Proposing an Effective Framework for Hybrid Clustering on Heterogeneous Data in Distributed Systems

Authors : Peyman Zohrevandi, Farhang Jaryani

Keywords :– Effective Framework, Hybrid Clustering, Heterogeneous Data, Distributed Systems.
Published Online : 09 september 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Combined clustering algorithms have more advantages than other clustering algorithms. These algorithms
often produce better clustering; they combine clustering that cannot be produced by any other clustering
algorithm alone, have less sensitivity to noise, and are able to integrate creating results from distributed
resources. The proposed process presented in this study will consist of three major parts: (1) the
recognition of two-sided clusters, (2) weighing primary clusters and (3) Aggregation clustering on
distributed heterogeneous data. To determine the two-sidedness of clusters between clustering, an
algorithm is proposed to determine which two clusters correspond to each other in two different clustering.
Weighing the clustering is done to the point that higher quality clusters in the production of the final result
have a greater impact on clustering that is of lower quality. The proposed strategy has been evaluated on
heterogeneously distributed data. The results of the evaluations have been compared with the other four
algorithms in the field of compromise clustering. The comparisons have shown that the proposed strategy
is more effective than other algorithms in most cases.  [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Proposing-an-Effective-Framework-in-order-to-Hybrid-Clustering.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] AKBARI, E., DAHLAN, H. M., IBRAHIM, R. & ALIZADEH, H. 2015. Hierarchical cluster ensemble
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Improving Performance of User-Based Collaborative Filtering Recommender Systems Using the Weighted Similarity Method

Authors : Younes Narouiee, Hassan Asadollahi

Keywords :– Recommender systems, Collaborative filtering, traditional similarity measures, Weighted Similarity. 
Published Online : 09 september 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Recommender systems are an example of the most successful web personalization tools. The most
important duty of a recommender system, is finding the user’s favorite items in a very large space of
selectable items. Similarity-based algorithms, often referred to as memory-based collaborative filtering
techniques, are one of the most successful methods in recommendation systems. When explicit ratings are
available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient,
cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion.
Therefore, two users have equal impact on each other in recommending new items. In this paper, we
introduce new weighting factors that allow us to consider new features in finding similarities between users.
These weighting factors, first, transform symmetric similarity to asymmetric similarity by considering the
number of ratings given by users on non-common items. Second, they take into account the habit effects
on users which are regarded on rating items by measuring the proximity of the number of repetitions for
each rate on common rated items. Experiments on dataset were implemented and compared to other
similarity measures. The results show that adding weighted factors to traditional similarity measures
significantly decreases Error resulting from them.  [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Improving-Performance-of-User-Based-Collaborative-Filtering-Recommender.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Zhang, F., Gong, T., Lee, V. E., Zhao, G., Rong, C., & Qu, G. (2016). Fast algorithms to evaluate
collaborative filtering recommender systems. Knowledge-Based Systems, 96, 96-103.

[2] Liu H, Hu Z, Mian AU, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of
collaborative filtering.Knowl-Based Syst 56:156–166

[3] Choi, S. M., Ko, S. K., & Han, Y. S. (2012). A movie recommendation algorithm based on genre
correlations. Expert Systems with Applications, 39(9), 8079-8085

[4] Pham XH, Jung JJ (2014) Recommendation system based on multilingualentity matching on linked
open data. J Intell Fuzzy Syst 27(2):589–599

[5] Rahul Katarya, Om Prakash Verma, An effective collaborative movie recommender system with cuckoo
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[6] Cleger-Tamayo, S., Fernández-Luna, J. M., & Huete, J. F. (2012). Top-N news recommendations in
digital newspapers. Knowledge-Based Systems, 27, 180-189.

[7] Koohi, H., & Kiani, K. (2016). User based Collaborative Filtering using fuzzy C-means. Measurement,
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[8] Massa P, Avesani P (2009) Trust metrics in recommender systems. In: Computing with social trust.
Springer, pp 259–285

[9] Garcia, I., Sebastia, L., & Onaindia, E. (2011). On the design of individual and group recommender
systems for tourism. Expert systems with applications, 38(6), 7683-7692.

