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Optimization Control Techniques using Soft Computing Approaches

Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353, 2017 4th International Conference on “Computing for Sustainable Global Development”, ISSN 0973-7529; ISBN 978-93-80544-24-3, 2017
By Nikhat Akhtar
Soft computing approaches have different capabilities in error optimization for controlling the complex system parameters, Soft computing approaches provide a learning and desion making support from the relevant datasets or others experts review experiences. Soft computing optimization approaches can be variety of many environmental and alsostability related uncertainties. This paper explain the different soft computing approaches viz., Genetic algorithms, Fuzzy logics, results of different error optimization control case studies. Mathematical Models refer to the Conventional error optimization control , which define dynamic control Conventional controllers are often inferior to the intelligent controllers, due to lack in comprehensibility. The results that controllers provide better control on errors than conventional controllers. Hybridization of technique such as fuzzy logic with genetic algorithms etc., provide a better optimization control for the designing and developing of intelligent systems....Read more
Proceedings of the 11 th INDIACom; INDIACom-2017; IEEE Conference ID: 40353 2017 4 th International Conference on “Computing for Sustainable Global Development”, 01 st - 03 rd March, 2017 Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA) Optimization Control Techniques using Soft Computing Approaches Yusuf Perwej Department of Computer Information System AI BAHA University, AL BAHA Kingdom of Saudi Arabia (KSA) Email ID:yusufperwej@gmail.com Nikhat Akhtar Deptt. of Computer Science & Engineering, Babu Banarasi Das University, Lucknow, INDIA Email ID: dr.nikhatakhtar@gmail.com Jai Pratap Dixit Deptt. of Computer Science & Engineering, Ambalika Institute Of Management and Technology , Lucknow , INDIA Email ID: jpdixit.iiita@gmail.com Abstract – Soft computing approaches have different capabilities in error optimization for controlling the complex system parameters, Soft computing approaches provide a learning and desion making support from the relevant datasets or others experts review experiences. Soft computing optimization approaches can be variety of many environmental and alsostability related uncertainties. This paper explain the different soft computing approaches viz., Genetic algorithms, Fuzzy logics, results of different error optimization control case studies. Mathematical Models refer to the Conventional error optimization control , which define dynamic control Conventional controllers are often inferior to the intelligent controllers, due to lack in comprehensibility. The results that controllers provide better control on errors than conventional controllers. Hybridization of technique such as fuzzy logic with genetic algorithms etc., provide a better optimization control for the designing and devepling of intelligent systems. Keywords - Fuzzy Logic; Evolutionary & Genetic Algorithms;Neural Networks; Soft Computing Approaches. I. INTRODUCTION Optimization control approaches have been defined for many decadesand recentlyprovid efficient algorithms with better performance with appropriate optimization algorithms.[4] Soft computing optimization techniques is a emerging field of computational intelligent systems which are integrated approaches and also highly suitable to determine solution for different problems such as illdefined , complex and difficult to modeled.[1] Optimization control define the adjusting the many inputes to determine the min or max output of results based on problems[3]. Desirable properties of optimization control algorithms as perspective of machine are in practice of execution processes[5]. Defining a algorithms for solving large scale problems we start by algorithms and identifying solution process which are extensible to adaptivity. Suppose a model is using a variational statement for a value of the parameter p∈ Π, then find the solution of function u = u(p) where the functional space V & A are dependent problem. The parameter p related with a function space for simlycity. With reference to define the optimization control problems, need a cost & space function f which depends on u & p. Soft computing techniques are enrich with the data sets of many techniqyes which are complementary within the nature of each one another. All optimization techniques include cobinotorial and continuous process uses the fuzzy logics concepts, neural networks, evoluationary and genetic algorithms.[1] A part of designing smart intelligent system is a crusical task which cannot be fulfill by the previous traditional approces of optimizations. Soft computing optimization paradigam is highly suitable to implement of many IS as they are real life domain applications. Soft computing optimization control problems is refers as a foundation element of the computational intelligence implementations. Many Industries , business portal problems are easly solved by the soft computing approaches. Soft computing approaches for optimization also controls the several problems which are highly complex and deficult to define. So we can say that there arethe following properties which narrated as first one In real life tthere many times become highly complicated as formulate description of model and it’s happens that different problems don’t require optimization control problems issues. And second one is as Different control optimization approaches deal with the real time applications. II. MAJOR TYPES OF APPLICATIONS SUPPORTED Soft computing family discussed with the Fuzzy logic concepts, Evolutionary Computing methods, Neural networks as the following : Fig. 1. Soft computing family Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 102
