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Lymphatic Filariasis

Lymphatic Filariasis

Avneet Kaur; Kavita Attri; Rahul Kumar

Lap Lambert Academic Publishing
2025
pokkari
Lymphatic filariasis, considered globally as a neglected tropical disease (NTD), is a parasitic disease caused by microscopic, thread-like worms, the parasite damages the lymph system. Most of the infected people have no symptoms and will never develop clinical symptoms. Fixed dose combination kits have emerged as a potential technique for the treatment of lymphatic filariasis. LF is treated with a mix of pharmacological therapy, vector control, and preventative measures. FDC kits for the treatment of LF are made up of two or more antifilarial medicines that target distinct phases of the parasite's life cycle. These kits make treatment regimens easier to follow, increase patient compliance, and improve overall treatment results. The use of FDC kits in LF therapy has various advantages. It removes the need for patients to take various pills or capsules at different times, decreasing therapy complexity and boosting adherence. Antifilarial medicine combination and dose standardisation decreases the possibility of incorrect drug selection or dosage mistakes. Furthermore, the combination of various medications in FDC kits may aid in the prevention of drug resistance.
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Recent Advances in Logo Detection Using Machine Learning Paradigms

Recent Advances in Logo Detection Using Machine Learning Paradigms

Yen-Wei Chen; Xiang Ruan; Rahul Kumar Jain

Springer International Publishing AG
2025
nidottu
This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues. This book provides numerous ways that deep learners can use for logo recognition, including: Deep learning-based end-to-end trainable architecture for logo detection Weakly supervised logo recognition approach using attention mechanisms Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images Approach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem. The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks. The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.
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