International Journal of Engineering and Advanced Technology(TM) Exploring Innovation| ISSN:2249-8958(Online)| Reg. No.:61902/BPL/CE/2011| Published By BEIESP| Impact Factor: 5.02| UGC Approved Journal
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 Volume-6 Issue-6 Published on August 30, 2017 01 Volume-6 Issue-5 Published on June 30, 2017 02 Volume-6 Issue-4 Published on April 30, 2017 03 Volume-6 Issue-3 Published on February 28, 2017 04 Volume-6 Issue-2 Published on December 30, 2016 05 Volume-6 Issue-1 Published on October 30, 2016 06 Volume-5 Issue-6 Published on August 30, 2016 07 Volume-5 Issue-5 Published on June 30, 2016 08 Volume-5 Issue-4 Published on April 30, 2016 09 Volume-5 Issue-3 Published on February 28, 2016 10 Volume-5 Issue-2 Published on December 30, 2015 11 Volume-5 Issue-1 Published on October 30, 2015 12 Volume-4 Issue-6 Published on August 30, 2015 13 Volume-4 Issue-5 Published on June 30, 2015 14 Volume-4 Issue-4 Published on April 30, 2015 15 Volume-4 Issue-3 Published on February 28, 2015 16 Volume-4 Issue-2 Published on December 30, 2014 17 Volume-4 Issue-1 Published on October 30, 2014 18 Volume-3 Issue-6 Published on August 30, 2014 19 Volume-3 Issue-5 Published on June 30, 2014 20 Volume-3 Issue-4 Published on April 30, 2014 21 Volume-3 Issue-3 Published on February 28, 2014 22 Volume-3 Issue-2 Published on December 30, 2013 23 Volume-3 Issue-1 Published on October 30, 2013 24 Volume-2 Issue-6 Published on August 30, 2013 25 Volume-2 Issue-5 Published on June 30, 2013 26 Volume-2 Issue-4 Published on April 30, 2013 27 Volume-2 Issue-3 Published on February 28, 2013 28 Volume-2 Issue-2 Published on December 30, 2012 29 Volume-2 Issue-1 Published on October 30, 2012 30 Volume-1 Issue-6 Published on August 30, 2012 31 Volume-1 Issue-5 Published on June 30, 2012 32 Volume-1 Issue-4 Published on April 30, 2012 33 Volume-1 Issue-3 Published on February 29, 2012 34 Volume-1 Issue-2 Published on December 30, 2011 35 Volume-1 Issue-1 Published on October 30, 2011 36
Volume-5 Issue-6 Published on August 30, 2016

S. No

Volume-5 Issue-6, August 2016, ISSN:  2249-8958 (Online)Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Greeshma T S, Subu Surendren

Paper Title:

Community Detection on Social Network – A Survey

Abstract: Social network is an important application in the internet which represent the geographically dispersed users. Social network provides a variety of methods for explaining patterns and entities. Social networks are mostly represented as graphs,   which contain nodes and edges. Nodes are used to represent actors such as people and organizations whereas edges show the relationship between these nodes. Several data sources involved in the social network forms communities which work in self-descriptive manner.  A collection of nodes which are connected by edges with high similarity is called a community. The community detection in social network, intend to partition the the graph with dense region which correspond to closely related entities. The selection of data sources and determination of community detection approaches can enhance the accuracy, efficiency and scalability of community. In this survey, different community detection approaches are discussed.

Keywords:
social network, community detection, community structure

References:

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1-3

 4. Authors: Jiin-Yuh Jang, Chien-Nan Lin, Sheng-Chih Chang, Chao-Hua Wang Paper Title: The 3-D Numerical Simulation of a Walking Beam Type Slab Heating Furnace with Regenerative Burners Abstract:    This study investigates the furnace thermal efficiency for a walking-beam type slab heating furnace with regenerative burners. The walking-beam type heating furnace is composed of five zones, namely, flameless, preheating, first heating, second heating and soaking zones with regenerator efficiency 90 %. The furnace uses a mixture of coke oven gas as a fuel to reheat the slabs. The numerical model considers turbulent combustion reactive flow coupled with radiative heat transfer in the furnace. It is shown that with regenerator burners, the furnace thermal efficiency is 72%, which is significantly higher than that of a furnace using the conventional burner without regenerator. Comparison with the in-situ experimental data from steel company in Taiwan shows that the present heat transfer model works well for the prediction of thermal behavior of the slab in the reheating furnace with regenerator burners. Keywords:    Reheating Furnace, Combustion, Radiative Heat Transfer, Regenerative burner References: 1.    T. Ishii, C. Zhang, and S. Suglyama, “Numerical simulations of highly preheated air combustion in an industrial furnace,” Transactions of the ASME, Vol. 120, 1989, pp. 276–284.  2.    Y. Suzukawa, S. Sugiyama, Y. Hino, M. Ishioka, and I. Mori, “Heat transfer improvement and NOx reduction by highly preheated air combustion,” Energy Convers, Mgmt Vol. 38, No. 10–13, 1997, pp. 1061–1071. 3.    J. G. Kim and K. Y. Huh, “Three-dimensional analysis of the walking-beam-type slab reheating furnace in hot strip mills,” Numerical Heat Transfer A38, 2000, pp. 589–609. 4.    T. Ishii, C. Zhang, and Hino. Y, “Numerical study of the performance of a regenerative furnace,” Heat Transfer Engineering, 23:4, 2002, pp. 23–33. 5.    N. Rafidi and W. Blasiak, “Thermal performance analysis on a two composite material honeycomb heat regenerators used for HiTAC burners,” Applied Thermal Engineering, Vol 25, 2005, pp. 2966–2982. 6.    J. P. Ou, A. C. Ma, S. H. Zhan, J. M. Zhou, and Z. O. Xiao, “Dynamic simulation on effect of flame arrangement on thermal process of regenerative reheating furnace,” J. Cent. South Univ. Technol., 2007. 7.    S. H. Han, D. Chang, and C. Y. Kim, “A numerical analysis of slab heating characteristics in a walking beam type reheating furnace,” International Journal of Heat and Mass Transfer, Vol 53, Issue 19–20, 2010, pp. 3855–3861. 8.    S. H. Han, D. Chang, and C. Huh, “Efficiency analysis of radiative slab heating in a walking-beam-type reheating furnace,” Energy, Vol 36, Issue 2, 2010, pp. 1265–1272. 9.    T. Morgado, P. J. Coelho, and P. Talukdar, “Assessment of uniform temperature assumption in zoning on the numerical simulation of a walking beam reheating furnace,” Applied Thermal Engineering, Vol 76, 2015, pp. 496–508. 10. J. M. Casal, J. Porteiro, J. L. Míguez, and A. Vazquez, “New methodology for CFD three-dimensional simulation of a walking beam type reheating furnace in steady state,” Applied Thermal Engineering, Vol 86, 2015, pp. 69–80. 13-19

 25. Authors: C. Ramachandra, Sarat Kumar Dash Paper Title: ESD Induced Reliability Problems in Space Grade Devices Abstract: ESD induced reliability problems in an IC have been studied in detail. PEM (Photon Emission Microscopy) analysis has indicated characteristic emission spots at same location from all the failed devices. Reprocessing of the failed device reveals Gate oxide rupture as root cause of the failure. Protection circuits have been designed to prevent ESD induced damage to the devices. The devices are found to be safe till 4500 V stress after protection circuit is implemented. Keywords: ESD (Electro Static Discharge), HBM (Human Body Model), PEM (Photon Emission Microscope), BPSG (Boron Phosphorous silicate glass) References: 1.       Jie Wu,“ Gate Oxide reliability under ESD – like pulse stress” IEEE Transactions on Electron Devices. Vol : 51, Issue : 7, pp : 1192 – 1196; July 2004 2.       Amerasekera and D. Campbell, "ESD pulse and continuous voltage breakdown in MOS capacitor structures", Proc. EOS/ESD Symp., pp. 208-213, 1986 3.       Y. Fong and C. Hu, "The effect of high electric field transients on thin gate oxide MOSFETs", Proc. EOS/ESD Symp., pp. 252-257, 1987 4.       H. Wolf, H. Gieser, and W. Wilkening, "Analyzing the switching behavior of ESD-protection transistors by very fast transmission line pulsing", Proc. EOS/ESD Symp. , pp. 28-37, 1999 5.       J. Wu, P. Juliano, and E. Rosenbaum, "Breakdown and latent damage of ultrathin gate oxides under ESD stress conditions", Proc. EOS/ESD Symp., pp. 287-293, 2000 6.       S. G. Beebe, "Simulation of complete CMOS I/O circuit response to CDM stress", Proc. EOS/ESD Symp., pp. 259-270, 1998 7.       P. E. Nicollian, W. R. Hunter, and J. C. Hu, "Experimental evidence for voltage driven breakdown models in ultrathin gate oxides", Proc. IRPS, pp. 7-15, 2000 8.       E. Wu, A. Vayshenker, E. Nowak, J. Sune, R.-P. Vollertsen, W. Lai, and D. Harmon, "Experimental evidence of ${t}_{\rm BD}$power-law for voltage dependence of oxide breakdown in ultrathin gate oxides", IEEE Trans. Electron Devices, vol. 49, pp. 2244-2253, 2002 9.       C. Leroux, P. Andreucci, and G. Reimbold, "Analysis of oxide breakdown mechanism occurring during ESD pulses", Proc. Int. Rel. Phys. Symp., pp. 276-282, 2000 10.    S.-J. Wang, I.-C. Chen, and H. L. Tigelaar, "TDDB on poly-gate single doping type capacitors ", Proc. IRPS, pp. 54-57, 1992 11.    T. Nigam, R. Degraeve, G. Groeseneken, M. Heyns, and H. Maes, "A fast and simple methodology for lifetime prediction of ultrathin oxides", Proc. IEEE Int. Rel. Phys. Symp., pp. 381-388, 1999 12.    T. Nigam, R. Degraeve, G. Groeseneken, M. Heyns, and H. Maes, "Constant current charge-to-breakdown: Still a valid tool to study the reliability of MOS structures?", IEEE Int. Rel. Phys. Symp., pp. 62-69, 1998 13.    R. Tu, J. King, H. Shin, and C. Hu, "Simulating process-induced gate oxide damage in circuits", IEEE Trans. Electron Devices, vol. 44, pp. 1393-1400, 1997 138-140 26. Authors: Neethu.M.S, Jayalekshmi.S Paper Title: Dependency Based Scheme for Load Balancing in Cloud Environment Abstract: Cloud computing provides an opportunity to dynamically share the resources among the users through virtualization technology. In this paper, a scheme for load balancing is proposed on the basis of dependency among the tasks. CMS consists of three algorithms including Credit-based scheduling for independent tasks, Migrating Task and Staged Task Migration for dependent tasks. The Credit-based method is used for scheduling the independent tasks considering both user priority and task length. Each task will be assigned a credit based on their task length and its priority. In the actual scheduling of the task, these credits values will be considered. Task Migration algorithm is used to guarantee balancing of loads among the virtual machines. Task migration is done such that the tasks gets migrated from heavily loaded machines to comparatively lighter ones. Thus, no rescheduling is required. For dependent tasks, the dependencies between tasks are considered and the technique termed as data shufﬂing is used. In data shuffling, a job is divided into several tasks according to the execution order. The method used here is that the tasks in one stage run independently, while the tasks in different stages must be executed serially. Finally the system is simulated and experiments are conducted to evaluate the proposed methods. This work also concentrates on a simulated study among some common scheduling algorithms in cloud computing on the basis of the response times. The algorithms being compared with the work includes: Random, Random Two Choices (R2C) and On-demand algorithms. The evaluations demonstrate that Credit-based scheduling algorithm significantly reduces the response time. Keywords:  Load Balancing, Virtual Machine, Task Scheduling, Dependency. References: 1.       Buyya, R., Ranjan, R., and Calheiros, R.N. “ Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities” , International Conference on High Performance Computing and Simulation, HPCS 2009. 2.       N. Susila, S. Chandramathi, Rohit Kishore, “A Fuzzy-based Fireﬂy Algorithm for Dynamic Load Balancing in Cloud Computing Environment”, Journal of Emerging Technologies in Web Intelligence, vol. 6, no. 4,pp.435-440, IEEE November 2014. 3.       DineshBabu.L.D,P.VenkataKrishna,“HoneyBeeinspiredloadbalancingoftasks in cloud computing environment”, Applied Soft Computing, vol.13,pp.2292-2303 ,Elsevier 2013. 4.       Elina Pacini,Cristian Mateos,Carlos Garcia Garino, “Balancing throughput and response time in online scientiﬁc clouds via Ant Colony Optimization”, Advances in Engineering Software, vol.8,pp.31-47 ,Elsevier 2015. 5.       Brototi Mondala, Kousik Dasgupta, Paramartha Dutta,“ Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach”, Procedia Technology, vol.4, pp.783-789, Elsevier 2012. 6.       Kousik Dasgupta, Brototi Mandal, Paramartha Dutta, Jyotsna Kumar Mondal, Santanu Dam,“ A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing”, International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), Elsevier, 2013. 7.       B. R. Kandukuri, R. Paturi V, A. Rakshit, “Cloud Security Issues”, IEEE International Conference on Services Computing, pp. 517-520, IEEE 2009. 8.       Yu Liu, Changjie Zhang, Bo Li, Jianwei Niu .“ DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters”, Journal of Network and Computer Applications, Elsevier 2015. 9.       GaochaoXu, Junjie Pang, and Xiaodong Fu, “A Load Balancing Model Basedon Cloud Partitioning for the Public Cloud”, vol.18 ,pp. 34-39,IEEE 2013. 10.    Aarti Singha, Dimple Junejab, Manisha Malhotraa ,“Autonomous Agent Based Load Balancing Algorithm in Cloud Computing ”, International Conference on Advanced Computing Technologies and Applications (ICACTA2015), vol.45, pp.832-841 , Elsevier 2015. 11.    Michael Mitzenmacher, “The Power of Two Choices in Randomized Load Balancing”, IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 10, pp.1094-1104 ,IEEE 2001. 12.    Antony Thomas, Krishnalal G, Jagathy Raj V P ,“Credit Based Scheduling Algorithm in Cloud Computing Environment”, Procedia Computer Science, vol.46, pp. 913 920, Elsevier 2015. 13.    Venubabu Kunamneni.,“ Dynamic Load Balancing for the Cloud”, International Journal of Computer Science and Electrical Engineering (IJCSEE), ISSN No. 2315-4209, vol-1 issue-1, 2012 14.    L. Wang, GregorLaszewski, Marcel Kunze, Jie Tao, “Cloud Computing: A Perspective Study”, New Generation Computing- Advances of Distributed Information Processing, pp. 137-146, vol. 28, no. 2, 2008. 15.    Ousterhout K, Wendell P, Zaharia M, Stoica I, “Batch sampling: low overhead schedulingforsub-secondparalleljob”, Berkeley: University of California; 2012. 16.    Weiwei Chen, Ewa Deelman, “Work ﬂow Sim: A Toolkit for Simulating Scientiﬁc Work ﬂows in Distributed Environments”, The 8th IEEE International Conference on E Science (E Science 2012), Chicago, 2012. 141-146 27. Authors: Sharafunisa S, Smitha E S Paper Title: Reversible Watermarking Technique for Relational Data using Ant Colony Optimization and Encryption Abstract:  Data is stored in different digital formats such as images, audio, video, natural language texts and relational data. Relational data in particular is shared extensively by the owners with communities for research purpose and in virtual storage locations in the cloud. The purpose is to work in a collaborative environment where data is openly available for decision making and knowledge extraction process. So there is a need to protect these data from various threats like ownership claiming, piracy, theft, etc. Watermarking is a solution to overcome these issues. Watermark is considered to be some kind of information that is embedded into the underlying data. While embedding the watermark, the data may modify, to overcome this we use reversible watermarking in which owner can recover the data after watermarking. In this paper, a reversible watermarking for relational data has been proposed that uses ant colony optimization and encryption for more accuracy and security. Keywords:   Ant colony optimization (ACO), Mutual information (MI), Reversible watermarking, Data recovery, Genetic Algorithm (GA). References: 1.       Raju Halder, Shanthanu Pal and Agostino Cortesi ,“Watermarking Techniques for Relational Databases: Survey, Classification and Comparison,” Journal of Universal Computer Science, Vol 16 ,2010, Number 21, pp.3164-3190 2.       J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia”, IEEE Trans. Image Process., vol. 6, no. 12, pp. 16731687, Dec. 1997 3.       Ifthikar, M. Kamran and Z. Anwar, “A Survey on Reversible Watermarking Techniques for Relational Databases,” Security and communication networks, 2015. 4.       Marco Dorigo and Thomus Stultze, ”Ant Colony Optimization“, 2004. 5.       T. M. Cover, J. A. Thomas, and J. Kieffer,’Elements of information theory,” SIAM Rev., vol. 36, no. 3, pp. 509510, 1994. 6.       R. Agarwal and J. Kiernan, “Watermarking relational databases”, in Proc. 28th Int. Conf. Very Large Data Bases, 2002, pp. 155166. 7.       G. Gupta and J. Pieprzyk, “Reversible and blind database watermarking using difference expansion,” in Proc. 1st Int. Conf. Forensic Appl. Tech. Telecommun., Inf., Multimedia Workshop, 2008, p. 24. 8.       G. Gupta and J. Pieprzyk, “Database relation watermarking resilient against secondary watermarking attacks,” in Information Systems and Security. New York, NY, USA: Springer, 2009, pp. 222–236. 9.       K. Jawad and A. Khan, “Genetic algorithm and difference expansion based reversible watermarking for relational databases,” J. Syst. Softw., vol. 86, no. 11, pp. 2742–2753, 2013. 10.    M. E. Farfoura and S.-J. Horng, “A novel blind reversible method for watermarking relational databases,” in Proc. IEEE Int. Symp. Parallel Distrib. Process. Appl., 2010, pp. 563–569 11.    Iftikhar S, Kamran M, Anwar Z.,“ RRW-a robust and reversible watermarking technique for relational data, IEEE transactions on Knowledge and Data Engineering , 2015, Volume: 27,Issue: 4, pp: 1132 – 1145 12.    K. Huang, H. Yang, I. King, M. R. Lyu, and L. Chan,”Biased minimax probability machine for medical diagnosis“, AMAI, 2004. 147-150 28. Authors: Jasher Nisa A J, Sumithra M D Paper Title: Adaptive Minimum Classification Error based KISS Metric Learning for Person Re-identification Abstract: Person re-identification becoming an interesting research area in the field of video surveillance and is taken as the area of intense research in the past few years. It is the task of identifying a person from a camera image, who is already been tracked by another camera image at different time at different location. Manual re-identification in large camera network is costly and mostly of inaccurate due to large number of camera that he had to simultaneously operate. In a crowded and unclear environment, when cameras are at a lengthy distance, face recognition is not possible due to insufficient image quality. So, visual features based on appearence of people, using their clothing, objects carried etc. can be exploited more reliably for re-identification. A person’s appearence can change between different camera views, if there is large changes in view angle, lighting, background and occlusion, so visual feature extraction is not possible accurately. For solving a person re-identification problem, have to focus on “developing feature representations which are discriminative for identity,but invarient to view angle and lighting”.  Recently, Minimum Classification Error (MCE) based KISS metric learning is considered as one of the top level algorithm for person re-identification. It uses VIPeR feature set as input, which contains the extracted features. MCE-KISS is more reliable with increasing the number of training samples.  It uses the smoothing technique and MCE criteria to improve the accuracy of estimate of eigen values of covarience metrics. The smoothing technique can compensate for the decrease in performance which arose from the estimate errors of small eigenvalues. Here, the value of average number of small eigen values of the covarience metrics is set as a constant. So it does not work well for a large number of samples. In such situation, introduce a new method to find the value of average of such small eigen values by maximizing the likelihood function. The new scheme is termed as Adaptive MCE-KISS and conduct validation experiments on VIPeR feature dataset. Keywords:  reidentification, matric learning, covarience matrics, likelihood method. References: 1.       Vezzani, R., Baltieri, D., Cucchiara, R.: People reidenti_cation in surveillance and forensics: A survey. ACM Computing Surveys (CSUR) 46(2) (2013) 29. 2.       Dapeng Tao, Lianwen Jin, Member, IEEE, Yongfei Wang, and Xuelong Li, Fellow, IEEE “Person Reidentification by Minimum Classification Error-Based KISS Metric Learning”,  ieee transactions on cybernetics, vol. 45, no. 2, february 2015. 3.       H. Hotelling, “Analysis of a complex of statistical variables into principal components,” J. Educ. Psychol., vol. 24, no. 6, pp. 417–441, 1933. 4.       McDermott, T. J. Hazen, J. Le Roux, A. Nakamura, and S. Katagiri, “Discriminative training for large-vocabulary speech recognition using minimum classification error,” IEEE Trans. Audio, Speech, Lang.Process., vol. 15, no. 1, pp. 203–223, Jan. 2007. 5.       Kimura, K. Takashina, S. Tsuruoka, and Y. Miyake, “Modified quadratic discriminant functions and the application to Chinese character recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 9, no. 1, pp. 149–153, Jan. 1987. 6.       K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res., vol. 10, pp. 207–244, Feb. 2009. 7.       J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Informationtheoretic metric learning,” in Proc. ICML, Corvallis, OR, USA, 2007, pp. 209–216. 8.       L. Yang, R. Jin, R. Sukthankar, and Y. Liu, “An efficient algorithm for local distance metric learning,” in Proc. AAAI, 2006, pp. 543–548. 9.       B. Prosser, W.-S. Zheng, S. Gong, T. Xiang, and Q. Mary, “Person re-identification by support vector ranking,” in Proc. BMVC, 2010. 10.    D. Tao, L. Jin, Y. Wang, Y. Yuan, and X. Li, “Person re-identification by regularized smoothing KISS metric learning,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 10, pp. 1675–1685, Oct. 2013. 11.    M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof, “Large scale metric learning from equivalence constraints,” in Proc. IEEE CVPR, Providence, RI, USA, 2012, pp. 2288–2295. 12.    M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof “Large scale metric learning from equivalence constraints,” in Proc. IEEE Conf. Comput. Vision Pattern Recogn., Jun. 2012, pp. 2288–2295. 13.    Kimura, K. Takashina, S. Tsuruoka, and Y. Miyake, “Modified quadratic discriminant functions and the application to Chinese character recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 9, no. 1, pp. 149–153, Jan. 1987. 14.    B.-H. Juang, W. Hou, and C.-H. Lee, “Minimum classification error rate methods for speech recognition,” IEEE Trans. Speech Audio Process., vol. 5, no. 3, pp. 257–265, May 1997. 15.    D. Gray, S. Brennan, and H. Tao, “Evaluating appearance models for recognition, reacquisition, and tracking,” in Proc. 10th PETS, 2007. 16.    T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002. 17.    Shamik Sural, Gang Qian and Sakti Pramanik, “segmentation and histogram generation using the hsv color space for image retrieval”. 18.    D. Gray, S. Brennan, and H. Tao, “Evaluating appearance models for recognition, reacquisition, and tracking,” in Proc. 10th PETS, 2007. 151-155 29. Authors: Rita Anitasari, Rizki Fitriani, Erna Triastutik, Alief Makmuri Hartono, Totok R. Biyanto Paper Title: Converting Fuel Oil to Gas in Combustion System for CO2 Emission Mitigation at PT. PJB UP Gresik Abstract:  In environmental point of view, natural gas is the cleanest of the fossil fuels. The combustion of natural gas releases virtually no sulphur dioxide and ash or particulate matter, and very small amounts of nitrogen oxides. Natural gas emits 22% less carbon dioxide than oil and 40% less than coal. NOx is reduced by more than 90% and SOx by more than 95%. This paper will describes the effort of PT. PJB UP Gresik as the owner of the bigest steam power plant in Indonesia to reduce the CO2 emission by converting fuel oil to gas at existing steam power plant fuel system. In order to achive operating conditions that assure mass, energy and momentum balances, some plant modifications and new installation were performed in combustion system area. The effort was performed succesfully. The evidents were compare with the same powerplant in the world. In term of CO2 emission, PT. PJB UP Gressik lay at the best ten compared to others power plant performance in America. It is shown PT. PJB UP Gresik have been performing best green practice especially in reducing CO2 emmision in the steam power plant by utilize fuel gas. Keywords:   CO2 Emission, Mitigation, Combustion System, Converting Fuel Oil to Gas References: 1.       Totok R. Biyanto, Green Concept in Engineering Practice, invited speaker at1St International Seminar on Science and Technology 2015, 5 August 2015, ITS Surabaya, ISSN 2460-6170 2.       EPA, Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion, US Environmental Protection Agency: 2014 3.       E. Dendy Sloan, Fundamental principles and applications of natural gas hydrates, Nature 426, 353-363 (20 November 2003 4.       SA Iqbal, Y Mido, Chemistry of Air & Air Pollution, Discovery Publishing, 2010 5.       Roberts, R. Brooks, P. Shipway, "Internal combustion engine cold-start efficiency: A review of the problem, causes and potential solutions", Energy Conversion and Management, Volume 82, June 2014, Pages 327–350 6.       D. Sarkar, Thermal power plant, 2015. 7.       Christopher E . Van Atten, Benchmarking Air Emissions, M .J. Bradley & Associates LLC, 2013 156-158 30. Authors: Nikhila A, Janisha A Paper Title: Lossless Visual Cryptography in Digital Image Sharing Abstract:   Security has gained a lot of importance as information technology is widely used. Cryptography refers to the study of mathematical techniques and related aspects of Information security. Visual cryptography is a secret sharing scheme which uses images distributed as shares such that, when the shares are superimposed, a hidden secret image is revealed. Visual cryptography schemes (VCSs) generate random and meaningless shares to share and protect secret images. The main issue in visual cryptography is quality of reconstructed image. The secret image is converted into shares; that mean black and white pixel images. There occurs an issue of transmission loss and also the possibility of the invader attack when the shares are passed within the same network. In this paper, a lossless TVC (LTVC) scheme that hides multiple secret images without affecting the quality of the original secret image is considered. An optimization model that is based on the visual quality requirement is proposed. The loss of image quality is less compared to other visual cryptographic schemes. The experimental results indicate that the display quality of the recovered image is superior to that of previous papers. In addition, it has many specific advantages against the well-known VCSs. Experimental results show that the proposed approach is an excellent solution for solving the transmission risk problem for the Visual Secret Sharing (VSS) schemes. Keywords: visual cryptography, visual secret sharing. References: 1.    Kai-Hui Lee and Pei-Ling Chiu “Sharing Visual Secrets in Single Image Random Dot Stereograms” IEEE Transactions on Image Processing, Vol.23, No. 10, October 2014 2.    Ross and A. A. Othman, “Visual Cryptography for Biometric Privacy”, IEEE Transactions on Information Forensics and Security, vol. 6, no. 1, pp. 70-81, 2011. 3.    M. Naor and A. Shamir, “Visual cryptography,” in Advances in Cryptology-EUROCRYPT 1994, ser. Lecture Notes in Computer Science, A. De Santis, Ed. 4.    R.-Z Wang and S.-F. Hsu, “Tagged visual cryptography,” IEEE Signal Process. Lett. vol. 18, no. 11, pp. 627-630, 2011. 5.    J.-B. Feng, H.-C. Wu, C.-S. Tsai, Y.-F. Chang and Y.-P. Chu, “Visual secret sharing for multiple secrets, “Patt. Recognition. vol. 41, no. 12, pp.35723581, 2008. 159-162 31. Authors: Neenu R S, Greeshma G Vijayan Paper Title: Data Mining using Meta Heuristic Approaches for Detecting Hepatitis Abstract: Clinical Data Mining involves the process of extracting, analyzing and finding the available data for clinical decision making. Mining data from clinical data set is not an easy task as they are inserted manually. In this paper, a solution for accurately predicting the presence or absence of hepatitis is proposed. The proposed technique is applied on clinical data sets taken from University of California at Irvine (UCI) machine learning repository. The proposed system contains two main subsystems for preprocessing and classifying. In the preprocessing subsystem the missing values in the data set is handled using missing data imputation methods like litwise deletion or mean/mode imputation method. If the percentage of missing values in a tuple is greater than 25%, then the tuple is rejected from the dataset else it was imputed by the most frequently used value. After handling the missing value, the relevant attributes are selected using meta-heuristic approaches like Particle Swarm Optimization (PSO) is used for feature selection. The reducts obtained after preprocessing are fed into the classification. In the classification subsystem the selected reducts are trained and tested using back propagation neural network. This paper aims at accurate prediction of diseases by analyzing clinical data sets. Keywords:  Back propagation neural network, Clinical Data Mining, Particle Swarm Optimization (PSO), University of California at Irvine (UCI). References: 1.       Fabricio Voznika and Leonardo Viana, “Data Mining Classifications”. 2.       What is clinical dataming? http://www.slideshare.net/empowerbpo/what-is-clinical-data-mining 3.       Yamille del Valle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi, Jean-Carlos Hernandez,  and Ronald G. Harley “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems”,  IEEE Transactions On Evolutionary Computation, VOL. 12, NO. 2, APRIL 2008 4.       R. C.Chakraborty, “Back Propagation Network: Soft Computing Course Lecture”, 15-20, Aug 10,2010. 5.       Y. Kaya and M. Uyar, “A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease”, ApplieSoft Computing Journal, vol. 13, no. 8, pp. 34293438, 2013. 6.       J. S. Sartakhti, M. H. Zangooei, and K. Mozafari, “Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)”, Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 570579, 2012. 7.       Support Vector  Mechanism.- https://en.wikipedia.org/wiki/Support_vector_machine 8.       D. Çalişir and E. Dogantekin, “A new intelligent hepatitis diagnosis system: PCALSSVM”, Expert Systems with Applications, “vol. 38, no. 8, pp. 1070510708, 2011. 9.       Kindie Biredagn Nahato, Khanna Nehemiah Harichandran and Kannan Arputharaj, “Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network”, Hindawi, 2015 10.    K. Bache and M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2013. 11.    Hany M. Harb, and  Abeer S. Desuky , “  Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization “, International Journal of Computer Applications (0975 – 8887) Volume104– No.5, October 2014. 12.    Ezgi Deniz Ülker and Sadık Ülker, “Application of Particle Swarm Optimization To Microwave Tapered Microstrip Lines”, Computer Science & Engineering: An International Journal (CSEIJ), Vol. 4, No. 1, February 2014. 163-167