[10] Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-
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knowledge-based recommender system. Application to the movies domain. Expert Systems with
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[13] Nilashi, M., & Ibrahim, O. B. (2014). A model for detecting customer level intentions to purchase in
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[14] Al-Shamri, M. Y. H., & Al-Ashwal, N. H. (2014). Fuzzy-weighted similarity measures for memory-based
collaborative recommender systems. Journal of Intelligent Learning Systems and Applications, 2014.

[15] Al-Shamri, M. Y. H. (2014). Power coefficient as a similarity measure for memory-based collaborative
recommender systems. Expert Systems with Applications, 41(13), 5680-5688.

[16] Chang, C. C., & Chu, K. H. (2013, June). A recommender system combining social networks for tourist
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[17] Bobadilla J, Ortega F, Hernando A, Bernal J (2012) A collaborative filtering approach to mitigate the
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[18] Gorgoglione, M., & Panniello, U. (2009, May). Including context in a transactional recommender
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Enhancement of the Safety of Vehicle Network Data by Controlling Data Access Policies

Authors : Sajad Karimi, Hassan Asadollahi

Keywords :– Network security, Ad-Hoc networks, Vehicular networks, Policy in networks. 
Published Online : 09 september 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Vehicular ad-hoc networks (VANETs) are a subclass of mobile ad-hoc networks (MANETs) in which the
mobile nodes are vehicles; these vehicles are autonomous systems connected by wireless communication.
This kind of network has the advantage of being able to be set-up and deployed anywhere and anytime
because it has no infrastructure set-up and no central administration. Distributing information between
these vehicles over long ranges in such networks, however, is a very challenging task, since sharing
information always has a risk of information disclosure attached to it especially when the information is
confidential like in military applications. This thesis therefore focuses on the issue of security in VANET and
especially controlling information flow. This thesis introduces a policy-based framework to control the
dissemination of messages communicated between nodes in order to ensure message confidentiality not
only during transmission, but also after it has been communicated to another peer. In this framework,
policies that specify how the information can be used by the receiver according the requirements of the
originator are attached to messages. These requirements are represented as a set of policy rules defined
by a policy language proposed in this thesis that explicitly instructs recipients how the information can be
disseminated to other nodes in order to avoid unintended disclosure. To evaluate the framework, Network
Simulator (NS-2) was used to test the performance of the proposed policy-based agent protocol using
average delay and overhead as performance metrics. Also, some scenarios are presented in this thesis to
show how data dissemination can be controlled based on the policy of the originator and the results of
these case studies show the feasibility of our research. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Enhancement-of-the-safety-of-Vehicle-network-data.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Ghebleh, R. (2017). A Comparative Classification of Information Dissemination Approaches in
Vehicular Ad hoc Networks from Distinctive Viewpoints: A Survey. Computer Networks, 131:15–37.

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Approach to Efficiently Allocated Resources and Independent Program Scheduling in Grid Systems using Fuzzy Inference

Authors : Jahanbakhsh Parmah, Javad Hosseinkhani

Keywords :– Resource Allocation, Scheduling, Grid Systems and Fuzzy Inference.
Published Online : 09 september 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Grid computing is a new technology that allows us to access various types of resources through the use of
communication infrastructure and computer networks, as well as the application of the finite degree of distributed
system concepts and facilities. There is no limit to the geographical scope and the type of resources covered. Since
resource source and source speed are two opposite factors, in other words, the fast source is usually expensive;
scheduler should apply the user’s resources in assigning tasks to resources. For example, if a user is concerned about
the cost of performing his work, assignment assigns it to a cheap resource, and if the user is concerned about the
completion time of his work, the scheduler should assign tasks to a quick source. This scheduler gives the user more
freedom to apply the comment. Research in this area suffers from a number of flaws, including optimization, completion
time and cost of execution. Similarly, in some other studies, it has been attempted to prioritize the importance of
completion time or cost, which has the disadvantage of prioritizing. Therefore, in this research, we will introduce a new
algorithm that, based on fuzzy decision, performs the best choice according to the user and system conditions. And
taking into account all the variables and the effect of each one in its own right, it will provide the most ideal solution
possible from both the system and the user’s point of view. In order to make a satisfactory decision, one should be able
to predict the value of any probable outcome that will be obtained after the decision is made, and indirectly compare
these values with a quantitative scale, and examine the probability of success. The proposed algorithm calculates the
most suitable source for satisfying the user’s needs, and calculates the lowest runtime for jobs on the resource. In the
end, the results of the comparison showed that the proposed algorithm can significantly improve the required repetition
and the time consumed (cost) to achieve an optimal solution. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Approach-to-Efficiently-Allocated-Resources-and-Independent-Program-Scheduling-in-Grid-Systems-using-Fuzzy-Inference.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Singh, Krishan Veer, and Zahid Raza. “A GA based job scheduling strategy for computational grid.”
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performance heterogeneous distributed computing systems.” Egyptian Informatics Journal 12.3 (2017):
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[6] Darmawan, Irfan, Yoga Priyana, and M. Ian Joseph. “Grid computing process improvement through
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Practice and Experience, pp. 1-32, May 2012.

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Proposing a Hybrid Approach in order to Increase the Efficiency and Security of Outsourced Data in Social Networks based on Data Mining

Authors : Javad Makaremi Isfahani, Farhang Jaryani

Keywords :– Privacy, Increase the Efficiency, Security of Outsourced Data, Data Mining.
Published Online : 03 April 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]A cloud based distributed anonymizer is proposed in this study. The anonymizer is placed on Virtual machines on cloud services. By expanding the use of social networks, privacy in these networks has become one of the most important security challenges. Today, anonymity and privacy techniques are used in many data environments (table data and network data). In this research, a method has been developed in which the data owner organizes the exploration of the dependency rules of the encrypted and outsourced data in the cloud environment to the n data server in the cloud environment, which together Explore the dependency rules and returns the data to the data owner in encrypted form. Data owner to reduce the risk of loss of privacy Data can select servers from different cloud service providers, which is at least one of the trusted servers. In this way, each server can calculate the support for an item set in a subset of the database, and each time before this, all transactions are tangled up so that the transaction structure can be hidden afterwards Encrypted dataset support and the minimum encryption threshold are compared with the conditional gate. Therefore, the purpose of privacy is that the existence of the relationship between its entities is represented by tags. The main goal is to balance optimality between the entity’s privacy and data applicability. Also, even though all nodes are K-unknown, communication disclosure can occur, which can improve the organization’s workflow and increase the efficiency and security of the previous methods significantly. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Proofread-Hybrid-Approach-to-Increase-the-Efficiency-and-Security-of-Outsourced-Data.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Birgani, Anoosh Mansouri, and Javad Hosseinkhani. Proposing an Algorithm in order to Detect Communities in Social Networks using Multi-Objective Evolutionary Algorithm. 2016.

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Providing an Effective Method for Maximizing the Load Balancing in Cloud Computing using the Memetic Algorithm

Authors : Iman Ahrari, Parisa Rahmani  

Keywords :– Cloud Computing, Memetic Algorithm, Load Balance, Dragonfly Algorithm, Genetic Algorithm. 
Published Online : 03 April 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Today’s users use cloud computing extensively, and most of their daily work are to use the services in the
cloud. The use of the cloud, despite the reduced complexity of the user, can create special problems for
cloud providers, one of them is the problem of balancing the load in the cloud and preventing the drowning
of cloud servers in a bunch of various tasks that users need. In this study, the load balancing in the cloud
was introduced using the Memetic algorithm, a kind of hybrid genetic algorithm, which was introduced by a
person called Dawkins, a genetic comparison of cultural evolution, and is now one of the growing research
spaces in Evolutionary calculations have been reviewed and the results have been compared with some of
the work done in this area. Investigating the results of the proposed algorithm, this study shows that the
use of the Memetic algorithm, which is an evolutionary algorithm, can optimally balance the load and
reduce the average waiting time for the response.  [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Proofread-Effective-Method-for-Maximizing-the-Load-Balancing-in-Cloud-Computing.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] P. Mell and T. Grance, “The NIST definition of cloud computing. 2011. Available at: h ttp,” csrc. nist.
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Providing a Method for Managing Efficient Resources in Cloud Computing based on Cuckoo’s Algorithm

Authors : Saeid Hosseinifar, Hassan Asadollahi

Keywords : Efficient Resource Management, Cloud Computing, Cuckoo Algorithm. 
Published Online : 03 April 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]In recent years, the cloud computing model has been very much considered due to its high scalability,
reliability, information sharing and low cost of single-processor machines. The term cloud is a metaphorical
concept that refers to the huge repository of hardware and software that can be easily accessed through
the Internet. Data centers, including those with high processing power, can respond to thousands of user-
generated computing needs. Today, most of these data centers use virtualization techniques, and thus
provide a distributed computing environment in which cloud services are provided on a virtual machine
platform, and virtual machines run on physical host’s platform. One of the major problems with data centers
is their high energy consumption, which is neglected following the competition of large companies for the
rapid development of data centers. One of the solutions to reduce energy consumption in data centers is to
reduce the processing burden of hosts. In order to minimize the processing burden of hosts, optimal
management should be done on virtual machine hosts in physical hosts so that the lowest possible number
of these hosts is used. Our goal in this research is to automate virtual machines in such a way. We make
physical hosts use fewer hosts. This issue is of the NP problem type, and we solved this problem using the
Cuckoo algorithm. Cuckoo algorithm is one of the most commonly used randomization optimization tools
that are used in many problems and simply problems with optimization functions. It solves various
problems and can simply transform the optimization functions. Here, our goal is to manage optimal
resources in cloud computing. Using the Cuckoo Optimization algorithm, we can perform a lot of virtual
machines and physical hosts better than other exploratory and spurious algorithms. Using optimal resource
management algorithms plays an important role in reducing the energy consumption of data centers. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Proofread-Managing-Efficient-Resources-in-Cloud-Computing-ba.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Maharana, Debendra, Bibhudatta Sahoo, and Srinivas Sethi. “Energy-efficient real-time tasks scheduling in cloud data centers.” Volume 4, Issue 12, 2017, pages 768-773.

[2] Jung ,Gihun, MongSim, Kwang , “Location-Aware Dynamic Resource Allocation Model for Cloud Computing Environment” ,
proceedings of International Conference on Information and Computer Applications ,Dubai ,2013, pages 256-269.

[3] G, ¬¬lovasz, F, Nieedermeier, Hermann DE Meer, “Performance tradeoffs of energy-aware virtual machine Consolidation”, 2012, DOI 10.1007/s10586, pages 481-496.

[4] T, Ferreto, ¬¬¬M, Netto, R,¬Calheiros, ” Server consolidation with migration control for virtualized data centers”, Elsevier,
Future Generation Computer systems, October 2013, Volume 27 Issue 8, pages 1027-1034.

[5] Beloglazov, Anton, Abawajy, Jemal, Buyy, Rajkumar,” Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing”, Elsevier Future Generation Computer Systems, Volume 28, 2012, pages 755-768.
[6] Chung Tam, Sik , Kuan, Tam , Hou, Mou Tam, Lap, Zhang, Tong, A New Optimization Method, the Algorithm of
Changes, for Bin Packing Problem, IEEE, 2010, 978-1-4244-6439-5/10, 2010.
[7] Andrea, Berl, Gelenbe, Erol, Girolamo, Giovanni, Giuliani, Hermann DE Meer, “Energy-Efficient Cloud Computing”,
published by Oxford University Press on behalf of The British Computer Society, 2009, Volume 53, DOi:
10.1093/comjnl/bxp080, pages 1046-1051.
[8] P. Venkata Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments,” Applied soft
computing, Volume 13, pages 2292-2303, 2013.
[9] B. Mondal, K. Dasgupta, and P. Dutta, “Load balancing in cloud computing using stochastic hill climbing-a soft computing
approach,” Procedia Technology, Volume 4, pages 783-789, 2012.
[10] Zhan, Shaobin, and Hongying Huo. “Improved PSO-based task scheduling algorithm in cloud computing.” Journal of
Information & Computational Science Volume 9, Issue 13, 2012, pages 3821-3829.
[11] Jena, R. K. “Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework.” Procedia Computer
Science, Volume 57, 2015, pages 1219-1227.

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Provide an Optimal Protocol to Improve Reliability and Assurance of Servility in Ad Hoc Networks with uzzy-Neural Logic Approach 

Authors : Saman Pazhoohesh , Mohammad Reza Vazifeh , Mohammad Reza Shahbazi

Keywords : Ad Hoc Networks, Reliability, Protection, Routing, fuzzy. 
Published Online : 06 February 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Mobile case networks are among the subsets of the technology of wireless networks, which nowadays are
considered in many researches and are used in various fields. The characteristics of mobile case networks
have made it especially important for the concept of security, especially in sensitive applications. On the
one hand, the unique architecture of this group of networks has led to the use of traditional methods
provided for wired networks and other wireless networks in this type of network are not efficient. And thus,
ensuring and maintaining security has a fundamental difference with other computer networks. The
importance of this factor, especially in some of key applications dedicated to these networks, has attracted
many researchers with the goal of providing mechanisms that support reliability and security. The focus of
past research in this regard is often on the issue of support for security. But so far, an efficient research has
not been provided with the capability to provide and support security assurance. Accordingly, and given the
remaining challenges in this area, there is also a need for more research in this field. In current research,
by considering the characteristics of mobile case networks, the limitations of prior research and reliability
and security requirements, an efficient and targeted protocol with support for both critical security and
security aspects, SRMRP Is presented. The protocol introduced by its method of operation through reliable
and trusted multimode routing, utilizes the capabilities of fuzzy sets based on the reliability and security
indexes, has been designed to provide the most security and confidence in the exchange, And minimizes
the problems caused by the absence of these two basic factors. The SRMRP protocol is highly compatible with mobile case networks and applications of these networks, and has expanded to fit the security and
security features. The proposed protocol provides the expectations of a reliable and secure mechanism
according to its capabilities. To evaluate the efficiency and capabilities of the proposed SRMRP protocol,
this protocol was evaluated using OPNET simulation tool in different scenarios. The results of the
simulations show the overall performance improvement of mobile cell network performance with the
proposed protocol as compared to previous studies, especially the TQR-based research. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Provide-an-Optimal-Protocol-to-Improve-Reliability-and-Assurance-of-Servility.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Maharana, Debendra, Bibhudatta Sahoo, and Srinivas Sethi. “Energy-efficient real-time tasks scheduling in cloud data centers.” Volume 4, Issue 12, 2017, pages 768-773.

 
[2] Jung ,Gihun, MongSim, Kwang , “Location-Aware Dynamic Resource Allocation Model for Cloud Computing Environment” ,
proceedings of International Conference on Information and Computer Applications ,Dubai ,2013, pages 256-269.

 
[3] G, ¬¬lovasz, F, Nieedermeier, Hermann DE Meer, “Performance tradeoffs of energy-aware virtual machine Consolidation”,
2012, DOI 10.1007/s10586, pages 481-496.

 
[4] T, Ferreto, ¬¬¬M, Netto, R,¬Calheiros, ” Server consolidation with migration control for virtualized data centers”, Elsevier,
Future Generation Computer systems, October 2013, Volume 27 Issue 8, pages 1027-1034.

 
[5] Beloglazov, Anton, Abawajy, Jemal, Buyy, Rajkumar,” Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing”, Elsevier Future Generation Computer Systems, Volume 28, 2012, pages 755-768.

 
[6] Chung Tam, Sik , Kuan, Tam , Hou, Mou Tam, Lap, Zhang, Tong, A New Optimization Method, the Algorithm of
Changes, for Bin Packing Problem, IEEE, 2010, 978-1-4244-6439-5/10, 2010.

 
[7] Andrea, Berl, Gelenbe, Erol, Girolamo, Giovanni, Giuliani, Hermann DE Meer, “Energy-Efficient Cloud Computing”,
published by Oxford University Press on behalf of The British Computer Society, 2009, Volume 53, DOi:
10.1093/comjnl/bxp080, pages 1046-1051.

 
[8] P. Venkata Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments,” Applied soft
computing, Volume 13, pages 2292-2303, 2013.

 
[9] B. Mondal, K. Dasgupta, and P. Dutta, “Load balancing in cloud computing using stochastic hill climbing-a soft computing
approach,” Procedia Technology, Volume 4, pages 783-789, 2012.

 
[10] Zhan, Shaobin, and Hongying Huo. “Improved PSO-based task scheduling algorithm in cloud computing.” Journal of
Information & Computational Science Volume 9, Issue 13, 2012, pages 3821-3829.

 
[11] Jena, R. K. “Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework.” Procedia Computer
Science, Volume 57, 2015, pages 1219-1227.

 

 

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Enhancing the Prediction Accuracy of Chronic Kidney Insufficiency using Fuzzy C-Means Algorithm and Neural Networks

Authors : Mohammad Karimi , Javad Hosseinkhani

Keywords : Accuracy of Forecasting, Chronic Renal Failure, Fuzzy C-Means Algorithm, Neural Network.
Published Online : 03 February 2018

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Data mining methods can replace time / cost tests in the medical field. In this regard, current research has presented a new method for predicting chronic renal disease, in which relevant data are clustered in a fuzzy fashion and then categorized using a neural network. The current research tries to improve the accuracy of the neural network using an uncontrolled method and thus combine an observation method with an uncontrolled method. The differentiation of the proposed method with previous methods is the use of an uncontrolled (clustering) algorithm in the structure of a method by the control. In addition, the proposed method, in view of its ability to improve the final accuracy of the improvement model, will eliminate the limitations of the inadequacy of predictive accuracy during the execution phase, and, on the other hand, due to the use of a batch method BD can be trained more quickly. Numerical tests implemented on the proposed model represent the superiority of the new model’s performance to the conventional methods in the subject literature. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Enhancing-the-Prediction-Accuracy-of-Chronic-Kidney-Insufficiency.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Celik, E., Atalay, M., & Kondiloglu, A. (2016). The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods, Expert Sytems with Applications, 12(170), 2027-2031.

[2] Rumelhart, D. (1986). Learning Representations by Back Propagation Error. Nature, 323, 533-536.

[3] Lakshmi, G. D., Kumar, P. R., Bharavi, K., Annapurna, P., Rajendar, B., Patel, P. T & Rao, G. S. (2012). Protective effect of Tribulus terrestris linn on liver and kidney in cadmium intoxicated rats, Spectrum, 73, 107- 128.

[4] Abhishek, G. S. M. T., & Gupta, D. (2012). Proposing Efficient Neural Network Training Model for Kidney Stone Diagnosis. International Journal of Computer Science and Information Technologies, 3(3), 3900-3904.

[5] Krishna Apparao, R. (2012). Statistical and Data Mining Aspects on Kidney Stones: A Systematic Review and Meta-analysis. Open Access Scientific Reports, 1-7.

[6] Van Eyck, J., Ramon, J., Guiza, F., Meyfroidt, G., Bruynooghe, M., & Van den Berghe, G. (2012). Data mining techniques for predicting acute kidney injury after elective cardiac surgery. Critical Care, 16(1), P344.

[7] Khavanin Zadeh, M., Rezapour, M., & Sepehri, M. M. (2013). Data mining performance in identifying the Risk Factors of early arteriovenous fistula failure in Hemodialysis Patients. International journal of hospital research, 2(1), 49-54.

[8] Lakshmi, K. R., Nagesh, Y., & Krishna, M. V. (2014). Performance comparison of three data mining techniques for predicting kidney dialysis survivability. International Journal of Advances in Engineering & Technology, 7(1), 242.

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