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Dr Misbah Uddin on Muck Rack

Dr Misbah Uddin

England
As seen in: Environmental Health Perspectives, Medium, Nature, Wiley Online Library, arXiv, PLOS, PLOS ONE, IEEE, MDPI, SAGE Publishing and
Covers:  Medical Negligence Law
Barrister Mohammad Misbah Uddin, better known as Misu was a student of the Unversity of Rajshahi before leaving for London to do his LLB.

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Dr Misbah Uddin’s Biography

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A barrister by profession, Blogger by hobby and Entrepreneur for money.

Dr Misbah Uddin’s Journalist Portfolio

Money Gelt - Personal Finance | Key to Savings

Money Gelt - Personal Finance | Key to Savings

Welcome to Money Gelt! A UK Personal Finance Blog helping you to take care of your money.

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Articles

Multi-directional control of a bio-inspired robot with a single undulating fin propulsion

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By Gonzalo Garcia Verified, Dr Misbah Uddin, Oscar Curet, Omar S. Odeh, Arshadul Karim
| Nature Verified
Abstract Control of agile and highly maneuverable underwater vessels is fundamental to explore complex maritime environments including survey closed to ocean structures and shallow waters. Currently, traditional autonomous underwater vehicles (AUVs) and remote operated vehicles (ROVs) can encounter challenges navigating through these tight spaces. In this work, we explore the use of a flexible undulating membrane as a propulsive and force control mechanism for an underwater vehicle.
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Deep learning and computer vision framework for tomato insect classification with XAI insights

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By Mohammad Islam, Anisur Rahman, Masum Billah, Morium Akter, Dr Misbah Uddin
| Nature Verified
Abstract Pest infestations pose a life-threatening challenge to global tomato production, often resulting in devastating yield losses and economic instability. Traditional manual identification practices are subjective, labor-intensive, and prone to error. To address this, within the paradigm of Agriculture 5.0, we introduce a novel hybrid deep learning model, ResNet50-Fusion (ViT), designed for the precise detection of six economically relevant tomato pests.
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FusionViT CNN adaptive framework for insect associated plant leaf disease classification

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By Mohammad Islam, Dr Misbah Uddin, Habibur Rahman Sajal, Morium Akhter
| Nature Verified
Abstract The timely and proper diagnosis of the leaf diseases caused by insects is important in reducing the losses of crops and also maintaining food security. Nonetheless, traditional Convolutional Neural Network (CNNs) can tend to be more susceptible to false alarms due to a lack of features in minority disease classes.
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