Volume-6 , Issue-11 , Nov - 2018 Go Back

All over the world, Internet is a dominant communication tool. Internet not only provides different ways of communication, but also increases the misuse of strong communication tool for advertisement and other personal beneficial activities. Progress of unwanted emails has encouraged the development of numerous spam filtering techniques. Since spammers are devising fresh techniques every time, anti-spamming techniques fails to filter out spam emails. E-mail spam is a difficult for the sustainability of the internet and global business. Millions of e-mails sent by spammers for advertisement of products and services. This paper describes an experimental analysis of spam e-mail classification along with proposed framework for feature selection and spam classification. The experimental result signifies performance of algorithm for standard dataset Enron. Origin based information selected for classification.

Spam, Spam Filter, Spam Detection

[1] D. Mallampati, "An Efficient Spam Filtering using Supervised Machine Learning Techniques," International Journal of Scientific Research in Computer Science and Engineering, vol. 6, no. 2, pp. 33-37, April 2018.
[2] G. V. Cormack and T. Lynam, "Spam Corpus Creation for TREC," in Second Conference on Email and Anti-Spam, California, USA, 2005.
[3] N. Advilkar, P. Mane and D. Walunj, "SPAM MAIL FILTERING," International Journal of Advanced Research in Computer Engineering & Technology, vol. 5, no. 1, pp. 99-104, 01 2016.
[4] D. Wang, D. Irani and C. Pu, "A Study on Evolution of Email Spam Over Fifteen Years," in 9th International Conference on Collborative COmputing: Networking, Application and Worksharing, Austin, TX, USA, 2013.
[5] A. Bhowmick and S. M. Hazarika, "Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends," in Advances in Electronics, Communication and Computing, 2016, pp. 583-590.
[6] B. Biggio, G. Fumera, I. Pillai and F. Roli, "A survey and experimental evaluation of image spam filtering techniques," Pattern Recognition Letters, July 2011.
[7] M. Siponen and C. Stucke, "Effective antispam strategies in companies: An international study," in International Conference on System Sciences, Kauia, HI, USA, 2006.
[8] Namrata and Suman, “Review Paper on Spam Detection Antiphishing Techniques,” International Journal of Computer Sciences and Engineering, vol. 6, no. 5, pp. 1156-1161, 2018.
[9] S. N. Raj , "Evaluation Of Cybercrime Growth And Its Challenges As Per Indian Scenario," International Journal of Informative & Futuristic Research, vol. 2, no. 9, pp. 3120-3128, May 2015.
[10] E. Blanzieri and A. Bryl, "A survey of learning-based techniques of email spam filtering," in Artificial Intelligence Review, 2009, pp. 63-92.
[11] E. P. Sanz, J. C. Cortizo Pérez and J. M. GOMEZ HIDALGO, "Email Spam Filtering," in Advances in Computers, vol. 74, 2008, pp. 45-114.
[12] P. G. Juneja and R. K. Pateriya, "A Survey on Email Spam Types and Spam Filtering Techniques," International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 3, pp. 2309-2314, March 2014.
[13] N. Agrawal and S. Singh, "Origin (Dynamic Blacklisting) Based Spammer Detection and Spam Mail Filtering Approch," International Conference on Digital Information Processing, Data Mining, and Wireless Communications, pp. 99-104, 6-8 july 2016.
[14] H. Guo, B. Jin and W. Qian , "Analysis of Email Header for Forensics Purpose," in International Conference on Communication Systems and Network Technologies, Gwalior, India, 2013.
[15] Rekha and S. Negi, "A Review on Different Spam Detection Approaches," International Journal of Engineering Trends and Technology (IJETT), vol. 11, no. 6, pp. 315-318, May 2014.
[16] P. Kulkarni and H. Acharya, "Comparative analysis of classifiers for header based emails classification using supervised learning," International Research Journal of Engineering and Technology, vol. 3, no. 3, March 2016.
[17] Enron-Spam datasets, Mountain View, CA, 2006.

Pramod P. Ghogare, Ajay U. Surwade, Manoj P. Patil, "Effective E-mail Spam Filtering Using Origin Based Information," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.359-362, 2018.

