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International Journal of Advances in Applied Sciences

IJAAS
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Published by Institute of Advanced Engineering and Science

ISSN : 22528814 EISSN : 27222594 DOI : http://doi.org/10.11591/ijaas

Core Subject : Science, Education, Social,

Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.

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Articles 11 Documents

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Search results for , issue "Vol 6, No 4: December 2017" : 11 Documents clear

Advances in Application of Fuzzy sets in electrical engineering Aditya Jain; Balakrushna Tripathy
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Initially a theory, today fuzzy logic has become an operational technique. Used alongside other advanced control techniques, it is making a discrete but appreciated appearance in various electric systems. In the majority of present-day applications, fuzzy logic allows many kinds of designer and operator qualitative knowledge in electrical automation to be taken into account. Fuzzy logic began to interest the media at the beginning of the nineties. The numerous applications in electrical and electronic household appliances, particularly in Japan, were mainly responsible for such interest. Washing machines not requiring adjustment, camcorders with Steadyshot (TM) image stabilization and many other innovations brought the term “fuzzy logic” to the attention of a wide public.

Dynamic Key Matrix of Hill Cipher Using Genetic Algorithm Andysah Putera Utama Siahaan
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

The matrix in Hill Cipher was designed to perform encryption and decryption. Every column and row must be inserted by integer numbers. But, not any key that can be given to the matrix used for the process. The wrong determinant result cannot be used in the process because it produces the incorrect plaintext when doing the decryption after the encryption. Genetic algorithms offer the optimized way to determine the key used for encryption and decryption on the Hill Cipher. By determining the evaluation function in the genetic algorithm, the key that fits the composition will be obtained. By implementing this algorithm, the search of the key on the Hill Cipher will be easily done without spending too much time. Genetic algorithms do well if it is combined with Hill Cipher.

Feature Selection Using Evolutionary Functional Link Neural Network for Classification Amaresh Sahu; Sabyasachi Pattnaik
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Computational time is high for Multilayer perceptron (MLP) trained with back propagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to train, and gives better result in the classification problems. The paper proposed an evolutionary functional link artificial neural network (EFLANN) using genetic algorithm (GA) by eliminating features having little or no predictive information. Particle swarm optimization (PSO) is used as learning tool for solving the problem of classification in data mining. EFLANN overcomes the non-linearity nature of problems by using the functionally expanded selected features, which is commonly encountered in single layer neural networks. The model is empirically compared to MLP, FLANN gradient descent learning algorithm, Radial Basis Function (RBF) and Hybrid Functional Link Neural Network (HFLANN) . The results proved that the proposed model outperforms the other models.

An Open Source Contact-Free Palm Vein Recognition System Ranjith Kumar M; Deepika G G; Meenakshi Krishnan; Karthikeyan B
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

In this document, we propose a novel palm vein recognition system using open source hardware and software. We have developed an alternative preprocessing and feature extraction technique. The proposed system is built on Raspberry Pi using OpenCV 2.4.12. The palm vein image is cropped to Region of Interest(ROI) to reduce the computational time in real time systems and then preprocessed to enhance the vein pattern visibility and to extract more number of key points using SIFT algorithm. Then the descriptors are stored in a dictionary like codebook file during training. Later the descriptors are tested with unknown patterns. The clustering is based on K-means algorithm and classification is done using Support Vector Machines (SVM).

Classification of Content based Medical Image Retrieval Using Texture and Shape feature with Neural Network Sweety Maniar; Jagdish S. Shah
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Medical image classification and retrieval systems have been finding extensive use in the areas of image classification according to imaging modalities, body part and diseases. One of the major challenges in the medical classification is the large size images leading to a large number of extracted features which is a burden for the classification algorithm and the resources. In this paper, a novel approach for automatic classification of fundus images is proposed. The method uses image and data pre-processing techniques to improve the performance of machine learning classifiers. Some predominant image mining algorithms such as Classification, Regression Tree (CART), Neural Network, Naive Bayes (NB), Decision Tree (DT) K-Nearest Neighbor. The performance of MCBIR systems using texture and shape features efficient. . The possible outcomes of a two class prediction be represented as True positive (TP), True negative (TN), False Positive (FP) and False Negative (FN).