Proceedings of the 11 th INDIACom; INDIACom-2017; IEEE Conference ID: 40353 2017 4 th International Conference on “Computing for Sustainable Global Development”, 01 st - 03 rd March, 2017 There are the following supported applications such as • Intelligent Search : • Optimization • Evolutionary Modeling • Machine Learning • Desission Support System • Robotics • Engineering System • Genetic Algorithms A. A. Intelligent Search: It can be happens with using evolutionary methods. GA (Genetic algorithms) dose’nt require specific knowledge problem of intelligent search due to string evoluted with qulity fitness so search execute made with possible through the string. They are basically structured . Fig. 2. Intelligent binary search B. Optimization It is the optimization process for dession making which satisfy all given constraints. Problem satisfied with all optimal outcomes . Traditional methods or approaches of optimization include both gradient based as well as direct different search approaches. GA satisfied the all the requirement providing optimum value. The global optimization determine the best process solution as nonlinear desission making models which are frequently have a number of sub –optimal solution value. Fig. 3. Optimization Cycle C. Evolutionary Modelling Basically EM approaches of optimization process computing which is based on structure of different problems determine with the help of process optimization evolution. The process of control evolution of optimization is change the inherited traits of a problems. Fig. 4. Basic process of Evolutionary Modelling D. Machine Learning It refers to the supervised learning as well as inductive learning. Neural Network is most important prime contributors in specific state area. Fig. 5. Supervise machine learning process Neural network capable providing incremental learning in intelligent system designing. E. Decission Support system: Different types of decision making have been design in various area deponds upon the specific domains. Fig. 6. Dicission Support System F. Robotics In this area soft computing hybridization approaches have been contributed . Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 103
Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353 2017 4 International Conference on “Computing for Sustainable Global Development”, 01st - 03rd March, 2017 Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA) th Optimization Control Techniques using Soft Computing Approaches Yusuf Perwej Nikhat Akhtar Jai Pratap Dixit Department of Computer Information System AI BAHA University, AL BAHA Kingdom of Saudi Arabia (KSA) Deptt. of Computer Science & Engineering, Babu Banarasi Das University, Lucknow, INDIA Deptt. of Computer Science & Engineering, Ambalika Institute Of Management and Technology , Lucknow , INDIA Email ID:yusufperwej@gmail.com Email ID: dr.nikhatakhtar@gmail.com Email ID: jpdixit.iiita@gmail.com Abstract – Soft computing approaches have different capabilities in error optimization for controlling the complex system parameters, Soft computing approaches provide a learning and desion making support from the relevant datasets or others experts review experiences. Soft computing optimization approaches can be variety of many environmental and alsostability related uncertainties. This paper explain the different soft computing approaches viz., Genetic algorithms, Fuzzy logics, results of different error optimization control case studies. Mathematical Models refer to the Conventional error optimization control , which define dynamic control Conventional controllers are often inferior to the intelligent controllers, due to lack in comprehensibility. The results that controllers provide better control on errors than conventional controllers. Hybridization of technique such as fuzzy logic with genetic algorithms etc., provide a better optimization control for the designing and devepling of intelligent systems. Keywords - Fuzzy Logic; Evolutionary & Genetic Algorithms;Neural Networks; Soft Computing Approaches. I. INTRODUCTION Optimization control approaches have been defined for many decadesand recentlyprovid efficient algorithms with better performance with appropriate optimization algorithms.[4] Soft computing optimization techniques is a emerging field of computational intelligent systems which are integrated approaches and also highly suitable to determine solution for different problems such as illdefined , complex and difficult to modeled.[1] Optimization control define the adjusting the many inputes to determine the min or max output of results based on problems[3]. Desirable properties of optimization control algorithms as perspective of machine are in practice of execution processes[5]. Defining a algorithms for solving large scale problems we start by algorithms and identifying solution process which are extensible to adaptivity. With reference to define the optimization control problems, need a cost & space function f which depends on u & p. Soft computing techniques are enrich with the data sets of many techniqyes which are complementary within the nature of each one another. All optimization techniques include cobinotorial and continuous process uses the fuzzy logics concepts, neural networks, evoluationary and genetic algorithms.[1] A part of designing smart intelligent system is a crusical task which cannot be fulfill by the previous traditional approces of optimizations. Soft computing optimization paradigam is highly suitable to implement of many IS as they are real life domain applications. Soft computing optimization control problems is refers as a foundation element of the computational intelligence implementations. Many Industries , business portal problems are easly solved by the soft computing approaches. Soft computing approaches for optimization also controls the several problems which are highly complex and deficult to define. So we can say that there arethe following properties which narrated as first one In real life tthere many times become highly complicated as formulate description of model and it’s happens that different problems don’t require optimization control problems issues. And second one is as Different control optimization approaches deal with the real time applications. II. MAJOR TYPES OF APPLICATIONS SUPPORTED Soft computing family discussed with the Fuzzy logic concepts, Evolutionary Computing methods, Neural networks as the following : Suppose a model is using a variational statement for a value of the parameter p∈ Π, then find the solution of function u = u(p) where the functional space V & A are dependent problem. The parameter p related with a function space for simlycity. Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 Fig. 1. Soft computing family 102 Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353 2017 4 International Conference on “Computing for Sustainable Global Development”, 01st - 03rd March, 2017 th There are the following supported applications such as • Intelligent Search : • Optimization • Evolutionary Modeling • Machine Learning • Desission Support System • Robotics • Engineering System • Genetic Algorithms A. A. Intelligent Search: It can be happens with using evolutionary methods. GA (Genetic algorithms) dose’nt require specific knowledge problem of intelligent search due to string evoluted with qulity fitness so search execute made with possible through the string. They are basically structured . Fig. 4. Basic process of Evolutionary Modelling D. Machine Learning It refers to the supervised learning as well as inductive learning. Neural Network is most important prime contributors in specific state area. Fig. 2. Intelligent binary search B. Optimization It is the optimization process for dession making which satisfy all given constraints. Problem satisfied with all optimal outcomes . Traditional methods or approaches of optimization include both gradient based as well as direct different search approaches. GA satisfied the all the requirement providing optimum value. The global optimization determine the best process solution as nonlinear desission making models which are frequently have a number of sub –optimal solution value. Fig. 5. Supervise machine learning process Neural network capable providing incremental learning in intelligent system designing. E. Decission Support system: Different types of decision making have been design in various area deponds upon the specific domains. Fig. 3. Optimization Cycle C. Evolutionary Modelling Basically EM approaches of optimization process computing which is based on structure of different problems determine with the help of process optimization evolution. The process of control evolution of optimization is change the inherited traits of a problems. Fig. 6. Dicission Support System F. Robotics In this area soft computing hybridization approaches have been contributed . Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 103 Optimization Control Techniques using Soft Computing Approaches III. OPTIMIZATION Optimization process control determine the various specific outputs after processing different contol functions technique or process which depends upon the various specific input or variable of problem mechanism. The optimization control technique using softcomputing describe as the figure 3.9 Fig. 7. Robotics arms optimization G. Engineering System Application domain in diffeennt disciplines of engineering problems likes mechanics,aeronotics etc. have been implementating using different optimization control techniques. H. Genetic Algorithms: There are the following programm applications such as Genetic Programming, Genetic Algorithms and Evalutionary Programming 1) Genetic Programming: The didderent operation are applicable to system control programs in various selected soft problems with reproduction an existing program by caping with new and creation of new system programs from existing system program. 2) Genetic Algorithms:GA used in problems of engineering and scientific controls applications. They also successfully applicable to all optimization problems which are determined to solve using conventional approaches such as m/c learning. 3) Evolutionary Programming:They are usefullapproaches of optimization when other are impossible to implement. It is also capable to predict a previous symbol based on symbol. Fig. 9. Optimization Process As the mechanism perspective of optimization algorithm [1] summarize as desirable properties such as scalability to large problems , generatlization and also a specific performance in the term of execution process. Computer is a perfect tool for optimization process which influencing the idea can be input in electronic format. Any Soft computing implementation technique for optimization control deponds upon mathematical theories which used to guarantee the performance of development. There are thespecific optimization technique classified such as Combinatorial Optimization , Continuous Optimization, Branch-and-Bound (BB) for Global Optimization and Alternating Minimization A. Combinatorial Optimization: Combinatorial control optimization discuss the technique in which the stages of unlabled points may be explicit optimization variables problems. Many control techniques describe a sytandard of supervisec as subroutine problems over the minimization for different fixed values. Control problems testing related to the many type of combinatorial problems in soft computing. Optimization control technique tackle with the classification as two category:[11] Exact Methods, Heuristic(approximate) Methods-Exact methods provide solution in hand but not sutable for the real life as necessary computation due to their complex nature.(not relastic for the large complex problems. Heuristic with meta heuristic as practically seek to find high quality solution (not optimal) with in resanable execution time.[14] Neural Network(NNS) [3] use in area of information driven rather than data driven.with good performance It Include the common serach methodology. Common mata search methodology performance is very slowin local opyima so faster coverage speed cover by dalta bar dalta training algorithm. Fig. 8. Genetic Algorithms process There are above applications in different field medicine, mathematics, social science ,management,and education etc. B. Continuous Optimization By Using continuous optimation for fixed argminy which eliminating continuous function objective which based on Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 104 Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353 2017 4 International Conference on “Computing for Sustainable Global Development”, 01st - 03rd March, 2017 th cluster assumption. A. APPROXIMATE MODELLING TECHNIQUE Soft computing optimization technique effectively solve real life probems application dealing with uncertainty and apprimation. It used fuzzy logic , probabilistic reasoning approaches. Fuzzy Logic: Fuzzy logic manipulating and representating data which was not precise . Fuzzy logic gives an interface that enables approximate capabilities applied to knowledgebased system. In intelligent system. Such type model enlisted with as Human Reasoning, Modal of Mathematical, Transaction (member & non members), Simple Relastively, System Fluctuation, Descripts or Lingustic Fig. 10. Optimization Process C. Branch-and-Bound (BB) for Global Optimization Fuzzy function optimization mapping with non fuzzy input values to fuzzy linguistic steps and vice versa for controlling numeric input data. Algorithm: 1. Intilise the linguistic variablesn 2.Function Construction s 3. Applied rule base process 4. Conversion fuzzy values 5. Evalutions rule 6. Result combination 7. Convert –output data as non fuzzy values 8. Probalistic Reasoning Probabilistic Reasoning deals with uncertainty ubiquitous property of knowledge property . Softcomputing also uses the theory of probabilistic reasoning theory. Incomplete knowledge causes the uncertainty as disagreement between information sources in approximation with measurement error. Fig. 11. Global Optimaization BB technique used for impractical for large different data sets. Which effectively gives a good perfiormance on