International Journal of Intelligent Engineering and Systems

1

Authors: Preeti Ivy Barrow, Swathi Jamjala Naryanan

While the genome sequences of different crops have been published and annotated, relatively little is known about the transcriptional networks that regulate gene expression. There are different ways of finding significant motifs in genome sequences, evolutionary motif finding algorithms being one of them. The work presented in this paper uses Differential Evolution (DE) Algorithm to discover new motifs in rice genome sequences and aims to provide an intuitive wizard to analyse the micro-array analysis of expression patterns (motifs) for the queried genes. The used DE algorithm involves crossover operation with fine tuning. Further, to search for already known motifs parallel version of adaptive hash based pattern matching algorithm has been implemented. The study is conducted on rice genome sequences for twelve chromosomes each consisting of approximately 70000 genome sequences. In this paper, experimental results provide a comparison of serial and parallel version of the algorithm applied for the same dataset which shows that parallel implementation improves the performance by almost 25-30% to search the patterns in genome sequences and provide a list of newly discovered significant motifs.

20

Authors: Sribindu Sattu, Moparthy Gurunadha Babu

In cognitive radio systems; primary clients can use auxiliary clients as agreeable transfers to expand their transmission rates while auxiliary clients will consequently acquire more range to get circumstances, prompting coordinated cognitive radio systems. Earlier research proposals in Coordinated Cognitive Radio Networks (CCRNs) predominantly concentrate on giving omnipresent access and high throughput primary client for clients yet have infrequently contemplated vitality proficiency. Moreover, most existing works expect that auxiliary clients are inactively chosen by primary clients, paying little respect to auxiliary client’s eagerness to help, which is clearly not feasible. To address energy issue, this paper identifies an Energy-Efficient Cooperative Strategy (EECS) by leveraging temporal and spatial diversity of the primary network. In particular, auxiliary clients with delay-tolerant packets can proactively settle on the agreeable choices by together thinking about primary channel accessibility, channel state data, primary client’s movement stack, and their own particular transmission necessities. Principle challenge for this cooperative relay determination issue is to choose a relay effectively and economically. Since the quantity of auxiliary clients is expanded step by step because of the quickly developing number of wireless communication gadgets, it is unfeasible to examine all the auxiliary clients and after that pick the best. Predominantly primary client transmitter successively watches auxiliary clients. An arrangement of auxiliary clients is chosen in view of transmission criteria. The hand-off choice plan selects the best hand-off from an arrangement of accessible transfers which are chosen in light of transmission criteria of coordinated connections. In the observation of a hand off primary client need to settle on choice in regards to whether to stop its observation and to pick that as best hand-off or to skip to see next transfer. This problem is addressed by optimal stopping theory and optimal stopping rule. To evaluate the performance of proposed scheme Optimal Selection Approach (OSA), it is compared with auxiliary client’s energy efficient and existing cooperative schemes. Extensive simulation study is conducted so that impacts of different system parameters are investigated and algorithm proposed can satisfy different system requirements.

42

Authors: Jeong-Su Kim, Jeong-Beom Kim

The challenge to solve in large capacity ternary content addressable memory (TCAM) is the large power consumption of search-line and match-line. In this paper, we propose a circuit that uses match-line selective-charging scheme and low-swing search-line scheme simultaneously to reduce power consumption of search-line and match-line which occupies a large ratio in power consumption of TCAM. The circuit was verified by Hspice simulation using a 1.8V supply voltage in a CMOS 0.18 um CMOS process. The proposed 128x128 bit TCAM shows 45.4% power savings in match-line and 74.7% power savings in search-line compared to the conventional precharge high scheme in search cycle. Also, the minimum search cycle time is 3 ns (333MHz).