In recent years, the various Deep Learning architectures have been applied in fields such as speech recognition, natural language processing, and many more classification tasks, where they have usually undergoing the traditional methods. The motivation for such an idea is inspired by the fact that the human brain is organized in a deep architecture, with a given input percept represented at several levels of abstraction. Previous research has problems like – complex unstructured data of satellite images to be observed in short period of time & result is misjudged. So, it is difficult to obtain accurate result immediately. Therefore, proposed paper addresses need of Convolutional Neural Network (CNN) for automatic object detection in military & space image. As per study of CNN we conclude as it is for accurate classification of object from image.

Convolutional Neural Network (CNN); Features; Kernels; Pooling; Rectified Linear Unit (ReLU ), etc

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[3] S. V. Shinde and U. V. Kulkarni, “Extended Fuzzy Hyperline-Segment Neural Network with Classification Rule Extraction”, Article in Press, Neurocomputing, April 2017.
[4] Parminder Kaur Birdi, Karbhari Kale, “Deep Convolutional Neural Network Models for Land Use and Land Cover Identification Using Dataset Created From LISS-IV Satellite Images”, in International Journal of Scientific Research in Computer Science, Engineering and Information Technology,Vol. 3, Issue 3, pp. 2123, 2018.
[5] Kuang ping, cao wei-na, wu qiao. “Preview on structures and algorithms of deep learning”. In IEEE, pp. 176, 2014.
[6] Nguyen Kien, Fookes Clinton, Sridharan Sridha. “Improving deep convolutional neural networks with unsupervised feature learning”. In IEEE 978-1-4799-8339-1/15/2015.
[7] Albawi saad .”Understanding of a Convolutional Neural Network”. In IEEE 978-1-5386-1949-0/17/ 2017.
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[9] Liu Lingqiao, Shen Chunhua, Hengel Anton van den.”Cross-Convolutional-Layer Pooling for Image Recognition”. In IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 11,pp. 2305, november 2017.
[10] Sun Deqing, Wulff Jonas, Sudderth Erik B., Pfister Hanspeter, Black Michael J.” A Fully-Connected Layered Model of Foreground and Background Flow”. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2451, 2013.
[11] Hiippala Tuomo. Recognizing military vehicles in social media images using deep learning. In IEEE, 978-1-5090-6727-5/17/$31.00, PP. 60-62 2017.
[12] Zhan Yongjie, Wang Jian, Shi Jianping, Cheng Guangliang, Yao Lele, And Sun Weidong. “Distinguishing Cloud And Snow In Satellite Images Via Deep Convolutional Network”. In IEEE Geoscience And Remote Sensing Letters, 1545-598x, pp.3-4 2017.
[13] Persello Claudio.” Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images”. In IEEE geoscience and remote sensing letters, vol. 14, no. 12, pp 2327-2328, december 2017.
[14] Siti Nor Khuzaimah Binti Amit, Shiraishi Soma, Inoshita Tetsuo, Aoki Yoshimitsu. “Analysis Of Satellite Images For Disaster Detection”. In IEEE, 978-1-5090-3332-4/16/$31.00, pp. 5190-5191, 2016.
[15] Zeng Haoyue, Xia Yong. “Space Target Recognition based on Deep Learning”. In 20th International Conference on Information Fusion Xi`an, China - July 10-13, 2017.

Chitra J. Patil, Swati V. Shinde, "Object Detection in Military & Space Image by Deep Learning with Convolutional Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.363-368, 2018.

“Cloud Computing” is an attractive model for enterprise businesses because of its on-demand, openness, reduced cost, scalability and pay-by-use business model. The DDoS attack on metered resources of Cloud environment is termed as Fraudulent Resource Consumption (FRC) attack. The FRC Attack leads to EDoS (economic Distributed Denial of Service) attack which aims to consume the cloud resource by attacker and impose financial burden to the legitimate user, where integrity, availability and confidentiality of the cloud services are never compromised but affects the accountability which leads to inaccurate billing. This paper surveys different techniques that generate, detect and mitigate the EDoS Attack on Cloud and proposes a Multilayered Framework for Mitigating EDoS Attack.

