International Journal of Convergence Computing (13 papers in press)
A New Feature Extraction Technique For Classifiers Using Self-Organizing Map
by Prasenjit Dey, Tandra Pal
Abstract: Neural network classifiers suffer from the overfitting problem which reduces its generalization capability. Due to generalization, a trained classifier will always have good test accuracy. The objective of the proposed work is to improve the input space of the data set which in consequence will increase the generalization of the classifiers. For this purpose, we have proposed a new feature extraction technique based on self-organizing map (SOM). Initially, two-dimensional SOM network is trained to achieve topological ordering in the input data set. Then, a Gaussian function is used in each node of the output map of SOM network. It processes the input patterns sequentially and produces $m^2$ dimensional new representation corresponding to each input pattern, where $m^2$ is the number of nodes present in the output layer. Thereafter, classifiers like probabilistic neural network (PNN) or multilayer perceptron (MLP) is used over this new representation of the input patterns. The whole process creates a new classifier based on hybrid neural network model. We have used twelve standard classification data sets and compared proposed model with conventional PNN and MLP classifiers. Comparison of results shows the superiority of the proposed method. Wilcoxon signed rank test also shows that our SOM-based model used for transformation of feature representation improves the performance of the classifiers.
Keywords: Feature extraction; Gaussian function; generalization; multilayer
perceptron; probabilistic neural network; self-organizing map.
Exploration of Neural Network Models for Defect Detection and Classification
by B. V. Ajay Prakash, D.V. Ashoka, V.N. Manjunath Aradhya
Abstract: In order to overcome the software development challenges like delivering a project on time`, developing quality software products and reducing development cost, software industries commonly uses defect detection software tools to manage quality in software products. Defects are detected and classified based on their severity, this can be automated in order to reduce the development time and cost. Nowadays to extract useful knowledge from large software repositories engineers and researchers are using data mining techniques. In this paper, software defect detection and classification method is proposed and neural network models such as Generalised Regression Neural Network (GRNN) and Probabilistic Neural Network model (PNN) are integrated to identify, classify the defects from large software repository. Based on defects severity proposed method discussed in this paper focuses on three layers: core, abstraction and application layer. The performance accuracy of the proposed model is compared with MLP and J48 classifiers.
Keywords: Software Bugs Tracking; Neural Network Models; Software Quality Assurance; Bug Tracking; Defect Classification.
Identification of AIDS Disease Severity Based on Computational Intelligence TechniquesUsing Clonal Selection Algorithm
by Dharmaiah Devarapalli, Srikanth Panigrahi, M.R. Narasinga Rao, Jonnadula Venkata Rao
The mining bioinformatics data is newly formed area in process of between bioinformatics and data mining. The process of developing the algorithmsmanipulated based on computational intelligence. The agitation cases be created everywhere in the world concerning of the Acquired Immunodeficiency syndrome (AIDS) disease which are complex. Data were collected throughout self-administered mail survey share nationwide at clinical and drug treatment centers and AIDS service organizations. Every country is facing the problem about AIDS. According to the occurring at a time immediately before the present survey of world health organization (WHO).Human Immunodeficiency Virus (HIV) is the retro virus which is the family retro viridae and it is a group of viruses that deficiency of the immune system, Especially that causing the death of many CD4 count cells, which coordinates the human immune system response extremely without invitation like intrudes. This project is identifying Patients severity and it is useful Doctors.
In this present machine learning algorithm of the clonal selection algorithm more effective diagnosis and optimize the data of AIDS disease. Computational intelligence techniques based on test of the performed the techniques used as AIDS data set. Calculated amore effective of rule length based on fitness function using sensitivity, specificity, comprehensibility are computed. Those techniques achievedto promising accurate results.
Keywords: AIDS data; CD4 Cells count,fitness function; intelligence technique; Clonal Selection Algorithm.
Reporting cell planning for cost reduction using binary genetic algorithm
by Smita Rani Parija, Prasanna Kumar Sahu, Sudhansu Sekhar Singh
Abstract: In mobile access networks, location management is a major and complex issue where location information of the mobile user is essential to deliver incoming calls/SMS to the appropriate mobile stations (MSs). Many metaheuristic algorithms are developed to solve such types of important issue which is responsible for determining the network configuration. This paper proposes a conventional binary genetic algorithm (BGA) approach implemented to reporting cells planning problem in complex multimodal search spaces. Since BGA is a conventional algorithm sufficiently able to solve the signalling cost problem along with the modifications in the generic operators. These modifications occur in the mutation operator. The simulation results obtained from the experiments conducted using the existing database along with the real time database. The proposed method shows better performance compared to existing algorithm. This simulation results obtained from the experiments reflects the effectiveness of the conventional BGA.