exhaustive rsearch for pruning large data search of the problem solution for many observation. The aim of probabilistic reasoning and logic is to combilne the probability capacity for handling uncertainty with detective capacity to exploit structure. It may be shown as analogous pattern to fuzzy reasoning as place of fuzziness. Bayesian Network is a powerfull mechanism and representation of knowledge using reasoning of probabilistic. B. Neural Networking Modelling D. Alternating Optimization Technique Minimization over the function(f) is a standard training –each unlabeled stages. The steps as the follow 1. Initilization (Problem) 2. Evaluation 3.Trach Fitness 4.Modification as per requirement 5.update the mechanism 6. Terminate if process met (3,4) 7.Execute 8. Repeat(2-6) IV. MODELLING TECHNIQUES • Approximate Optimization Techniques • Neural Networking Modelling Technique • Hybrid Optimization Modelling Fig. 12. General Structure of Artificial Neural Network Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 105 Optimization Control Techniques using Soft Computing Approaches Neural Network Optimization Modelling as soft computing play a important role as as designing of meachine learning system by simulating optimization control technique system. Massively parallel processing of distributed system made up of interconnected neural computing lelements. It have a ability acquire knowledge of the whole system 16]. Neural Model are generly use the simple processors(small amount of local memory) which recognize specific pattern algorithms to discover large databases[18] represent normal structure for optimization. C. Hybrid Optimization Modeling Hybrid Optimization Modeling approaches in softcomputing derived from many domains such as statistic, logics and physiology etc. The initial development of position as control independent methods are utilized among different community of practitions.Hybridization is capable to eliminate the limitation and generate problem siolution for individual approaches. Encoding capability methods involve rule base aggregation , semantics and antecedent aggregation oparators and de-fuzzification[25]. Hybrid system of neural network provide a complex nonlinear relationship which appropriately suitable for predetermined classification of different domais.But output precision are often limited and admit zero error. Hybridization with fuzzy logic is more powerfull designing for intelligent system. The architecture involve the mechanism which considerd as adaptive fuzzy system as general framework. There are the following hybrid modeling techniques as :Evolutionary Fuzzy hybrid system,Neural Fuzzy And This leads to: Gradient Methods: An alternative substitute optimal function which obtain objective function using gradient technique.as: A. A. Contineuous Process The Blalancing function become relatively easy to implement for all algorithms using balancing constraints such as : Primal Optimation optimization in soft linear classification boundaries require technique. also part of contineuous process for computing. It related with the directly and variables in process. Non linear kernel function to implements the Neural Genetic Fuzzy V. ALGORITHMS, OBSERVATION AND RESULT Basically observation required as the follows :Experimental Setup, Hyperparameters, Multi-class, Objective value, Balancing constraint etc. B. B. Contineous Optimization This type of optimization control problem illustrate how independent cluster assumption with given standard Function (f) determine as objective function as non fuzzy system Optimazation algorithms based on specific Deterministic and non deterministic control processing of different parameter. Algorith for Deterministic Annealing. Here we focus on using alternate and gradient methods for optimization Alternate Minimization control Algorithm: Here the procedure proposed by sindwani 2008 over unlabled functions.Contribution of two loss terms by C , pi parameter. Fig. 13. The effective optimization output for unlabled point. Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 106 Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353 2017 4 International Conference on “Computing for Sustainable Global Development”, 01st - 03rd March, 2017 th Soft computing uses the hybridization of approaches to overcome the errors and optimize the procedure based on resulting challenges. These procedures provide may algorithms for development of new approaches for solving optimization techniques. Optimization control technique algorithms present different significant implementation . The optimization problems can be handle by the different control technique different approaches with their goal bydemonstrate mentions algorithms with in different types of simulation codes in designing of different control issues. Fig. 14. The degree of non fuzzy system optimized by different parameter processing VII. ACKNOWLEDGEMENT I acknowledge my great gratitude and immense to different researchers for their encouragement, inspiration and insightful resurch. VIII. REFERENCES [1] [2] [3] [4] [5] [6] Fig. 15. The piecewise loss optimized function [7] VI. CONCLUSION This paper explain the different optimization control technique with significance of soft computing approaches such as fuzzy logic system methods of evolutionary concepts and also neural network, which are the prime contributions in developing and designing different intelligent systems.There are many N intelligent systems , which require various task performing such as impressions ,intelligent searchs ,optimization, uncertainty, machine learning and different automatic dession support making systems etc. Softcomputing optimationapproaches are very efficient to explain satisfactory desion making procedure for the requirement of designing and developing of intelligent envirnments. Fuzzy logic concepts , evolutionary technique process , neural networks and probalistics reasoning methods have different capabilities in error optimization for controlling the complex system parameters. There are major constituents by hybridratzation such as fuzzy with evolutionary, neural with evolutionary and neural-fuzzy-evolutionary which have contributed for achiving a successfully design and implementation of intelligent system for next generation. This paper discussed many contribution of soft computing optimization process in search ,optimizating, controlling, machine learning, engineering design and many fuzzy, linguistic modeling etc. The paper has provide major types of supported applications, optimization methods and approximate designing with significant reviews algorithms under utilities and architecture. Indetitification of technique with performance of model identification provide a generalizationform of soft computing. 