Informatics Publishing Limited

Falguni Raval ¹, Y. P. Kosta ², Amit V. Patel ¹, Dhara Patel ¹

Affiliations
1 Department of Electronics and Communication Engineering, Charotar University of Science and Technology, Changa, Pin: 388 421, Gujarat, IN
2 Mawadi Education Foundation’s Group of Institutions, Rajkot Morbi Road, At & PO Gauridad 360003, IN

Abstract

This paper proposes Microstrip Rectangular Patch Antenna in which metamaterial properties applied at ground Plane. The paper also analyzed the performance of Microstrip Patch Antenna with and1 without using the metamaterial structure at 10 GHz. The works mainly include Metamaterial as Defected ground plane. The Open Slot Split Ring resonator is used as metamaterial. The effective permeability parameters are retrieved from s parameters for unit cell Split Ring Resonator, revealing the presence of magnetic resonances around resonance frequency 10 GHz. All antenna parameters such as Return Loss, Gain, and Band width were checked. The main focus of this paper was to check the effect of metamaterial so that Patch antenna used for multi band operation.

Keywords

Defected Ground Plane (DGP), Open Slot Split Ring Resonator (OSSRR), Rectangular Patch Antenna, Microstrip, Metamaterial, Split Ring Resonator (SRR).

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Shobhit K. Patel ¹, Y. P. Kosta ²

Affiliations
1 Charotar University of Science and Technology, Changa-388221, Gujarat, IN
2 Marwadi Education Foundation’s Group of Institutions, Rajkot, Gujarat, IN

Abstract

The area of micro strip antennas has seen some inventive work in recent years and is currently one of the most dynamic fields of antenna theory. Mobile personal communication systems and wireless computer networks are commonly used nowadays and they need antennas in different frequency bands. The design adopts patch structure and introducing the novel truncated patch offering multiband applications. The proposed patch has a compact dimension. The antenna works on four bands having center frequencies, 5.6, 7.1, 8,1, and 9.5 GHz. The design is suitable for applications in C-band and X-band Communications. Design results are obtained by a HFSS (High Frequency Structure Simulator) which is used for simulating microwave passive components.

Keywords

Microstrip Patch Antenna, Multiband, C-Band, X-Band.

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Jaymin Bhalani ¹, Dharmendra Chauhan ¹, Y. P. Kosta ², A. I. Trivedi ³

Affiliations
1 Electronics and Communication Engineering Department, Charotar University of Science and Technology, Changa, IN
2 Marvadi Institute of Technology, Rajkot, IN
3 Electrical Engineering Department, M.S. University, Baroda, IN

Abstract

In this paper, we propose two novel semi-blind channel estimation techniques based on QR decomposition for Rayleigh flat fading Multiple Input Multiple output (MIMO) channel using different receiver antenna combinations and various pilot symbols. In the first technique, the flat-fading MIMO channel matrix H can be decomposed as a upper triangular matrix R and a unitary rotation matrix Q as H=RQ. The matrix R is estimated blindly from only received data by using orthogonal matrix triangularization based house holder QR decomposition, while the optimum rotation matrix Q is estimated exclusively from pilot based Orthogonal Pilot Maximum Likelihood Estimator (OPML) algorithm. In the second technique, joint semi-blind channel and data estimation is performed using QR decomposition based Least Square (LS) algorithm. Simulations have taken under 4-PSK data modulation scheme for two transmitters and different combinations of receiver antennas as well as various training symbols. Finally, these two new techniques compare with Whitening Rotation (WR) based semi-blind channel estimation technique and results shows that those new techniques achieve very nearby performance with low complexity compare to Whitening rotation based technique. Also first technique with perfect R outperforms Whitening Rotation based technique.

Keywords

Multiple Input Multiple Output, Orthogonal Pilot ML Estimator, QR Decomposition, Semi Blind Channel Estimation.

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Amit Thakkar ¹, Y. P. Kosta ²

Affiliations
1 Chandubhai S. Patel Institute of Technology, Changa, Gujarat, IN
2 Marwadi Group of Institutions, Rajkot, Gujarat, IN

Abstract

Most of today’s structured data is stored in relational databases. Thus, the task of learning from relational data has begun to receive significant attention in the literature. Unfortunately, most methods only utilize “flat” data representations. Thus, to apply these single-table data mining techniques, we are forced to incur a computational penalty by first converting the data into this “flat” form. As a result of this transformation, the data not only loses its compact representation but the semantic information present in the relations are reduced or eliminated. As an important task of multi-relational data mining, multi-relational classification can directly look for patterns that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. According to the differences in knowledge representation and strategy, the paper addressed different kind of multi-relational classification approaches that are ILP-based, graph-based and relational database-based classification approaches and discussed each relational classification technology, their characteristics, the comparisons and several challenging researching problems in detail.