Keywords: binary genetic algorithm; BGA; cost function; location update; mobile location management; paging; reporting cell planning problem.
Singularity preserving collocation methods for weakly singular Fredholm integral equations and the corresponding eigenvalue problem
by Bijaya Laxmi Panigrahi
Abstract: In this paper, the singularity preserving collocation methods are being developed for Fredholm integral equations of the second kind with the weakly singular kernel and for the corresponding eigenvalue problem. In this method, the interpolatory projected space is the direct sum of singular space and spline function space. The convergence rates for the approximated solution and iterated solution in weakly singular Fredholm integral equations of the second kind are being calculated. The error bounds for eigenvectors, eigenvalues, and iterated eigenvectors are also being obtained. We illustrate our results with numerical examples.
Keywords: Singularity preserving collocation methods; Fredholm integral equations; Eigenvalue problems; compact operator; weakly singular kernel.
Multiobjective Bilevel Optimization Method to solve Environmental-Economic Power Generation and Dispatch Problem with Interval Data Uncertainty
by Bijay Baran Pal, Mousumi Kumar
Abstract: This paper presents a multiobjective bilevel programming (MOBLP) based interval goal programming (IVGP) method for modeling and solving to solve environmental-economic power generation and dispatch (EEPGD) problem using genetic algorithm (GA) in an inexact environment. In the proposed method, the objective functions to be optimized are represented as goals by introducing target intervals for the achievement of objective values in decision horizon. Then, using interval arithmetic rules, the defined planned -interval goals are converted into standard goals as in conventional goal programming (GP) to formulate minsum IVGP model of the problem. In the solution search process, a genetic algorithm (GA) computational scheme is employed in an interactive way to reach the solution of EEPGD problem
The effective use of the approach is illustrated through a case example of the standard IEEE 30-Bus 6-Generator test system. The model solution is also compared with the solutions obtained by using other approaches studied previously.
Keywords: genetic algorithm; goal programming; interval goal programming; mathematical programming; multiobjective bilevel programming; quadratic bilevel programming.
Special Issue on: Selected Topics in Mathematical and Computational Sciences
Ball Convergence for a third order method based on Newton's method and the Adomian decomposition method
by Ioannis Argyros, P. Jidesh, Santhosh George
Abstract: The aim of this paper is to extend the usage of a third order method based on the Adomian decomposition in cases not covered before. The hypotheses are based only on the first derivative while in earlier works, hypotheses requiring the existence of the fifth derivative are utilized to show the convergence of the method. Moreover, we also provide radii of convergence as well as error estimates based on Lipschitz constants not given before in earlier works. Numerical examples emphasizing the superiority of the new results over the earlier ones complete this paper.
Keywords: Adomian decomposition method ; Newton’s method; order of convergence; local convergence.
Special Issue on: Smart Converging Technology
Development of an Adaptive non-parametric model for estimating maximum efficiency of Disc membrane
by Anirban Banik, Tarun Kanti Bandyopadhyay, Sushant Kumar Biswal, Mrinmoy Majumder
Abstract: Membrane separation and filtration process are a technique of removing the impurities from the feed stream based on the pore size of the membrane bed. Permeate stream produced by the membrane are good quality due to this membrane found wide application in the field of water purification, gas-gas separation etc. In the concerned study, the ability of the cellulose acetate disc membrane for improving the quality of the rubber industrial effluent of Tripura has been investigated in pilot scale. GMDHmultilayered feedback algorithm has been implemented to predict the maximum efficiency of the membrane. The efficiency of the membrane is maximized for the optimal value of pore size, inlet velocity, and operating pressure. It has been found that efficiency of the membrane is maximized when the pore size of the membrane is kept 2.060538 μm, inlet velocity is 0.201896 m/sec and operating pressure is 694.7201 kPa. The performance of the prepared GMDH model is evaluated by using model evaluation technique like NSE, PBIAS, slope and Y-intercept, RSR. It has been found that software predicted data can be used for trouble shooting and optimal design of the membrane bed.
Keywords: membrane; GMDH; neural network; membrane separation technique; optimization.
Designing and Modelling of Grid Connected Photovoltaic System (Case Study: EEU Building at Hawassa City)
by Yishak Kifle, Baseem Khan, Jitendra Singh
Abstract: Renewable energy sources are integral part of the present energy system because of the various problems, associated with conventional energy sources. Solar and wind are the major renewable energy sources. The renewable energy sources are sufficient enough to meet the world energy requirement. In this paper, a comprehensive analysis on energy output calculation of the photo-voltaic (PV) system is presented. The solar radiation effect is articulated based on monthly average sun shine hours. After local area solar potential is assessed, small plant is developed and designed for EEU building electric power supply, which is connected to the grid. Therefore, there is huge reduction in grid supply demand. The study uses different metrological data and simulates the grid connected PV technologies to end users, for decision making in PV energy investment. Performance ratio and specific energy is calculated as indicator for the plant feasibility in the area.
