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International Journal of Computational Systems Engineering

 

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International Journal of Computational Systems Engineering (34 papers in press)

 

Regular Issues

 

  • KNN based Ensemble Selection for Imbalance Learning   Order a copy of this article
    by Guirong Zheng, Huaping Guo 
    Abstract: Classification of imbalance datasets is one of the crucial issues in the field of machine learning. Since the distribution of imbalance dataset is extremely skew, the traditional classifications always come up with a disappointed performance. Different with the traditional methods, this paper reconsiders class imbalance problem from the viewpoint of ensemble learning. However, many ensembles tend to build defective base classifiers which are helpless to improve the generalized ability of ensemble would be produced. To solve this problem, a novel ensemble algorithm named NNES (k-Nearest Neighbor based Ensemble Section) is proposed in this paper. To evaluate the local properties of an unlabeled instance, NNES tends to pay more attention to minority and improve its performance on imbalance datasets. Experimental results show that NNES can improve the classification performance of the imbalance datasets effectively. Moreover, the improvement would be strengthened when some sampling techniques are introduced in.
    Keywords: imbalance datasets; ensemble; k-Nearest Neighbor; classification; sampling techniques.

  • Design of Type-2 Fuzzy Logic Power System Stabilizer Using Limit cycle approach   Order a copy of this article
    by Naidu I. E. S, Sudha K. R 
    Abstract: Power system is subjected to wide range of operating conditions. Heavily loaded power systems are subjected to Hopf bifurcations resulting in oscillatory instability. A power system shifts to the dynamic instability region encountered by unstable limit cycles, this result in increase of oscillatory behavior of power system. Many of the authors suggested the theories like Poincar
    Keywords: Dynamic analysis; Dynamic stability limit; Hopf bifurcation; Limit cycles; Eigen value analysis; Genetic algorithm;.
    DOI: 10.1504/IJCSYSE.2017.10012582
     
  • Optimal Path Planning of UAV using Grey Wolf Optimizer   Order a copy of this article
    by Soundarya M S, Anusha Danashaker, Rohith P, Kavitha Panneerselvam, Seshadari Srinivasan 
    Abstract: This paper aims at proposing a novel approach for path planning of Unmanned Aerial Vehicle (UAV) along with obstacle avoidance. The path planning is achieved through swarm intelligence algorithm inspired by the behaviour of grey wolves known as Grey Wolf Optimizer (GWO). The optimal path planning of UAV using GWO is obtained by proper choice of objective function for targets and obstacle avoidance condition. The algorithm has three search agents namely alpha, beta and gamma which help in proper convergence of solution to the target while avoiding obstacle. The proposed approach is tested with different test cases of target and obstacles conditions and the simulated results have been reported. Simulation is carried out in MATLAB environment.
    Keywords: GWO; Path Planning; UAV.

  • A review on gamma interconnection network   Order a copy of this article
    by Shilpa Gupta, G.L. Pahuja 
    Abstract: In past decades considerable development has been made in big data communication and computation in super computer systems. Multistage interconnection networks are widely used in these super computer systems for reliable communication, because of their cost effectiveness, fault tolerance property and low transmission delay. Multistage interconnection networks (MIN) are classified according to their switch element size, number of stages, connection pattern between stages etc. Lot of researchers have compared various MIN on basis of different aspects of reliability and fault tolerance but selection of different MIN for comparison was not precise to one or more class of interconnection networks (IN) but was random. In this paper an attempt has been made to discuss all Gamma networks proposed till date to the best of our knowledge, and compared on various reliability issues so as to achieve a comprehensive statement about all Gamma MIN which has been broadly used in high speed packet switching super computers. It has been observed that other researchers have taken same reliability value for different types of switches, wherein here in this paper different reliability values have been applied to different type of switches to compute all reliability parameters of all Gamma Networks. also terminal reliability (TR)of all Gamma Networks have been computed and compared for all possible Tag Values for Network size of 8
    Keywords: Fault-Tolerance; Gamma Interconnection Network (GIN); Multistage Interconnection Network (MIN); Reliability; Switch Element (SE).

  • Elicitation of Software Testing Requirements from the Selected Set of Softwares Requirements in GOREP   Order a copy of this article
    by Mohd Sadiq, Sanjida Nazneen 
    Abstract: Requirements engineering (RE) is employed to elicit, model, and analyse the requirements of software. Software requirements elicitation is the first sub-process of RE; and it is used to identify the requirements of software according to the need of the stakeholders. In literature, we identify that goal oriented requirements elicitation process (GOREP) do not support the identification of testing requirements from the functional requirements (FR) of software in early phase of RE. Therefore, to tackle this research issue, we proposed a method for the identification of the testing requirements from the FR in GOREP. In real life applications, only those requirements are implemented and tested, which are selected by the stakeholders. So in the proposed method we used fuzzy based technique for FR selection on the basis of non-functional requirements (NFR). The canonical representation of multiplication operation (CRMO) associated with L-1 R-1 inverse arithmetic principle and graded mean integration representation (GMIR) based on triangular fuzzy numbers (TFNs) have been applied for the selection of the FR. In our work, an Institute Examination System (IES) is used as a case study to explain the steps of the proposed method.
    Keywords: Functional Requirements; Non-Functional Requirements; Testing Requirements; Test Cases; Fuzzy Based Approach; and Triangular Fuzzy Numbers.