Cloud computing; Distributed Denial of Service (DDoS) attack; Fraudulent Resource Consumption (FRC) attack,; Economic Distributed Denial of Service (EDoS) Attack

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[3] Al-Haidari, Fahd, Mohammed H. Sqalli, and Khaled Salah. "Enhanced EDoS-Shield for Mitigating EDoS Attacks Originating from Spoofed IP Addresses."Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on. IEEE, 2012.
[4] Armin Slopek and Natalija Vlajic, ”Economic Denial of Sustainability (EDoS) Attack in the Cloud Using Web-Bugs”, in 17th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2014), Gothenburg, Sweden , September 2014.
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[10] http://www.rationalsurvivability.com/blog/2009/01/a-couple-of-follow-ups-on-the-edos-economic -denial-of-sustainability-concept
[11] Joseph Idziorek and Mark Tannian, “Exploiting Cloud Utility Models for Profit and Ruin”, Proceedings of IEEE International Conference on Cloud Computing, pp. 33-40, 2011.
[12] Joseph Idziorek, Mark F. Tannian, and Doug Jacobson,” The Insecurity of Cloud Utility Models”, IEEE Computer Society, March/April 2013.
[13] Keromytis, A., Misra, V., AND Rubenstein, D., 2002. SOS : Secure Overlay Services. SIGCOMM, pp. 61–72.
[14] Koduru, A. ; Comput. Sci. & Eng., Nat. Inst. of Technol., Trichy, India ; Neelakantam, T. ; Saira Bhanu, S.M. Detection of Economic Denial of Sustainability Using Time Spent on a Web Page in Cloud, IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) 2013
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[16] Massimo Ficco and Massimiliano Rak, Stealthy Denial of Service Strategy in Cloud Computing, IEEE Transactions On Cloud Computing, January 2015.
[17] Mirkovic, G. Prier, and P. Reiher. 2002a. Attacking DDoS at the source. In Net-work Protocols, 2002. Proceedings.10th IEEE International Conference on. 312–321.DOI:http://dx.doi.org/10.1109/ICNP.2002.1181418
[18] Mohit Kumar, Nirmal Roberts.”A Technique to reduce the Economic Denial of Sustainability (EDoS) Attack in Cloud”. Fourth International Conference on Recent Trends in Information, Telecommunication and Computing – ITC 2013
[19] Mor Sides , Anat Bremler-Barr, Elisha Rosensweig in “ Yo-Yo Attack - Vulnerability in auto-scaling mechanism “ in SIGCOMM ’15 August 17-21, 2015, London, United Kingdom
[20] Naresh Kumar, M. , Sujatha, P. ; Kalva, V. ; Nagori, R. ; Katukojwala, A.K. ; Kumar, M., Mitigating Economic Denial of Sustainability (EDoS) in Cloud Computing Using In-cloud Scrubber Service, 2012 Fourth International Conference on Computational Intelligence and Communication Networks (CICN),IEEE 2012.
[21] Naresh Kumar, M., Sujatha, P., Kalva, V., Nagori, R., Katukojwala, A., Kumar, M. “Mitigating economic denial of sustainability (edos) in cloud computing using in-cloud scrubber service”, In: Computational Intelligence and Communication Networks (CICN), 2012 Fourth International Conference on. 2012, p. 535–539. doi:10.1109/CICN.2012.149.
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[23] S VivinSandar, SudhirShenai ”Economic Denial of Sustainability (EDoS) in Cloud Services using HTTP and XML based DDoS Attacks”. International Journal of Computer Applications (0975 – 8887) Volume 41– No.20, March 2012.
[24] Soon HinKhor Akihiro Nakao, “sPow On-Demand Cloud-based eDDoS Mitigation Mechanism”Fifth Workshop on Hot Topics in System Dependability
[25] Sqalli, Mohammed H., Fahd Al-Haidari, and Khaled Salah. "EDoS Shield-A Two-Steps Mitigation Technique against EDoS Attacks in Cloud Computing." Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. IEEE, 2011.
[26] Wael Alosaimi and Khalid Al-Begain. An Enhanced Economical Denial of Sustainability Mitigation System for the Cloud. 2013 Seventh International Conference on Next Generation Mobile Apps, Services and Technologies.

A. Somasundaram, "Multilayered Framework for Mitigating (MLFM) EDoS Attack," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.369-375, 2018.

The goal of building Intrusion Detection System is conceptualized with need of making secure and protected publically and privately accessible data so that it can be easily avoided from its unauthorized uses. Since increase of network density and heavy use of development of internet has generated a major challenge of making these network data and traffic protected from intruded attacks. Security of network traffic is becoming a major issue of computer network system. Attacks on the network are increasing day-by day. The most publicized attack on network traffic is considered as Intrusion. Data mining techniques are used to monitor and analyze large amount of network data & classify these network data into anomalous and normal data. Since data comes from various sources, network traffic is large. Data mining techniques such as classification and clustering are applied to build Intrusion Detection system. An effective Intrusion detection system requires high detection rate, low false alarm rate as well as high accuracy. This research paper includes effective Data mining techniques applied on IDS for the effective detection of pattern for both malicious and normal activities in network by strong classification mechanism, it will simplify the task of securing information system through this proposed Intrusion Detection system which is developed by the optimized use of newly Ant Colony optimization followed by Hyper pipes classifier classification. Intrusion detection system has been used for ascertaining intrusion and to preserve the security goals of information from attacks.