Keywords: solar potential assessment; grid connected PV; performance ratio; specific energy.
Wheat Yield Forecasting Using Fuzzy Logic
by Tanya Sah
Abstract: Forecasting in general and crop yield forecasting, in particular, is considered a compound problem. Food and Agriculture Organizations report Global Agriculture towards 2050 accentuates the challenges which agriculture sector is going to face in near future. The report draws the attention towards the disparity between the demand and supply. It also highlights the issues that automation has introduced into the food and agriculture sector. Where there is a need to increase the production to feed the rapidly increasing population there is an equal necessity for coping up with the dwindling numbers of farm workers. As such, there is a need for a reliable forecasting algorithm capable of handling time series data. In this paper, we have proposed a fuzzy time series forecasting algorithm to forecast wheat yield. The reason to choose fuzzy over other forecasting methods is its capability of dealing with the vague, imprecise data and it outperforms many statistical conventional models in such conditions. The neural network has been used for training and defuzzification of the forecasted values.To attests the efficacy and the performance of the proposed method, it has been tested against the wheat production dataset.
Keywords: Fuzzy Logic; Wheat yield forecasting; Neural Network.
A novel method for reduction of leakage current in MOSFET
by Debasis Mukherjee, B.V.Ramana Reddy
Abstract: In this paper, structural modification of conventional bulk MOSFET has been proposed for minimization of subthreshold leakage current. Key structural features of bulk MOSFET have been kept unaltered. Comparison of conventional and proposed structure has been presented for a 20 nm NMOS with 0.8 volt Vdd. The proposed structure is capable of reducing subthreshold leakage current even at very low drain voltage when the gate voltage is zero. Around 55 percent reduction of OFF current has been obtained when drain voltage is at Vdd and gate voltage is zero. The methodology proposed does not have any special requirement at the circuit level, and can be combined with all circuit level methodologies. The proposed structure is named as Defensive MOSFET as it looks like a defensive shield. Structural dimensions of 20 nm MOSFET generation have been taken from the 2011 edition of International Technology Roadmap for Semiconductors or ITRS. All simulation processes have been executed by Sentaurus G-2012.06 Technology Computer Aided Design or TCAD software.
Keywords: 20 nm; leakage current reduction; NMOS.
Development of Real Time Monitoring System for Intake Water of Surface Water Treatment Plant with the help of Segmented Polynomial Neural Network
by Paulami De, Mrinmoy Majumder
Abstract: The Surface Water Treatment Plant (WTP) has an important role in the maintenance of water quality supplied to the consumers It was found from various studies that inflow water quality to a WTP plays an important role in the operation of the treatment plants. The nature and characteristics of the inflow water control the duration of treatment, dozing pattern etc. The temporal variation only one of the parameters like Turbidity of the inflow water can induce major adjustment in the plant operation and may also affect the efficiency of the WTP. Identification of temporal pattern can help the engineers to adapt the WTP operations and can save unnecessary wastage of plant resources. That is why in the present study a new model was proposed for prediction of the temporal patterns of various chemical parameters by adopting a new type of Artificial Neural Network model. The model was applied to predict the Turbidity concentration at the intake point of Bardowali WTP of Tripura which have a peri-urban settlement where Turbidity varies due to the urban runoff which disposes its suspended matters into the at the intake point of the WTP. According to performance metrics utilized the model was able to predict the temporal pattern of the model at 99% accuracy at a lag of 1 hour.
Keywords: Soft-computation; Polynomial Neural Network; Segment Separation; Water Treatment Plant.
Special Issue on: ICES 2017 Innovations in Communication and Engineering
CONFIDENT EXPLORATION BESIDE WOMEN ENTREPRENEUR PROBLEMS AND ISSUES AVAILING CREDIT SUPPORT IN MICRO, SMALL AND MEDIUM ENTERPRISES COMPUTING SECTOR
by Ponsindhu Thilagar
Abstract: Availability of timely and adequate finance is the foremost important thing for the growth of any sector. Micro, Small and Medium Enterprises (MSMEs) contribution to Indian economy in terms of production, employment and exports is very significant. To support MSMEs a large number of commercial banks and financial institutions are operating in our country.There is a need to study the problem and issues behind obtaining of credit support and subsidies from the financial institutions are another area was an attempt can be made to understand the difficulties faced by the women entrepreneur owning MSME. Hence this research is designed to study the above mentioned problems related to women entrepreneurs.
Keywords: Women Entrepreneurs; Financial Institutions; Credit Support; Rigidity; Cumbersome Formalities; Insisting compulsory deposit.