Security architectures for sensitive Data in Cloud Computing

Mohammed, Tareq Abed; Mohammed, Ahmed Burhan

doi:10.1145/3410352.3410828

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Author details 1 Collage of Engineering, Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey, 2 College of Computer Science and Information Technology, Department of Information Technology, University of Kirkuk, Kirkuk -Iraq., 3 Collage of Engineering, Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey.…”

Section: Code Availabilitymentioning

confidence: 99%

“…Polypharmacy, the concurrent use of multiple medications to manage complex health conditions, holds significant promise for enhancing therapeutic efficacy [1]. It represents a strategic approach to target multiple risk factors in diseases such as heart failure, metabolic syndrome, and diabetes, which are often rooted in intricate and interrelated biological processes [2]. By addressing various facets of these diseases through a combination of drugs, polypharmacy has the potential to provide superior clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Big data Drug Interactions and associated side effects by Using Artificial Neural Networks (ANN) over Traditional Graph Convolutional Networks (GCNs)

IBRAHIM,

Mohammed,

DARA

2024

Preprint

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Rapid advances in machine learning have enabled the prediction of complex drug-drug interactions (DDIs) and associated harmful effects. This study aims to develop a neural network model that can predict drug-drug interactions (DDIs) for various side effects. Our study intends to create a reliable and easy-to-understand tool that can transform pharmaceutical research and healthcare by lowering polypharmacy risks. Our method begins with the careful selection and compilation of large datasets on medicine combinations, side effects, drug-side effect connections, and drug-protein interactions. We use an adjacency matrix to establish a drug-protein network. Then, we use PCA to shrink the network. Using artificial neural networks, our neural network is designed for binary categorization. This model is rigorously trained, validated, and tested using performance metrics to ensure its strength and adaptability. Our model has remarkable accuracy, with AUC-ROC scores of 98.67% for certain interactions. Reading and handling structured input is a major advantage of Artificial Neural Networks (ANNs) over Graph Convolutional Networks (GCNs). The findings demonstrate our approach's versatility in pharmaceutical research and healthcare, including medication development and real-time clinical decision help. To conclude, this study advances DDI prediction and management. Resilience, comprehensibility, and accuracy make the model a flexible polypharmacy solution. By solving DDI prediction and side effect control, our strategy might improve pharmaceutical research, patient safety, and healthcare results. This study shows how advanced machine learning may be used in pharmaceuticals.

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Section: Code Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Big data Drug Interactions and associated side effects by Using Artificial Neural Networks (ANN) over Traditional Graph Convolutional Networks (GCNs)

IBRAHIM,

Mohammed,

DARA

2024

Preprint

Self Cite

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“…These delays prolong stays in refugee camps. The Trump administration also continued to deport immigrants, with numerous deportees testing positive for COVID-19 immediately on arrival in their home countries (Mohammed et al, 2020). At one point, at least 23% of cases in Guatemala could be traced to returned detainees (Montoya-Galvez, 2020b).…”

Section: Impact Of Asylum Policies On the United States-mexico Bordermentioning

confidence: 99%

Health Sciences Interprofessional Collaborative: A Perspective on Migration, COVID-19, and the Impact on Indigenous Communities

et al. 2021

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At the United States-Mexico border, the impacts of immigration policy are dynamic with political, humanitarian, and health outcomes. This article highlights the experiences at the Casa Alitas migrant shelter in Tucson, Arizona. Casa Alitas aims to meet the needs of the im/migrants it serves, including the unique needs of indigenous asylum-seekers from Central America. We highlight the importance of community-based humanitarian response to support asylum-seekers in a way that acknowledges our shared humanity and implements specific approaches (e.g., language justice and trauma informed care). The effort at Casa Alitas is unique because in addition to other partnerships, Casa Alitas established an interprofessional collaboration between the University of Arizona Health Sciences Colleges and the Arizona State University School of Social Work. The interprofessional collaboration encourages mutual education amongst our professions and the use of our extended networks to meet the needs of im/migrants and asylum seekers in our community and the United States. We recommend the development of best practices in asylum health care, the importance of creating border-wide networks to build on local resources, and highlight the importance of exposing future health practitioners to trauma informed and culturally and linguistically appropriate care.

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A Comparative Study of Attribute Selection Algorithms on Intrusion Detection System in UAVs: A Case Study of UKM-IDS20 Dataset

Mohammed

Fourati

Fakhrudeen

2023

Lecture Notes in Computer Science

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Security architectures for sensitive Data in Cloud Computing

Cited by 4 publications

References 29 publications

Predicting Big data Drug Interactions and associated side effects by Using Artificial Neural Networks (ANN) over Traditional Graph Convolutional Networks (GCNs)

Predicting Big data Drug Interactions and associated side effects by Using Artificial Neural Networks (ANN) over Traditional Graph Convolutional Networks (GCNs)

Health Sciences Interprofessional Collaborative: A Perspective on Migration, COVID-19, and the Impact on Indigenous Communities

A Comparative Study of Attribute Selection Algorithms on Intrusion Detection System in UAVs: A Case Study of UKM-IDS20 Dataset

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