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Suthar H.A.,Parul Institute of Technology
Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017 | Year: 2017

Design of control system demands optimization of many variables simultaneously, which results in problem of multiobjective optimization. The control degree of freedom is defined as the number of variables that can be controlled in the system. Hence, two-degree-of-freedom (abbreviated as 2DOF) PID control system has advantages over one degree-of-freedom (abbreviated as 1DOF) PID control system. One degree-of-freedom PID controller is used where there are two distinct objectives such as set-point tracking and disturbance rejection. The main objective of 2DOF controller is to control both set point tracking and disturbance rejection simultaneously. The process of heat exchanger which is widely used in process industry having two predominant disturbances one is due to flow variation of input fluid and second is due to temperature variation of input fluid. Hence, heat exchanger is selected as test bench to verify 2DOF control algorithm both for set point tracking and disturbance rejection. Recently, many evolutionary and nature inspired algorithm based controller tuning is attracted researchers due to their advantage to optimize parameters based on cost function, without any knowledge about process. Hence, optimization of 2DOF controller using genetic algorithm, for shell and tube heat exchanger system is proposed. MATLAB is used as software tool to verify 2DOF control strategy for heat exchanger system using genetic algorithm. © 2017 IEEE.


Bhatt U.Y.,Parul Institute of Technology | Patel P.A.,Parul Institute of Technology
Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015 | Year: 2015

Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent itemsets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. Rare association rule contains Rare Items. Rare Association Rules represents unpredictable or unknown associations, so that it becomes more interesting than frequent association rule mining. The main goal of rare association rule mining is to discover relationships among set of items in a database that occurs uncommonly. We have proposed a Maximum Constraint based method for generating rare association rule with tree structure. Tentative results show that MCRP-Tree takes less time for rule generation compared to the existing algorithm as well as it finds more interesting rare items. © 2015 IEEE.


Shah C.,Parul Institute of Technology | Goyal B.,Parul Institute of Technology | Patel V.,Parul Institute of Technology
Materials Today: Proceedings | Year: 2015

In recent years, composite materials have gained more and more attention in aerospace, automotive and structural applications. In many engineering applications, high strength-weight ratio of materials are important while designing the components. Now a days, to fulfil the above requirements conventional materials are replaced by composite materials. In present work, stir casting method is used for uniform distribution of the reinforcement material (SiC) in Aluminium as a matrix material. Friction stir welding (FSW) is a relatively new solid-state joining process. In particular, it can be used to join high-strength aerospace aluminium alloys that are hard to weld by conventional fusion welding. For analysis, L9 orthogonal array is used with four different variables (Wt % of Sic, welding speed, tool rotation speed and tool geometry) for the analysis of UTS. ANOVA is performed for the optimization of the process parameters. © 2015 Elsevier Ltd.


Kothari A.A.,Parul Institute of Technology | Patel W.D.,Parul Institute of Technology
Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015 | Year: 2015

Contexts and aspects have been distinguished as the significant factors in fabricating recommender systems. Most recommender systems aim at utilizing either non-contextual preferences or contextual preferences distinctly, while very few endeavors have been made to identify the significance of both. Hence an attempt has been made to study the influence of both, users' context dependent and context independent preferences in the single recommender system. In this case, accuracy has always been a challenge. Therefore, there exists a need for such a classification technique which can be commonly applied to both types of preferences that helps enhance the accuracy of the retrieved results. For this purpose, use of a standard Machine learning technique well known as Support Vector Machines was proposed. The idea behind using Support vector machines is to split the data in an optimal way and classify the data precisely to aid prediction purpose. For generating recommendations, these context-dependent preferences are further combined with users' context-independent preferences. Finally this technique is applied on a real-life dataset to demonstrate that our method is proficient in dealing with contextual preferences of users and well classify them to achieve better recommendation accuracy than the relative works. © 2015 IEEE.


Pilavare M.S.,Parul Institute of Technology | Desai A.,Parul Institute of Technology
ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems | Year: 2015

As cloud computing is having connected via network with servers so there are so many issues are there to be solved. Load balancing is the main issue over the cloud to be resolved. Various techniques are used to improve the load balancing in cloud computing. Among them various techniques are outperformed by the Genetic Algorithm. The GA uses random selection of processors as the input to it and then processes. Previously it is taken as the processors and jobs are having same priority but that is not actual case. So to improve the efficiency of GA the input processors are given first to the priority algorithm that is Logarithmic Least Square Matrix that is proposed here. The problem of being idle and starvation is taken under observation to resolve them by the proposed algorithm. © 2015 IEEE.


