Anugrah Jain

Henish Soliya, Anugrah Jain

Metaheuristic algorithms are widely used for solving complex problems due to their ability to provide near-optimal solutions. But the execution time of these algorithms increases with the problem size and solution space. And, to get more promising results, we have to execute these algorithms for a large number of iterations, requiring a large amount of time and this is one of the main issues found with these algorithms. To handle the same, researchers are now-adays working on design and development of parallel versions of state of the art metaheuristic optimization algorithms. We, in this paper, present a parallel implementation of state of the art Artificial Protozoa Optimizer using NVIDIA CUDA framework to leverage GPU acceleration. Our implementation optimizes the state of the art Artificial Protozoa Optimizer (APO) to achieve high performance. We implement both the existing sequential version and the proposed parallel version of Artificial Protozoa Optimizer in this paper. The experimental results calculated over benchmarks functions of CEC2022 demonstrate a significant performance gain i.e. up to 6.7 times speed up achieved in case of proposed parallel version. We also use two real world applications (1) Tension/Compression Spring Design in engineering optimization and (2) Image Thresholding using otsu method for testing the performance of proposed implementation in handling real tasks.

Vivek Yadav, Anugrah Jain

India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.