61

Authors: Nandoori Srikanth, Muktyala Siva Ganga Prasad

Due to shortage of power source, energy is the significant concern part in Wireless Sensor Systems. Wireless Sensor Networks (WSNs) are currently used in numerous kinds of WSN simulators have been established to visually illustrate the coverage, energy consumption status and expected lifetime of WSN sensors. Most of the Researchers have proposed and develop routing algorithm to increase the network lifetime. Among them clustering is the best technique and it is well known for accomplishing energy effectiveness in WSNs. In this paper, Energy Efficient Multi-hop Routing Clustering Protocol (EEMRCP) based on Fuzzy K-Means and Centralized Mid-point Algorithm (FKM-CMA) is proposed for network lifetime improvement. An existing and frequently used algorithm is K- means algorithm in WSN. The main limitation of this K-means algorithm is random initial centroid selection. To improve K-means algorithm, a Midpoint method in initial centroid selection is used. This is named as FKM-CMA. There are two main considerations for cluster head selection in proposed work, one is it’s residual energy, and another one is Euclidean distance used in basic Fuzzy K-means algorithm. Finally, Multi-hop communication is performed for transmitting the packets from CHs to Base Station depending on the distance between them. Furthermore, stimulated outcomes showed that the total network performance of the proposed approach is improved than the other existing approaches.

88

Authors: Udaya Dayanandan, Vivekanandan Kalimuthu

Software quality assessment at the architectural level is efficient because it provides various advantages to find and fix design errors at the earliest stage of software development life cycle (SDLC).Numerous methods have been evolved to validate quality-related issues at the architectural level. The software quality comprises of a total of eight attributes where maintainability is one of the important attribute which affects the overall system quality. Though few methods are available for maintainability analysis, none of the state of art method employs fuzzy logic with Quality Model for Object Oriented Design (QMOOD) metric suite. In this paper, the fuzzy analytic hierarchy process (FAHP) method is proposed with Buckley method to evaluate the influence of maintainability and its sub-attributes in software quality at the architectural level. The proposed evaluation model is tested against several versions of the MFC and OWL windows application frameworks and the obtained results are compared with the state-of-the-art methods. This experimental analysis shows that the FAHP with Buckley method is superior to the other compared methods.

107

Authors: Hemanth Somasekar, Kavya Naveen

Text categorization means dividing a set of input documents into the two or more classes to which these documents belong. Because of increase in availability of data in digital form in large amount, it becomes necessary to organize it. Feature extraction is the crucial step in text classification. Most of the existing text classifiers are lacking in finding out the relations among the terms. In this research work proposed text clustering of Improved Markov Clustering Model (IMCM) and text classification in which the nonlinear relations among the terms are also considered. This approach is scalable to huge dataset also and its classification power is not affected if relations among terms are large. The proposed IMCM method improved the text document clustering significantly and helped to avoid the human interaction in text clustering. Experimental results have shown that proposed system is 91% accurate for 8 categories and decreases, as increase the classes from 8 to 12, from 96% to 94%, respectively. Also, compared proposed model with existing Naive Bayes and k-Nearest Neighbor classifiers. Experimental results show that the proposed model is more accurate than these two classifiers. The better results demonstrated that presented system is developed effectively.

128

Authors: Mallaiah Vorsu, Vinaya Babu Arramraju, Sujatha Bhanothu

Object Oriented Program (OOP) provides the way to reuse the program by pre-implementing functionalities in the software. It is difficult to develop the object oriented software, which is important in the computer programing. For OOP, modularization mainly depends on the class. There are many methods present for the assigning responsibilities, but most of them are based on the human for decision making. In this research, back propagation neural network (BPNN) was used to provide the solution to object oriented design of the software. The Cinema Booking System (CBS) was taken as the input documentation and Formal Concept analysis (FCA) then found the relationship of the element in the lattice manner, after that the relationship was set with each other. The result showed that the proposed system outperformed the existing system and also the design made manually.

147

Authors: Mohammed Lahraichi, Khalid Housni, Samir Mbarki

Object tracking is a crucial step in video analysis, as well as detection and recognition. Particle filter, based on color histogram, is considered among multiple approaches that prove their effectiveness in this domain. It is apparent that if an object and its background, or more objects to be tracked, having nearly the same color histogram, the quality of tracking will be negatively affected. Therefore, combining color histogram and texture features for pixels in motion is considered a potential solution to this issue. In this paper, we propose a particle filter tracking method consisting of two stages: at the prediction stage, the state space model is represented by Box–Muller transform, and at the correction stage, the observation likelihood model is calculated in three steps: firstly, the background subtraction is used to extract the moving objects in order to reduce the impact of background pixels on color histogram. Secondly, the texture Gabor features are computed using moving pixels to distinguish between objects having the same color histogram. Finally, the histogram and texture features are combined to define a likelihood observation model. The proposed method is implemented using several video sequences. The visual and numerical results obtained are compared with those of traditional particle filter method, and show that our method is very efficient particularly in case of similar objects, and clearly improves the quality of tracking.