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Van Bloemen Waanders, Brain R. Carness “Optimization under adaptive error control for a contact tank reactor”,I. proc. Of IJNME, 2002 C. A. Pena-Reyes, “ Co-evolutionary Fuzzy Modeling”, Ph.D. dissertation, Swiss Federal Inst. Tech., Lausanne, EPFL, Switzerland, 2002. E.Cox, Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, The Morgan Kaufmann Series, CA, 2005. Kristin P. Bennett ,Emilio Parrado-Hernadez, “ The interplay of optimization and machine learning research , in proc. Journal of machine learning research , 2006(1265-1281). C. Pena-Reyes,” Co-evolutionary Fuzzy Modelling” [dissertation]. Federal Inst. Tech.:Univ. Lausanne; 2002. K.B.Mankad,” The Significance of Genetic Algorithms in Search, Evolution, Optimization and Hybridization: A Short Review”, International Journal of Computer Science and Business Informatics, Vol. 9, No. 1, pp. 103-115, 2014. D. Chapelle and A. Zien. Semi-supervised classification by low density separation. In Tenth International Workshop on Artificial Intelligence and Statistics, 2005. A.Abraham, Recent Advances in Intelligent Paradigms and Applications, Jain L. and Kacprzyk J. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, pp.135, 2002. Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 107 Optimization Control Techniques using Soft Computing Approaches [19] Ramaprabha R. and Mathur B.L. ,soft computing optimization [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] for solar photovoltaic arrays. In proc. APRN journal of engineering and applied sciences , vol 6,n0. 10, octo 2011,18196608 P.Bajpai, and M.Kumar, Genetic Algorithm – an Approach to Solve Global Optimization Problems, Indian J. Computer Sci. and Eng., vol. 1, no. 3, pp. 199-206, Oct.-Nov. 2010. K.B.Mankad, and P.S.Sajja, “The Impact of Genetic Fuzzy Modeling for Machine Intelligence”, Information Technology Research Journal.Vol .3(1), pp. 1 – 8, May, 2013. G. Serag-Eldin, S. Souafi-Bensafi, J. K. Lee,W.K.Chan,M.Nikravesh,Web Intelligence: Web-Based BISC Decision Support System (WBISC-DSS), in: Y.- Q. Zhang, et al.,(Eds.), Computational Web Intelligence, Univ. of CA, Berkely,2004, pp. 391-429. R. Collobert, F. Sinz, J. Weston, and L. Bottou. Large scale transductive SVMs. Journal of Machine Learning Research, 7:1687–1712, 2006. Olivier Chapelle , Vikash Sindhwani “ Optimization techniques for semisupervised support vector machines , in proc. Journal of machine learning , 2008, 203-233 W.M. Cheung and U. Kaymak, A fuzzy logic based trading system, in Proc. of the Third European Symposium on Natureinspired Smart Information Systems, St. Julians, Malta, 2007,pp. 141-148, T.Hayat andK.M.J. Knanim,“New Fuzzy CSP for an Optimized Mobile Robot’s Path Tracking using Genetic Algorithms”, International Journal of Computer and Information Technology (ISSN: 2279 – 0764), Vol.3 (3), May 2014,pp.523-531. Rundolf, Christian, “ The role of soft computing in intelligent data analsysis: in proc. Ijermt . in 2014 O.Cordon, F.Herrera, F.Hoffmann andL.Magdalena, Genetic Fuzzy Systems Evolutionary tuning and learning of fuzzy knowledgebases,Singapore: World Scientific, 2001. Kunjal Bharatkumar Mankad, An architechural perspective of soft computing methods. In proc. Ijrmt , in 2015,vol-4,issues-2 2278-9359 Bengio and Y. Grandvalet. Semi-supervised learning by entropy minimization. In Advances in Neural Information Processing Systems, volume 17, 2004 Copy Right © INDIACom-2017; ISSN 0973-7529; ISBN 978-93-80544-24-3 108
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Proceedings of the 12th INDIACom; INDIACom-2018; IEEE Conference ID: 42835, 2018
Ashish Agrawal
This Paper Presents a Fuzzy Logic based approach for student performance evaluation in colleges. The parameters to consider for evaluation were academic performance, placement, and extra-curricular activities to analyze the student performance for future evaluation. In our proposed approach, we work on three domains-future perspective, placement, and result evaluation. This study helps the educational institutes how to enhance their current evaluation techniques for better evaluation.
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A Study On Human Activity Recognition From Video
Debashis Barman
Analysis of human activities from video is currently one of the ongoing research areas in computer science. Recognition of human activity from video has gained lot of attention because of its increasing demand in many real life applications, for e.g. video surveillance, entertainment, healthcare, child and old age homes, etc. In this paper, several steps involved in automatic human activity recognition systems, such as segmentation, tracking of motion, pose estimation and recognition of activities are studied. Common segmentation techniques such as background subtraction and Gaussian Mixture Model (GMM) are also discussed. Two different approaches for tracking of motion in video, i.e. representation and localization of the target and filtering and data association are also discussed. Finally some of the commonly available datasets used in automatic analysis of human activities from video are also mentioned in detail.
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Enhancing the Security of CAPTCHA based on the New Character Locations
Sarvesh Kumar
— Since that time i.e. 1864 till today, research on CAPTCHA is implementing day by day in different designs different technique over a security level. Generally as we know that CAPTCHA is mainly used in website to block the bots, which means a program or a code which is generate by a spammer to search the valid email address to be used as a target for spam and also want to know the weakness of system by which the spammers may attack. CAPTCHA is implemented as a Turing test with respect to Artificial Intelligence. So, to differentiate whether the user who are operating the website are human or a computer program. And the test which is consider to be solve in the form of CAPTCHA is solved and success rate more than 80% by the human and less than 0.01% by the computer system. In this paper we will come to know usability, new techniques that have been used to change different CAPTCHA schemes, types of CAPTCHA. We than introduce the new level to solve CAPTCHA whether it would be in form of text, audio, image, video. Main goal is to reduce the time for user surfing internet by spending time to solve the CAPTCHA. The idea shows that the proposal is indeed feasible and if implemented it will be another technique to reduce the time limit because many technique were implemented present few days before CAPTCHA is introduced.
