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Hao S, Huang C, Heidari AA, Xu Z, Chen H, Althobaiti MM, Mansour RF, Chen X. Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Althobaiti MM, Ashour AA, Alhindi NA, Althobaiti A, Mansour RF, Gupta D, Khanna A. Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images. Biomed Res Int 2022; 2022:3714422. [PMID: 35572730 PMCID: PMC9098312 DOI: 10.1155/2022/3714422] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 02/13/2022] [Revised: 03/29/2022] [Accepted: 04/07/2022] [Indexed: 01/28/2023]
Abstract
The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods.
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Affiliation(s)
- Maha M. Althobaiti
- Department of Computer Science, College of Computing and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia
| | - Amal Adnan Ashour
- Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Nada A. Alhindi
- Oral Diagnostic Sciences Department, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Asim Althobaiti
- Regional Laboratory and Blood Bank, Taif Health, Taif, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - Deepak Gupta
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Ashish Khanna
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
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Ahmed ZAT, Aldhyani THH, Jadhav ME, Alzahrani MY, Alzahrani ME, Althobaiti MM, Alassery F, Alshaflut A, Alzahrani NM, Al-madani AM. Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models. Comput Math Methods Med 2022; 2022:3941049. [PMID: 35419082 PMCID: PMC9001065 DOI: 10.1155/2022/3941049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 11/30/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 12/04/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.
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Affiliation(s)
- Zeyad A. T. Ahmed
- Department of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, India
| | - Theyazn H. H. Aldhyani
- Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
| | - Mukti E. Jadhav
- Shri Shivaji Science & Arts College, Chikhli Dist. Buldana, India
| | - Mohammed Y. Alzahrani
- Department of Computer Sciences and Information Technology, Albaha University, Albaha, P.O. Box 1988, Saudi Arabia
| | - Mohammad Eid Alzahrani
- Department of Engineering and Computer Science, Al Baha University, Albaha, P.O. Box 1988, Saudi Arabia
| | - Maha M. Althobaiti
- Department of Computer Science, College of Computing and Information technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia
| | - Fawaz Alassery
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahmed Alshaflut
- College of Computer Science and Information Technology, Albaha University, Albaha, P.O. Box 1988, Saudi Arabia
| | - Nouf Matar Alzahrani
- College of Computer Science and Information Technology, Albaha University, Albaha, P.O. Box 1988, Saudi Arabia
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Nilashi M, Ali Abumalloh R, Alrizq M, Alghamdi A, Samad S, Almulihi A, Althobaiti MM, Yousoof Ismail M, Mohd S. What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology. Telematics and Informatics 2022; 69:101795. [PMID: 36268474 PMCID: PMC9556033 DOI: 10.1016/j.tele.2022.101795] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/14/2022] [Accepted: 02/21/2022] [Indexed: 10/31/2022]
Abstract
Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers’ trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers’ reviews in TripAdvisor’s COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k-means to segment the users’ data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample.
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Althobaiti MM, Almulihi A, Ashour AA, Mansour RF, Gupta D. Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans. J Healthc Eng 2022; 2022:2872461. [PMID: 35070232 PMCID: PMC8769827 DOI: 10.1155/2022/2872461] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 10/29/2021] [Revised: 12/10/2021] [Accepted: 12/17/2021] [Indexed: 12/18/2022]
Abstract
Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.
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Affiliation(s)
- Maha M. Althobaiti
- Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahmed Almulihi
- Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Amal Adnan Ashour
- Department of Oral & Maxillofacial Surgery and Diagnostic Sciences Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - Deepak Gupta
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
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Asghar J, Tabasam M, Althobaiti MM, Adnan Ashour A, Aleid MA, Ibrahim Khalaf O, Aldhyani THH. A Randomized Clinical Trial Comparing Two Treatment Strategies, Evaluating the Meaningfulness of HAM-D Rating Scale in Patients With Major Depressive Disorder. Front Psychiatry 2022; 13:873693. [PMID: 35722557 PMCID: PMC9197773 DOI: 10.3389/fpsyt.2022.873693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/02/2022] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION Due to the complexity of symptoms in major depressive disorder (MDD), the majority of depression scales fall short of accurately assessing a patient's progress. When selecting the most appropriate antidepressant treatment in MDD, a multidimensional scale such as the Hamilton Depression Rating scale (HAM-D) may provide clinicians with more information especially when coupled with unidimensional analysis of some key factors such as depressed mood, altered sleep, psychic and somatic anxiety and suicidal ideation etc. METHODS HAM-D measurements were carried out in patients with MDD when treated with two different therapeutic interventions. The prespecified primary efficacy variables for the study were changes in score from baseline to the end of the 12 weeks on HAM-D scale (i.e., ≤ 8 or ≥50% response). The study involved three assessment points (baseline, 6 weeks and 12 weeks). RESULTS Evaluation of both the absolute HAM-D scores and four factors derived from the HAM-D (depressed mood, sleep, psychic and somatic anxiety and suicidal ideation) revealed that the latter showed a greater promise in gauging the anti-depressant responses. CONCLUSION The study confirms the assumption that while both drugs may improve several items on the HAM-D scale, the overall protocol may fall short of addressing the symptoms diversity in MDD and thus the analysis of factor (s) in question might be more relevant and meaningful.
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Affiliation(s)
- Junaid Asghar
- Faculty of Pharmacy, Gomal University, D. I. Khan, Pakistan
| | - Madiha Tabasam
- Faculty of Pharmacy, Gomal University, D. I. Khan, Pakistan
| | | | - Amal Adnan Ashour
- Department of Oral & Maxillofacial Surgery, Taif University, Taif, Saudi Arabia
| | - Mohammed A Aleid
- College of Education, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Osamah Ibrahim Khalaf
- Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad, Iraq
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Ali A, Iqbal MM, Jamil H, Akbar H, Muthanna A, Ammi M, Althobaiti MM. Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing. Sensors (Basel) 2021; 22:s22010108. [PMID: 35009649 PMCID: PMC8747413 DOI: 10.3390/s22010108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 06/12/2023]
Abstract
With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices' trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.
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Affiliation(s)
- Abid Ali
- Department of Computer Science, The University of Engineering and Technology, Taxila 47080, Pakistan; (A.A.); (M.M.I.); (H.A.)
- Department of Computer Science, Govt Akhtar Nawaz Khan (Shaheed) Degree College KTS, Haripur 22620, Pakistan
| | - Muhammad Munawar Iqbal
- Department of Computer Science, The University of Engineering and Technology, Taxila 47080, Pakistan; (A.A.); (M.M.I.); (H.A.)
| | - Harun Jamil
- Department of Electronic Engineering, Jeju National University, Jejusi 63243, Jeju-do, Korea;
| | - Habib Akbar
- Department of Computer Science, The University of Engineering and Technology, Taxila 47080, Pakistan; (A.A.); (M.M.I.); (H.A.)
| | - Ammar Muthanna
- Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia;
- Department of Computer Science, RUDN University, Peoples’ Friendship University of Russia, 6 Miklukho-Maklaya Str., 117198 Moscow, Russia
| | - Meryem Ammi
- Department of Computational Sciences, Naif Arab University for Security Sciences, Riyadh 13216, Saudi Arabia;
| | - Maha M. Althobaiti
- Department of Computer Science, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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