  • Grenade-Cauchy Operator Integrated Artificial Bee Colony Optimization for Reliable QoS based Web Service Composition   Order a copy of this article
    by Udhaya Shree S., Amuthan A. 
    Abstract: Web service composition is considered as the potential method of integrating diversified number of applications independent of the characteristic features of service providers. Majority of the dynamic web service composition techniques in the literature addresses the problem of web service composition through the perspectives of Quality of Service (QoS) or transactional characteristics. GCO-ABC algorithm is proposed as an attempt of developing a dynamic service composition methodology that depends on the integration of transactional and QoS-based properties of web service. The transactional characteristics of web services are analyzed and the problem of dynamic web service composition is modeled using a directed acyclic graph with constraints. GCO-ABC algorithm is applied on the work flow sequence of web service composition developed as a constrained directed acyclic graph for determining the near optimal solution with efficacy and it is also predominant in comparison to the traditional Ant Colony Optimization Algorithm (ACOM) and its variant Improved Ant Colony Optimization Algorithm (IACOM). The performance of GCO-ABC is investigated through empirical and simulation means and the determined results infer that the proposed scheme is not only efficient than IACOM, ACOM and Brute force scheme but also capable of approximating the searching solutions into a much more optimal candidate solution.
    Keywords: Grenade Explosion; Cauchy operator; Reliable QoS; Web Service Composition; Ant Colony Optimization; Artificial Bee Colony Algorithm.

  • Performance Comparison of Multiagent Cooperative Reinforcement Learning Algorithms for Dynamic Decision Making in Retail Shop Application   Order a copy of this article
    by Deepak A. Vidhate 
    Abstract: A novel approach by Expertise based on Multiagent Cooperative Reinforcement Learning Algorithms (EMCRLA) for dynamic decision making in the retail application is proposed in this paper. Performance comparison between Cooperative Reinforcement Learning Algorithms and Expertise based Multiagent Cooperative Reinforcement Learning Algorithms (EMCRLA) is demonstrated. Different cooperation schemes for multi-agent cooperative reinforcement learning i.e. EGroup scheme, EDynamic scheme is proposed here. Implementation outcome includes demonstration of recommended cooperation schemes that are competent enough to speed up the collection of agents that achieves excellent action policies. This approach is developed for a three retailer stores in the retail marketplace. Retailers will be able to help each other and obtain profit from cooperation knowledge through learning their own strategies that exactly stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train helpfully in the circumstances. Assuming significant hypothesis on the vendors stock policy, restock period, arrival process of the consumers, the approach is modelled as Markov decision process model that make it possible to design learning algorithms. Dynamic consumer performance is noticeably learned using the proposed algorithms. The paper illustrates results of Cooperative Reinforcement Learning Algorithms of three shop agents for the period of one year sale duration and then demonstrated the results using proposed approach for three shop agents for one year sale duration. The results obtained by the proposed expertise based cooperation approach show that such methods will be able to put a quick convergence of agents in the dynamic environment.
    Keywords: Cooperation schemes; Multiagent learning; Reinforcement learning.

  • Towards Recent Developments in the Methods, Metrics and Datasets of Software Fault Prediction   Order a copy of this article
    by Deepak Sharma, Pravin Chandra 
    Abstract: The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modeling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modeling techniques based on traditional and computational intelligence based methods are also reviewed. This paper is an endeavor to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios.
    Keywords: Software Fault Prediction; Fault Tolerance; Computational Intelligence; Software Metrics; Evaluation Metrics.

  • Performance analysis of WOA optimized PID controllers for LFC of interconnected thermal power systems   Order a copy of this article
    by Rajesh Bhatt, Girish Parmar, Rajeev Gupta 
    Abstract: Nature inspired Whale optimization algorithm (WOA) has been implemented for load frequency control (LFC) of two areas interconnected non reheat thermal power systems in order to maintain the system frequency at a prescheduled value in the presence of disturbances. Two similar PID controllers have been used in each area and control optimization problem for proper tuning of controllers parameters is defined by considering the integral of time multiplied absolute error (ITAE) as an objective function. The superiority of WOA optimized PID controllers has been established by comparing the results with other existing approaches such as; BFOA/PI, DE/PI, NSGA-II/PI, GSA/PI and GWO/PI for the same system in the presence of load changes of different magnitude and location. For performance analysis, the percentage reduction in ITAE, settling times of frequency & tie line power deviations and minimum damping ratio (MDR), etc. have also been calculated. The simulation results show that the WOA/PID approach gives far better results in terms of system dynamic responses, ITAE, MDR, settling times and overshoots of 〖∆f〗_1,〖∆f〗_2,and 〖∆P〗_Tie for the same system under study when compared with some existing approaches.
    Keywords: Load frequency control (LFC); two area interconnected non reheat thermal power system; PID controller; Whale optimization algorithm (WOA).

