7,268 research outputs found
A novel hybrid edge detection technique: ABC-FA
Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods. The results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.Publisher's Versio
A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier
International audienceThe patient's heart disease status is obtained by using a heart disease detection model. That is used for the medical experts. In order to predict the heart disease, the existing technique use optimal classifier. Even though the existing technique achieved the better result, it has some disadvantages. In order to improve those drawbacks, the suggested technique utilizes the effective method for heart disease prediction. At first the input information is preprocessed and then the preprocessed result is forwarded to the feature selection process. For the feature selection process a proficient feature selection is used over the high dimensional medical data. Hybrid Fish Bee optimization algorithm (HFSBEE) is utilized. Thus, the proposed algorithm parallelizes the two algorithms such that the local behavior of artificial bee colony algorithm and global search of fish swarm optimization are effectively used to find the optimal solution. Classification process is performed by the transformation of medical dataset to the Multi kernel support vector machine (MKSVM). The process of our proposed technique is calculated based on the accuracy, sensitivity, specificity, precision, recall and F-measure. Here, for test analysis, the some datasets used i.e. Cleveland, Hungarian and Switzerland etc., that are given based on the UCI machine learning repository. The experimental outcome show that our presented technique is went better than the accuracy of 97.68%. This is for the Cleveland dataset when related with existing hybrid kernel support vector machine (HKSVM) method achieved 96.03% and optimal rough fuzzy classifier obtained 62.25%. The implementation of the proposed method is done by MATLAB platform. Rundown phrases-Artificial bee colony algorithm, Fish swarm optimization, Multi kernel support vector machine, Optimal rough fuzzy, Cleveland, Hungarian and Switzerland
Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
Back-propagation algorithm is one of the most widely used and popular
techniques to optimize the feed forward neural network training. Nature
inspired meta-heuristic algorithms also provide derivative-free solution to
optimize complex problem. Artificial bee colony algorithm is a nature inspired
meta-heuristic algorithm, mimicking the foraging or food source searching
behaviour of bees in a bee colony and this algorithm is implemented in several
applications for an improved optimized outcome. The proposed method in this
paper includes an improved artificial bee colony algorithm based
back-propagation neural network training method for fast and improved
convergence rate of the hybrid neural network learning method. The result is
analysed with the genetic algorithm based back-propagation method, and it is
another hybridized procedure of its kind. Analysis is performed over standard
data sets, reflecting the light of efficiency of proposed method in terms of
convergence speed and rate.Comment: 14 Pages, 11 figure
Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes
Construction over peat area have often posed a challenge to geotechnical engineers.
After decades of study on peat stabilisation techniques, there are still no absolute
formulation or guideline that have been established to handle this issue. Some
researchers have proposed solidification of peat but a few researchers have also
discovered that solidified peat seemed to decrease its strength after a certain period of
time. Therefore, understanding the chemical and biological reaction behind the peat
solidification is vital to understand the limitation of this treatment technique. In this
study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and
Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom
ash at various ratio. The mixtures of peat-binder-filler were subjected to the
unconfined compressive strength (UCS) test, bacterial count test and chemical
elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over
curing period were observed. Mixing 1 samples showed a steadily increase in strength
over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern
at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial
count and enzymatic activity with increase quantity of crystallites. Samples with lower
strength recorded increase in bacterial count and enzymatic activity with less
crystallites. Analysis using XRD showed that pargasite
(NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in
the lower strength samples, pargasite was predicted to be converted into monosodium
phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated
as 303.60. This showed that reaction which happened during solidification work was
inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as
135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount
of enzyme secretes is higher
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
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