Search

Your search keyword '"Shimaa, A"' showing total 16,626 results
Start Over Author "Shimaa, A"
16,626 results on '"Shimaa, A"'

1. Colistin susceptibility among multidrug resistant Gram-negative bacilli isolated from Tertiary hospital in Egypt

Author

Shimaa, A. Abdel Salam and Raghda, Hager

Subjects
colistin susceptibility, mcr-1 gene, multi-drug resistant gnb, pcr, Microbiology, QR1-502
Abstract

Colistin is considered as the last resort for treatment of bacterial infections caused by carbapenem-resistant Gram-negative bacilli (GNB). Colistin resistance can increase due to the spread of plasmid-borne mcr-1 gene. This study aimed to determine colistin susceptibility and to detect the presence of mcr-1 gene in the clinical isolates of GNB recovered from different clinical samples collected from Ain Shams University Hospital, Cairo, Egypt. About thirty-five GNB isolates were recovered from the different clinical samples that were collected during the period from February-April, 2019. These isolates were subjected to antibiotic susceptibility testing using disc diffusion assay, and Colistin susceptibility through the E-test. In addition, conventional Polymerase chain reaction (PCR) was carried out for detection of mcr-1 gene among the colistin GNB resistant isolates. Most of the GNB isolates (60 %) were recovered from blood samples. Klebsiella pneumoniae (K. pneumoniae) was the most common isolated bacterium; that was represented by 24 isolates (68%). Out of the 35 GNB, only five isolates (14.3 %) were resistant to colistin by E-test, with MIC >256 𝜇g/ ml. The mcr-1 gene was detected only in one Pseudomonas aeruginosa (P. aeruginosa) isolate. This study concluded the low frequency of mcr-1 gene among the current GNB isolates. However, a large scale study is recommended to detect colistin resistance among GNB.

Published
2020
Full Text
View/download PDF

2. Synthetic Counterfactual Faces

Author

Ramesh, Guruprasad V, Rosenberg, Harrison, Hooda, Ashish, and Fawaz, Shimaa Ahmed Kassem

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Computer vision systems have been deployed in various applications involving biometrics like human faces. These systems can identify social media users, search for missing persons, and verify identity of individuals. While computer vision models are often evaluated for accuracy on available benchmarks, more annotated data is necessary to learn about their robustness and fairness against semantic distributional shifts in input data, especially in face data. Among annotated data, counterfactual examples grant strong explainability characteristics. Because collecting natural face data is prohibitively expensive, we put forth a generative AI-based framework to construct targeted, counterfactual, high-quality synthetic face data. Our synthetic data pipeline has many use cases, including face recognition systems sensitivity evaluations and image understanding system probes. The pipeline is validated with multiple user studies. We showcase the efficacy of our face generation pipeline on a leading commercial vision model. We identify facial attributes that cause vision systems to fail., Comment: Paper under review. Full text and results will be updated after acceptance

Published
2024

3. On Kakeya's Geometric Proof of Enestr\'om-Kakeya's Theorem

Author

Annaby, Mahmoud and Elsayed-Abdullah, Shimaa

Subjects
Mathematics - Complex Variables, 26C10
Abstract

This paper is devoted to demonstrate Kakeya's geometric proof of his theorem (1912), independently established earlier by Enestr\"om (1893). By calculating centers and radii of the interlacing circles of Kakeya's method, we prove Kakeya's geometric structure, which has not been previously established. We give an equivalent proof, which is based on the construction of internally interlacing circles, which has been geometrically considered by Tomic (1948)., Comment: 9 pages, 9 Figures

Published
2024

4. Large Language Model-Driven Curriculum Design for Mobile Networks

Author

Erak, Omar, Alhussein, Omar, Naser, Shimaa, Alabbasi, Nouf, Mi, De, and Muhaidat, Sami

