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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 477 Documents

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Self-Tuning VGPI Controller Based on ACO Method Applied for WTGS system Madaci Mansour; Djallel Kerdoun
International Journal of Advances in Applied Sciences Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

The stability and the fast response are two parameters to evaluate the efficiency of any system, and the acknowledgement of the mathematic model studied and its parameters are strongly required. In order to build the regulation and the control of the system, different methods are used. Some are traditional (PI, PD, PID…); whereas, others are modern (Fuzzy logic, neural networks, statistical algorithms, genetic algorithms, VGPI and so on…).In this paper, we focused on the presentation of a new method which we call the scheduling regulation based on a particle swarm optimization. A stochastic diffusion search method that takes inspiration from the social behaviors of real ants with their environment. Ant colony optimization algorithms (ACO) presents a promising performance which is a self-organized regulation system with no need to the acknowledgment of both the mathematic model and the parameters of the systems from a side, and it can insure the stability and the fast response of the system from another side.

Photonic Crystal Slab Add-Drop Filter Mohammad Reza Rakhshani; Mohammad Ali Mansouri-Birjandi
International Journal of Advances in Applied Sciences Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

A new type of optical add drop filter (ADF) based on slab photonic crystals resonant cavities is proposed. ADF operation is based on coupling between the photonic crystal waveguides. Using the finite difference time domain (FDTD) method and plane wave expansion (PWE) method, the ADF characteristics and band structure of the filter, respectively are obtained. The proposed structure is optimized to work as an ADF. Dropping efficiency at 1560nm and quality factor (Q) of our proposed structure are 90% and 195, respectively. The quantities of quality factor and transmission efficiency are suitable for optical applications. This structure is highly attractive for photonic integrated circuits (PICs).

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.

ARIMA Model for Gold Bullion Coin Selling Prices Forecasting Lazim Abdullah
International Journal of Advances in Applied Sciences Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forecast gold prices. This paper attempts to address the forecasting of gold bullion coin selling prices. The forecasting models ARIMAs are applied to forecast the gold bullion coin prices. The result suggests that ARIMA (2, 1, 2) is the most suitable model to be used for forecasting gold bullion coin prices. Closer examination suggests that the gold bullion coin selling prices are in upward trends and could be considered as a worthy investment.

CP-NR Distributed Range Free Localization Algorithm in WSN Deepak Prashar; Kiran Jyoti; Dilip Kumar
International Journal of Advances in Applied Sciences Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Advancements in wireless communication technology have empowered the researchers to develop large scale wireless networks with huge number of sensor nodes. In these networks localization is very active field of research. Localization is a way to determine the physical position of sensor nodes which is useful in many aspects such as to find the origin of events, routing and network coverage. Locating nodes with GPS systems is expensive, power consuming and not applicable to indoor environments. Localization in three dimensional space and accuracy of the estimated location are two factors of major concern. In this paper, a new three dimensional Distributed range-free algorithm which is known as CP-NR is proposed. This algorithm has high localization accuracy and resolved the problem of existing NR algorithm. CP-NR (Coplanar and Projected Node Reproduction) algorithm makes use of co-planarity and projection of point on plane concepts to reduce the localization error. Results have shown that CP-NR algorithm is superior to NR algorithm and comparison is done for the localization accuracy with respect to variations in range, anchor density and node density.

Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product Sumit Goyal; Gyanendra Kumar Goyal
International Journal of Advances in Applied Sciences Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Feedforward multilayer machine learning models were developed to predict the shelf life of burfi stored at 30oC. Experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output. Bayesian regularization algorithm was used for training the network. The transfer function for hidden layers was tangent sigmoid, and for the output layer it was purelinear function. The network was trained with 100 epochs, and neurons in each hidden layers varied from 3:3 to 20:20. Excellent agreement was found between the actual and predicted values establishing that feedforward multilayer machine learning models are efficient in predicting the shelf life of burfi.

Polyhouse Agricultural Marketing System Using Big Data Hadoop Ayesha Bhandralia; Resham Arya; S. N. Panda; Sachin Ahuja
International Journal of Advances in Applied Sciences Vol 5, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science

Agriculture is to cultivate and produce crops and livestock products. Agriculture in its associated sectors is unquestionably the largest livelihood provider, in the broad areas of rural India. With the emergence of high tech agriculture farming known as Polyhouse farming came into existence. Polyhouse farming is an alternative new technique which reduces clinging on to natural procedures that are very basic needs of agriculture i.e. rainfall, temperature, humidity and geographical conditions and this result in the most advantageous use of water and land resources. Polyhouse advances to create suitable microclimates, which favour the crop production by simulation of artificial environment using latest computer assisted technology. Yet polyhouse product marketing is an uncovered area which is still neglected and there is a strong need to for an effective marketing intervention or channelization. The liberalized trade environment in general has added another dimension to the poverty of farmers and uncertainty about the future. The paper describes the long established approach of marketing concept of polyhouse agriculture. The relationship between development, marketing and accessibility in polyhouse agriculture is reviewed. We have detected various inadequate parameters, and recognized the need of an effective polyhouse marketing system which may have beneficial impact on farmers.

SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images Durga Prasad Kondisetty; Mohammed Ali Hussain
International Journal of Advances in Applied Sciences Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy c-means and self organizing maps clustering methods.

Performance analysis of security framework for software defined network architectures K. A. Varun Kumar; D. Arivudainambi
International Journal of Advances in Applied Sciences Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Software defined data centers (SDDC) and software defined networking (SDN) are two emerging areas in the field of cloud data centers. SDN based centrally controlled services takes a global view of the entire cloud infrastructure between SDDC and SDN, whereas Network Function Virtualization (NFV) is widely used for providing virtual networking between host and Internet Service Providers (ISP’s). Some Application as a Service used in NFV data centers have a wide range in building security services like Virtual firewalls, Intrusion Detection System (IDS), load balancing, bandwidth allocation and management. In this paper, a novel security framework is proposed to combat SDDC and SDN based on NFV security features. The proposed framework consists of a Virtual firewall and an efficient bandwidth manager to handle multiple heterogeneous application requests from different ISPs. Real time data were taken from an experiment for a week and A new simulation based proof of concept is admitted in this paper for validation of the proposed framework which was deployed in real time SDNs using Mininet and POX controller.

Song Recommendation System Using Maximal b-Matching Deepa S; Varsha R; Parvathi R
International Journal of Advances in Applied Sciences Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

The last decade has witnessed a fundamental paradigm shift on how information content is distributed among people. Nowadays, an increasing number of platforms allow everyone to participate both in information production and information consumption. The phenomenon has been coined as democratization of content. However, as the opportunities to find relevant information and relevant audience increases, so does the complexity of a system that would allow suppliers and consumers to meet in the most efficient way. Our motivation is building a “featured item” component for social-media applications. Such a component would provide recommendations to consumers each time they login the system. The existing system follows either collaborative filtering or content based filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on user’s behaviours, activities or preferences and predicting what users will like based on their similarity to other users. Content-based filtering methods are based on a description of the item and a profile of the user's preference. Both of these methods require input from the user in the form of ratings or other user's likes. But social content matching takes into account only the user's preferences and also the capacity constraints. For each item 't' and each user 'u', consider constraints on the maximum number of edges that t and u can participate in the matching. These capacity constraints can be estimated by the activity of each user and the relative frequency with which items need to be delivered. Here we introduce the concept called b-matching goal is to find a matching that satisfies all capacity constraints and maximizes the total weight of the edges in the matching. The result of b-matching is the set of songs that are to be recommended to the user based on his likes.

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