Accuracy, Attack, Ant Colony, Classifier, Clustering, Data mining, Detection, Information, Intrusion, Signature, optimization,etc.

[1] Weiwei Chen, Fangang Kong, Feng Mei, Guigin Yuan, Bo Li, "a novel unsupervised Anamoly detection Approach for Intrusion Detection System", 2017 IEEE 3rd International Conference on big data security on cloud, May 16–18, 2017.
[2] S. Aljawarneh, M. Aldwairi, M.B. Yassein, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model", Journal of Computational Science, 2017.
[3] J. Yang, T. Deng, R. Sui, "An adaptive weighted one-class svm for robust outlier detection", Proceedings of the 2015 Chinese Intelligent Systems Conference, pp. 475-484, 2016
[4] Anbar Mohammed, Abdulah Rosni, H. Hasbullah Izan, Yung-Wey Chong, E. Elejla Omar, "Comparative Performance Analysis of classification algorithm for Internal Intrusion Detection", 2016 14th Annual Conference on Privacy Security and Trust (PCT), Dec 12–14, 2016.
[5] Mariem Belhor, Farah Jemili, "Intrusion Detection based on genetic fuzzy classification system", 2016 IEEE 13th International Conference on Computer Systems and Application (AICCSA), Nov 29 2016-Dec 2, 2016.
[6] Anbar Mohammed, Abdulah Rosni, H. Hasbullah Izan, Yung-Wey Chong, E. Elejla Omar, "Comparative Performance Analysis of classification algorithm for Internal Intrusion Detection", 2016 14th Annual Conference on Privacy Security and Trust (PCT), Dec 12–14, 2016.
[7] Gong Shang-fu ; Zhao Chun-lan,Intrusion detection system based on classification, IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment,2012.
[8] J.H. Lee, J.H. Lee, S.G. Sohn, J.H. Ryu, T.M. Chung, "Effective value of decision tree with KDD 99 intrusion detection datasets for intrusion detection system", Advanced Communication Technology 2008 ICACT 2008 10th International Conference on, vol. 2, pp. 1170-1175, 2008.
[9] ShailendraSahu and B M Mehtre ,"Network Intrusion Detection System Using J48 Decision Tree," IEEE , 2015.
[10] Sara Chadli,Mohamed Emharraf and Mohammed Saber "The design of an IDS architecture for MANET based on multi-agent" International Colloquium on Information Science and Technology (CiSt),IEEE,2014.
[11] Difan Zhang, Linqiang Ge, Rommie Hardy, Authersi Yu, Hanlin Zhang and Robert Reschly, "On Effective Data Aggregation Techniques In Host-based Intrusion Detection in MANET," The 10th Annual IEEE CCNC- Green Communications and Computations Track 2013 IEEE.
[12] Jitendra S Rathore, Praneet Saurabh, Bhupendra Verma “AgentOuro: A Novelty Based Intrusion Detection and Prevention System” Computational Intelligence and Communication Networks (CICN), 2012 Fourth International Conference on 3-5 Nov. 2012.
[13] Vasima Khan, Anomaly Based Intrusion Detection And Prevention System, IJERT, 2013.
[14] Mukesh Sharma,Akhil Kaushik, Amit Sangwan Performance Analysis of Real Time Intrusion Detection and Prevention System using Snort.,IJERT, 2012 .
[15] Vaishali T.Deshmukh, Shubhangi Vaikole, Layered Crf A Model To Build More Accurate Intrusion Detection System, IJERT, 2012.
[16] Bhavana G.Rathwa,Prof.Purnima Singh Genetic Algorithm Methodology for Intrusion Detection System, IJERT, 2012.
[17] Bin Zeng , Lu Yao and ZhiChen Chen"A network intrusion detection system with the snooping agents",International Conference on Computer Application and System Modeling, 2010.
[18] Vera Marinova-Boncheva, “A Short Survey of Intrusion Detection Systems”, Problems of Engineering Cybernetics and Robotics, 2007.
[19] A. Moore, D. Zuev and M. Crogan, "Descriminators for use in flow based Classification," Queen Marry University of London, August 2005.
[20] Khaled Labib, Computer security and intrusion detection, Crossroads, Volume 11, Issue 1, August 2004.
[21] Yong Zhong, Xiao-lin Qin, Database Intrusion Detection Based on User Query Frequent Itemsets Mining with Item Constraints [J], Proceedings of the 3rd international conference on information security, 2004.
[22] Dheeraj Gupta ; P.S. Joshi ; A.K. Bhattacharjee ; R.S. Mundada, IDS alerts classification using knowledge-based evaluation, Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).
[23] David Ahmad Effendy ; Kusrini Kusrini, Classification of intrusion detection system (IDS) based on computer network; Sudarmawan Sudarmawan, 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) 2017.
[24] Murthy, S.K. , Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery, 24, 1998.
[25] I Nyoman, Trisna Wirawan, I. E., "Penerapan Naive Bayes Pada Intrusion Detection System Dengan Diskritisasi Variabel", Jurnal Ilmiah Teknologi Informasi, vol. 2, 2015.
[26] Aurobindo Sundaram, An Introduction to Intrusion Detection, Crossroads, Volume 2, Issue 4, Pages: 3 –7, 1996.
[27] Northcutt, S. “Network Intrusion Detection: An Analyst’s Handbook.” New Riders, Indianapolis 1999.
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[29] KDD99, KDDCup 1999 data, 1999, http://kdd.ics.uci.edu/ Databases/kddcup99/10 percent.gz, 1999.
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K Shukla, R K Gupta, V. Namdeo, "A Hybrid Intrusion Detection System Using Hypper-Pipe Classifier and Ant Colony Optimization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.376-380, 2018.