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Time Synchronization in Wireless Sensor Networks using Optimally Powered Overheard Nodes
Dr. Garima Mathur
— Time synchronized nodes in WSN play an important role in many services such as event monitoring, data collection. Because of unsynchronized clock of sensor motes, energy use of network increases considerably. Recently, a scheme for time synchronization using pairwise synchronization was proposed. The scheme used overheard nodes to synchronize nodes in the network thereby relaxing the burden of the network. But as the size of the network increases, nodes cannot perform overhearing from the parent node because of the constraint of limited range of motes. This paper proposes the good at producing time synchronized network using optimally powered overheard nodes in multilayered scenario. These nodes offer greater range to nodes, hence reducing the limitations such as high message delays, low throughput. The control to get an effect of the offered design has been made certain through many simulations. Results make clear that time taken to synchronize the network using optimally powered overheard nodes outdoes the existing designs in terms of performance parameters of the network such as throughput, network lifetime, end to end delay.
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Hyperspectral Imaging-a New Era of Imaging
Shalini Yadav
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A Naive Approach for Handling Uncertainty Inherent in Query Optimization of Probabilistic Databases
bvicam.ac.in
Ritika Wason
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Fuzzy Inference to Rule – Based Risk Calculation for Risk Adaptive Access Control
IEEE, 2018
pratik aher
Quantifying risk in a real time system was always challenging. There cannot be a uniform solution for calculating risk as it depends on the perception of the system. For some system data availability is of higher importance and other system accuracy of results. Hence there is a need to develop an adaptive solution for real time ERP system. In today's world where data is huge, unstructured and dynamic, developing a solution for calculating risk and finding out malicious activities needs to include static as well as dynamic parameters. In this paper we have developed a risk calculation framework, which can dynamically adapt to the changing needs and appropriately find if there is any malicious activity happening in the system. The objective of the system to is grant access to genuine users in regular as well as emergency scenario.
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SMX Algorithm: A Novel Approach to Avalanche Effect on Advanced Encryption Standard AES
Bhargavi Goswami
— Any algorithm is evaluated based on its confusion and diffusion property determining its strength to withstand the intruding attacks. AES (Advanced Encryption Standard), using 128-bit key size has the number of rounds processed over plaintext to obtain unbreakable ciphertext. The key factor that determines the security of the algorithm is the processing of four different stages in each round during encryption and decryption. The algorithm has confusion property implemented in the form of S-Box (Substitution Box) which works in a static manner in each round. This may prove to be the loophole where an attacker may analyze the static S-Box processing and may get successful in determining plaintext by implementing reverse S-Box during the transmission of encrypted data. In this paper, authors propose a solution to strengthen the S-BOX implemented in the stage of "SubBytes" by implementing novel approach of "SMX (Sum Modulus XOR) Algorithm" with a gain of added complexity with reduced processing time.
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A Cost Optimal Implementation of CORDIC Algorithm on Virtex-4 FPGAs using Technology Dependent Optimizations
Burhan Khurshid
In this paper we present an efficient FPGA implementation of common CORDIC architecture for circular coordinates. CORDIC is one of the most versatile algorithms available for developing high-performance computing hardware. It is a multiplier-less approach and involves only shift and add operations. Since fixed-point arithmetic is efficiently realizable on FPGAs, CORDIC algorithm is very apt for implementation on FPGAs. Unfortunately, majority of the published work related to FPGA implementation of CORDIC algorithm has focused only on technology independent (architectural) modification. However, in this paper we consider the technology dependent optimization of CORDIC architectures that can implement a desired functionality with reduced cost. We particularly focus on 4-input LUTs that are inherent basic logic elements in Virtex-4 FPGAs. Technology dependent optimizations are carried out to utilize this FPGA primitive efficiently. The cost of the circuit is expressed in terms of the r...
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Network connectivity maintenance of WSNs in node failure-prone environment: A detailed survey
2014 International Conference on Computing for Sustainable Global Development (INDIACom), 2014
Dr. Virender Ranga
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Globalized Intelligent System
Naveen Kumar Jain
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A Globalized Intelligent System
Sarika Jain
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AI Techniques in Knowledge Management
Brojo Mishra
In this paper we have discussed the importance of information in the field of business and economics. Valuable information in certain pattern leads to knowledge. We have described how knowledge is one of the major factors for getting the desired results. In today’s world, information technology plays a frontier role in gathering knowledge, storing it and processing with it. Artificial Intelligence can be directly related with Knowledge Management. Techniques of Artificial Intelligence are used to process this knowledge and getting predictions on outcome.
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Question Systematization using Templates
Urmila Shrawankar
– Question is a crucial construct of natural language. Systematic, error free question is a basic need of different applications of natural language. Many research works have been focused on 'statement' formation but the issue of 'systematic question' formation is less focused. This research work resolves above issue through systematization process using Template based approach which is accompanied by Dictionary approach and powerful NLP technique like Maximum Entropy based POS Tagging technique. Systematization process aims to reform proper flawless question from the erroneous input question by removing existing errors present in order of words, word spelling and removing ambiguous synonyms of the words. This work deals with domain specific WH-questions of English language. Additionally it also works on imperative questions. Template based approach is supported with a key concept of 'Question Templates' which are designed with human intelligence keeping detail knowledge of various lingual constructs, their grammar and domain specific questionnaire. This work is useful in various fields , for example in academics to set question papers, to assist English learners, to produce intermediate output for complex systems like question-answering system to retrieve correct answer from a huge dataset.
