�ݺ�ߣshows by User: YusufBrima

�ݺ�ߣshows by User: YusufBrima / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: YusufBrima / Sun, 02 Feb 2025 13:09:01 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: YusufBrima Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model /slideshow/assessing-fusarium-oxysporum-disease-severity-in-cotton-using-unmanned-aerial-system-images-and-a-hybrid-domain-adaptation-deep-learning-time-series-model/275313608 presentation-250202130901-9145ff47
A talk on deep learning for smart agriculture at the Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany.]]>
A talk on deep learning for smart agriculture at the Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany.]]> Sun, 02 Feb 2025 13:09:01 GMT /slideshow/assessing-fusarium-oxysporum-disease-severity-in-cotton-using-unmanned-aerial-system-images-and-a-hybrid-domain-adaptation-deep-learning-time-series-model/275313608 YusufBrima@slideshare.net(YusufBrima) Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model YusufBrima A talk on deep learning for smart agriculture at the Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-250202130901-9145ff47-thumbnail.jpg?width=120&height=120&fit=bounds" /> A talk on deep learning for smart agriculture at the Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany.

Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model from Yusuf Brima

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0 Trustworthy Healthcare AI for Mental Health Risk Prediction /slideshow/trustworthy-healthcare-ai-for-mental-health-risk-prediction/275311658 talkatnru-250202110056-25400a8c
A talk on building trustworthy AI systems for mental health management at the Neurobiology Research Unit,Copenhagen University Hospital, Denmark. ]]>
A talk on building trustworthy AI systems for mental health management at the Neurobiology Research Unit,Copenhagen University Hospital, Denmark. ]]> Sun, 02 Feb 2025 11:00:56 GMT /slideshow/trustworthy-healthcare-ai-for-mental-health-risk-prediction/275311658 YusufBrima@slideshare.net(YusufBrima) Trustworthy Healthcare AI for Mental Health Risk Prediction YusufBrima A talk on building trustworthy AI systems for mental health management at the Neurobiology Research Unit,Copenhagen University Hospital, Denmark. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talkatnru-250202110056-25400a8c-thumbnail.jpg?width=120&height=120&fit=bounds" /> A talk on building trustworthy AI systems for mental health management at the Neurobiology Research Unit,Copenhagen University Hospital, Denmark.

Trustworthy Healthcare AI for Mental Health Risk Prediction from Yusuf Brima

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0 Assessing Explainability in Deep Learning for Medical Image Analysis /slideshow/assessing-explainability-in-deep-learning-for-medical-image-analysis/275311589 talkatscai-250202105644-d049314e
A talk on assessing XAI methods for deep learning applied to medical image analysis at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Germany]]>
A talk on assessing XAI methods for deep learning applied to medical image analysis at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Germany]]> Sun, 02 Feb 2025 10:56:44 GMT /slideshow/assessing-explainability-in-deep-learning-for-medical-image-analysis/275311589 YusufBrima@slideshare.net(YusufBrima) Assessing Explainability in Deep Learning for Medical Image Analysis YusufBrima A talk on assessing XAI methods for deep learning applied to medical image analysis at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Germany <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talkatscai-250202105644-d049314e-thumbnail.jpg?width=120&height=120&fit=bounds" /> A talk on assessing XAI methods for deep learning applied to medical image analysis at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Germany

Assessing Explainability in Deep Learning for Medical Image Analysis from Yusuf Brima

