Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections.

This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustainable agriculture, this research focuses on developing a highly efficient and accurate automated system for identifying plant diseases, thereby contributing to enhanced crop protection and yield optimization. The proposed model is trained on a comprehensive dataset encompassing various plant species and disease categories, ensuring robust performance and adaptability.

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification.

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA).

Single-cell analysis of memory B cells from top neutralizers reveals multiple sites of vulnerability within HCMV Trimer and Pentamer.

Human cytomegalovirus (HCMV) can cause severe diseases in fetuses, newborns, and immunocompromised individuals. Currently, no vaccines are approved, and treatment options are limited. Here, we analyzed the human B cell response of four HCMV top neutralizers from a cohort of 9,000 individuals.

Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.

Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection.