Providing an Effective Strategy in order to Proportional Distribution Nodes Meanwhile Energy Saving Network and Appropriate Coverage in Sensor Network

Authors : Marjan Golzari , Javad Hosseinkhani

Keywords : Effective Strategy, Proportional Distribution Nodes, Energy Saving Network, Appropriate Coverage, Sensor
Published Online :– 23 October 2017

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Wireless sensor networks are characterized by wireless loss links and resource constrained nodes. Energy efficiency is a major concern in such networks. Among the resource constraints, energy is probably the most crucial one since sensor nodes are typically battery powered and the lifetime of the battery imposes a limitation on the operation hours of the sensor network. The main problem of ad hoc networks is Resource constraints. Due to moving of nods, network topology continually changes and routing protocols must be aware of these changes. The central argument, finding dynamic routing protocols which in such an environment, able to find a suitable way to communicate and exchange information between two nodes. In this thesis, an algorithm is proposed that built on multi-streaming strategy, there are multiple paths between both origin and destination and for sending packages all paths are used at the same time. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Providing-an-Effective-Strategy-in-order-to-Proportional-Distribution-Nodes-Meanwhile-Energy-Saving-Network-and-Appropriate-Coverage-in-Sensor-Network.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem] [1] Hosseinkhani, Javad, et al. 2014, “Detecting Suspicion Information on the Web Using Crime Data Mining Techniques.” International Journal of Advanced Computer Science and Information Technology 3(1) : 32-41.

 

[2] Hosseinkhani, J., S. Chuprat, and H. Taherdoost. 2012, “Criminal Network Mining by Web Structure and Content Mining.” 11th WSEAS International Conference on Information Security and Privacy (ISP’12), Prague, Czech Republic September.

 

[3] Hosseinkhani, Javad, Suriayati Chuprat, and Hamed Taherdoost. 2012, “Discovering criminal networks by Web structure mining.” Computing and Convergence Technology (ICCCT), 7th International Conference on. IEEE.

 

[4] Ghoreyshi, Saeedodin, and Javad Hosseinkhani. 2015, “Developing a Clustering Model based on K-means Algorithm in order to Creating Different Policies for Policyholders in Insurance Industry.” International Journal of Advanced Computer Science and Information Technology (IJACSIT) 4(2): 46-53.

 

[5] Xueping Zhang, Jiayao Wang, Fang Wu, Zhongshan Fan and Xiaoqing Li, 2006, “A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids”, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications,Vol.1: 605 – 610.

 

[6] Thiemo Krink and Sandra Paterlini, 2006, “Differential Evolution and Particle Swarm optimization in Partitional Clustering”, Journal of Computational Statistics and Data Analysis, Vol. 50(5): 1220-1247.

 

[7] Ian Davidson, Kiri L. Wagstaff, and Sugato Basu, 2006, “Measuring Constraint-Set Utility for Partitional Clustering Algorithms”, Proceedings of the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases, Vol. 4213: 115-126.

 

[8] Taher Niknam and Babak Amiri, 2010, “An efficient hybrid approach based on PSO, ACO and K-Means for cluster analysis”, Applied Soft Computing, Vol.10(1): 183-197.

 

[9] M. Arshad, 2012, “Implementation of Kea-Key phrase Extraction Algorithm by Using Bisecting K-Means Clustering Technique for Large and Dynamic Data Set”, International Journal of Advanced Technology & Engineering Research, Vol.2(2), 134-137.

 

[10] Nor Ashidi Mat Isa, Amy A. Salamah and Umi Kalthum Ngah, 2009, “Adaptive Fuzzy Moving K-means Clustering Algorithm for Image Segmentation”, IEEE Transactions On Consumer Electronics, Vol.55(4): 2145-2153.

 

[11] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z.H. Zhou, M. Steinbach, D.J. Hand, D. Steinberg, 2008, “Top 10 algorithms in data mining”, Knowledge and Information Systems, January, Volume 14(1): 1-37.

 

[12] O. A. Abbas, 2008, “comparisons between data clustering algorithms”, international Arab journal of information technology, Vol. 5(3): 320-325.

 

[13] Y. S. Patil, M.B. Vaidya, 2012, “A Technical Survey on cluster analysis in data mining”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Vol 2(9): 03-513.

 

[14] C. McGregor, C. Christina and J. Andrew, 2012, “A process mining driven framework for clinical guideline improvement in critical care”, Learning from Medical Data Streams 13th Conference on Artificial Intelligence in Medicine (LEMEDS). Vol. 765.

