Mangena Venu Madhavan¹, Dang Ngoc Hoang Thanh², Aditya Khamparia^1,*, Sagar Pande¹, Rahul Malik¹, Deepak Gupta³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2939-2955, 2021, DOI:10.32604/cmc.2021.012466 - 28 December 2020

Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying More >

Ahmet Elbir^1,∗, Hamza Osman Ilhan¹, Nizamettin Aydin¹

Computer Systems Science and Engineering, Vol.34, No.2, pp. 101-107, 2019, DOI:10.32604/csse.2019.34.101

Abstract The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and More >

N. Mohanapriya¹, Dr. B. Kalaavathi²

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 663-672, 2019, DOI:10.31209/2018.100000041

Abstract Medical images are obtained straight from the medical acquisition devices so that, the image quality becomes poor and may contain noises. Low contrast and poor quality are the major issues in the production of medical images. Medical imaging enhancement technology gives way to solve these issues; it helps the doctors to see the interior portions of the body for early diagnosis, also it improves the features the visual aspects of an image for a right diagnosis. This paper proposes a new blend of Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO) called Hybrid… More >