بازیابی تصویر با استفاده از الگوریتم کلونی زنبور عسل با تعادل-تکامل و مهار جانبی(یا افزایش رزولوشن محرک)

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این پروژه حاوی فایلهای کدهای متلب مربوط به روش و گزارشی در قالب ورد از تئوری روش مقاله می باشد. برای توضیح مفصل کدها در قالب فیلم آموزشی یا ورد نیاز به سفارش است. برای سفارش با ایمیل یا شماره تماس موجود در سایت تماس حاصل نمائید.

رفرنس مقاله:

Li, B., Zhou, C., Liu, H., Li, Y., & Cao, H. (2016). Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition. Optik, ۱۲۷(۲۴), ۱۱۷۷۵-۱۱۷۸۵.

:Abstract

Image retrieval is a fundamental issue in pattern recognition. In this work, lateral inhibition (LI) model is adopted as a pre-processing step, which widens the gray level gradients so as to facilitate the image retrieval scheme. In searching for a perfect match between a predefined template and a reference image, we adopt metaheuristic algorithms for good seach capability. Artificial bee colony (ABC) algorithm is a bio-inspired optimization technique, which imitates the foraging behavior of honey bee swarms. It is well known that the algorithm is good at exploration but poor at exploitation. We present balance-evolution artificial bee colony (BE-ABC) algorithm that aims to strike a balance between exploration and exploitation rather than just focusing on improving the latter. BE-ABC algorithm adaptively manipulates the search intensity at the exploration and exploitation stages during the iterations. Besides that, it incorporates an overall degradation procedure to prevent premature convergence. Simulation results confirm that BE-ABC algorithm is more capable than several state-of-the-art metaheuristic algorithms in this image retrieval scheme.

طبقه بندی با شبکه تابع پایه شعاعی فرا شناختی و الگوریتم یادگیری مبتنی بر پرتو

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این پروژه حاوی فایلهای کدهای متلب مربوط به روش و گزارشی در قالب ورد از پیاده سازی می باشد

رفرنس پروژه :

Babu, G. S., & Suresh, S. (2013). Meta-cognitive RBF network and its projection based learning algorithm for classification problems. Applied Soft Computing, ۱۳(۱), ۶۵۴-۶۶۶.

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and its ‘Projection Based Learning’ (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, namely the cognitive component and the meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.

 

رمزگذاری تصویر با مولد آشوب عصبی کارآمد

Avatar for مهندس برات زادهمهندس برات زاده

این پروژه حاوی فایلهای کدهای متلب مربوط به روش و گزارشی در قالب ورد از پیاده سازی می باشد

رفرنس پروژه:

Kassem, A., Hassan, H. A. H., Harkouss, Y., & Assaf, R. (2014). Efficient neural chaotic generator for image encryption. Digital Signal Processing, ۲۵, ۲۶۶-۲۷۴. 

In this paper, we propose a new implementation of chaotic generator using artificial neural network. Neural network can act as an efficient source of perturbation in the chaotic generator which increases the cycleʼs length, and thus avoid the dynamical degradation due to the used finite dimensional space. On the other hand, the use of neural network enlarges the key space of the chaotic generator in an enormous way. The efficiency of the proposed neural chaotic generator is illustrated using some dynamical and NIST statistical tests. We also propose in this paper, a new image encryption method based on chaotic sequence, and the obtained results emphasize the efficiency of our technique.