“Steganography” is a technique that thwarts unauthorized users to have access to the crucial data, to invisibility and payload capacity using the different technique like discrete cosine transform (DCT) and discrete shearlet transform (DST).The available methods till date result in good robustness but they are not independent of file format. The aim of this research work is to develop a independent of file format and secure hiding data scheme. The independent of file format and secure hiding data scheme is increased by combining DST and least significant bits (LSB) technique. Accordingly an efficient scheme is developed here that are having better MSE and PSNR against different characters.

Discrete Shearlet Transform, SVD, PSNR, MSE

[1] Nazir A. Loan, Nasir N. Hurrah, Shabir A. Parah, Jong Weon Lee, Javaid A. Sheikh, and G. Mohiuddin Bhat, “Secure and Robust Digital Image Watermarking Using Coefficient Differencing and Chaotic Encryption”, Received January 4, 2018, accepted February 7, 2018, date of publication March 16, 2018, date of current version April 25, 2018.
[2] N. Senthil Kumaran, and S. Abinaya, “Comparison Analysis of Digital Image Watermarking using DWT and LSB Technique”, International Conference on Communication and Signal Processing, April 6-8, 2016, India.
[3] Aase, S.O., Husoy, J.H. and Waldemar, P. (2014) A Critique of SVD-Based Image Coding Systems, IEEE International Symposium on Circuits and Systems VLSI, Orlando, FL, Vol. 4, Pp. 13-16.
[4] Ahmed, F. and Moskowitz, I.S. (2014) Composite Signature Based Watermarking for Fingerprint Authentication, ACM Multimedia and Security Workshop, New York, Pp.1-8.
[5] Akhaee, M.A., Sahraeian, S.M.E. and Jin, C. (2013) Blind Image Watermarking Using a Sample Projection Approach, IEEE Transactions on Information Forensics and Security, Vol. 6, Issue 3, Pp.883-893.
[6] Ali, J.M.H. and Hassanien, A.E. (2012) An Iris Recognition System to Enhance E-security Environment Based on Wavelet Theory, Advanced Modeling and Optimization, Vol. 5, No. 2, Pp. 93-104.
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[9] Baaziz, N., Zheng, D. and Wang, D. (2011) Image quality assessment based on multiple watermarking approach, IEEE 13th International Workshop on Multimedia Signal Processing (MMSP), Hangzhou, Pp.1-5.
[10] Bao, F., Deng, R., Deing, X. and Yang, Y. (2008) Private Query on Encrypted Data in Multi-User Settings, Proceedings of 4th International Conference on Information Security Practice and Experience (ISPEC 2008), Pp. 71-85, 2008.
[11] Barni, M. and Bartolini, F. (2004) Watermarking systems engineering: Enabling digital assets security and other application, Signal processing and communications series, Marcel Dekker Inc., New York.
[12] Barni, M., Bartolini, F. and Piva, A. (2001) Improved Wavelet based Watermarking Through Pixel-Wise Masking, IEEE Transactions on Image Processing, Vol. 10, Pp. 783-791.