Keywords

Multi-Relational Data Mining, Multi-Relational Classification, Inductive Logic Programming (ILP), Graph, Selection Graph, Tuple ID Propagation.

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Nidhi Shah ¹, Amit Ganatra ¹, C. K. Bhensdadia ², Y. P. Kosta ¹

Affiliations
1 Department of Computer Engineering CIT-Changa, Gujarat, IN
2 Department of Computer Engineering, Dharmsinh Desai University Nadiad, Gujarat, IN

Abstract

Given the explosive rate of data deposition on the web; classification has become a complex and dynamic phenomenon. As classification complexity is continuing to grow, so is the need in direct proportion to designing and developing data mining algorithms & techniques. Classification is the most commonly applied data mining technique, a process of finding a set of models or functions that describes and distinguishes data classes, for the purpose of using it – so classification is a specialist with specialized skills, which is moving toward universality. A classification problem is considered as a supervised learning problem. The aim of the classification task is to discover a kind of relationship between the attributes (input) and class (output), so that the discovered knowledge can be used to predict the class of a new unknown object. Classification of the records or data is done based on the classification rules. Ant colony optimization is a method that derives its inspiration from real ants that forage for food by selecting the shortest path from multiple possible paths available to reach food. Thus merging the concept of Ant Colony Optimization (ACO) with data mining brings in a new approach to designing classification rule that will be helpful in extraction of information for a specialized dataset. In this paper a survey is done on Ant-miner algorithm for classification Rule extraction. The Ant miner algorithm extract classification rule from data using if-then-else pattern; similar to other traditional algorithm available for classification task or purposes. Extraction of classification Rule from data is an important task of data mining. We present, detailed description about the algorithm available for classification rule mining using Ant colony optimization. Variations to the ant colony based an Ant-miner algorithm is discussed along with the comparison of the algorithms with critical parameters like predictive accuracy, No. of Rules Discovered, No. of terms per No. of rules Discovered, using different data sets. Hence the paper will help to study various ant miner algorithms and comparison carried out will help the data miner to select and use algorithm according to need based on the specialized properties associated with the algorithm.

Keywords

Ant Colony Optimization (ACO), Classification, Data Mining.

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Kamal Sutaria ¹, Amit Ganatra ², Y. P. Kosta ¹, C. K. Bhensdadia ³, Kruti Khalpada ¹

Affiliations
1 Charotar University of Science and Technology, Changa, Gujrat, IN
2 Charotar University of Science and Technology Changa, Gujrat, IN
3 Department of Computer Engineering, Dharamsinh Desai University, Nadiad, Gujarat, IN

Abstract

Incremental Flexible Frequency Discretization (IFFD) is a recently proposed discretization approach for Naïve Bayes (NB).IFFD performs satisfactory by setting the minimal interval frequency for discretized intervals as a fixed number. In this paper, we first argue that this setting cannot guarantee that the selecting MinBinSize is on always optimal for all the different datasets. So the performance of Naïve Bayes is not good in terms of classification error. We thus proposed a sequential search method for NB: named Optimum Binning. Experiments were conducted on 4 datasets from UCI machine learning repository and performance was compared between NB trained on the data discretized by OB, IFFD, and PKID.

Keywords

Discretization, Naïve Bayes, Optimum Binning.

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Kruti Khalpada ¹, Amit P. Ganatra ², C. K. Bhensdadia ³, Y. P. Kosta ¹, Kamal K. Sutaria ¹

Affiliations
1 Charotar University of Science and Technology, Charusat, Changa, IN
2 Charotar University of Science and Technology, Charusat, Changa, IN
3 Dharmsinh Desai University, D.D.U., Nadiad, IN

Abstract

Ant Colony Optimization (ACO) is one of the techniques of swarm intelligence motivated by real world foraging behavior of ants. ACO has been successfully applied to so many combinatorial optimization problems successfully. However, ACO has not achieved excellent solutions to constraint satisfaction problems. Traveling Tournament Problem (TTP) is a real world sports time tabling problem that abstracts the important issues in creating time tables where teams‘ travel is an important issue and is one of the constraint satisfaction problems. In the existing approaches of ACO to TTP have some of the issues like poor quality solution (sum of the total distance traveled by each team in the tournament is large) and large solution construction time. So here, we have made efforts to deal with some of the above mentioned issues. First we have compared different ACO family algorithms and have analyzed that ACS is the most successful algorithm of ACO family. So here by using Ant Colony System (ACS) as the base algorithm with backtracking integration, we are getting better solution quality. Cranky ant approach has been used for better exploration. Apart from the solution quality, number of iterations and the number of local search solutions needed to construct the solution have been reduced up to a large extent.

