The democratization of the Internet has opened vistas of opportunities for young people coming from underprivileged backgrounds to gain scholarships for further studies. This massive outpour of information has also saturated facts with fiction about what it takes to gain a prestigious scholarship program amid the ever-growing competitiveness among candidates for a few slot of opportunities. Thus, our aim in this webinar, the first of its kind (at least to our knowledge for the West African context), is to distill the essence of a scholarship, the does and don't in one's quest to get a scholarship and a general thinking paradigm to have to become a successful scholar. The paradigm in question is problem-solving and leadership-oriented ways of thinking because to be successful, one has to be so in the selfless service to humankind.
My experience was great personally and professionally. As I am very passionate about traveling and meeting people from different cultures, I learned a lot about the country and the people of Bangladesh. I still maintain ties with some friends from here. Read more
As mentioned in my previous post, scholarships are offered to candidiate base on specific eligibility criteria which could range from merit (whatever its definition might be) to diversity and inclusion of underrepresented groups. However, the common denominator is, these candidates need financial assistance for professional development.
One key advice which you should bear in mind in your hunt for scholarship opportunities is to avoid plagiarism. I know, we all at one point in time have to stand on the shoulders of giants in order to grow but give credit to whom it is due. Intellectual honesty is very important. You may wonder, why am I taking a tangent and being stoic on this subject?! Simple. Primarily because it is very crucial not only in getting a scholarship but to toward your profession success (or failure) altogether. Pay attention to this truism. You are going to get suggestively tempted to not be original but doing so could be at your detriment. So avoid using other people's motivation letters, study plan or similar documents as your own. Be yourself. Tell your own unique story. That will help you stand out.
Having got that out of the way, here are a few sample documents and templates to help inspire you develop your own application portfolio. This is not exhaustive, neither it is the best there exist. However, it could be a great first step in developing an intuition of how these documents are developed.
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021
In our research, we utilized Magnetic Resonance Imaging (MRI) as a principal diagnostic tool in radiology to detect soft tissue abnormalities, particularly in the brain. To address the laborious process of manually interpreting a large number of MRI scans, we developed a framework using Machine Learning methodologies. Our approach, based on Deep Transfer Learning and the Deep Residual Convolutional Neural Network (ResNet50) architecture, achieved remarkable classification accuracies of 86.40% on our dataset, 93.80% on the Harvard Whole Brain Atlas dataset, and an impressive 97.05% accuracy on the School of Biomedical Engineering dataset. These results demonstrate the effectiveness of our proposed transfer learning framework for multi-classification of brain tumors in MRI images, offering a potential and impactful method to improve medical diagnosis in radiology. Read more
Recommended citation: Yusuf Brima, Mossadek Hossain Kamal, Upama Kabir, and Tariqul Islam (2019). "Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification." IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1(1). https://doi.org/10.48550/arXiv.2106.07333
Published in MDPI Special Issue on Machine Learning Applications for COVID-19 and Its Complications: Screening, Diagnosis, Treatment, and Prognosis, 2021
Accurate early diagnosis of COVID-19 viral pneumonia, primarily in asymptomatic people, is essential to reduce the spread of the disease, the burden on healthcare capacity, and the overall death rate. It is essential to design affordable and accessible solutions to distinguish pneumonia caused by COVID-19 from other types of pneumonia. In this work, we propose a reliable approach based on deep transfer learning that requires few computations and converges faster. Experimental results demonstrate that our proposed framework for transfer learning is a potential and effective approach to detect and diagnose types of pneumonia from chest X-ray images with a test accuracy of 94.0%. Read more
Recommended citation: Yusuf Brima. (2021). "Deep Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images." MDPI Special Issue on Machine Learning Applications for COVID-19 and Its Complications: Screening, Diagnosis, Treatment, and Prognosis. 1(1). https://doi.org/10.3390/diagnostics11081480
Published in arXiv preprint biorxiv:2023.06.08.544240, 2023