  • A Computerized Framework for Characterization of Breast Tissue Using Mammographic Images   Order a copy of this article
    by Indrajeet Kumar, Jitendra Virmani, Harvendra Singh Bhadauria 
    Abstract: In this study various experiments have been conducted for four-class and two-class breast tissue pattern characterization using various texture feature models in transform domain. These experiments have been conducted on 480 mammographic images taken from the DDSM dataset. However, for two-class breast tissue pattern characterization problem, images belonging to {BIRADS-I, BIRADS-II} class has been considered in fatty class and images belonging to {BIRADS-III, BIRADS-IV} class has been considered in dense class. From each image, ROI of predefined size 128
    Keywords: Mammography; Breast tissue characterization; BIRADS classification; Texture feature models; Support vector machine classifier.

  • Contest of Strength Game Based Algorithm for Decline of Active Power Loss   Order a copy of this article
    by LENIN KANAGASABAI 
    Abstract: In this paper, Contest of Strength (COS) algorithm is proposed to solve the reactive power problem. It inspired by the game of Contest of Strength & the algorithm imitates the features of the game. Each candidate solution is alike a team participating in a series of rope pulling competitions & based on exert pulling forces on each other the superiority of the solutions they exemplify. According to Newtonian laws of mechanics the challenging teams move to their new-fangled positions. Contest of Strength (COS) algorithm maintains equal level of exploration & exploitation in search of near to global optimal solution in reactive power problem. Proposed Contest of Strength (COS) algorithm has been tested in standard IEEE 30, 57,118 & practical 191 bus test systems and simulation results show clearly the better-quality performance of the projected algorithm in tumbling the real power loss.
    Keywords: Optimal Reactive Power; Transmission loss; Contest of Strength.

Special Issue on: SCDA 2018 Advances and Challenges of Soft Computing in Data Mining

  • A Framework for Ensemble Classification and Sensitivity Analysis in Privacy Preserving Data Mining   Order a copy of this article
    by Chandrakanth Patrapati 
    Abstract: The perturbation mechanism for data streams is a challenging task. In the emerging world, data is erupting from various sources. The core applications need care on the data streams for further analysis and experimentation. As the micro data available with the core applications shall not be revealed to the public without taking any chance of breach, the perturbation challenges the analysis to get through the like results as of on the original data sets. In this paper we have applied a concept of Perlin noise to distract the original data from the eyes of the analysts, however allowing them to perform their activities well. The data evolving in the trendy applications of the today world needs security, almost of the applications in the world are related directly or indirectly to the personally identifiable information (micro data) of the people. The security that assures the privacy on these data is hitherto static in the applications. Applying security dynamically on such data is a challenging task. The perturbation mechanism canonically applies the noise on the datasets, where some of them do not require. This paper deals about the concepts of generation of smooth noise and syntactic perturbation mechanism on the selective tuples as selective perturbation.
    Keywords: privacy preserving data mining;data streams;ensemble classifier;sensitivity; smooth noise.
    DOI: 10.1504/IJCSYSE.2020.10016937
     
  • Feed Forwarded CT Image Registration for Tumor and Cyst Detection using rigid transformation with HSV color segmentation   Order a copy of this article
    by PRABHAT BARIK, R.A.M. BARIK 
    Abstract: This paper automates the Medical diagnosis process by Image Registration feed forwarded to object detection with prediction of tumour and cyst by using K-means clustering over HSV colour features. Diagnosis of life killer disease is a complex process which requires Bio-medical image such as MRI, CT, and Endoscopy etc. Many Biomedical Images is used for the same case to predict the disease. Because of different view point of different Photographic sensors at different time obtained medical images are not aligned. So the manual diagnosis makes harder because of the images are not registered or not aligned properly. The inherent cause is the distortion of the imaging signal where object may be miss-transformed due to different camera focus and projection. Image registration is an essence to bypass the non-alignment issue. Here we have proposed and analysed a combined solution towards the miss transformed object or region of interest by performing reverse geometric transformation with different angle to produce the better perspective image for diagnosis which is feed forwarded to HSV colour model based segmentation to predict the cyst, tumour presence.
    Keywords: Image Registration; Color Segmentation; HSV (Hue Saturation Value); MRI (Machine Resonance Imaging); CT (Computer Tomography); Rigid Transformations; K-means Clustering.

