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Your search keyword '"Samuel, Oluwarotimi W."' showing total 8 results
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8 results on '"Samuel, Oluwarotimi W."'

3. On the Clinical Use of Artificial Intelligence and Haematological Measurements for a Rapid Diagnosis and Care of Paediatric Malaria Patients in West Africa †.

Author

Nsugbe, Ejay, Mathebula, Dephney, Viza, Evi, Samuel, Oluwarotimi W., Connelly, Stephanie, and Mutanga, Ian

Subjects
MACHINE learning, CHILD patients, CHILD mortality, ARTIFICIAL intelligence, THERAPEUTICS, BLOOD cell count
Abstract

Malaria continues to be a major cause of death worldwide, with a broad range of people spread over 90 countries being at risk of contracting the disease, and a significant cause of death in children under the age of 5. Due to this, there continues to be substantial investment towards not just the treatment of the disease, but also a more rapid and accurate means towards its diagnosis. In this work, we look to explore how measurements obtained from the complete blood count (CBC) technique from patients' blood, alongside artificial intelligence (AI) methods, could form an affordable analytical pipeline that could be adopted in hospital settings in both developed and developing countries. As part of this work, we utilize patient blood measurements acquired from paediatric patients from Ghana, West Africa, alongside various configurations of AI models towards distinguishing between malaria vs. non-malaria cases in a sample set comprising over 2000 patients. Class balancing algorithms are utilized to first balance the classes for the various patient groups, followed by the use of AI algorithms to train machine learning models to differentiate between a malaria vs. a non-malaria patient. The results showcased a generally high prediction accuracy, especially in the case of models with nonlinear decision boundaries, therein showing how the proposed analytic pipeline can serve as a high-throughput approach towards tackling the malaria epidemic from a diagnostics perspective and ultimately enhancing patient care strategies. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Big data, analytics and artificial intelligence for sustainability

Author

Ojokoh, Bolanle A., primary, Samuel, Oluwarotimi W., additional, Omisore, Olatunji M., additional, Sarumi, Oluwafemi A., additional, Idowu, Peter A., additional, Chimusa, Emile R., additional, Darwish, Ashraf, additional, Adekoya, Adebayo F., additional, and Katsriku, Ferdinand A., additional

Published
2020
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7. Muscle activity-driven green-oriented random number generation mechanism to secure WBSN wearable device communications

Author

Cao, Yuanlong, Zhang, Guanghe, Liu, Fanghua, You, Ilsun, Zheng, Guanglou, Samuel, Oluwarotimi W., Chen, Shixiong, Cao, Yuanlong, Zhang, Guanghe, Liu, Fanghua, You, Ilsun, Zheng, Guanglou, Samuel, Oluwarotimi W., and Chen, Shixiong

Abstract

Wireless body sensor networks (WBSNs) mostly consist of low-cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle-computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity-driven green-oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128-bit RN, which is much more efficient when compared to the electrocardiography-based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes.

Published
2018

8. Muscle activity-driven green-oriented random number generation mechanism to secure WBSN wearable device communications

Author

Cao, Yuanlong, Zhang, Guanghe, Liu, Fanghua, You, Ilsun, Zheng, Guanglou, Samuel, Oluwarotimi W., Chen, Shixiong, Cao, Yuanlong, Zhang, Guanghe, Liu, Fanghua, You, Ilsun, Zheng, Guanglou, Samuel, Oluwarotimi W., and Chen, Shixiong

Abstract

Cao, Y., Zhang, G., Liu, F., You, I., Zheng, G., Samuel, O. W., & Chen, S. (2018). Muscle Activity-Driven Green-Oriented Random Number Generation Mechanism to Secure WBSN Wearable Device Communications. Wireless Communications and Mobile Computing, 2018. Available here

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