Multiscale Fractal Descriptors Applied to Nanoscale Images

This work proposes the application of fractal descriptors to the analysis of nanoscale materials under different experimental conditions. We obtain descriptors for images from the sample applying a multiscale transform to the calculation of fractal dimension of a surface map of such image. Particularly, we have used the}Bouligand-Minkowski fractal dimension. We applied these descriptors to discriminate between two titanium oxide films prepared under different experimental conditions. Results demonstrate the discrimination power of proposed descriptors in such kind of application

Texture descriptor combining fractal dimension and artificial crawlers

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the details richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that each agent can interact with the environment and each other. Since this swarm system alone does not achieve a good discrimination, we developed a new method to increase the discriminatory power of artificial crawlers, together with the fractal dimension theory. Here, we estimated the fractal dimension by the Bouligand-Minkowski method due to its precision in quantifying structural properties of images. We validate our method on two texture datasets and the experimental results reveal that our method leads to highly discriminative textural features. The results indicate that our method can be used in different texture applications.Comment: 12 pages 9 figures. Paper in press: Physica A: Statistical Mechanics and its Application

Improved texture image classification through the use of a corrosion-inspired cellular automaton

In this paper, the problem of classifying synthetic and natural texture images is addressed. To tackle this problem, an innovative method is proposed that combines concepts from corrosion modeling and cellular automata to generate a texture descriptor. The core processes of metal (pitting) corrosion are identified and applied to texture images by incorporating the basic mechanisms of corrosion in the transition function of the cellular automaton. The surface morphology of the image is analyzed before and during the application of the transition function of the cellular automaton. In each iteration the cumulative mass of corroded product is obtained to construct each of the attributes of the texture descriptor. In a final step, this texture descriptor is used for image classification by applying Linear Discriminant Analysis. The method was tested on the well-known Brodatz and Vistex databases. In addition, in order to verify the robustness of the method, its invariance to noise and rotation were tested. To that end, different variants of the original two databases were obtained through addition of noise to and rotation of the images. The results showed that the method is effective for texture classification according to the high success rates obtained in all cases. This indicates the potential of employing methods inspired on natural phenomena in other fields.Comment: 13 pages, 14 figure

PEMODELAN 3D MOTIF CINCIN DAN PERHIASAN LAINNYA DENGAN FRAKTAL

Pada penelitian ini, penulis membahas Pemodelan Motif CinCin dan Perhiasan dengan Fraktal Tiga Dimensi (3D). Penelitian ini dilatarbelakangi dengan fakta bahwa kearifan lokal untuk motif cincin emas dan perak serta perhiasan lainnya dari Kota Gede Yogyakarta dan Kendari Sulawesi nampak sudah diatur dan berpola tetap. Walaupun motifnya beragam, namun desain kurang bervariasi, sehingga Nampak monoton. Untuk itu, diperlukan desain motif yang lebih unik, menarik dan bernilai jual tinggi. Pemodelan motif 3D ini menggunakan OpenGL dan bahasa pemrograman C. Pemodelan telah diuji dengan menggunakan sistem Operasi Windows. Pada penelitian ini telah dihasilkan 340 desain cincin unik yang bernuansa tradisional dan modern. Secara keseluruhan, luaran yang dihasilkan dari penelitian ini pada tahun ke dua adalah (i) aplikasi mobile untuk pembuatan model 3D untuk cincin (iii) satu makalah yang diseminarkan di 2015 The Annual Conference on Engineering and Technology di Nagoya Jepang pada 4-6 Nov 2015 (iv) draft buku ajar: “Pemodelan Fraktal 3D untuk Cincin dan Perhiasan Lainnya”

Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using Functional Data Analysis

This work proposes and study the concept of Functional Data Analysis transform, applying it to the performance improving of volumetric Bouligand-Minkowski fractal descriptors. The proposed transform consists essentially in changing the descriptors originally defined in the space of the calculus of fractal dimension into the space of coefficients used in the functional data representation of these descriptors. The transformed decriptors are used here in texture classification problems. The enhancement provided by the FDA transform is measured by comparing the transformed to the original descriptors in terms of the correctness rate in the classification of well known datasets

PENERAPAN PENDEKATAN MACHINE LEARNING PADA PENGEMBANGAN BASIS DATA HERBAL SEBAGAI SUMBER INFORMASI KANDIDAT OBAT KANKER

Cancer is still an epidemiological disease in Indonesia. Drug development against cancer still relies to pharmacological laboratories and natural chemicals, which could have side effects. Cancer drug development has entered the stage of molecular biology, where the interaction of ligand chemical structure with receptor protein can be studied with high accuracy. Various chemical compounds, ranging from synthetic, semi-synthetic, to natural materials, developed for the purpose to fight one of the most dangerous diseases. In the context of the development of herbal-based drugs, there has been found heaps of natural compounds, curated and annotated, in various databases belonging to China, Taiwan, Indonesia, Japan, and several other countries. However, problems arise when choosing the best bioactive compounds to develop against cancer. Complexity arises because the metabolic pathway of cancer is very diverse, depending on the type and phase of cancer. Therefore, in this systematic review, we developed a machine learning approach to screen for these bioactive compounds, then took the best candidates for molecular simulation operations that would be tested for validity in wet experiments. Thus, the automation of the candidate drug development process for cancer could be achieved with great significance. It is known that the most effective and efficient machine learning method was Naïve Bayes, but the best in processing large amounts of compound data was classfier SVM. The future of complex bioactive compounds data could be secured by employing deep learning method. Keywords: machine learning, drug development, natural material compounds, metabolic pathways, cancer