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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 6, No 1: March 2017" : 5 Documents clear
CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns Athira TR; Abraham Varghese
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (781.473 KB) | DOI: 10.11591/ijai.v6.i1.pp8-17

Abstract

Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.
Time-Based Raga Recommendation and Information Retrieval of Musical Patterns in Indian Classical Music Using Neural Networks Samarjit Roy; Sudipta Chakrabarty; Debashis De
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.807 KB) | DOI: 10.11591/ijai.v6.i1.pp33-48

Abstract

In Indian Classical Music (ICM) perspective, Raga is formed from the different and correct combination of notes. If it is observed the history of Indian Classical Raga in ICM, the playing or serving each of the ragas has some unique sessions. The procedure is to suggest the classifications of playing a raga has been attempted to display by explaining unique musical features and pattern matching. This contribution has been represented how music structures can be advanced through a more conceptual demonstration and consent to unambiguously describe process of computational modeling of Musicology which signify the challenge on complete musical composition from the elementary vocal objects of ICM usage using Neural Networks. In Neural network the samples of various ragas have been taken as input and classify them according to the times of the performance. Over 90% accuracy level has achieved using entire Confusion Matrices and Error Histogram performance evaluation technique.
Fuzzy Information Modeling in a Database System Salam Ismaeel; Ayman Al-Khazraji; Karama Al-delimi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (845.274 KB) | DOI: 10.11591/ijai.v6.i1.pp1-7

Abstract

A Fuzzy logic (FL) provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. In this paper a fuzzy information modeling system was developed then used in a database, which contains fuzzy data and real data, to create new information assistance capable of making any decision about this data. The proposed system is implemented on a special database used to evaluation workers or users in any formal organizations.
A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes D. Geraldine Bessie Amali; Dinakaran M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.121 KB) | DOI: 10.11591/ijai.v6.i1.pp26-32

Abstract

Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization.
A Low Power, Low Noise Amplifier for Recording Neural Signals G. Deepika; K.S. Rao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (344.281 KB) | DOI: 10.11591/ijai.v6.i1.pp18-25

Abstract

The design of a low power amplifier for recording EEG signals is presented. The low noise design techniques are used in this design to achieve low input referred noise that is near the theoretical limit of any amplifier using a differential pair as input stage. To record the neural spikes or local field potentials (LFP’s) the amplifier’s bandwidth can be adjusted. In order to reject common-mode and power supply noise differential input pair need to be included in the design. The amplifier achieved a gain of 53.7dB with a band width of 0.5Hz to1.1 kHz and input referred noise measured as 357 nVrms operated with a supply voltage of 1.0V. The total power consumed is around 3.19µW. When configured to record neural signals the gain measured is 54.3 dB for a bandwidth of 100 Hz and the input referred noise is 1.04µ Vrms. The amplifier was implemented in 180nm technology and simulated using Cadence Virtuoso.

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