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0 A Talk on Deep Causal Representation Learning /slideshow/a-talk-on-deep-causal-representation-learning/270011604 copyofcausalrepresentationlearning-240702062520-8fb4e2c5
This presentation explores the emerging field of causal representation learning, which aims to combine causal modeling with deep learning techniques. It discusses: Differences between natural and machine intelligence Challenges in current AI approaches The importance of causal reasoning and discovery A framework for deep causal representation learning Potential applications in healthcare, computer vision, and speech processing Key research directions and open challenges Ideal for researchers, data scientists, and AI enthusiasts interested in the future of robust and interpretable machine learning models.]]>
This presentation explores the emerging field of causal representation learning, which aims to combine causal modeling with deep learning techniques. It discusses: Differences between natural and machine intelligence Challenges in current AI approaches The importance of causal reasoning and discovery A framework for deep causal representation learning Potential applications in healthcare, computer vision, and speech processing Key research directions and open challenges Ideal for researchers, data scientists, and AI enthusiasts interested in the future of robust and interpretable machine learning models.]]> Tue, 02 Jul 2024 06:25:20 GMT /slideshow/a-talk-on-deep-causal-representation-learning/270011604 YusufBrima@slideshare.net(YusufBrima) A Talk on Deep Causal Representation Learning YusufBrima This presentation explores the emerging field of causal representation learning, which aims to combine causal modeling with deep learning techniques. It discusses: Differences between natural and machine intelligence Challenges in current AI approaches The importance of causal reasoning and discovery A framework for deep causal representation learning Potential applications in healthcare, computer vision, and speech processing Key research directions and open challenges Ideal for researchers, data scientists, and AI enthusiasts interested in the future of robust and interpretable machine learning models. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/copyofcausalrepresentationlearning-240702062520-8fb4e2c5-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation explores the emerging field of causal representation learning, which aims to combine causal modeling with deep learning techniques. It discusses: Differences between natural and machine intelligence Challenges in current AI approaches The importance of causal reasoning and discovery A framework for deep causal representation learning Potential applications in healthcare, computer vision, and speech processing Key research directions and open challenges Ideal for researchers, data scientists, and AI enthusiasts interested in the future of robust and interpretable machine learning models.

A Talk on Deep Causal Representation Learning from Yusuf Brima

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0 On Intelligence /slideshow/on-intelligence-251600645/251600645 onintelligence-220417091142
This talk takes us through a journey of human urge to tapping into the inner workings of the mind and how to create echos of that in machines.]]>
This talk takes us through a journey of human urge to tapping into the inner workings of the mind and how to create echos of that in machines.]]> Sun, 17 Apr 2022 09:11:41 GMT /slideshow/on-intelligence-251600645/251600645 YusufBrima@slideshare.net(YusufBrima) On Intelligence YusufBrima This talk takes us through a journey of human urge to tapping into the inner workings of the mind and how to create echos of that in machines. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/onintelligence-220417091142-thumbnail.jpg?width=120&height=120&fit=bounds" /> This talk takes us through a journey of human urge to tapping into the inner workings of the mind and how to create echos of that in machines.

On Intelligence from Yusuf Brima

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0 Guides to Securing Scholarships Overseas /slideshow/guides-to-securing-scholarships-overseas-251473096/251473096 guidestosecuringscholarshipsoverseas-220330103710
This short talk distills the roadmap toward getting a scholarship to study abroad. It touches on the key ideas to make you stand out and the pitfalls to avoid in your hunt for financial aid to study abroad.]]>
This short talk distills the roadmap toward getting a scholarship to study abroad. It touches on the key ideas to make you stand out and the pitfalls to avoid in your hunt for financial aid to study abroad.]]> Wed, 30 Mar 2022 10:37:09 GMT /slideshow/guides-to-securing-scholarships-overseas-251473096/251473096 YusufBrima@slideshare.net(YusufBrima) Guides to Securing Scholarships Overseas YusufBrima This short talk distills the roadmap toward getting a scholarship to study abroad. It touches on the key ideas to make you stand out and the pitfalls to avoid in your hunt for financial aid to study abroad. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/guidestosecuringscholarshipsoverseas-220330103710-thumbnail.jpg?width=120&height=120&fit=bounds" /> This short talk distills the roadmap toward getting a scholarship to study abroad. It touches on the key ideas to make you stand out and the pitfalls to avoid in your hunt for financial aid to study abroad.

