Santwana Sagnika

Plant diseases pose a significant threat to agricultural productivity and global food security, accounting for 70-80% of crop losses worldwide. Traditional detection methods rely heavily on expert visual inspection, which is time-consuming, labour-intensive, and often impractical for large-scale farming operations. In this paper, we present PlantDiseaseNet-RT50, a novel fine-tuned deep learning architecture based on ResNet50 for automated plant disease detection. Our model features strategically unfrozen layers, a custom classification head with regularization mechanisms, and dynamic learning rate scheduling through cosine decay. Using a comprehensive dataset of distinct plant disease categories across multiple crop species, PlantDiseaseNet-RT50 achieves exceptional performance with approximately 98% accuracy, precision, and recall. Our architectural modifications and optimization protocol demonstrate how targeted fine-tuning can transform a standard pretrained model into a specialized agricultural diagnostic tool. We provide a detailed account of our methodology, including the systematic unfreezing of terminal layers, implementation of batch normalization and dropout regularization and application of advanced training techniques. PlantDiseaseNet-RT50 represents a significant advancement in AI-driven agricultural tools, offering a computationally efficient solution for rapid and accurate plant disease diagnosis that can be readily implemented in practical farming contexts to support timely interventions and reduce crop losses.

Oral cancer is a serious worldwide health problem. It might be seen in the face, oral glands, neck, or mouth of the patient. Cancer detection utilizing histopathology images aids in easing and forecasting abnormality. Furthermore, it delivers better outcomes if biological procedures are implemented appropriately, however, there are large opportunities for human errors and blunders during physical examinations. The advancement of deep learning techniques may open the door to a better detection of oral cancer from histopathology images, which will be advantageous to lab staff and medical professionals. Our study presents an in-depth analysis of 10 pre-trained deep convolutional neural network models using transfer learning approach for the detection of oral cancer. The experiment was performed for two classes, i.e., normal and oral squamous cell carcinoma. The results show that the VGG19 model with data augmentation was able to attain the highest classification accuracy of 96.26% using the transfer learning technique. We have also introduced an approach by integrating the VGG19 pre-trained model with a custom naïve inception block. This fusion of two well-known models harnesses their complementary strengths and results in a more robust architecture. The incorporation of the inception block addresses limitations observed in the VGG19 framework, such as vanishing gradient issues and excessive computational requirements. The proposed model for detection of oral cancer has undergone a thorough block-wise fine tuning. Our results demonstrate the superior performance of our deep learning architecture compared to existing literature, highlighting its potential to enhance the detection and diagnosis of oral cancer.

Cognitive radio systems have taken a fore-running position in the wireless communication technology. With most of the communication taking place through multi-carrier systems, the allocation of available spectrum to various carriers is a prominent issue. Since cognitive systems provide an environment of dynamic spectrum allocation, it becomes necessary to perform dynamic spectrum allocation swiftly with due consideration of parameters, like power consumption, fair distribution and minimal error. This paper considers a Particle Swarm Optimization-based approach, popularly used for solving large problems involving complex solution spaces to reach an optimal solution within feasible time. The mentioned spectrum allocation problem has been solved using PSO with a view to maximize the total transfer rate of the system, within specified constraints of maximum error rate, maximum power consumption and minimum transfer rate per user. The results have been compared with the existing Genetic Algorithm-based approach and have proved to be more effective.