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.
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Affiliation(s)
- Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Aravinda C. V
- N. M. A. M. Institute of Technology, Nitte 574110, Karkala, India
| | - Meng Lin
- Ritsumeikan University, Kyoto, Japan
| | - B Ashwini
- N. M. A. M. Institute of Technology, Nitte 574110, Karkala, India
| | - Mohamed Yaseen Jabarulla
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
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