Browsing by Subject "Artificial Neural Networks"

Now showing 1 - 4 of 4

Open Access
Artificial neural network modeling and simulation of in-vitro nanoparticle-cell interactions
(American Scientific Publishers, 2014) Cenk, N.; Budak, G.; Dayanik, S.; Sabuncuoglu, I.
In this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA), all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.
Open Access
An evolutionary basic design tool
(2010) Akbulut, Dilek
As a creative act, design aims at achieving innovative solutions to fulfill the requirements provided in the problem definition. In recent years, computational methods began to be used not only in design presentation but also in solution generation. The study proposes a design methodology for a particular basic design problem on the concept of emphasis. The developed methodology generates solution alternatives by carrying out genetic operations used in evolutionary design. The generated alternatives are evaluated by an objective function comprising an artificial neural network. The creative potential of the methodology is appraised by comparing the outputs of test runs with the student works for the same design task. In doing so, three different groups of students with diverse backgrounds are used.
Open Access
A rotation, scaling, and translation invariant pattern classification system
(Elsevier, 1993) Yüceer, C.; Oflazer, K.
This paper describes a hybrid pattern classification system based on a pattern preprocessor and an artificial neural network classifier that can recognize patterns even when they are deformed by transformation of rotation, scaling, and translation or a combination of these. After a description of the system architecture we provide experimental results from three different classification domains: classification of letters in the English alphabet, classification of the letters in the Japanese Katakana alphabet, and classification of geometric figures. For the first problem, our system can recognize patterns deformed by a single transformation with well over 90% success ratio and with 89% success ratio when all three transformations are applied. For the second problem, the system performs very good for patterns deformed by scaling and translation but worse (about 75%) when rotations are involved. For the third problem, the success ratio is almost 100% when only a single transformation is applied and 88% when all three transformations are applied. The system is general purpose and has a reasonable noise tolerance. Times Cited: 32 (from All Datab
Open Access
Ship recognition and classification using silhouettes extracted from optical images
(IEEE, 2016) Yüksel, Göktuğ Kağan; Yalıtuna, Buğra; Tartar, Ömer Faruk; Adlı, Faiz Caner; Eker, Kaan; Yörük, Onur
In this paper, extraction of ship signatures from silhouette images of three-dimensional ship models and ship recognition from optical images are investigated. First of all, from the silhouette images of 3-dimensional ship models, with the help of feature vectors, ship signatures are created. Using three-dimensional ship models gets rid of the difficulty of obtaining real videos for the database and makes it possible to obtain information about ships from every angle. Then, created ship signatures are collected in a synthetic database. In the next stage, using segmentation and Artificial Neural Networks, ship recognition and classification are performed. In this paper, how all of the stages are done is explained and obtained numerical results are provided to illustrate used theoretical solutions.