arXiv subjects

Anugrah Jain

Publications and source records attributed to Anugrah Jain.

3 recordsLinked to original sources

arXiv2025

cuAPO: A CUDA-based Parallelization of Artificial Protozoa Optimizer

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/or 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-a-days working on design and development of parallel versions of state-of-the-art metaheuristic optimization algorithms. We, in this paper, present a CUDA-based parallelization of state-of-the-art Artificial Protozoa Optimizer leveraging GPU acceleration. We implement both the existing sequential version and the proposed parallel version of Artificial Protozoa Optimizer for a performance comparison. Our experimental results calculated over a set of CEC2022 benchmark functions demonstrate a significant performance gain i.e. up to 6.7 times speed up is achieved with proposed parallel version. We also use a real world application, i.e., Image Thresholding to compare both algorithms.

cs.NE↗

arXiv2025

Detecting Multiple Diseases in Multiple Crops Using Deep Learning

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.

cs.CV↗

arXiv2015

Fault Tolerant Synthesis of Reversible Circuits

Reversible computing has emerged as a possible low cost alternative to conventional computing in terms of speed, power consumption and computing capability. In order to achieve reliable circuits in reversible computing, provision for fault tolerance is necessary. A number of fault models, fault tolerant techniques (such as parity-preserving) and testing approaches have proposed in literature. This dissertation exploits parity-preserving characteristics of two reversible gates which provide low cost parity-preserving based fault tolerance. In order to extend online testability of reversible circuits, the substitution of Peres gate has been presented. The online testing capabilities of MCF including Swap and Fredkin gates were also identifies. Finally a tool was developed to implement all above substitutions and converting any reversible circuit to parity-preserving based fault tolerant circuit.

cs.ET↗