Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN

In recent years, computer vision systems have been used in almost every field of industry. In this study, image processing algorithm has been developed by using CUDA (GPU) which is 79 times faster than CPU. We had used this accelerated algorithm in destemming process of pepper. 65 percent of total national production of pepper is produced in our cities, Kahramanmaras and Gaziantep in Turkey. Firstly, hybrid intuitionistic fuzzy algorithm edge detection has been used for preprocessing of original image and Otsu method has been used for determining automatic threshold in this algorithm. Then the multilayer perceptron artificial neural network has been used for the classification of patterns in processed images. Result of ANN test for detection direction of pepper has shown high accuracy performance in CPU-based implementation and in GPU-based implementation

Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

Corneal ulcer is one of the most devastating eye diseases causing permanent damage. There exist limited soft techniques available for detecting this disease. In recent years, deep neural networks (DNN) have significantly solved numerous classification problems. However, many samples are needed to obtain reasonable classification performance using a DNN with a huge amount of layers and weights. Since collecting a data set with a large number of samples is usually a difficult and time-consuming process, very large-scale pre-trained DNNs, such as the AlexNet, the ResNet and the DenseNet, can be adapted to classify a dataset with a small number of samples, through the utility of transfer learning techniques. Although such pre-trained DNNs produce successful results in some cases, their classification performances can be low due to many parameters, weights and the emergence of redundancy features that repeat themselves in many layers in som cases. The proposed technique removes these unnecessary features by systematically selecting images in the layers using a genetic algorithm (GA). The proposed method has been tested on ResNet on a small-scale dataset which classifies corneal ulcers. According to the results, the proposed method significantly increased the classification performance compared to the classical approaches

Accelerated Intuitionistic Fuzzy Edge Detection Algorithm by Using CUDA

Intuitionistic fuzzy edge detection (IFED) algorithm has been used in the signification or characterization of images. IFED algorithm has been designed by the experts and the algorithm provides to aim to minimize errors of them. To be applicable in parallel of IFED is pave the way for accelerating of algorithm by performing in the graphics card. In this study, IFED algorithm was tested by transferring different size images to graphics cards which has different computing capacity via Compute Unified Device Architecture (CUDA) programming environment which is manufactured by NVIDIA. Parallel model of the algorithm adapted to CUDA platform, compared to serial application running on processor, and has seen that shortened runtime at least 67 times, most 641 times

Improved quick artificial bee colony (iqABC) algorithm for global optimization

Artificial bee colony (ABC) algorithm inspired by the complex behaviors of honey bees in foraging is one of the most significant swarm intelligence-based meta-heuristics and has been successfully applied to a number of numerical and combinatorial optimization problems. In this study, for increasing the early convergence performance of the ABC algorithm while protecting the qualities of the final solutions, a new exploitation mechanism from the best food source that is managed by the number of evaluations is described and its efficiency on both employed and onlooker bee phases is analyzed. The results of the experimental studies obtained from a set of benchmark problems showed that the ABC algorithm with the proposed method performs significantly better than the standard implementation of ABC algorithm and its other variants in terms of convergence speed and solution quality especially for the difficult problems that should be solved before completion of the relatively small number of fitness evaluations

A new artificial bee colony algorithm employing intelligent forager forwarding strategies

Artificial Bee Colony (ABC) algorithm that mimics the intelligent foraging behaviors of real honey bees has been successfully applied to different types of optimization problems in recent years. Actually, the main reason lying behind the high preference of the ABC algorithm is related with its good performance on solving difficult optimization problems due to the effective search mechanisms existing in a single cycle and easily-implementable bee phases. However, with the purpose of increasing the implementation simplicity and generalizing the principal concept of the algorithm, some significant behaviors of the real foraging bees are not closely simulated and integrated into the workflow of the ABC or tried to be managed by employing simple randomized or conditional operations. In this study, in order to increase the performance of ABC algorithm, the complex behavior of the foraging employed bees related with how they decide to pass through to the dance area and how long they stay on there for informing onlookers is modeled in detail and then a new variant of ABC called intelligent forager forwarding ABC for short iff - ABC is proposed. For analyzing the possible contributions of the intelligent forager forwarding strategy on the performance of the ABC algorithm, thirteen classical benchmark problems and fifteen computationally expensive benchmark problems presented at the CEC 2015 were tested. The results obtained from the experimental studies were compared with the results of the different meta-heuristics in addition to the well-known variants of the standard ABC algorithm. From the experimental studies, it was concluded that the intelligent forager forwarding strategy significantly improves the quality of the final solutions and the convergence speed of the ABC algorithm. (C) 2020 Elsevier B.V. All rights reserved