Mistry B.R.,Parul Institute of Technology | Desai A.,Parul Institute of Technology
ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems | Year: 2015

Association rule mining is a powerful model of data mining used for finding hidden patterns in large databases. The challenges of data mining is to secure the confidentiality of sensitive patterns when releasing database of third parties. Privacy Preserving in this paper is used as hide association rule. Association rule hiding algorithm sanitize database such that certain sensitive association rule cannot be discovered through Association rule mining techniques. There are various approach this describe in this paper but used the Heuristic approach in Data Distortion Technique. The proposed algorithm is the extension of MDSRRC algorithm, which hides multiple R.H.S items. In Proposed work MDSRRC algorithm works on the distributed database. We will show experimental results in comparisons with MDSRRC algorithm in single database and MDSRRC algorithm in distributed database. © 2015 IEEE.


Patel S.J.,Parul Institute of Technology | Bhoi U.R.,Parul Institute of Technology
Proceedings - 2014 4th International Conference on Advances in Computing and Communications, ICACC 2014 | Year: 2014

Cloud Computing is a platform for computing resources (Hardware and Software) that are delivered as a service over an internet network to the customers. Its intention is to share large scale equipments and resources for computation, storage, information and knowledge for scientific researches. There are many jobs that are required to be executed by the available resources to achieve best performance, minimal total time for completion, shortest response time, utilization of resource usage and etc. Because of these different objectives and high performance of computing environment, we need to design, develop, and propose a scheduling algorithm that outperforms appropriate allocation map of jobs due to different factors. Job scheduling is one of the major issue in cloud computing environment. In job scheduling priority is the biggest issue because some jobs need to be scheduled first then all other remaining jobs which can wait for a long time. In this paper, we have proposed an improvement in priority based job scheduling algorithm in cloud computing which is based on multiple criteria and multiple attribute decision making model. © 2014 IEEE.


Kadiwala S.,Parul Institute of Technology | Andhariya S.R.,Parul Institute of Technology
ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems | Year: 2015

A unified trust management scheme that enhances the security in MANETs using uncertain reasoning is proposed. In the proposed scheme, the trust model has two components: Trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory, which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. Performance of a routing protocol including this trust management scheme is evaluated under attack. Trust management considers the capability of the node along with its behavior while calculating the trust. Hence the performance of the routing protocol is improved when both behavior and capability is considered for trust evaluation. © 2015 IEEE.


Desai J.M.,Parul Institute of Technology | Andhariya S.R.,Parul Institute of Technology
ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems | Year: 2015

With the rapid growing of IT development and e-commerce web sites, increasing trends in people to posting online reviews. Sentiment lexicons has offend used to analyzing the large volume of online review data available and gain useful knowledge from it. Most of the sentiment lexicon are aspect base, uses dependence parsing for extracting the word which are not be able to classify the sentimental word so accurately. Try to propose a method which combines sentiment lexicon and shallow parsing. Which determine aspect and domain base sentiment analysis and then assign polarity to a lexicon. Main merits of proposed methods is that it highly accurate and automatically generating structured to avoiding the cost of manually labelling data. The shallow parsing used to analyses sentence and get the constituents words. It will not considering the internal structure of constituent word, nor specifying their value in sentence. Then using polarity of words positive or negative evolution of the product conclude. © 2015 IEEE.


Bhatt U.Y.,Parul Institute of Technology | Patel P.A.,Parul Institute of Technology
Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015 | Year: 2015

Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent item sets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. Rare association rule contains Rare Items. Rare Association Rules represents unpredictable or unknown associations, so that it becomes more interesting than frequent association rule mining. The main goal of rare association rule mining is to discover relationships among set of items in a database that occurs uncommonly. We have proposed a Maximum Constraint based method for generating rare association rule with tree structure. Tentative results show that MCRP-Tree takes less time for rule generation compared to the existing algorithm as well as it finds more interesting rare items. © 2015 IEEE.

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