Bhawna Ahirwar, Sanjay K. Sharma, "High Security Text and Image Message for Steganography and Watermarking Through Modified Least Significant Bits Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.381-385, 2018.

Image Compression is the art and Science of reducing the amount of data required to represent an image. It is one of the useful and commercially successful technique in the field of Digital Image Compression. The innumerable images are compressed and decompressed daily. Image compression techniques are classified into lossless and lossy compression techniques. This paper covers three lossy compression techniques such as Tree Structured Vector Quantization (TSVQ), TSVQ reduces the quantizer search complexity by replacing full search encoding with a sequence of tree decisions , and Multi Stage Vector Quantization (MSVQ) , Multistage Vector Quantization is a modification of Unconstrained Vector Quantization technique. It is also called as Multistep, Residual or Cascaded Vector Quantization. Multistage Vector Quantization (MSVQ) technique preserves all the features of Unconstrained Vector Quantization technique while decreasing the computational complexity, memory requirements and spectral distortion. And Side Match Vector Quantization (SMVQ), In SMVQ Neighbor pixels within an image are similar unless there is an edge across. But the topic of our interest is Side Match Vector Quantization.

SMVQ, MSVQ, TSVQ

[1]. V.Krishna, Dr. V.P.C.Rao, P.Naresh, P. Rajyalakshmi,“Incorporation of DCT and MSVQ to Enhance Image Compression Ratio of an image” International Research Journal of Engineering and Technology (IRJET), Volume: 03, Issue: 03 | Mar-2016.
[2]. Sarita S. Kamble, A.S. Deshpande,“Image Compression Based on Side Match Vector Quantization” International Journal of Engineering Science and Computing, Volume 6 ,Issue No. 5,ISSN 2321 3361 © May 2016.
[3]. Malwinder Kaur, Navdeep Kaur, “A Litreature Survey on Lossless Image Compression”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2015.
[4]. Chuan Qin, Chin-Chen Chang and Yi-Ping Chiu, “A Novel joint Data-Hiding and compression scheme based on SMVQ and Image Inpainting”, IEEE Trans. on Image Process., vol. 23, no. 3, pp. 969-978, March 2014.
[5]. Richa Goyal, Jasmeen Jaura, “A Review of Various Image Compression Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 7, July 2014.
[6]. Kaur M. and Kaur G. “A survey of lossless and lossy Image compression technique” International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 2, February 2013.
[7]. Mukesh Mittal, Ruchika Lamba,“Image Compression Using Vector Quantization Algorithms: A Review” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013.
[8]. Arup Kumar Bhattacharjee, Tanumon Bej, Saheb Agarwal, "Comparison Study of Lossless Data Compression Algorithms for Text Data", IOSR-JCE Volume 11, Issue 6, May-June 2013, pp 15-19.
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[12]. S.Mitra, R.Shuyu Yang Kumar, B.Nutter. “An optimized Hybrid Vector Quantization for efficient source Encoding IEEE 2002, 45th Midwest Symposium, Vol. 2, 2002.
[13]. Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1, pp 19-23, FebMarch 2001.
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Cibi Castro. V, Arul Raj. T, Ilam Parithi. T , Balasubramanian. R, "Comparative Study of Image Compression Techniques based on Vector Quantization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.386-890, 2018.

Cloud Computing is a latest technology where the usage of this service by different clients was increasing rapidly day by day. Cloud provides different services with best facilities to utilize it. Presently many small and large scale businesses were using Cloud services to meet their requirements. The one of the main service in cloud is cloud storage. The major requirements for achieving security in outsourced databases are confidentiality, privacy, integrity, availability. Now a day’s many companies gives access their clients to store their data in remote storage server i.e. cloud .as well as one of the main issue in cloud is data security. In consideration with the cloud security, in this paper we proposed architecture for storing the data in cloud using the fragmentation method. The detail architecture is discussed in the paper.