167

Authors: Pushpa Mohan, Kiran Kumari Patil

One of the most experimentally difficult problem in the world is weather forecasting, which is a basic mechanism in meteorology. Especially in data mining system, there are different information mining strategies are available, for example, K-Means, Artificial Neural Network (ANN) and Support Vector Machine (SVM), etc. These weather predicting strategies are financially high and also very inconsistent for large datasets. To overcome these issues, an effective dimensionality reducing strategy: Self Organizing Map (SOM) is proposed along with Latent Dirichlet Allocation (LDA). The SOM strategy is one of the proper dimensionality reducing strategy to highlight the self-arranging outline. After reducing the measurement, the dimensionality reduced information are used to forecast climate for a reasonable outcome. A reasonable season for an appropriate crop is arranged with the guide of Deep Neural Network (DNN) classification system. This research work depends on finding appropriate information model, which helps in accomplishing high precision and simplification for value forecast. Finally, the experimental outcome shows that the proposed approach improved accuracy in weather and crop prediction up to 7-23% compared to the existing methods.

188

Authors: Pranai Kungwalrut, Viriya Kongratana, Thanit Trisuwannawat, Vittaya Tipsuwanporn, Arjin Numsomran

Optimized water management strategies are among the most crucial concerns in a biomass power plant. Especially during the times of water scarcity, an improper water supply planning and operation result in a detrimental power generation or shut down processes. This paper aims to propose the cyber-physical cloud computing (CPCC), a mechanism of analysis and control physical process using cloud-based framework, in order to improve the efficiency of water management system in a biomass power plant. In this study, a 9.9 MW biomass power plant is implemented to investigate the performance of CPCC. The architecture of the proposed system consists of three physical tiers. The first tier is a tier of physical devices included with pressure, flow, pH sensors, water pumps and valves that are responsible for detecting the physical data and interacting with the water production process. The second tier is an edge computing device which functions as a controller, embedded server, data storage, gateway and switch to manage all operational tasks in an intranet area. The third tier is a cloud computing system which enables big data applications such as online monitoring and visualization of process operation, adaptive filtration fouling control, consuming water and total water cost analysis. The results validate the effectiveness of the proposed system as the ability of an adaptive fouling control system to adjust backwash scheduling and chemical dosing so that achieving the target of purified water quality even during the fluctuation of raw water qualities. Subsequently, the water treatment system can achieve the capability for optimal total water cost operations under a target permeate flow rate and the percent of the recovery. Significantly, the data analytics in the CPCC reflect current operational requirements of water under local climate conditions, likewise contribute to the practical solution for sustainable water resource planning in the energy production.

212

Authors: Mylam Chinnappan Babu, Sankaralingam Pushpa

Discrimination is one of the most important challenging tasks in web mining due to its many legal and ethical features in social media and enterprise based industries. There are an enormous amount of anti-discrimination measures available to prevent discrimination such as using some features like race, religion, gender, nationality, disability, marital status, and age besides situations like employment and training, access to public services, credit, insurance, etc. Practically, those systems are not possible to use in industries due to large datasets. Indirect discrimination contains a set of rules or techniques which are not explicitly specifying discriminatory features, deliberately or accidentally and could create unfair decisions. Existing systems have low classification accuracy and data loss with high discrimination data detection time. To overcome these limitations, an Efficient Discrimination Prevention and Rule Protection (EDPRP) approach has been proposed for removing the discrimination and protects the rule without damaging the data quality. The proposed system designing pre-processing discrimination prevention approach and specify the different features and represent to deal with direct or indirect discrimination. EDPRP is capable of preventing Indirect and direct discrimination, and it allows automatic and routine collection of large amounts of data from the public. In EDPRP, the discrimination prevention model is based on partial data sets as part of the automated decision making. Based on Experimental evaluations, proposed method improves 8% (percentages) of Support and 8 ms (milliseconds) of Execution Time compared than existing methods.

