Geo-Blockchain Intelligence Risk Assessment (GBIRA) technologies for secure and sustainable internet of geospatial big data computing ecosystems: a survey Hana M. Saraya, Ahmed A. Saleh, Amira Rezk Geojournal, 2026 In a world driven by geospatial big data, simulating past, present, and future scenarios is increasingly viable, giving rise to advanced geospatial intelligence sciences and technologies. These innovations enhance geospatial data management, which constitutes 80% of global data and is vital for addressing natural hazards like global warming and climate change, key areas of spatial big data research tied to environmental challenges. However, the growth of geospatial big data, associated with advancements in cloud computing, has increased cyberattack risks. Each technology for managing geospatial data has its complexities, benefits, and limitations. This paper examines Geo-Blockchain Intelligence Risk Assessment technologies, focusing on secure and sustainable geospatial big data computing for predicting environmental disasters in smart IoT ecosystems. It also explores new computing paradigms, providing robust capabilities to transform how environmental disasters and human responses interact through a comprehensive theoretical and scientific framework.
Automated lung cancer diagnosis from chest X-ray images using convolutional neural networks Aya Aboelghiet, Samaa M. Shohieb, Amira Rezk, Ahmed Abou Elfetouh, Ahmed Sharaf, et al. Peerj Computer Science, 2025 Background/Objectives Lung cancer is the leading cause of cancer-related deaths worldwide. While computed tomography (CT) scans provide more comprehensive medical information than chest X-rays (CXR), the high cost and limited availability of CT technology in rural areas pose significant challenges. CXR images, however, could serve as a potential preliminary diagnostic tool in diagnosing lung cancer, especially when combined with a computer-aided diagnosis (CAD) system. This study aims to enhance the accuracy and accessibility of lung cancer detection using a custom-designed convolutional neural network (CNN) trained on CXR images. Methods A custom-designed CNN was trained on an openly accessible CXR dataset from the Japanese Society for Radiological Technology (JSRT). Prior to training, the dataset underwent preprocessing, where each image was divided into overlapping patches. A t-test was applied to these patches to distinguish relevant from irrelevant ones. The relevant patches were retained for training the CNN model, while the irrelevant patches were excluded to enhance the model’s performance. Results The proposed model yielded a mean accuracy of 83.2 ± 2.91%, demonstrating its potential as a cost-effective and accessible preliminary diagnostic tool for lung cancer. Conclusions This approach could significantly improve the accuracy and accessibility of lung cancer detection, making it a viable option in resource-limited settings.
Enhanced Optimized Classification Model of Chronic Kidney Disease Shahinda Elkholy, Amira Rezk, Ahmed Abo El Fetoh Saleh International Journal of Advanced Computer Science and Applications, 2023 —Chronic kidney disease (CKD) is one of the leading causes of death across the globe, affecting about 10% of the world's adult population. Kidney disease affects the proper function of the kidneys. As the number of people with chronic kidney disease (CKD) rises, it is becoming increasingly important to have accurate methods for detecting CKD at an early stage. Developing a mechanism for detecting chronic kidney disease is the study's main contribution to knowledge. In this study, preventive interventions for CKD can be explored using machine learning techniques (ML). The Optimized deep belief network (DBN) based on Grasshopper's Optimization Algorithm (GOA) classifier with prior Density-based Feature Selection (DFS) algorithm for chronic kidney disease is described in this study, which is called "DFS-ODBN." Prior to the DBN classifier, whose parameters are optimized using GOA, the proposed method eliminates redundant or irrelevant dimensions using DFS. The proposed DFS-ODBN framework consists of three phases, preprocessing, feature selection, and classification phases. Using CKD datasets, the suggested approach is also tested, and the performance is evaluated using several assessment metrics. Optimized-DBN achieves its maximum performance in terms of sensitivity, accuracy, and specificity, the proposed DFS-ODBN demonstrated accuracy of 99.75 percent using fewer features comparing with other techniques.
Adaptive classification in data stream mining Journal of Theoretical and Applied Information Technology, 2020
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Geo-Blockchain Intelligence Risk Assessment (GBIRA) technologies for secure and sustainable internet of geospatial big data computing ecosystems: a survey HM Saraya, AA Saleh, A Rezk GeoJournal 91 (2), 43 , 2026 2026
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MOST CITED SCHOLAR PUBLICATIONS
Early prediction of chronic kidney disease using deep belief network SMM Elkholy, A Rezk, AAEF Saleh IEEE Access 9, 135542-135549 , 2021 2021 Citations: 109
Standardized electronic health record data modeling and persistence: A comparative review A Gamal, S Barakat, A Rezk Journal of biomedical informatics 114, 103670 , 2021 2021 Citations: 108
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An Efficient Classification Model for Unstructured Text Document M Mowafy, A Rezk, HM El-bakry Am J Compt Sci Inform Technol 6 (1), 16 , 2018 2018 Citations: 39
A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks N Elaraby, S Barakat, A Rezk Scientific reports 12 (1), 16271 , 2022 2022 Citations: 35
A comprehensive fuzzy ontology-based decision support system for Alzheimer’s disease diagnosis N Shoaip, A Rezk, S El-Sappagh, L Alarabi, S Barakat, MM Elmogy IEEE Access 9, 31350-31372 , 2020 2020 Citations: 30
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The impact of cyber crime on E-Commerce A Rezk, S Barakat, H Saleh International Journal of Intelligent Computing and Information Sciences 17 … , 2017 2017 Citations: 23
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Alzheimer’s disease diagnosis based on a semantic rule-based modeling and reasoning approach N Shoaip, A Rezk, S El-Sappagh, T Abuhmed, S Barakat, M Elmogy Computers, Materials, & Continua 69 (3), 3531 , 2021 2021 Citations: 18
An effective SQL injection detection model using LSTM for imbalanced datasets KS Fathi, S Barakat, A Rezk Computers & Security 153, 104391 , 2025 2025 Citations: 15
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Minimize the false positive rate in a database intrusion detection system A Rezk, H Ali, M El-Mikkawy, S Barakat International Journal of Computer Science & Information Technology 3 (5), 29 , 2011 2011 Citations: 10
A transfer learning-enabled optimized extreme deep learning paradigm for diagnosis of covid-19 R Ahmed, S Barakat, A Rezk Computers, Materials, & Continua 70 (1), 1381 , 2022 2022 Citations: 7
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