Special Issue on: ICMMCMSE-2017 Computational Problems in Science and Engineering

  • Stochastic maximum principle for mean-field type singular optimal control problem with discounted cost   Order a copy of this article
    by Muthukumar Palanisamy, Deepa Ravi 
    Abstract: In this article, mean-field type stochastic singular optimal control problem with discounted cost is studied over an infinite time interval. The discounted cost makes the cost functional is bounded, which guarantees the existence of optimal control. The control variable has two components as classical and singular control. Moreover the singular control satisfies bounded variation, non-decreasing continuous on the left with right limits. The proposed system is investigated in two different cases, such as without and with delay. In addition, infinite horizon version of stochastic maximum principle is established by using the convex control domain in each case. The obtained theoretical results are applied to optimal harvesting problem and optimal consumption problem.
    Keywords: Infinite horizon; Mean-field; Optimal control; Singular control; Stochastic maximum principle.

  • Discrete MDP Problem in Service Facility Systems with Inventory Management   Order a copy of this article
    by Selvakumar Chinnakkalai, Maheswari Pandiyaraj, Elango Chellappan 
    Abstract: In this article, we consider a discrete time(equally spaced) service facility system in which arrival of customers to the system is controlled by taking decision at the beginning of each period. Customers arrive for service are placed in a specified queue called Potential queue. Controller observe the number of customers in the system (eligible queue + server), the number of items in inventory and decides the quantity of customers to be admitted, others are rejected. Demands arrive throughout the period but they are satisfied only at the end of the period. Inventory is maintained in the server to satisfy the customers at the service completion time. Inventory is replenished instantaneously when the level become zero. The system is formulated as a Markov Decision Process. A stationary cost structure is imposed at each decision epoch and optimal policy is obtained by using policy iteration algorithm. A numerical example is provided to illustrate the problem.
    Keywords: Discrete-time MDP; Admission control; Service facility with inventory; Markov Decision Process.

  • Edge Fixed Edge Steiner Number of a Graph   Order a copy of this article
    by PERUMALSAMY M 
    Abstract: For a non-empty set W of vertices in a connected graph G, {itshape the Steiner distance d(W)} of W is the minimum size of a connected subgraph of G containing W. S(W) denotes the set of vertices that lies in Steiner W-trees. Let G be a connected graph with at least 2 vertices. A set W $subseteq$ V(G) is called a {itshape Steiner set} of G if S(W) = V(G). The {itshape Steiner number} s(G) is the minimum cardinality of a Steiner set. An edge Steiner set of G is a set W $subseteq$ V(G) such that every edge of G is contained in a Steiner W- tree. The edge Steiner number $s_1 (G)$ is the minimum cardinality of its edge Steiner sets and any edge Steiner set of cardinality $s_1 (G)$ is a minimum edge Steiner set of G. Let G be a connected graph with at least 3 vertices. For an edge e = xy in G, a set W $subseteq$ V(G) $-$ {x, y} is called an {itshape edge fixed edge Steiner set} of G if linebreak W' = W $cup$ {x, y} is an edge Steiner set of G. The minimum cardinality of an edge fixed edge Steiner set is called the {itshape edge fixed edge Steiner number of G} and is denoted by $s_{e1}(G)$. Also the Steiner W-tree necessarily contains the edge e and is called {itshape edge fixed edge Steiner W-tree}. In this paper, we study some of the characteristics of edge fixed edge Steiner number and provide the bounds for edge fixed edge Steiner number. For any two positive integers a and b with 2 $leq$ a $leq$ b, there exists a connected graph G with $s(G)$ = a and $s_{e1}(G)$ = b for some edge e = $xy$ in G.
    Keywords: Steiner set; edge fixed Steiner set; Steiner number; edge fixed Steiner number; edge fixed edge Steiner set; edge fixed edge Steiner number.

  • On the Generalized Degree Distance of Graphs   Order a copy of this article
    by Manzoor Ahmad Bhat, K. Pattabiraman 
    Abstract: The generalized degree distance of a connected graph G, denoted by Hλ(G), is defined as Hλ(G) = 1/2 ∑u,v∈V(G)(dG (u) + dG (v))dGλ(u,v), where λ is any real number.In this paper, we investigate the mathematical properties of the generalized degree distance of a connected graphs. In addition, we present the relation between the generalized degree distance and other graph invariants.
    Keywords: Wiener index; generalized degree distance; graph invarients.

