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1000 tulosta hakusanalla Rahul Kumar Ghosh

Recent Advances in Deep Learning for Medical Image Analysis

Recent Advances in Deep Learning for Medical Image Analysis

Yen-Wei Chen; Lanfen Lin; Rahul Kumar Jain

Springer International Publishing AG
2025
sidottu
This book is a valuable resource for understanding the transformative role of artificial intelligence in modern healthcare and aims to inspire continued research and collaboration across disciplines. In recent years, deep learning has emerged as a transformative technology across various fields, with medical image analysis standing out as one of its most impactful applications. This book offers a comprehensive overview of the latest developments in this fast-evolving domain, bridging foundational principles with state-of-the-art techniques that are redefining the future of medical imaging. This book is structured in two parts—Part I: Deep Learning Fundamentals and Paradigms and Part II: Advanced Deep Learning for Medical Image Analysis. The book provides in-depth coverage of essential topics, including convolutional neural networks, attention mechanisms, transformer architectures, multimodal analysis, semi-supervised learning, domain adaptation, generative models, and foundation models for large-scale pretraining. This book is intended for a broad audience, including graduate students, academic researchers, and industry professionals in computer science, biomedical engineering, and healthcare technologies. It serves as both an introductory guide and a reference resource for those seeking to deepen their knowledge in this rapidly evolving area.
Nutrient Management Module for Mustard (Brassica juncea) Crop

Nutrient Management Module for Mustard (Brassica juncea) Crop

Shashi Prakash Vishwakarma; Preetam Saran; Rahul Kumar Verma

LAP Lambert Academic Publishing
2019
pokkari
Rapeseed and mustard is a major oil seed crop of India after groundnut. The chemical fertilizer is the mainstay of agricultural production due to insufficient availability of alternative nutrient source. The organic resources available presently, could meet nearly one-third of total nutrients required to achieve the targeted level of food production. The soils of Indo-Gangetic Plain, are showing signs of fatigue due to falling organic carbon levels and deficiencies of macro, secondary and micro nutrients. The integrated use of organic and inorganic source of nutrient ensures better and sustainable yields. In the light of present investigation it may be concluded that 100% NPK + 5t FYM + 40 kg S ha-1 can be recommended to the farmers for successful cultivation of mustard in addition to improving fertility status viz., N, P, K, S and Zn of soil.
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.
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.