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Round Robin Selection of Datacenter Simulation Technique Cloudsim and Cloud Analsyt Architecture and Making it Efficient by Using Load Balancing Technique
Sarvesh Kumar, Sumitra Beniwal
— Cloud Service Providers always offer Communication services that are flexible, on demand and measured. Multimedia services are used to be delivered through Cloud computing with severe QoS necessities. So there is a recent research is going on as a challenge between researchers, the QoS-aware, cost-efficient selection of data centers, due to these all necessities. Here in this work, there is an optimization approach to observe Cloud Data Center Selection Problem. The main feature of cloud computing is to achieve a high user agreement and enlarged resource consumption ratio by ensuring an well-organized and fair allotment of every computing resource is efficient load balancing. It helps to get appropriate load balancing aids in minimizing resource utilization, implementing fail-over, enabling scalability, avoiding bottleneck etc. In cloud computing situation load balancing is self-possessed of selecting data center for future demand and virtual machine organization at entity data center. Cloud computing resources have a variety of technology that make easy the implementation of big scale requirement. Therefore, the selection of appropriate data center for execution of a task is a significant feature to improve the performance of cloud computing. In this paper available policy for data center choice is Round-robin with simulation technique and also how CloudSim and cloud analyst help in selection of datacenter many more.
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PMKBEA: A Process Model Using Knowledge Base Software Engineering Approach
bvicam.ac.in
MERAJ AHMAD SIDDIQUI
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Construction of News Headline from Detailed News Article
Urmila Shrawankar
Usually long news article contains large amount of information. Many a times due to lack of time people are unable to read whole news article. Therefore, headline is required in order to get complete idea of news without reading whole news article. The extractive and abstractive approaches are conventionally used for news headline generation. In this paper, a combinational approach is used for headline construction by using keywords/keyphrases along with parsing technique of Natural Language Processing (NLP). The Keyphrase Extraction Algorithm (KEA) is used to extract keyphrases from input news text. Respective news domain word thesaurus and some other approaches are used for retrieving keywords from news text. Proper headline syntax can be constructed by using parsing technique. Headline is useful to reduce the reading and interpretation time for getting the complete idea of entire news article. The objective is to save reader’s time and effort in finding the useful information in a detail news article.
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Web-Application Attacks: A Survey
P Sangwan
Attackers have an over-growing list of vulnerabilities to exploit in order to maliciously gain access to particular web applications. New vulnerabilities are being discovered all the time by computer security researchers, by attackers and even by users. Each time changes are made at any level of the web-application infrastructure; there is the potential for new vulnerabilities to be created. In this paper, we discuss about the vulnerabilities that today's web applications face and what work has been done to tackle these vulnerabilities. Firstly, different types of these vulnerabilities are presented by dividing them into relevant groups. Then a survey is presented in tabular form to present the research done in static and dynamic approaches to tackle web application vulnerabilities. After that, a survey dealing primarily with the research done in the field of cross-site scripting attacks is presented. Finally, future scope and pathway for research is suggested for preventing cross-site scripting attacks.
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Review of Soft Computing Models in Design and Control of Rotating Electrical Machines
Energies, 2019
K.W. Chau, Amir Mosavi
Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.
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A Secure Approach of Image Encryption using QR Code on Social Media
Brahmdutt Bohra, Surendra Yadav
— Social data as the name suggest is the large collection of the data for which the traditional data processing techniques are not sufficient. Social data is now gaining popularity very fast in the internet era. Many social media sites like Face book are using the vast data storage mechanism. In this paper we will discuss about that data storage using big data and its security. Now-a-days, every second millions of profiles are accessed around the world where thousands of thousand photos are uploaded. To store these photos and many other data uploaded by user we use big data. But due to some security issues and loop holes, these photos can be hacked or downloaded easily and also millions of fake ids are created. These loop holes degrades the security of social media sites. So, to make the advancement in the security, in this paper we suggest the use of QR code concept. Further in this paper we will also discuss the approach which is used for the security of personal data over social media and also prevent from the fake ids.