Special Issue on: Advances in Computational Systems

  • Analysis of THD In Various Power Electronic Converters to Regulate the Voltage from Renewable Energy Sources   Order a copy of this article
    by Jency Joseph, Josh F. T., Ronaldo Lamare, Blessen Varghese Mathew 
    Abstract: This work presents a performance analysis of various power electronic converters with RL load to reduce the total harmonic distortion. The power converters inspected are: ZETA converter, single-ended primary inductance converter, often written SEPIC converter and Z SOURCE converter. The objective is to analyze which power electronic converter exhibits less total harmonic distortion (THD) and more efficiency in order to select the suitable converter for electric vehicle propulsion system. Three above mentioned converters are designed, modeled, and simulated. The zeta converter is advantageous over other converters inspected and have reduced total harmonic distortion , higher output power and hence improved efficiency. The simulations are done with MATLAB/SIMULINK and the results are presented.
    Keywords: Total Harmonic Distortion (THD); MATLAB simulation ,Z-SOURCE converter (ZSC); Zeta converter; Sepic converterrnrn.

  • Pipel: Exploiting Resource Reorganization to Optimize Performance of Pipeline-Structured Applications in the Cloud   Order a copy of this article
    by Vinicius Meyer, Rodrigo Da Rosa Righi, Vinicius Facco Rodrigues, Cristiano André Da Costa, Guilherme Galante, Cristiano Both 
    Abstract: Workflow has become a standard for many scientific applications that are characterized by a collection of processing elements and an arbitrary communication among them. In particular, a pipeline application is a type of workflow that receives a set of tasks, which must pass through all processing elements (also named here as stages) in a linear fashion, where the output of a stage becomes the input of the next one. To compute each stage, it is possible to use a single compute node or to distribute its incoming tasks among the nodes of a cluster. However, the strategy of using a fixed number of resources can cause under- or over-provisioning situations, besides not fitting irregular demands. In addition, the selection of the number of resources and their configurations are not trivial tasks, being strongly dependent of the application and the tasks to be processed. In this context, our idea is to deploy the pipeline application in the cloud, so executing it with a feature that differentiates cloud from other distributed systems: resource elasticity. Thus, we propose Pipel: a reactive elasticity model that uses lower and upper load thresholds and the CPU metric to on-the-fly select the most appropriated number of compute nodes and virtual machines (VMs) for each stage along the pipeline execution. This article presents the Pipel architecture, highlighting load balancing and scaling in and out operations at each stage, as well as the elasticity equations and rules. Based on Pipel, we developed a prototype which was evaluated with a three stages graphical application and four different task workloads (Increasing, Decreasing, Constant and Oscillating). The results were promising, presenting an average gain of 38% in the application time when comparing non-elastic and elastic executions.
    Keywords: Cloud elasticity; Pipeline applications; Performance Optimization; Dynamic Resource Management; Adaptivity.
    DOI: 10.1504/IJCSYSE.2019.10015444
     
  • An Efficient Payload Distribution Method for High Capacity Image Steganography   Order a copy of this article
    by Sandeep Rathor, Anand Singh Jalal, Soumendu Chakraborty 
    Abstract: To produce higher level of security most of the irreversible and reversible image steganography techniques stress upon encrypting the secret image (payload) before embedding it to the cover image. In this case the steganographic system requires more computation time if a payload is large. Therefore, technique that can have lower computation time, higher embedding capacity and provoke same level of distortion, as any state of art encryption technique, can enhance the performance of stego systems. In this paper we propose a payload distribution method for secure secret image sharing that has lower computation time. The proposed scheme can be consider as a better option than any encryption technique. In the proposed scheme the payload is distributed over sign map (SM), error factor (EF) and normalized error (NE) using primary cover image. A payload is a grayscale image which is divided into signmap, error factor and normalized error then embedded into RGB secondary cover image using high capacity steganography algorithm. Proposed method is an alternate for encryption which reduces the overall complexity of the stego system. Result analysis shows that the distortion introduced into the resulting signmap, error factor and normalized error is significant enough to conceal the original payload with lower computation time than any state of art encryption schemes.
    Keywords: Irreversible Steganography; Payload; Cover Image; LSB; Sign Map Error Factor.

  • Armor on Digital Images Captured Using Photoelectric Technique by Absolute Watermarking Approach   Order a copy of this article
    by Suresh Annamalai 
    Abstract: Nowadays digital image captured through Photoelectric Technique have undergone with malicious modifications. The proposed paper tells on a high quality recovery of digital document using absolute watermarking approach. The recoveries of lost informations are identified using bit values. Whereas the problem on recovering scaled, rotated and translated images exist still. Thus the absolute watermarking approach assures the recovery of digital images from any format of manipulations providing high quality pictures. The different sort of bits used for this purpose is classified into audit bit, carrier bit and output bit. Here the consistent feature of the original image is coded and the output bit is protected using a carrier encoder. This enables the audit bit to detect the erasure locations and retrieve the manipulated areas of the image with high quality pictures in low cost.
    Keywords: Carrier Encoding; Tamper Proofing; SIFT; Predictive Coding; Spoofing Detection; Compression.

