Underwater object detection is one of the most challenging research topics in computer
vision technology. The complex underwater environment makes underwater images suffer …

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide.
Over this period, the economic and healthcare consequences of COVID-19 infection in …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …

The rice leaves related diseases often pose threats to the sustainable production of rice
affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice …

Batch Normalization (BN) is a milestone technique in the development of deep learning,
enabling various networks to train. However, normalizing along the batch dimension …

Despite the steady progress in video analysis led by the adoption of convolutional neural
networks (CNNs), the relative improvement has been less drastic as that in 2D static image …

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent
years, which has good applications in medical image analysis. In this paper, DenseNet is …

Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Agriculture is the major occupation in India and it loses 35% of the crop productivity annually
owing to plant diseases. Earlier plant disease detection is a tedious process because of …