Keywords

Ant Colony Optimization, Traveling Tournament Problem, Ant Colony System.

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Harshita L. Patel ¹, Amit P. Ganatra ², C. K. Bhensdadia ¹, Y. P. Kosta ³

Affiliations
1 Computer Department, Dharamsinh Desai University, Nadiad, Gujarat, IN
2 Computer Department, Charusat University, Changa, Gujarat, IN
3 CHARUSAT University, Gujarat, IN

Abstract

At present, an active research topic is the use of ensembles of classifiers. They are obtained by generating and combining base classifiers, constructed using other machine learning methods. It is an obvious approach to making decisions more reliable to combine the output of different models. Several machine learning techniques do this by learning an ensemble of models and using them in combination:prominent among these are schemes called bagging, boosting, and stacking. They can all, more often than not, increase predictive performance over a single model. And they are general techniques that can be applied to numeric prediction problems and to classification tasks. Bagging, boosting, and stacking have only been developed over the past decade, and their performance is often astonishingly good. In this paper, we do a comparative study of Boosting algorithms and also their performance comparison with Bagging and Stacking. ML algorithms implemented in WEKA (Waikato Environment for Knowledge Analysis) are used for comparative study. Results obtained over different datasets by different algorithms are compared.

Keywords

Bagging, Bayesian Network, Boosting, Classifiers, Ensemble Learning, Feature Selection, Machine learning, MLP (Multi Layer Perceptron), Naive Bayes Classifier, Predictive Accuracy, SMO (Sequential Minimal Optimization Algorithm for Training a Support Vector Classifier), and Stacking.

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Amit Ganatra ¹, Y. P. Kosta ¹

Affiliations
1 Patel Department of Computer Engineering, Charotar University of Science & Technology, CHARUSAT, Gujarat, IN

Abstract

Stock prediction is, so far, one of the popular topics not only for research purposes but also for commercial applications. Owing to its importance, a well-established school of concepts and techniques, including fundamental and technical analysis, has developed in recent decades. However, because these techniques or tools are based on totally different analytical approaches, they often yield contradictory results. More importantly, these analytical tools are heavily dependent on human expertise and justification in areas such as the location of reversal (or continuation) patterns, market patterns, and trend prediction. Predicting stock data with traditional time series analysis has proven to be difficult. An artificial neural network may be more suitable for the task primarily because no assumption about a suitable mathematical model has to be made prior to forecasting. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. Our focus of study is to build neural network for stock market prediction. We propose to study feed forward back propagation network and their predictive accuracy. We propose to study architecture model of neural network and its different network parameters. The study attempts to understand network parameter like momentum, learning rate, number of neurons etc. We will compare architecture and result of above models. Our aim is to build best model by studying various parameters of the neural network. And also study other related model to compare accuracy of the model. In this study we have used R tool to implement the neural network. We have taken closing price, turnover, global indices, interest rate, and inflation as a neural network input. We proposed to include other indicator like news, currency rate, and crude price as input to the neural network. We compared stock prediction accuracy by setting different network parameters. Subsequently, an attempt is made to build and evaluate a neural network with different network parameters.  Technical as well as fundamental data are used as input to the network. In benchmark comparisons, the price prediction proves to be successful.

Keywords

Classification, Neural Network, Feature Selection, Prediction, Stock Market.

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Amit Ganatra ¹, Y. P. Kosta ¹, Gaurang Panchal ¹, Chintan Gajjar ²

Affiliations
1 Department of Computer Engineering, Charotar Institute of Technology, Charotar University of Science and Technology, Changa, Anand-388 421, IN
2 Information Technology Department, Charotar Institute of Technology, Charotar University of Science and Technology, Changa, Anand-388 421, IN

Abstract

An Artificial Neural Network classifier is a nonparametric classifier. It does not need any priori knowledge regarding the statistical distribution of the class in a giver selected data Source. While, neural network can be trained to distinguish the criteria used to classify easily in a generalized manner that allows successful classification the newly arrived inputs not used during training. Through this paper it is eastliblished that back propagation neural network works successfully for the purpose of classification. Back propagation suffers from getting stuck into Local Minima. Weight optimization in Back propagation can be optimized using the Genetic Algorithm (GA). The back propagation algorithm is improved by invoking Genetic algorithm, to improve the overall performance of the classifier. The performance of a fitness algorithm using the approach suggested by us is a Hybrid System that is being analyzed in this paper. In this paper the issue of improving the fitness (weight adjustment) of Back propagation algorithm is addressed. Some of the Advantages of Hybrid algorithms are: convergence speed will be increased and the local minima problem can be overcome. The proposed Hybrid Algorithm is to perform learning as a back propagation and optimize weights using GA for classification.

Keywords

Artificial Neural Network, Back Propagation Algorithm, Genetic Algorithm.

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