Guides to Securing Scholarships Overseas from Yusuf Brima

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0 Transfer Learning for the Detection and Classification of traditional pneumonia and pneumonia induced by the COVID-19 from Chest X-ray Images /slideshow/transfer-learning-for-the-detection-and-classification-of-traditional-pneumonia-and-pneumonia-induced-by-the-covid19-from-chest-xray-images/249944830 aimsthesispresentation-210809064634
A presentation of my MSc in Mathematical Sciences thesis at the African Institute of Mathematical Sciences (AIMS), Rwanda. This presentation explores the application of Deep Transfer Learning towards the diagnosis and classification of traditional pneumonia and pneumonia induced from COVID-19 using chest X-ray images.]]>
A presentation of my MSc in Mathematical Sciences thesis at the African Institute of Mathematical Sciences (AIMS), Rwanda. This presentation explores the application of Deep Transfer Learning towards the diagnosis and classification of traditional pneumonia and pneumonia induced from COVID-19 using chest X-ray images.]]> Mon, 09 Aug 2021 06:46:32 GMT /slideshow/transfer-learning-for-the-detection-and-classification-of-traditional-pneumonia-and-pneumonia-induced-by-the-covid19-from-chest-xray-images/249944830 YusufBrima@slideshare.net(YusufBrima) Transfer Learning for the Detection and Classification of traditional pneumonia and pneumonia induced by the COVID-19 from Chest X-ray Images YusufBrima A presentation of my MSc in Mathematical Sciences thesis at the African Institute of Mathematical Sciences (AIMS), Rwanda. This presentation explores the application of Deep Transfer Learning towards the diagnosis and classification of traditional pneumonia and pneumonia induced from COVID-19 using chest X-ray images. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aimsthesispresentation-210809064634-thumbnail.jpg?width=120&height=120&fit=bounds" /> A presentation of my MSc in Mathematical Sciences thesis at the African Institute of Mathematical Sciences (AIMS), Rwanda. This presentation explores the application of Deep Transfer Learning towards the diagnosis and classification of traditional pneumonia and pneumonia induced from COVID-19 using chest X-ray images.

Transfer Learning for the Detection and Classification of traditional pneumonia and pneumonia induced by the COVID-19 from Chest X-ray Images from Yusuf Brima

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0 AIMS Block Presentation]{Deep Transfer Learning for Magnetic Resonance Image Multi-class Classification /slideshow/aims-block-presentationdeep-transfer-learning-for-magnetic-resonance-image-multiclass-classification/242482887 aimspresentation-210209214939
This paper adopted a Deep Residual Convolutional Neural Network (ResNet50) architecture for the experiments amongst other discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40\% on the proposed dataset, 93.80% on the Harvard Whole Brain Atlas, and 97.05% accuracy on the School of Biomedical Engineering dataset. Our experimental results significantly demonstrate our proposed framework for Transfer Learning is a potential and effective approach for brain tumour multi-classification tasks.]]>
This paper adopted a Deep Residual Convolutional Neural Network (ResNet50) architecture for the experiments amongst other discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40\% on the proposed dataset, 93.80% on the Harvard Whole Brain Atlas, and 97.05% accuracy on the School of Biomedical Engineering dataset. Our experimental results significantly demonstrate our proposed framework for Transfer Learning is a potential and effective approach for brain tumour multi-classification tasks.]]> Tue, 09 Feb 2021 21:49:38 GMT /slideshow/aims-block-presentationdeep-transfer-learning-for-magnetic-resonance-image-multiclass-classification/242482887 YusufBrima@slideshare.net(YusufBrima) AIMS Block Presentation]{Deep Transfer Learning for Magnetic Resonance Image Multi-class Classification YusufBrima This paper adopted a Deep Residual Convolutional Neural Network (ResNet50) architecture for the experiments amongst other discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40\% on the proposed dataset, 93.80% on the Harvard Whole Brain Atlas, and 97.05% accuracy on the School of Biomedical Engineering dataset. Our experimental results significantly demonstrate our proposed framework for Transfer Learning is a potential and effective approach for brain tumour multi-classification tasks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aimspresentation-210209214939-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper adopted a Deep Residual Convolutional Neural Network (ResNet50) architecture for the experiments amongst other discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40\% on the proposed dataset, 93.80% on the Harvard Whole Brain Atlas, and 97.05% accuracy on the School of Biomedical Engineering dataset. Our experimental results significantly demonstrate our proposed framework for Transfer Learning is a potential and effective approach for brain tumour multi-classification tasks.