A Fuzzy Logic Based Mppt Controller For Wind-Driven Three-Phase Self-Excited Induction Generators Supplying Dc Microgrid B.Murali Mohan; M.Pala Prasad Reddy; M. Lakshminarayana
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

In this paper, a straightforward strategy for tracking the maximum power (MP) accessible in the wind energy conversion system for dc microgrid is proposed. A three-phase diode bridge rectifier alongside a dc-dc converter has been utilized between the terminals of wind-driven induction generator and dc microgrid. Induction generator is being worked in self-energized mode with excitation capacitor at stator. The output current i.e., dc grid current act as a control variable to track the MP in the proposed WECS. In this manner, the proposed calculation for maximum power point tracking (MPPT) is autonomous of the machine and wind-turbine parameters. Further, a technique has been created for deciding the obligation proportion of the dc-dc converter for working the proposed system in MPPT condition utilizing wind turbine qualities, relentless state proportionate circuit of prompting generator and power balance in power converters. Circuit straightforwardness and basic control calculation are the significant points of interest of the proposed setup for supplying energy to the dc microgrid from WECS. The fruitful working of the proposed calculation for Fuzzy logic based MPPT has been shown with broad exploratory results alongside the simulated values.

Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction J.P. Kharat
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed forward and Time Delay Neural Network. The results show that cascade feed forward network produces minimum error as compared to other networks. This paper also compares the results of traditional prediction method of averaging of frames for future frame prediction with neural based methods. The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models.

Comparison of Modeling and Simulation results Management Micro Climate of the Greenhouse by Fuzzy Logic between a Wetland and Arid region Didi Faouzi; N. Bibi-Triki; B. Draoui; A. Abène
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Currently the climate computer offers many benefits and solves problems related to the regulation, monitoring and controls. Greenhouse growers remain vigilant and attentive, facing this technological development. they ensure competitiveness and optimize their investments / production cost which continues to grow. The application of artificial intelligence in the industry known for considerable growth, which is not the case in the field of agricultural greenhouses, where enforcement remains timid. it is from this fact, we undertake research work in this area and conduct a simulation based on meteorological data through MATLAB Simulink to finally analyze the thermal behavior -greenhouse microclimate energy . In this paper we present comparison of modeling and simulation management of the greenhouse microclimate by fuzzy logic between a wetland (Dar El Beida Algeria) and the other arid (Biskra Algeria).

Improved Color Satellite Image Segmentation Using Tsallis Entropy and Granular Computing Jagan kumar. N; Agilandeeswari. L; Prabukumar. M
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

The research work is to improve the segmentation of the color satellite images. In this proposed method the color satellite image can be segmented by using Tsallis entropy and granular computing methods with the help of cuckoo search algorithm. The Tsallis and granular computing methods will used to find the maximum possibility of threshold limits and the cuckoo search will find the optimized threshold values based on threshold limit that is calculated by the Tsallis entropy and granular computing methods and the multilevel thresholding will used for the segmentation of color satellite images based on the optimized threshold value that will find by this work and these methods will help to select the optimized threshold values for multiple thresholding effectively.

Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique Anirudh Yadav; Vinay Kumar Harit
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Fault identification and its diagnosis is an important issue in present scenario of power system, as huge amount of electric power is utilized. Random types of faults occur in substation, which leads to irregular and discontinue supply of power from generating to consumer point. Fault detection is an important concept of power system which is to be studied and new method has to develop for fault detection and removal of it. This paper proposed on-line fault detection and identification of fault-type by using Neuro-Fuzzy method in substation. Combination of Artificial Neural Network (ANN) and Fuzzy Logic (FL), results in gaining learning capabilities of fuzzy logic. Variation of current according to fault is used for identification. Fuzzy controller display output condition in form of (0,1).Here, single line-to ground (LG) fault, line-to-line (LL) fault, double line-to ground (LLG)/ LLL fault are considered.

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