  • SVM Classification of Brain images from MRI Scans using Morphological Transformation and GLCM Texture Features   Order a copy of this article
    by Usha Ramasamy, Perumal K 
    Abstract: This paper introduces a novel HTT based GLCM texture feature extraction procedure for an automatic MRI (Magnetic Resonance Images) brain image classification. The method has three phases, (1) Hierarchical Transformation Technique (HTT), (2) texture feature extraction and (3) classification. The new proposed HTT method incorporates optimum disk-shaped mask selection, top-hat and bottom-hat morphological operations, and some mathematical operation for both image pre-processing and enhancement. The gray level co-occurrence matrix is computed to extract statistical texture features such as contrast, correlation, energy, entropy, and homogeneity from an image. And these extracted images features of co-occurrence matrix can very well be fed into SVM (Support Vector Machine) for further MRI brain normal and abnormal image classification. The alternate approach of the HTT based GLCM also compared with conventional GLCM texture feature extraction method.
    Keywords: magnetic resonance images; classification; texture feature extraction; grey level co-occurrence matrix; support vector machine; top hat transform; bottom hat transform.
    DOI: 10.1504/IJCSYSE.2018.10011250
     
  • Performance Analysis of Lyapunov Stability Based and ANFIS Based MRAC   Order a copy of this article
    by Kalpesh Pathak, Dipak Adhyaru 
    Abstract: Analysis of two adaptive controller parameter adjustment laws for a model reference adaptive controller has been discussed in this paper. The comparison has been done about applying Lyapunov stability rule and using adaptive Neuro fuzzy inference system (ANFIS) to adjust parameter for model reference adaptive control. Discussion of the nature of system, adaptive controller, basic block diagram and control law has been presented. For intense analysis two case studies have been considered. Simulation of two bench-mark process control applications, level control in coupled tank and concentration control in biochemical reactor (BCR) has been discussed. Comparative results have been plotted and discussed for each proposed algorithm. Considered systems have mutual parameter interaction and nonlinear parameter dynamics. Introduction part discusses literature survey, development of the topic and importance of the work. Initially Lyapunov rule based technique has been applied for control in both cases. With ANFIS based algorithm, new values of adjustment parameter have been generated. Results shows that performance of ANFIS based MRAC gives improved results in presence of system uncertainties.
    Keywords: Coupled Tank; Biochemical Reactor; Model Reference Adaptive Control; Lyapunov Stability; ANFIS.

  • An Efficient Load Balancing Mechanism with Deadline Consideration on GridSim   Order a copy of this article
    by DEEPAK KUMAR PATEL, CHITA RANJAN TRIPATHY 
    Abstract: GridSim is a very popular Grid simulation tool. The GridSim toolkit is used to simulate application schedulers for different parallel and distributed computing systems such as Clusters and Grids. Many researchers have proposed various load-balancing techniques in Grid, but all those cannot be used in GridSim due to the structural differences of Grid. In this paper, we propose an enhanced load balancing method called Enhanced GridSim with Load Balancing based on Deadline Consideration (EGLBD) for GridSim. The proposed algorithm balances the load by providing an effective selection method for efficient scheduling of Gridlets among heterogeneous resources which maximizes the utilization of the resources and increases the efficiency of the Grid system. Also, the proposed algorithm shows the details of the load estimation method for every levels of GridSim. We simulate the proposed algorithm on the GridSim platform. The proposed mechanism on comparison is found to outperform the existing schemes in terms of finished Gridlets, unfinished Gridlets, total execution time and resubmitted time. The simulation results are presented.
    Keywords: GridSim; Gridlet; Gridresource; Load Balancing.

  • Optimal Control of Magnetic Levitation System based on Cuckoo Search Algorithm   Order a copy of this article
    by Ananthababu Palaparthi 
    Abstract: The classical Proportional-Integral-Derivative (PID) controller and its variants remain the controllers of choice in many applications, despite the efforts put in the development of advanced control schemes over the past two decades. Magnetic levitation or MagLev systems are highly nonlinear and unstable systems. Because of the inherent nonlinearities of the MagLev systems, position tracking of Magnetic Levitation System (MLS) is a challenging task. In the present paper, a noval method of global optimization algorithm, cuckoo search algorithm is proposed, to tune the parameters of PID controller. The proposed PID controller is tested on real time magnetic levitation system. Simulation results show the effectiveness of the cuckoo search based PID controller for position tracking of magnetic levitation system.
    Keywords: Cuckoo Search algorithm;Magnetic Levitation; Optimization algorithms; PID Controller; tuning controller parameter;.