We propose a theoretical framework for biological recognition, integrating category theory, information theory, dynamical systems modeling, and optimization theory. This framework defines individual recognition (IR) and class-level recognition (CR) as functors between stimuli and responses. We identify five conditions for robust IR systems, termed “signature systems,” and model them as attractor states, offering insights into communication and language evolution. Read more
Recommended citation: Brima, Yusuf. "A Mathematical Framework for Understanding Recognition Systems" biorxiv preprint biorxiv:2023.06.08.544240 (2023). https://doi.org/10.1101/2023.06.08.544240
Published in arXiv preprint arXiv.2208.00953, 2023
We evaluated attribution methods to illuminate how deep neural networks analyze medical images, using adaptive path-based gradient integration to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets, highlighting possible biomarkers, exposing model biases, and offering insights into the links between input and prediction, demonstrating the method’s ability to elucidate model reasoning and improve deep learning transparency for domain experts by revealing the rationale behind predictions. Read more
Recommended citation: Brima, Yusuf and Marcellin Atemkeng. “Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images.” (2022). https://doi.org/10.48550/arXiv.2208.00953
Published in arXiv preprint arXiv.2309.03619, 2023
In this study, we investigate the impact of different formulations of the Barlow Twins (BT) objective on downstream task performance for speech data, proposing Modified Barlow Twins (MBT) with normalized latents to enforce scale-invariance, and our results demonstrate that MBT improves representation generalization over original BT, particularly when fine-tuning with limited target data, highlighting the importance of designing objectives that encourage invariant and transferable representations for self-supervised speech representations. Read more
Recommended citation: Brima, Yusuf, et al. "Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction." arXiv preprint arXiv:2309.03619 (2023). https://doi.org/10.48550/arXiv.2309.03619
Published in Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195, 2023
This work aims to investigate a novel self-supervised learning (SSL) method for speech representation that leverages redundancy reduction techniques to learn robust representations capturing speaker characteristics. Our proposed approach builds upon the Barlow Twins framework, introduced in computer vision, and we adapt it to speech processing. The primary objective is to assess the quality of the learned representations through comprehensive evaluations of various downstream tasks, including speaker identification, gender recognition, emotion recognition, and more. By exploiting the statistical relationships between different views of the same speech input, the proposed method encourages the model to capture speaker-specific information while attenuating the impact of irrelevant variations. This enables extraction of features invariant to non-speaker-related factors, such as language content or background noise. Read more
Recommended citation: Brima, Yusuf (2023): Self-Supervised Learning of Speech Representation via Redundancy Reduction. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-02. Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195 https://doi.org/10.18420/ki2023-dc-02
[MSc. Mathematical Sciences thesis] Transfer Learning for the Detection and Classification of traditional pneumonia and pneumonia induced by the COVID-19 from Chest X-ray Images. Available here Read more
Workshop Title: BRIDGING THE TECHNOLOGICAL GAP – SPREADING TECHNOLOGICAL INNOVATIONS IN THE STUDY OF THE HUMAN AND NON-HUMAN MIND Date: JULY 31ST, 2022 – AUGUST 6TH, 2022 URL: https://www.primate-cognition.eu/de/veranstaltungen/bridging-the-technological-gap-workshop.html — Read more
Poster title: “Supervised Contrastive Deep Learning for Individual Recognition in Chimpanzees in the Wild “ This talked peered into the study of the origin of human language as a communicative tool. It narrowed down on Individual Recogntition (IR) – the ability of an individual (a receiver) to uniquely identify another individual (a signaler) based on signature cues, a ubiquitous practice of animals that live in fission-fusion systems. IR is crucial to startification of groups, formation of trust and behaviour direction towards others. This talk connect the problem of IR from the perpective of a receiver as an computational problem modelled using deep learning. Read more
Talk title: “Supervised Contrastive Representation Learning for Individual Recognition in the Wild.” On-going reaching into the application of state-of-the-art deep learning techniques for speaker recognition in the wild. Read more
Undergraduate courses, University of Makeni, Department of Computer Science, 2017
Unit I
Data mining – Introduction – Information and production factor – Data mining vs query tools– Data mining in marketing – Self learning computer systems – concept learning – Data mining and the data warehouse. Read more