 

[15] R. Bellazzi and B. Zupan, 2008, “Predictive data mining in clinical medicine: current issues and guidelines”, Int. J. Med. Inform., Vol. 77 (2008): 81-97.

 

[16] M. Kumari and S. Godara, 2011, “Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction”, International Journal of Computer Science and Technology (IJCST) ISSN: 2229- 4333, Vol. 2(2): 304- 308.

 

[17] S. Gupta, D. Kumar and A. Sharma, 2011, “Data Mining Classification Techniques Applied For Breast Cancer Diagnosis and Prognosis”, Indian Journal of Computer Science and Engineering, Vol. 2(2):188-195.

 

[18] T. S. Chen, T. H. Tsai, Y. T. Chen, C. C. Lin, R. C. Chen, S. Y. Li and H. Y. Chen, 2005, “A Combined K-Means and Hierarchical Clustering Method for improving the Clustering Efficiency of Microarray”, Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems.

 

[19] Chipman and R. Tibshirani, 2006, “Hybrid hierarchical clustering with applications to microarray data”, Biostatistics, Vol. 7(2): 286-301.

 

[20] J. J. Tapia, E. Morett and E. E. Vallejo, 2009, “A Clustering Genetic Algorithm for Genomic Data Mining”, Foundations of Computational Intelligence, vol. 4 Studies in Computational Intelligence, Vol. 204: 249-275.

 

[21] T. H. A. Soliman, A. A. Sewissy and H. A. Latif, 2010, “A Gene Selection Approach for Classifying Diseases Based on Microarray Datasets”, 2nd International Conference on Computer Technology and Development (lCCTD 2010).

 

[22] S. Belciug, 2009, “Patients length of stay grouping using the hierarchical clustering algorithm”, Annals of University of Craiova, Math. Comp. Sci. Ser., ISSN: 1223-6934, Vol. 36(2): 79-84.

 

[23] Schulam. P., Wigley. F., Saria. S. 2015, “Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery”, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2956-2964.[/accordionitem][/cq_vc_accordion]

A Data Mining Approach for Analysis of Customer Behavior in order to Improve Policies in Insurance Industry based on Combination of Particle Swarm Optimization and k-Means Algorithm

Authors : Hamid Moradi, Javad Hosseinkhani

Keywords : Data Mining Approach, Analysis of Customer Behavior, Policies in Insurance Industry, Particle Swarm
Optimization

Published Online :– 24 October 2017

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]One of the important branches of insurance products is individual insurance policies, which is directly cover insurer’s life. Among individual insurance policies, health insurance policies because of diversity of coverages and contracts and high loss ratio are very important in insurance company. Due to high competition in the sale of policies between insurance companies and climb increasing of demand for them, knowing the customers of these products is considered important in the maintenance and survival of insurance organizations. This study is considering the practical application of data mining in an insurance company on health insurance policy customers to investigate whether in this way can help insurance companies to identify different customer groups and their characteristics in order to make suitable patterns for offering suitable services to customers. In this way the maximum value of the relationship with the customer is achieved. In this research, the customers of health insurance policies have been clustered by means of some features. The Clustering was done using proposed algorithm based on PSO and k-Means algorithms. Evaluation has shown that the proposed method has high accuracy in data clustering. The proposed model has clustered data in four clusters which each cluster differ from others in terms of usefulness to the organization. The result has shown that the third cluster is the most profitable and fourth cluster is the most harmful. According to the proposals made to each cluster, organization can maximize benefits from the relationship with its policyholders. The Clustering was done using proposed algorithm based on k-Means algorithm. The proposed model has clustered data in four clusters which each cluster differ from others in terms of usefulness to the organization. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/A-Data-Mining-Approach-for-Analysis-of-Customer-Behavior-in-order-to-Improve-Policies-in-Insurance-.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Hosseinkhani, Javad, et al. 2014, “Detecting Suspicion Information on the Web Using Crime Data Mining Techniques.” International Journal of Advanced Computer Science and Information Technology 3(1) : 32-41. [2] Hosseinkhani, J., S. Chuprat, and H. Taherdoost. 2012, “Criminal Network Mining by Web Structure and Content Mining.” 11th WSEAS International Conference on Information Security and Privacy (ISP’12), Prague, Czech Republic September. [3] Hosseinkhani, Javad, Suriayati Chuprat, and Hamed Taherdoost. 2012, “Discovering criminal networks by Web structure mining.” Computing and Convergence Technology (ICCCT), 7th International Conference on. IEEE. [4] Ghoreyshi, Saeedodin, and Javad Hosseinkhani. 2015, “Developing a Clustering Model based on K-means Algorithm in order to Creating Different Policies for Policyholders in Insurance Industry.” International Journal of Advanced Computer Science and Information Technology (IJACSIT) 4(2): 46-53. [5] Xueping Zhang, Jiayao Wang, Fang Wu, Zhongshan Fan and Xiaoqing Li, 2006, “A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids”, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications,Vol.1: 605 – 610. [6] Thiemo Krink and Sandra Paterlini, 2006, “Differential Evolution and Particle Swarm optimization in Partitional Clustering”, Journal of Computational Statistics and Data Analysis, Vol. 50(5): 1220-1247. [7] Ian Davidson, Kiri L. Wagstaff, and Sugato Basu, 2006, “Measuring Constraint-Set Utility for Partitional Clustering Algorithms”, Proceedings of the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases, Vol. 4213: 115-126.
[8] Taher Niknam and Babak Amiri, 2010, “An efficient hybrid approach based on PSO, ACO and K-Means for cluster analysis”, Applied Soft Computing, Vol.10(1): 183-197. [9] M. Arshad, 2012, “Implementation of Kea-Key phrase Extraction Algorithm by Using Bisecting K-Means Clustering Technique for Large and Dynamic Data Set”, International Journal of Advanced Technology & Engineering Research, Vol.2(2), 134-137. [10] Nor Ashidi Mat Isa, Amy A. Salamah and Umi Kalthum Ngah, 2009, “Adaptive Fuzzy Moving K-means Clustering Algorithm for Image Segmentation”, IEEE Transactions On Consumer Electronics, Vol.55(4): 2145-2153. [11] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z.H. Zhou, M. Steinbach, D.J. Hand, D. Steinberg, 2008, “Top 10 algorithms in data mining”, Knowledge and Information Systems, January, Volume 14(1): 1-37. [12] O. A. Abbas, 2008, “comparisons between data clustering algorithms”, international Arab journal of information technology, Vol. 5(3): 320-325. [13] Y. S. Patil, M.B. Vaidya, 2012, “A Technical Survey on cluster analysis in data mining”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Vol 2(9): 03-513. [14] C. McGregor, C. Christina and J. Andrew, 2012, “A process mining driven framework for clinical guideline improvement in critical care”, Learning from Medical Data Streams 13th Conference on Artificial Intelligence in Medicine (LEMEDS). Vol. 765. [15] R. Bellazzi and B. Zupan, 2008, “Predictive data mining in clinical medicine: current issues and guidelines”, Int. J. Med. Inform., Vol. 77 (2008): 81-97. [16] M. Kumari and S. Godara, 2011, “Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction”, International Journal of Computer Science and Technology (IJCST) ISSN: 2229- 4333, Vol. 2(2): 304- 308. [17] S. Gupta, D. Kumar and A. Sharma, 2011, “Data Mining Classification Techniques Applied For Breast Cancer Diagnosis and Prognosis”, Indian Journal of Computer Science and Engineering, Vol. 2(2):188-195. [18] T. S. Chen, T. H. Tsai, Y. T. Chen, C. C. Lin, R. C. Chen, S. Y. Li and H. Y. Chen, 2005, “A Combined K-Means and Hierarchical Clustering Method for improving the Clustering Efficiency of Microarray”, Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems. [19] Chipman and R. Tibshirani, 2006, “Hybrid hierarchical clustering with applications to microarray data”, Biostatistics, Vol. 7(2): 286-301. [20] J. J. Tapia, E. Morett and E. E. Vallejo, 2009, “A Clustering Genetic Algorithm for Genomic Data Mining”, Foundations of Computational Intelligence, vol. 4 Studies in Computational Intelligence, Vol. 204: 249-275. [21] T. H. A. Soliman, A. A. Sewissy and H. A. Latif, 2010, “A Gene Selection Approach for Classifying Diseases Based on Microarray Datasets”, 2nd International Conference on Computer Technology and Development (lCCTD 2010). [22] S. Belciug, 2009, “Patients length of stay grouping using the hierarchical clustering algorithm”, Annals of University of Craiova, Math. Comp. Sci. Ser., ISSN: 1223-6934, Vol. 36(2): 79-84. [23] Schulam. P., Wigley. F., Saria. S. 2015, “Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery”, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2956-2964.[/accordionitem][/cq_vc_accordion]

Providing an Effective Strategy in order to Proportional Distribution Nodes Meanwhile Energy Saving Network and Appropriate Coverage in Sensor Network

Authors : Marjan Golzari, Javad Hosseinkhani

Keywords : Effective Strategy, Proportional Distribution Nodes, Energy Saving Network, Appropriate Coverage, Sensor
Network

Published Online :– 20 July 2017

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Wireless sensor networks are characterized by wireless loss links and resource constrained nodes. Energy efficiency is a major concern in such networks. Among the resource constraints, energy is probably the most crucial one since sensor nodes are typically battery powered and the lifetime of the battery imposes a limitation on the operation hours of the sensor network. The main problem of ad hoc networks is Resource constraints. Due to moving of nods, network topology continually changes and routing protocols must be aware of these changes. The central argument, finding dynamic routing protocols which in such an environment, able to find a suitable way to communicate and exchange information between two nodes. In this thesis, an algorithm is proposed that built on multi-streaming strategy, there are multiple paths between both origin and destination and for sending packages all paths are used at the same time. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Providing-an-Effective-Strategy-in-order-to-Proportional-Distribution-.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Kuhn. F, Wattenhofer .R, Zollinger. A, 2012, “Asymptotically optimal geometric mobile ad-hoc routing” ACM. Discrete algorithms and methods for mobile computing and communications, PP.24-33, doi>10.1145/570810.570814. [2] Alotaibi. E, Mukherjee. B, 2012, “A survey on routing algorithms for wireless Ad-Hoc and mesh networks”, Elsevier. Contents lists available at SciVerse ScienceDirect, pp.940-968, doi:10.1016/j.comnet.2011.10.011.
[3] Mitchel.C, 2003,”Security for Mobility “, IEEE Press Piscataway, NJ, USA ©2003, ISBN: 0863413374. [4] X. Hong, K. Xu, M. Gerla, 2002,”Scalable Routing Protocol for Mobile Ad Hoc Networks”, IEEE Network Magazine, July-Aug, pp.11-21. [5] C.E. Perkins, T.J. Watson, , 1994,”Highly dynamic destination sequenced distance vector routing (DSDV) for mobile computers”, in: ACM IGCOMM94 Conference on Communications Architectures, London, UK. [6] R. Di Pietro; S. Guarino; N.V. Verde; J. Domingo-Ferrer, 2014, Security in wireless ad-hoc networks – A survey ,ELSEVIER, Computer Communications, Volume 51: 1-20. [7] S. Murthy J.J. Garcia-Luna-Aceves, 1995,”A routing protocol for packet radio networks, in: Proceedings of the First Annual ACM International Conference on Mobile Computing and Networking”, Berkeley, CA, , pp. 86–95. [8] D. Johnson, D. Maltz, J. Jetcheva, 2002,” The dynamic source routing protocol for mobile ad hoc networks”, Internet Draft, draft-ietf-manet-dsr-07.txt, work in progress. [9] R. Di Pietro, S. Guarino, N.V .Verde, J. Domingo-Ferrer, 2014,” Security in wireless ad-hoc networks – A survey”, Elsevier, Computer Communications, Vol. 51: 1–20. [10] S. Das, C. Perkins, E. Royer, 2012, “Ad hoc on demand distance vector (AODV) routing, Internet Draft”, draft-ietfmanetaodv-11.txt, work in progress, 2012. [11] Mahesh K. Marina, Samir R.Das, 2011, “On-demand Multipath Distance Vector Routing in Ad hoc network”, Proceedings of the International Conference for Network Protocols (ICNP), pp. 14–23. [12] Z.J. Hass, R. Pearlman, 1999,” Zone routing protocol for ad-hoc networks”, Internet Draft, draft-ietf-manet-zrp-02.txt, working progress. [13] T. Ozaki, J. Bae_kim, T .Suda, 2001,”Bandwidth-Efficient Multicast Routing for Multihop, Ad Hoc wireless Networks”, in proceeding of IEEE INFOCOM, pp.1181-1191. [14] S. Toumpis, D. Tompakaris, 2016, “Wireless ad hoc networks and related topologies: application and research challenges”, Electrotechnik & informationstechnik, pp.232-241.

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An ECR-driven Distributed Retail RFID System

Authors : Amine Karmouche, Yassine Salih-Alj, Jawad Abrache

Keywords : ECR, middleware, RFID, supply chain management.
Published Online :– 07 July 2017

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Efficient Consumer Response, commonly referred to as ECR, is being increasingly put at the center of the retail industry strategies. Retailers are now collaborating with all the supply chain stakeholders in order to reach an optimal service level. In this regard, new emerging wireless technologies are currently offering new opportunities to support ECR-driven logistics information systems. RFID is among these technologies, which extends the potential of wireless identification, and represents a potential replacement to old-fashioned identification systems such as the barcode system in retail sales. This paper presents a new RFID-based cost efficient approach for pervasive retail sales. The suggested new system architecture is based on aisle-level scanning and new event management procedures at the level of RFID middleware. It also discusses the impact of deploying such systems in retail stores, such as supermarkets, on the overall supply chain. The motivation behind such approach is not only reducing the number of RFID readers compared to existing RFID based systems, but providing customers with an interactive shopping experience and fast checkout and bill payment, making this new approach transparent, efficient and cost effective. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/IJACSIT-invited-paper_An-ECR-driven-Distributed-Retail-RFID-System.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Want R.(2006). An Introduction to RFID Technology. IEEE Pervasive Computing, Vol. 5, pp. 25-33.
[2] Al-Ali A. R., Aloul F. A., Aji N. R., Al-Zarouni A. A.,& Fakhro N. H. (2008). Mobile RFID Tracking System. 3rd International Conference on Information and Communication Technologies (ICTTA): From Theory to Applications, pp. 1-4.
[3] Roussos G. (2006). Enabling RFID in Retail. Computer. London, UK: Birkbeck College, London University.
[4] Karmouche A. & Salih-Alj Y. (2012). Aisle-level scanning for pervasive RFID-based Shopping Applications. International Conference on Computer Systems and Industrial Informatics ICCSII’12, pp. 1-4.
[5] Chunli L. & Donghui L. (2012). Application and Development of RFID Technique. Second International Conference on Consumer electronics, Communications and Networks, pp. 900-903.
[6] Karmouche A. & Salih-Alj Y. (2012). Distributed RFID Shopping System. Journal of E-Technology, Vol. 3, pp. 119-125.
[7] Nikitin P. V., Lam S., &Rao K. V. S. (2005). Low Cost Silver Ink RFID Tag Antennas. Antennas and Propagation Society International Symposium, Vol. 2B, pp. 353-356.
[8] Redinger D., Yin S., Farschi R., & Subramanian V. (2004). An Ink-Jet-Deposited Passive Component Process for RFID. IEEE Transactions on Electron Devices, Vol. 51, No. 12, pp. 1978-1983.
[9] Kawahara Y., Georgiadis A. & Collado A. (2013) Low-Cost inkjet-printed fully passive RFID tags using metamaterial-inspired antennas for capacitive sensing applications. Microwave Symposium Digest (IMS) IEEE MTT, pp. 1-4.
[10] Swedberg C. (2010). Chip-size EPC Gen 2 Tag Promises to Enable New Applications. RFID Journal.
[11] Roussos G. (2008). Networked RFID: Systems, Software and Services. Computer Communications and Networks, Springer.
[12] Mohaisen M. (2008). Radio Transmission Performance of EPCglobal Gen-2 RFID System. 10th International Conference on Advanced Communication Technology ICACT 2008, Vol. 2, pp. 1423-1428.
[13] Finkenzeller K. (2003). RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification, 2nd ed., New York: Wiley.
[14] Fernie J. & Sparks L. (2004). Logistics and Retail Management. Insights into Current Practice and Trends from Leading Experts. US: Kogan-Page.
[15] Sabbaghi A. &Vaidyanathan G. (2008). Effectiveness and Efficiency of RFId Technology in Supply Chain Management: Strategic Values and Challenges. Journal of Theoretical and Applied Electronic Commerce Research. Vol. 3(2), pp. 71-81.
[16] Karmouche A., Salih-Alj Y. & Abrache J. (2014). Distributed aisle-level Scanning approach for RFID Shopping Systems.2nd IEEE International Conference on Logistics Operations Management GOL’14, pp. 1-7.

