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Abstract: Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. The image segmentation is used for various applications such as medical images, satellite images, content based image retrieval, machine vision, recognition tasks and video surveillance etc. Many methods such as compression based methods, thresholding, and clustering has been proposed in literature for segmentations. The clustering methods can be divided into two parts, namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised image segmentation segments by its own cluster values. The spine segmentation method validates cluster extraction and subsequently vertebral image is obtained. The previous methods for segmenting images in the medical field are taking more time and less accuracy of vertebral outputs. In order to overcome the disadvantages a new methodologies proposed. In this proposed work three methods have been implemented, namely lumbar spine image Segmentation using linked outlyingness tree. Supervised image segmentation using LOT, Unsupervised. Comparing each other Lumbar spine image segmentation provides the best solution for medical images. The performance results also proved that the proposed system has better performance over other existing algorithms.

Index Terms: Computed Tomography (CT), Magnetic Resonance Image (MRI), Linked Outlyingness Tree (LOT), Robust Outlyingness Ratio (ROR).

REFERENCES

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Paper Title:

Abstract: The information security research that has been the subject of much attention in recent years is that intrusion detection systems.   Intrusion-detection systems (IDS) intend at detecting attacks against computer systems and networks or, in general, against information systems. In fact, it is difficult to provide efficient IDS and to maintain them in such a secure state during their lifetime and utilization. Intrusion–detection systems have the task of detection of any insecure states. Machine learning in data mining field plays an essential role in the Network Intrusion Detection research area. Although there are several technological advancements in field of IDS still there are challenges. IDS are intended at detecting attacks against computer systems and networks or, in general, against information systems. The problem of developing an ability to detect novel attacks or unknown attacks based on audit data in IDS is still on verge. Also, the classification accuracy is one such inadequacy, the Weka tool is tested for the few machine learning techniques in this work. This paper presents comparison of K-NN, Decision tree, Naïve Bayes based classifiers using Weka tool, for IDS. This paper will provide an insight for the future research. The KDD CUP'99 data set is employed for experiment, result analysis and evaluation. The methods tested based on Detection rate and False Alarm rate.

Keywords: Classification, Data Mining, Intrusion Detection System (IDS), Machine Learning techniques, Weka, KDD CUP’99 dataset.

REFERENCES

1. Hua TANG, Zhuolin CAO “Machine Learning-based Intrusion Detection Algorithms” Journal of Computational Information Systems5:6(2009) 1825-1831 Available at http://www.JofCI.org
2. YU-XIN MENG “The Practice on Using Machine Learning For Network Anomaly Intrusion Detection” 2011 IEEE.
3. Chi Cheng, Wee Peng Tay and Guang-Bin Huang “Extreme Learning Machines for Intrusion Detection” - WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012 - Brisbane, Australia
4. Naeem Seliya , Taghi M. Khoshgoftaar “Active Learning with Neural Networks for Intrusion Detection” IEEE IRI 2010, August 4-6, 2010, Las Vegas, Nevada, USA 978-1-4244-8099-9/10/$26.00 ©2010 IEEE
5. Kamarularifin Abd Jalill, Mohamad Noorman Masrek “Comparison of Machine Learning Algorithms Performance in Detecting Network Intrusion” 201O International Conference on Networking and Information Technology  978-1-4244-7578-0/$26.00 © 2010 IEEE
6. Jingbo Yuan , Haixiao Li, Shunli Ding , Limin Cao “Intrusion Detection Model based on Improved Support Vector Machine” Third International Symposium on Intelligent Information Technology and Security Informatics 978-0-7695-4020-7/10 $26.00 © 2010 IEEE
7. Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey “Intrusion Detection Using Data Mining Techniques” IEEE 2010.
8. Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani “A Detailed Analysis of the KDD CUP 99 Data Set” Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009)
9. Megha Aggarwal, Amrita “Performance Analysis Of Different Feature Selection Methods In Intrusion Detection” international    journal of scientific & technology research volume 2, issue 6, june 2013
10. Huy Anh Nguyen and Deokjai Choi “Application of Data Mining to Network Intrusion Detection: Classifier Selection Model” Weka, University of Waikato, Hamilton, New Zealand

8-13

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Paper Title:

Keywords: importance, platform, Network, developed, transformative power, Dalits.

REFERENCES

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3. Alejandro, Jennifer (2010) Journalism in the Age of Social Media, Working Paper, Reuters Institute Fellowship Paper, University of Oxford, USA.
4. Bedell, J. (2010) What is the Difference Between Social Media and Social Networking?, jasontbedell.com
5. Beilin, R., Reichelt, N. T. King, B. J. Long, A. and Cam. S. (2013) Transition landscapes and social networks: examining on-ground community resilience and its implications for policy settings in multiscalar systems, Ecology and Society18(2):30-42,  dx.doi.org
6. Beisel, D. (2006) Vertical Social Networks, February 9, genuinevc.com
7. Bodin, O and Crona B. I. (2009) The role of social networks in natural resource governance: what relational patterns make a difference?Global Environmental Change 19:366-374, dx.doi.org
8. Bodin, O., Crona, B. I. and Ernstson H. (2006) Social networks in natural resource management: what is there to learn from a structural perspective?Ecology and Society 11(2):2-8, ecologyandsociety.org
9. Boyd, d. (2009) Friendship. In Mizuko Ito et al. (Eds.), Hanging out, messing around, geeking out: Kids living and learning with new media. Cambridge, MA: MIT Press.
10. Cohen, P.J., Evans, L.S. and Mills, M (2012) Social networks supporting governance of coastal ecosystems in Solomon Islands,Conservation Letters 5:376-386.
11. Dhillon, Amrit (2016) How social media is empowering India’s dalits, The Globe and Mail, July 25, beta.theglobeandmail.com
12. Donelan, Helen (2016) Social media for professional development and networking opportunities in academia, Journal of Further and Higher Education, 40(5):706–729.
13. Elaheebocus, R.M.S (2013) Impact of online social networking on Youth: Case study of Mauritius, Engineering Department University of Mauritius.
14. Gartner (2008) Gartner Says Citizen Social Networks Will Complement and May Replace, Some Government Functions, October 3, Gartner,  gartner.com
15. Haider, H. (2011) Social Media and Reform Networks, Protests, Social Movements and Coalitions, Birmingham: Birmingham University GSDRC, www.gsdrc.org
16. Hauck, J., Schmidt J.and A. Werner (2016) Using social network analysis to identify key stakeholders in agricultural biodiversity governance and related land-use decisions at regional and local level, Ecology and Society21(2):49, www.doi.org 
17. Haythornthwaite, C (2005) Social networks and Internet connectivity effects, Information, Communication and Society, 8(2): 125–147.
18. International Dalit Solidarity Network (2017) Dalit Solidarity Network, hrdn.eu
19. International Dalit Solidarity Network (2017) Human Rights Defenders, India, October 09, Copenhagen, Denmark, idsn.org
20. Ito, M., Baumer, S., Bittanti, M., Boyd, d., Cody, R., Herr-Stephenson, B., et al. (2010). Hanging out, messing around, geeking out: Kids living and learning with new media. Cambridge, MA: MIT Press.
21. Lachapelle, Paul (2011) The Use of Social Networking in Community Development, Community Development Society, Columbus, OH, CDS@AssnOffices.
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27. Pempek, T. A., Yemolayeva, Y. A. and Chivert, S. L. (2009) College Students Social Networking Experiences on Facebook, Journal of Applied Development Psychology, 30(3):227 – 238.
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Authors:

Paper Title:

Abstract: Every day, a massive amount of information is stored, processed, and transmitted digitally. The primary goal of image compression is to minimize the number of bits required to represent the original images by reducing the redundancy in images, while still meeting the User defined quality requirements. Uncompressed images normally require a large amount of storage capacity and transmission bandwidth. In this paper we proposed a hybrid image compression technique for the image which is better in the terms of result by measuring performance evaluation parameters to increase the value of PSNR; our empirical results study shows that hybrid methods are better than existing techniques.

Keywords: Discrete Wavelet Transform (DWT), discrete Cosign Transform (DCT), PSNR, RGB, HVS, Image Compression.

REFERENCES

1. Shruthi K N, Shashank B M, Y.SaiKrishna Saketh, Dr. Prasantha .H.S and Dr. S.Sandya “Comparison Analysis Of A Biomedical Image For Compression Using Various Transform Coding Techniques”, IEEE, 2016, Pp 297-303.
2. Sunil Kumar and M. Indra Sena Reddy “Image Compression Techniques by using Wavelet Transform”, Journal of Information Engineering and Applications, 2012, Pp 35-40.
3. Maneesha Gupta and Dr.Amit Kumar Garg “Analysis Of Image Compression Algorithm Using DCT”, IJERA, 2012, Pp 515-521.
4. Kamrul Hasan Talukder and Koichi Harada “Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image”, AJAM, 2010, Pp 1-8.
5. Kiran Bindu, Anita Ganpati and Aman Kumar Sharma “A Comparative Study Of Image Compression algorithms”, International Journal of Research in Computer Science, 2012, Pp 37-42.
6. Miguel Hernandez-Cabronero, Victor Sanchez, Michael W. Marcellin, Joan Serra-Sagrista “A distortion metric for the lossy compression of DNA microarray images” 2013 Data Compression Conference.
7. Seyun Kim, Nam Ik Cho “Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression” I EEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 1, JANUARY 2014. Pp 445-449.
8. Seyun Kim, Nam Ik Cho “Lossless Compression of Color Filter Array Images by Hierarchical Prediction and Context Modeling” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 24, NO. 6, JUNE 2014. Pp 1040-1046.
9. Mai Xu, Shengxi Li, Jianhua Lu, Wenwu Zhu “Compressibility Constrained Sparse Representation With Learnt Dictionary for Low Bit-Rate Image Compression” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 24, NO. 10, OCTOBER 2014. Pp 1743-1757.
10. Vikrant Singh Thakur, Kavita Thakur “DESIGN AND IMPLEMENTATION OF A HIGHLY EFFICIENT GRAY IMAGE COMPRESSION CODEC USING FUZZY BASED SOFT HYBRID JPEG STANDARD” 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies. Pp 484-489.
11. Chandan Singh Rawat and Sukadev Meher “A Hybrid Image Compression Scheme using DCT and Fractal Image Compression”, International Arab Journal of Information Technology, 2013, Pp 553-562.
12. Navpreet Saroya and Prabhpreet Kaur “Analysis of IMAGE COMPRESSION Algorithm Using DCT and DWT Transforms”, International Journal of Advanced Research in Computer Science and Software Engineering, 2014, Pp 897-900.
13. M.Ramesh and Dr.A.Shanmugam “Medical Image Compression using WaveletDecomposition for Prediction Method”, IJCSIS, 2010, Pp 262-265.
14. Fouzi Douak, Redha Benzid and Nabil Benoudjit “Color image compression algorithm based on the DCT transform combined to an adaptive block scanning”, Elsevier, 2011, Pp 16-26.
15. Azam Karami, MehranYazdiand Grégoire Mercier “Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition”, IEEE, 2012, Pp 444-450.
16. MFerni Ukrit, G.R.Suresh “Effective Lossless Compressionjor Medical Image Sequences Using Composite Algorithm” 2013 International Conference on Circuits, Power and Computing Technologies. Pp 1122-1126.
17. Krishan Gupta, Dr Mukesh Sharma, Neha Baweja “THREE DIFFERENT KG VERSION FOR IMAGE COMPRESSION” 2014. Pp 831-837.
18. Antonio Lopes F. And Roberto D'amore, (2010). A Low Complexity Image Compression Solution For Onboard Space Applications, Sbcci, Pp.174-179.
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20. Chandrajit Choudhury, Yellamraju Tarun, Ajit Rajwade And Subhasis Chaudhuri, (2015). Low Bit-Rate Compression Of Video And Light-Field Data Using Coded Snapshots And Learned Dictionaries, IEEE, Pp.1-6.
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Authors:

Paper Title:

Abstract: The amount of municipal solid waste (MSW) has been increasing steadily over the last decade by reason of population growth and waste generation rate. The management of municipal solid waste collection and transportation is a challenge. Efforts should be made to provide these systems with the best methods to improve their overall efficiency, thus to reduce fuel consumption, pollutant emissions and costs. In this paper, a model for optimizing municipal solid waste collection and also pollutant emissions will be proposed. Firstly, the optimization plan is developed in a static context, and then it is integrated into a dynamic context using multi-agent based modelling and simulation. A case study related to Hagiang City, Vietnam, is presented to show the efficiency of the proposed model. From the optimized results, it has been found that the cost of MSW collection and the pollutant emissions (CO2, CO, NOx, PM, HC) are respectively reduced by 15.8 % and 16 %.

Keywords: Municipal Solid Waste Management, Route Optimization, Environmental Modelling, Dynamic Modelling, Agent-based Model Equation-Based Model, Vehicle Routing Model.

REFERENCE

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3. DasandB.K.Bhattacharyya,“Optimizationofmunicipalsolidwaste collection and transportation routes,” Waste Management, vol. 43, pp. 9 – 18, 2015. 

4. V. Karadimas, G. Rigopoulos, and N. Bardis, “Coupling multi- agent simulation and gis: An application in waste management,” in Proceedings of the 10th WSEAS International Conference on Systems, ser. ICS’06. Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS), 2006, pp. 656–660. 

5. M. Hua, T. K. Nguyen, H. Van Dinh Thi, and N. A. N. Thi, “Towards a decision support system for municipal waste collection by integrating geographical information system map, smart devices and agent-based model,” in Proceedings of the Seventh Symposium on Information and Communication Technology, ser. SoICT ’16. New York, NY, USA: ACM, 2016, pp. 139–146. [Online]. Available: http://doi.acm.org/10.1145/3011077.3011129 

6. Drogoul, E. Amouroux, P. Caillou, B. Gaudou, A. Grignard, N. Mar- illeau, P. Taillandier, M. Vavasseur, D.-A. Vo, and J.-D. Zucker, GAMA: A Spatially Explicit, Multi-level, Agent-Based Modeling and Simulation Platform. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 271–274.
7. -H. Huang and P.-C. Lin, “Vehicle routing scheduling for municipal waste collection system under the ’keep trash off the ground’ policy,” Omega, vol. 55, pp. 24 – 37, 2015. 

8. A. Maimoun, D. R. Reinhart, F. T. Gammoh, and P. M. Bush, “Emissions from {US} waste collection vehicles,” Waste Management, vol. 33, no. 5, pp. 1079 – 1089, 2013. 

9. Mes, M. Schutten, and A. P. Rivera, “Inventory routing for dynamic waste collection,” Waste Management, vol. 34, no. 9, pp. 1564 – 1576, 2014. 

10. Haghshenas, M. Vaziri, and A. Gholamialam, “Evaluation of sustain- able policy in urban transportation using system dynamics and world cities data: A case study in isfahan,” Cities, vol. 45, pp. 104 – 115, 2015. 

11. Buenrostro-Delgado, J. M. Ortega-Rodriguez, K. C. Clemitshaw, C. Gonzlez-Razo, and I. Y. Hernndez-Paniagua, “Use of genetic algo- rithms to improve the solid waste collection service in an urban area,” Waste Management, vol. 41, pp. 20 – 27, 2015. 

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