AIMS Block Presentation]{Deep Transfer Learning for Magnetic Resonance Image Multi-class Classification from Yusuf Brima

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0 African Accents International Institute (AAII-SL): Work overview /slideshow/african-accents-international-institute-aaiisl-work-overview/242482846 workoverview-210209214407
African Accents International Institute (AAII-SL) is a grassroots NGO with a vision of creating a literate (knowledge) society that contributes to the economic, social, political and cultural growth of Sierra Leone.]]>
African Accents International Institute (AAII-SL) is a grassroots NGO with a vision of creating a literate (knowledge) society that contributes to the economic, social, political and cultural growth of Sierra Leone.]]> Tue, 09 Feb 2021 21:44:07 GMT /slideshow/african-accents-international-institute-aaiisl-work-overview/242482846 YusufBrima@slideshare.net(YusufBrima) African Accents International Institute (AAII-SL): Work overview YusufBrima African Accents International Institute (AAII-SL) is a grassroots NGO with a vision of creating a literate (knowledge) society that contributes to the economic, social, political and cultural growth of Sierra Leone. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/workoverview-210209214407-thumbnail.jpg?width=120&height=120&fit=bounds" /> African Accents International Institute (AAII-SL) is a grassroots NGO with a vision of creating a literate (knowledge) society that contributes to the economic, social, political and cultural growth of Sierra Leone.

African Accents International Institute (AAII-SL): Work overview from Yusuf Brima

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0 Introduction to internet /slideshow/introduction-to-internet-226928415/226928415 introductiontointernet-200204230438
Introduction to Web Engineering]]>
Introduction to Web Engineering]]> Tue, 04 Feb 2020 23:04:38 GMT /slideshow/introduction-to-internet-226928415/226928415 YusufBrima@slideshare.net(YusufBrima) Introduction to internet YusufBrima Introduction to Web Engineering <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontointernet-200204230438-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to Web Engineering

Introduction to internet from Yusuf Brima

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0 Big data for healthcare analytics final -v0.3 miz /slideshow/big-data-for-healthcare-analytics-final-v03-miz/155896995 bigdataforhealthcareanalytics-final-v0-190716143813
Sources of Big Data in Health (a comparative description of national and international data sources and identification of new/emerging sources of data)]]>
Sources of Big Data in Health (a comparative description of national and international data sources and identification of new/emerging sources of data)]]> Tue, 16 Jul 2019 14:38:13 GMT /slideshow/big-data-for-healthcare-analytics-final-v03-miz/155896995 YusufBrima@slideshare.net(YusufBrima) Big data for healthcare analytics final -v0.3 miz YusufBrima Sources of Big Data in Health (a comparative description of national and international data sources and identification of new/emerging sources of data) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdataforhealthcareanalytics-final-v0-190716143813-thumbnail.jpg?width=120&height=120&fit=bounds" /> Sources of Big Data in Health (a comparative description of national and international data sources and identification of new/emerging sources of data)

Big data for healthcare analytics final -v0.3 miz from Yusuf Brima

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0 Detecting malaria using a deep convolutional neural network /slideshow/detecting-malaria-using-a-deep-convolutional-neural-network/147543191 detectingmalariausingadeepconvolutionalneuralnetwork-190525075320
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized) Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture. Uses Keras with a Tensorflow backend. ]]>
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized) Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture. Uses Keras with a Tensorflow backend. ]]> Sat, 25 May 2019 07:53:20 GMT /slideshow/detecting-malaria-using-a-deep-convolutional-neural-network/147543191 YusufBrima@slideshare.net(YusufBrima) Detecting malaria using a deep convolutional neural network YusufBrima Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized) Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture. Uses Keras with a Tensorflow backend. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/detectingmalariausingadeepconvolutionalneuralnetwork-190525075320-thumbnail.jpg?width=120&height=120&fit=bounds" /> Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized) Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture. Uses Keras with a Tensorflow backend.

Detecting malaria using a deep convolutional neural network from Yusuf Brima

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0 https://cdn.slidesharecdn.com/profile-photo-YusufBrima-48x48.jpg?cb=1738662191 Research Scientist at the Research Training Group in Computational Cognition, Osnabrück University. yusufbrima.github.io https://cdn.slidesharecdn.com/ss_thumbnails/presentation-250202130901-9145ff47-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/assessing-fusarium-oxysporum-disease-severity-in-cotton-using-unmanned-aerial-system-images-and-a-hybrid-domain-adaptation-deep-learning-time-series-model/275313608 Assessing fusarium oxy... https://cdn.slidesharecdn.com/ss_thumbnails/talkatnru-250202110056-25400a8c-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/trustworthy-healthcare-ai-for-mental-health-risk-prediction/275311658 Trustworthy Healthcare... https://cdn.slidesharecdn.com/ss_thumbnails/talkatscai-250202105644-d049314e-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/assessing-explainability-in-deep-learning-for-medical-image-analysis/275311589 Assessing Explainabili...