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Issue 01

Proposing a Data Mining Approach for Cost Reduction of Heart Disease based on Decision Tree

Authors : Alireza Motaharzadeh , Javad Hosseinkhani

Keywords : Heart Disease, Data mining, Diagnosis and treatment, Cost Reduction
Published Online : 19 April 2017

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem]Nowadays heart disease is very common and is a major cause of mortality .proper and early diagnosis of
this disease is very important. Diagnostic methods and treatments for this disease have many side effects
and are so expensive. Therefore, researchers are looking for cheaper ways with high precision to diagnose
this disease. Studies used characteristic collected from patients and various algorithms of data mining to
increase the accuracy. In this thesis, a data set including important and effective characteristics for
diagnosis of heart disease is collected. The data set in this thesis is collected from 118 cardiac patients that
were referred to Heart Specialized Hospital in Jamaran. Data mining with extracting knowledge from the
abundance of medical information can effectively help doctors in prediction and diagnosis proper treatment
of diseases. To predict , diagnose and treatment of cardiac patients ,four models of decision tree data
mining models and also artificial network was carried out on data set, which decision tree algorithms had
the highest accuracy with 74/74 percent , that in my study is the highest achieved accuracy. Attention to
high risks of performing invasive diagnostic methods in cardiac patients, including coronary angiography.
On the other hand, successful experiences in data mining methods in medicine have been obtained;
therefore this study has been presented the model based on data mining techniques that have capability to
predict heart disease and diagnosis proper treatment for cardiovascular diseases. In this study, data mining
with predicting that a person have heart disease or not, or diagnosis proper treatment method, can help
doctors to reduce the cost of treatments of cardiac patients and quality of presenting better services. [/accordionitem][/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/Proposing-a-Data-Mining-Approach-for-Cost-Reduction-of-Heart-Disease-based-on-Decision-Tree.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Hosseinkhani, Javad, Suhaimi Ibrahim, Suriayati Chuprat, and Javid Hosseinkhani Naniz. “Web
Crime Mining by Means of Data Mining Techniques.” Research Journal of Applied Sciences,
Engineering and Technology 7, no. 10 (2014): 2027-2032.

[2] Oztekin, Asil, Dursun Delen, and Zhenyu James Kong. “Predicting the graft survival for heart–lung
transplantation patients: An integrated data mining methodology.” international journal of medical
informatics 78, no. 12 (2009): e84-e96.

[3] Dangare, Chaitrali S., and Sulabha S. Apte. “A data mining approach for prediction of heart disease
using neural networks.” International Journal of Computer Engineering & Technology (IJCET) 3, no.
3 (2012): 30-40p.

[4] Roa, Diego, Javier Bautista, Nicolás Rodríguez, María Del Pilar Villamil, Andres Jiménez, and Oscar
Bernal. “Data mining: A new opportunity to support the solution of public health issues in Colombia.”
In Computing Congress (CCC), 2011 6th Colombian, pp. 1-6. IEEE, 2011.

[5] Pandey, Atul Kumar, Prabhat Pandey, and K. L. Jaiswal. “A heart disease prediction model using
Decision Tree.” IUP Journal of Computer Sciences 7, no. 3 (2013): 43.

[6] Srinivas, K., B. Kavihta Rani, and A. Govrdhan. “Applications of data mining techniques in healthcare
and prediction of heart attacks.” International Journal on Computer Science and Engineering (IJCSE)
2, no. 02 (2010): 250-255.

[7] Leung, KwongSak, KinHong Lee, JinFeng Wang, Eddie YT Ng, Henry LY Chan, Stephen KW Tsui,
Tony SK Mok, Pete Chi-Hang Tse, and Joseph JY Sung. “Data mining on dna sequences of hepatitis b
virus.” IEEE/ACM transactions on computational biology and bioinformatics 8, no. 2 (2011): 428-440.

[8] Dangare, Chaitrali S., and Sulabha S. Apte. “Improved study of heart disease prediction system using
data mining classification techniques.” International Journal of Computer Applications 47, no. 10
(2012): 44-48.

[9] Colombet, Isabelle, Alan Ruelland, Gilles Chatellier, François Gueyffier, Patrice Degoulet, and Marie-
Christine Jaulent. “Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.” In Proceedings of the AMIA Symposium, p. 156. American Medical
Informatics Association, 2000.

[10] Ordonez, Carlos. “Comparing association rules and decision trees for disease prediction.” In
Proceedings of the international workshop on Healthcare information and knowledge management,
pp. 17-24. ACM, 2006.

[11] Karaolis, Moutiris, J. A. Moutiris, L. Papaconstantinou, and C. S. Pattichis. “Association rule analysis
for the assessment of the risk of coronary heart events.” In 2009 Annual International Conference of
the IEEE Engineering in Medicine and Biology Society, pp. 6238-6241. IEEE, 2009.

[12] Soni, Jyoti, Ujma Ansari, Dipesh Sharma, and Sunita Soni. “Predictive data mining for medical
diagnosis: An overview of heart disease prediction.” International Journal of Computer Applications
17, no. 8 (2011): 43-48.

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