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Metrics for Requirements Engineering and Automated Requirements Tools
Proceedings of the 5 th National Conference; INDIACom-2011, 2011
Shams Siddiqui, Prof. M. U. Bokhari
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Spintronics-A New Light in Nanoscale Devices
bvicam.ac.in
Namit Gupta
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Predictions and Recommendations for the Higher Education Institutions from Facebook Social Networks
Meenu Dave
We are living in the age of Higher Education (HE) 2.0, where the fastest growing social networking sites (SNS) is Facebook. It's been widely accepted as a platform for communications and collaborations. The students and Higher Education Professionals (HEP) which includes faculty members, administrators, management, etc. use to share their valuable resources. It's been notified that students are far more ahead in using communication technologies as compared to the Higher Education Institutions (HEIs) Professionals. In our research work, we analyse the social network graphs which are Facebook pages or groups having members like HEP, students, etc., related to Higher Education in India. We discovered that in some networks, members are less, but online posts and messages are huge in number. Student's active participations or interactions in the social networks can be done by Social Network Analysis (SNA) approaches. In our study, we explore the techniques for the SNA to detect the motifs or patterns in the Online Social Media Networks (OSMNs). These are produced by the interactions and the content exchanged among the users, students and professionals of HE in the social graph. In our previous research work, we used Network Analysis Software Applications (NASA) tools able to discover important architecture and fetching the core, perimetric and outlined members in the network. By these NASA applications, we can visualize, generate and summarize the buzz topics or the words by the active members of the network, which gives us a glimpse of the topic under discussion. We exploited the mining algorithms provided by the NASA tools like (Gephi, NodeXL, LikeAnalyzer and Wolfram Alpha). This helped us in assessing their presence and participation factor of the students and the higher education professionals in the social network graphs. Our study attempted to test for an affiliation that exists between Higher Education (HE) and the Social Networking Site (SNS) Facebook. Our research in analysis and finding related to social network analysis predicts that social networking on Facebook and higher education can work in parallel.
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Clustering with Multiple networks applying K-means
Proceedings of the 12thINDIACom; INDIACom-2018; IEEE Conference ID: 42835 2018 5th International Conference on “Computing for Sustainable Global Development”, 14th - 16th March, 2018 BharatiVidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA), 2018
Anuranjan Misra, Naina Pal
Many multiple view network clustering and multiple domain network clustering methods were proposed to get good MN clustering. These methods believe that there must be some common clustering which is shared by all networks. Clustering on multiple networks when done has been proved to have much more accuracy when compared to the clustering done on individual networks which is performed separately, but there are different networks that provide information which would be complementary on the underlying clustering structure. If the groups are considered differently than better clustering performance can be attained. Thus a method is needed which is able to find out the network groups on its own so that networks which are there in one group shares clustering which is common. To resolve this problem, we are applying K-means to categorize and perform clustering on multi networks.
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BVICAM-Empirical Analysis of Image Compression using Wavelets, Discrete Cosine Transform and Neural Network.pdf
Gaurav Gupta
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Prevention against DDOS Attack on Cloud Systems using Triple Filter: An Algorithmic Approach
Proceedings of the 10thINDIACom; INDIACom-2016; IEEE Conference ID: 37465, 2016
Anuranjan Misra
DDoS or simply DoS attack is one of the main threats in the cloud networks which can prevents the legitimate users from getting cloud services and sometimes it may crash cloud servers after receiving vast amount of requests. Therefore, the identification of most appropriate solution directives to strengthen security and privacy in the cloud environment has become paramount to secure the cloud networks. In this paper we have proposed algorithms uses triple filtering techniques for discard the spoofed packets and also strengthen the security of cloud computing.
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Bioinformatics- Tools and applications
Dr. Parul Thapar
A research not only requires the collection of results and data, but also interpretation of that data is an essential step in fulfilling the objectives of a research. Bioinformatics is a part of computer science which is involved in discoveries related to biological science. Bioinformatics is an emerging field of science which is progressing in every field of life science i.e. biotechnology, molecular biology, agriculture, medicine and other such areas. The present paper highlights the advancement of computer science in uplifting the areas of research related to biological sciences and its contribution in analysis and interpretation of data related to research. Keywords-Bioinformatics, biological science, computer science, data analysis, interpretation of results and research and development.
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A Review of Solving Real Domain Problems in Engineering for Computational Intelligence Using Soft Computing
Proceedings of the 11th INDIACom; INDIACom-2017; IEEE Conference ID: 40353, 2017 4th International Conference on “Computing for Sustainable Global Development”, ISSN 0973-7529; ISBN 978-93-80544-24-3, Pages 706–711, 2017
Dr. Yusuf Perwej, Prof. (Dr.) Firoj Parwej
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A Comparative Study of a Class of Nature Inspired Algorithms
bvicam.ac.in
Shubhra Goyal
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Review of Chronic Kidney Disease Based on Data Mining Techniques Challenging issues of CKD and Data mining and Machine Learning (ML) algorithms
Dr.G. Suseendran
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Dr.G. Suseendran
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An Analytical Review of Exigent Research Issues in Data Mining Application Fields: Current Scenario and Future Prospects An Analytical Review of Exigent Research Issues in Data Mining Application Fields: Current Scenario and Future Prospects
Meenu Dave
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RAiSE : Hybrid Recommender Engine for Agricultural Domain
Priyanka Rastogi
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Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM
rosy bhatia
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Real Time Dynamic Relative Positioning Of An Unmanned Ground Vehicle
Devendra Yadav
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An Efficient Heart Disease Prediction System Using Modified Firefly Algorithm Based Radial Basis Function with Support Vector Machine
researchgate, 2018
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Performance Analysis of Cascaded Denoising Block for ECG Signal Analysis using Different Filters
Ashish Saini
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Job Shop Scheduling Algorithms-A Shift from Traditional Techniques to Non-Traditional Techniques
Kumar Kirti, Meenu Dave
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A Study of Academic Performance Evaluation Using Fuzzy Logic Techniques
Saurabh Pal
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Comprehensive Study of Popular Software Testing Problems
Madhulika Malik
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Revolution in Existing Network under the Influence of Software Defined Network
Bhargavi Goswami
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Listening deaf through Tactile sign language
Urmila Shrawankar
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