Security as a service, Cloud Models, Fragmentation, Database

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V. Kiran Kumar, E. Hari Prasad, "Proposed Model for Ensuring More Security in Cloud by Data Fragmentation Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.391-394, 2018.

The main goal of this study is to discover the iron oxide content in soil using Vis-NIR spectroscopy with the help of ASD FieldSpec4 instrument. Estimation of iron Oxide content can be utilized as an indicator for soil fertility. The instrument ASD FieldSpec4 Spectroradiometer is utilized for capturing spectral signature of gathered soil samples from various areas in Jalna district of Maharashtra state in India. In the Vis-NIR wavelength range we utilized Partial Least Squares Regression (PLSR) method to estimate the iron oxide content present in the samples of soil. Also few pre-processing methods were applied such as savitzky golay and first derivative preprocessing. The outcomes were assessed by root mean square error (RMSE) and coefficients of determination (R2). The observations with the help of PLSR models with the first derivative pre-processing was (RMSE =0.008711, R2 =0.91 for calibration and RMSE= 0.001624, R2=0.92 for validation) and with savitzky golay pre-processing was (RMSE =0.004415, R2 =0.87 for calibration and RMSE=0.004209, R2=0.89 for validation). It is inferred that, 444nm, 480nm, 529nm, 680nm, 880nm and 920nm wavelength bands are sensitive to soil iron oxide. Taking everything into account, concentrations of iron oxide in soils could be surveyed by soil spectra; therefore, spectral reflectance would be an elective tool for monitoring soil metals.

Iron oxide content, ASD fieldspec4 spectroradiometer, PLSR, Vis-NIR

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Kanchan Sukhdev Kayande, Ratnadeep R.Deshmukh, Pooja Vinod Janse, Jaypalsing N. Kayte, "Hyper spectral Analysis of Soil Iron Oxide using Fieldspec4 Spectroradiometer," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.395-399, 2018.

The Index of Industrial Production (IIP) is an important indicator and a univariate time series data in nature. In the present study, the authors endeavored to develop forecasting models for twenty three (23) selected IIPs of India. The models were developed using Multilayer Perceptron. The study focused at (i) development of forecasting models, (ii) visualization of them, and (iii) analyzing the accuracies of the developed models. The study showed a mixed result with approximately twenty two percent (22%) i.e. five (5) out of twenty three (23) of the IIPs under study gave very good forecasting accuracy in terms of Mean Absolute Percentage Error (MAPE less than five), approximately twenty six percent (26%) i.e. six (6) out of twenty three (23) of the IIPs under study gave good forecasting accuracy (MAPE greater than or equal to five and MAPE less than ten) and approximately thirteen percent (13%) i.e. three (3) out of twenty three (23) of the IIPs under study gave moderate forecasting accuracy (MAPE greater than or equal to ten & MAPE less than twelve).

Multilayer Perceptron, Index of Industrial Production, Mean Absolute Percentage Error, Forecasting, Time Series

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Dipankar Das, Awanish Kumar Tripathi, Ayushi Shah, Samarth Mehta, "Application of Multilayer Perceptron for Forecasting of Selected IIPs of India – An Empirical Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.400-406, 2018.

VANET Technology is the tremendously increasing day to day and a prominent research technology in the area of vehicular nodes which provides with concrete and real applications. Prominent development of applications and inherent characteristics such as frequent disconnection of network, rapid dynamic topology, and predictable mobility have made data dissemination technique inside VANET system as a crucial challenging task in the significant area of wireless sensor networks. Many of VANET applications require real-time communication with high reliability. Data dissemination is the base for whole network and it is a mechanism of spreading the data or information over dispersed networks. Data dissemination is intended to use the resources of a network in an optimal way to provide the needs to end users in dedicated network environment. To initiate efficient data dissemination technique in VANET system we have to consider both data forwarding node and not participated nodes in that data dissemination. So, data dissemination protocol should be designed exclusively so that achieves dissemination of data to end point without affecting data in dense and sparse network of VANET system. In this paper, we discuss the data dissemination protocols history thoroughly. The main objective of this paper is to provide fundamental concepts of data dissemination in VANET system environment.

Data Dissemination Prortocol, VANET, Store and Forwarding, Geocast, Beacon, MAC, BPSK

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Mamillapally Raghavender Sharma, "Efficient and Reliable Data Dissemination Approaches for VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.407-412, 2018.