  • EMOTION RECOGNITION BY DANCE MOVEMENT-A SURVEY   Order a copy of this article
    by PALLAVI JO 
    Abstract: Abstract-Emotion plays an important role in our daily lives. Recognize emotions such as happy, sad, fear, surprise etc from facial expressions is an easy task for human. But it is a complex task for computers. Many researches have done to recognize emotions from users facial expression by computers. In that, a challenging thing today is recognize emotions from body pose. In this paper, we review the methods of emotion recognition by body pose. Body pose means, it may be dance movement or person person communication. In this paper, first we review some methods of emotion recognition from dance movements. Secondly, we propose our future method of recognize emotions from the Indian classical dance bharathanatyam navarasas i.e. nine emotions. We will use the techniques of orientation filtering, max pooling and template filtering for recognizing navarasas of bharathanatyam dance.
    Keywords: Keywords- kinect sensor; log gabor filter; max pooling; template matching.

  • REVENUE MAXIMIZATION MODEL USING CUSTOMIZED PLANS IN COMPUTER SERVICE ALLOCATION   Order a copy of this article
    by Showkat Ahmad Dar 
    Abstract: The rapid development of the Internet and the emergence of computing technologies like grid or cloud computing have enabled a novel trend of purchasing and consuming Information Technology services. These services are offered at different prices using various pricing schemes and techniques. End users will favor the service provider offering the best quality with the lowest price. Therefore, applying a fair pricing model will attract more customers and achieve higher revenues for service providers. This work focuses on a novel static/dynamic pricing model which is able to satisfy advance users requirements based on normal fixed price model. This paper considers many factors that affect pricing and user satisfaction, such as fairness, QoS, SLA, and more, by highlighting their importance in recent markets and propose a flexible model which tries to utilize all resources to the highest capacity and offers low prices for underutilized resources.
    Keywords: Computer Resource; Pricing; Resource Allocation.rn rn.

  • A Survey on Word Embedding Techniques and Semantic Similarity for Paraphrase Identification   Order a copy of this article
    by Divesh Kubal, Anant Nimkar 
    Abstract: In Natural Language Processing (NLP), Paraphrase Identification (PI) determines the relatedness between the pair of sentences having fewer or negligible lexical overlap but still pointing towards the same meaning. The major challenge faced while attempting to solve this problem is the many possible linguistic variations conveying the same purpose. This paper aims to provide a detailed survey of traditional similarity measures, Statistical Machine Translation metrics, Machine Learning and Deep Learning techniques and a well-defined flow between them. This article encompasses various word embedding methods and step-wise derivation of its learning module. This survey paper also provides a definite flow pointing towards the evolution of Deep Learning in an unambiguous manner. A comparative analysis of various techniques to solve PI is presented and it will provide research directions to work in the similar domain.
    Keywords: Paraphrase Identification; Word embedding; Deep Learning; Convolutional Neural Network; Semantic Similarity.

  • IMAGE STYLE TRANSFER USING CONVOLUTIONAL NEURAL NETWORKS BASED ON TRANSFER LEARNING   Order a copy of this article
    by Varun Gupta, Rajat Sadana, Swastikaa Moudgil 
    Abstract: The purpose of an image style transfer system is to extract the semantic image content from the target image and then using a texture transfer procedure display the semantic content of target image in the style of the source image. The uphill task in this context is to render the semantic content of an image but with the advent of Convolutional Neural Networks, image representations have been made much more explicit. In this work, we explore the method for image style transfer using transfer learning from pre-trained models of Convolutional Neural Networks (CNN).Use of these models gives us the power to produce images of a high perceptual quality that are a union of the content of an arbitrary image and the appearance of renowned artworks. Further, this paper compares pre-trained CNN models for image style transfer task and highlights the potential of CNN to deliver appealing images using modern manipulation techniques.
    Keywords: Image Style Transfer; Convolution Neural Networks; Transfer Learning; Deep Learning; Machine Learning; Artificial Intelligence.

  • Performance Analysis and Evaluation of Software Defined Networking Distributed Controllers in Datacenter Networks   Order a copy of this article
    by Worku Muluye, Mesfin Abebe, Satheesh Kumar Nagineni 
    Abstract: A computer network is a critical issue in our day to day activity; however, this day it works under several problems. To solve these problems OpenFlow-based programmable Software-Defined Network (SDN) was released. OpenFlow is a protocol for SDN which vertically separates the control plane from the data plane of the network devices. In SDN the controllers are the brains of the network that manages and controls the network devices. Datacenter network is one of the application areas that required successful integration of distributed OpenFlow controllers by making the network more consistent. However, the need for high-performance SDN controllers increases with the increasing of network devices in the datacenter network. SDN distributed controller is a controller which can increase or decrease the number of controllers according to the change of traffics. The aim of this paper is to perform a study and performance analysis of SDN distributed controllers in a datacenter networks by considering three designed topologies and a different number of devices. Initially, has been performed a detailed study and comparison of SDN distributed controllers and selected the best ONOS (Open Networking Operating System), controller. Then, created three ONOS controllers in a cluster using Linux Container (LXC) and designed tree topologies using python in Mininet. Then tested and evaluated three ONOS controllers throughput and latency and presented the results of single, two and three ONOS controllers. The single ONOS controller has 60665.22 to 82566.85 flow/s throughput and 62.89 to 94.45 responses/ms latency. Two ONOS controllers have 105564.30 to 118911.20 flow/s throughput and 117.53 to 147.91 responses/ms latency. Three ONOS controllers have 216816.80 to 229388.90 flows/s throughput and 297.61 to 245.32 responses/ms latency. Single, two and three ONOS controllers have high throughput and high response per ms. Finally, based on these results the distributed ONOS controller better performance than single SDN controllers in a datacenter networks.
    Keywords: Software-defined networking; SDN distributed controller; OpenFlow; Open networking operating system; Datacenter networks; Tree topologies.