231

Authors: Doaa Elsayed, Eman Nasr, Alaa El Din Ghazali, Mervat Gheith

Web Service Composition (WSC) has attracted considerable attention and research to support Service Oriented Architecture (SOA). WSC constructs complex applications by combing atomic Web services together to achieve users’ requirements. The selection of the best Web service that fulfils the Functional Requirements (FRs) and optimizes the Quality of Service (QoS) requirements, such as response time, cost, reliability, etc., is a critical part of WSC, especially in a dynamic environment. Since requirements frequently change, this demands that WSC must be adapted to provide the most suitable composite services and fulfil the users’ requirements that emerge. This paper presents a new hybrid approach for dynamic optimization of WSC using Parallel Genetic Algorithm (PGA) based on Q-learning, which we integrate with K-means clustering. We call it PGAQK. Q-learning is utilized to generate an initial population to enhance the effectiveness of PGA. K-means clustering is used to cluster Web services based on a fitness function. It is applied in the mutation operator, and used for Web service substitution to prune the Web services in the search space to find the best Web services for the environment changes that might occur at runtime. To the best of our knowledge such a hybrid approach has not been used for WSC before. We implemented PGAQK over the .NET Framework using C# programming language. A series of comparable experiments were carried out showed that PGAQK outperforms traditional PGA and Q-learning approaches in terms of fitness values. PGAQK allows WSC to modify itself dynamically and achieve a better fitness value to fit the changing environment, where the characteristics of composite web services continue to change.

250

Authors: Deepak Nandal, Om Prakash Sangwan

This paper estimates the effort for software by optimizing the COnstructive COst MOdel (COCOMO) model parameters using hybrid BATGSA (Bat inspired Gravitational Search Algorithm) algorithm. The performance of the COCOMO model completely depends upon its parameters which can be optimized by using meta-heuristic algorithms. This paper uses hybrid BATGSA algorithm which hybrids the improved bat algorithm with the gravitation search algorithm (GSA) to optimize the COCOMO model. The bat algorithm demonstrates the hunting and routing behavior of the bat which is improved by using a random walk in the exploration phase. The exploration phase is further improved by using GSA as gravitation force affects the velocity of the bat. The algorithm has been analyzed on four NASA datasets downloaded from promise repository. The comparison of the algorithm has been made with existing three states of art techniques i.e. COCOMO model, BAT algorithm, Improved BAT(IBAT) algorithm by using normalized error as a parameter. The reduction in error ranges from 2% to 10% on different dataset as compared other state of art algorithms proves the significance of proposed algorithm.

275

Authors: Mukesh Bansal, Chaitanya Purushottam Agrawal

This paper develops a technique by using Jaya algorithm and feed-forward neural network to determine the quality of object-oriented software by using Chidamber & Kemerer (CK) along with Li & Henry metrics. The technique basically focuses on the maintainability factor of software quality which in turn depends upon the software complexity. The software complexity is directly proportional to the number of changes done per class which is determined by the technique. The analysis has been done on UIMS (User Interface Management System) and QUES (Quality Evaluation System) datasets by using the mean absolute error as the analysis parameter. The reduction in the mean absolute error as compared to the existing state of art techniques along with the individual component of proposed technique proves the significance of the technique.

293

Authors: Maddela Vijaya Lakshmi, Pardhasaradhi Pokkunuri, Boddapati Taraka Phani Madhav

A coplanar waveguide fed asymmetric ground frequency reconfigurable antenna is designed and analyzed in this paper. The proposed antenna is occupying the compact dimension of 50X45X1.6 mm on FR4 substrate with dielectric permittivity 4.4. The designed antenna is operating in the wideband from 2 to 5.8 GHz with coverage in applications like Bluetooth, LTE, Wi-Fi and WLAN. The reconfigurable nature of the antenna is analyzed with the switching of PIN diodes placed on the antenna structure. The on and off conditions of the diodes D1 and D2 providing switching between the LTE, Wi-Fi and WLAN bands with good impedance matching. The advantage of PIN diodes includes high power handling capability, very low driving voltage and extremely low cost. Here the switch placement and control/biasing strategies depend on the type of the switch and the topology of the antenna. The prototyped antenna with biasing is providing excellent correlation with High Frequency Structure Simulator (HFSS) and Computer Simulation Technology (CST) simulation results. The reconfigurability of the antenna and its parametric optimization is analyzed and presented in this work.