Subjects
Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobile networks evolve towards the 6G era, managing their increasing complexity and dynamic nature poses significant challenges. Conventional RL approaches often suffer from slow convergence and poor generalization due to conflicting objectives and the large state and action spaces associated with mobile networks. To address these shortcomings, we introduce curriculum learning, a method that systematically exposes the RL agent to progressively challenging tasks, improving convergence and generalization. However, curriculum design typically requires extensive domain knowledge and manual human effort. Our framework mitigates this by utilizing the generative capabilities of LLMs to automate the curriculum design process, significantly reducing human effort while improving the RL agent's convergence and performance. We deploy our approach within a simulated mobile network environment and demonstrate improved RL convergence rates, generalization to unseen scenarios, and overall performance enhancements. As a case study, we consider autonomous coordination and user association in mobile networks. Our obtained results highlight the potential of combining LLM-based curriculum generation with RL for managing next-generation wireless networks, marking a significant step towards fully autonomous network operations., Comment: To appear in IEEE/CIC ICCC

Published
2024
Full Text
View/download PDF

16. Using moringa (Moringa oleifera) seed protein hydrolysate as a dietary protein supplement modulates hemato-biochemical indices, antioxidant response, and tissue histo-morphology of Nile tilapia (Oreochromis niloticus)

Author

Ibrahim, Rowida E., Amer, Shimaa A., Rhouma, Nasreddin R., Younis, Elsayed M., Abdelwarith, Abdelwahab A., Abdel-Ghany, Heba M., Elshobaky, Gehad E., El-Saber, Mahmoud M., Osman, Ali, Davies, Simon J., and Rahman, Afaf N. Abdel

Published
2024
Full Text
View/download PDF

29. A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local Machine Learning Models for Instagram and TikTok

Author

West, Jack, Thiemt, Lea, Ahmed, Shimaa, Bartig, Maggie, Fawaz, Kassem, and Banerjee, Suman

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computers and Society, K.4.2, C.4, D.2.2
Abstract

Mobile apps have embraced user privacy by moving their data processing to the user's smartphone. Advanced machine learning (ML) models, such as vision models, can now locally analyze user images to extract insights that drive several functionalities. Capitalizing on this new processing model of locally analyzing user images, we analyze two popular social media apps, TikTok and Instagram, to reveal (1) what insights vision models in both apps infer about users from their image and video data and (2) whether these models exhibit performance disparities with respect to demographics. As vision models provide signals for sensitive technologies like age verification and facial recognition, understanding potential biases in these models is crucial for ensuring that users receive equitable and accurate services. We develop a novel method for capturing and evaluating ML tasks in mobile apps, overcoming challenges like code obfuscation, native code execution, and scalability. Our method comprises ML task detection, ML pipeline reconstruction, and ML performance assessment, specifically focusing on demographic disparities. We apply our methodology to TikTok and Instagram, revealing significant insights. For TikTok, we find issues in age and gender prediction accuracy, particularly for minors and Black individuals. In Instagram, our analysis uncovers demographic disparities in the extraction of over 500 visual concepts from images, with evidence of spurious correlations between demographic features and certain concepts., Comment: 18 pages, 13 figures, to appear at IEEE Symposium on Security and Privacy 2024

Published
2024

30. A novel interleukin 36 gamma for diagnosing and differentiating malignant from infectious pleural effusion

Author

Nashwa A. Amin, Asmaa E. Mohamed, Shimaa M.E. Wahdan, Amira E. Ramadan, Shimaa M. Mohammed, Marwa S.E. Abd-Elraouf, and Kout A.M. Diab

Subjects
interleukin 36, pleural effusion, tuberculous pleural effusion, Diseases of the respiratory system, RC705-779
Abstract

Background Pleural effusions can be caused by various diseases, making their diagnosis challenging. Light’s criteria are often used to distinguish transudative and exudative effusions. Exudative effusions have a more complex etiology, including parapneumonic, tuberculous, and malignant effusion. Objectives The main objective of this study was to evaluate the effectiveness and sensitivity of Interleukin 36 gamma (IL-36γ) as a diagnostic marker for differentiating between malignant and infectious pleural effusions. Methods The study involved 100 patients with pleural effusion. Patients were categorized into five groups based on their final diagnosis. An ELISA technique was used to quantitatively measure the levels of pleural fluid IL-36γ. Results The results showed that pleural IL-36γ levels were higher in tuberculous pleural effusion compared with malignant, transudative, and uncomplicated pleural effusion. In contrast, pleural IL-36γ levels were higher in complicated pleural effusion compared with tuberculous, malignant, transudative, and uncomplicated pleural effusion. Conclusion Based on the study’s findings, it was concluded that pleural IL-36γ is a novel biomarker that can be used to diagnose and differentiate malignant pleural effusion from infectious pleural effusion.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results