Special Issue on: Soft Computing Approaches in Wireless Networks with IoT and Medical Health Care System (SI-SWNMS)

  • Reduced Mutual Coupling MIMO Antenna   Order a copy of this article
    by Hari Krishna, MATURI THIRUPATHI 
    Abstract: Abstract: In this paper, a reduced mutual coupling 1x2 inset feed rectangular patch antenna is presented. The antenna elements are separated by a distance of λ0/4 exhibiting excellent isolation of -55 dB at 5 GHz band. To improve the isolation between closely placed antennas, a compact planar meander line based Electronic Bandgap Structure (EBG) behaves like a double negative (DNG) material is placed between them. The proposed EBG structure is implemented on the MIMO antenna with continuous as well as discontinuous ground plane. It is found that the EBG structure with discontinuous ground plane improves at least 6 dB isolation between antenna elements than continuous ground. The proposed antenna structures are fabricated showing good agreement between simulated and measured results.
    Keywords: Keywords: EBG Structure; MIMO Antenna; Miniature Antenna; Wideband Antenna.rn.

  • Hybrid Converter for Electric Vehicle Battery Charging with Power Quality Features   Order a copy of this article
    by B.R. Ananthapadmanabha, Rakesh Maurya, Sabharaj Arya 
    Abstract: The power factor correction converters for electric vehicle battery chargers result in higher conduction losses and reverse recovery losses when fed from low voltage supply line.To overcome the above problem, ahybrid switched capacitor Cuk converter based power factor correction converter is proposed which has high step-up gain and low-voltage stress across the switch.The converter offers a very significant efficiency improvement at low voltage supply line over the single-switch buck-boost converters (including fly-back, SEPIC and Cuk topologies) and conventional two-switch buck-boost cascaded converters. In this paper, the design and simulation of the proposed converter is carried out in continuous current mode (CCM).The modeling and simulation are done in Matlab-Simulink environment. The power quality indices like THDs (vTHD, iTHD), PF are evaluated to demonstrate the performance of the converter. The converter is evaluated both in steady state and transient conditions.
    Keywords: Battery charging; Cuk converter; Harmonics; Electric vehicle; Power factor.

  • Automation of home and its Management Using IoT   Order a copy of this article
    by Hari Krishna, VANGA YASHWANTH REDDY, E. Naga Booshanam 
    Abstract: This paper proposes a smart home management system that depicts automation of home and its management. IoT (Internet of Things) is used to post the information to physical devices that are interfaced to environment. The acquisition of data considered in this work is humidity, temperature, smoke or hazardous gas detection, water level indication. This collected data is sent to internet using network and protocols. The proposed system works on real time monitoring of data and maintains security of home. Controlling action taken through internet. Reducing power consumption is major achievement and energy conservation happens with the implementation of automation. ARM 7 LPC2148 microcontroller is used in this work.
    Keywords: IoT; Temp; LDR; IR Sensor,GPRS,WI-FI.

  • Analysis of particle swarm and artificial bee colony optimization based clustering protocol for WSN   Order a copy of this article
    by Ankit Gambhir, Ashish Payal 
    Abstract: Wireless sensor networks (WSNs) have attracted may research scholars in recent years. WSNs are significantly resource-restrained by their bound power supply. Due to which, energy utilization is a key issue in the designing of protocols for WSNs. Current researchers proposed the appropriate uses of routing protocols to increase network life. To such as hierarchical routing (clustering such as LEACH) is an efficient approach, in which cluster has been organized; each cluster has numerous nodes and lone cluster head (CH). Node transmits their sensed data to CH; CH cumulates that information and forwards that to sink. Soft-computing (SC) techniques such as nature inspired algorithms (particle swarm optimization, ant colony optimization, artificial bee colony optimization etc) vastly tackle their compatibility and adaptableness to deal with the complex constraints in WSNs. In this paper, performance of different versions of LEACH, obtained by applying soft computing approaches has been evaluated. Comparative analysis has been also presented.
    Keywords: wireless sensor network; soft computing approaches; particle swarm optimization; artificial bee colony optimization;.