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HERG is the protein which is found in the potassium ion channel which is mainly used to check the cardiac action of heart in which we can see the heart rhythm and its abnormality by the Q.T syndrome through electro gram where Q.T interval was extended or prolonged for that the various reports are coming for sudden death due to cardiac arrest.

Liagand, Potassium Membrane,Gene,HERG

[1] Zeltser D, Justo D, Halkin A, Prokhorov V, Heller K, Viskin S. Torsade de pointes Due to Noncardiac Drugs: Most Patients Have Easily Identifiable Risk Factors. Medicine (Baltimore). 2003; 82:282–290.
[2] Hancox JC, McPate MJ, El Harchi A, Zhang YH. The HERG Potassium Channel and HERG Screening For Drug-Induced Torsades de Pointes. Pharmacol Ther. 2008; 119:118–132
[3] Lees-Miller, J. P., Duan, Y., Teng, G. Q. & Duff, H. J. Molecular Determinant of High-Affinity Dofetilide Binding to HERG1 Expressed in Xenopus Oocytes: Involvement of S6 Sites. Mol. Pharmacol. 57, 367–374 (2000).
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[5] Mitcheson JS, Chen J, Sanguinetti MC. Trapping of a Methane Sulfonanilide by Closure of the HERG Potassium Channel Activation Gate. J Gen Physiol. 2000; 115:229–240
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[7] Carmeliet,e.(1993). Use-Dependent Block and Use-Dependent Unblock of the Delayed Rectifier k+ Current by Almokalant in Rabbit Ventricular Myocytes. circ. Res.,73,857–868.
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[9] Bucchi A, Baruscotti M, Nardini M, Barbuti A, Micheloni S, Bolognesi M, DiFrancesco D. Identification of the Molecular Site of Ivabradine Binding to HCN4 Channels. PLoS One. 2013; 8:e53132

Kavita Varma Shukla, Vishal Sharma, "Protein Ligand Docking Study of Cetirizine on HERG Receptor", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.1-3, 2019.

This paper covers the implementation of the implementation of Network Intrusion Detection System (NIDS) using International Data Encryption Algorithm (IDEA). The current era has seen an explosive growth in communications. Applications like online banking, personal digital assistants, mobile communication, smartcards, etc. have emphasized the need for security in resource constrained environments. International Data Encryption Algorithm (IDEA) cryptography serves as a perfect network intrusion detection system (NIDS) tool because of its 128 bits key sizes and high security comparable to that of other algorithms. However, to match the ever increasing requirement for speed in today’s applications, hardware acceleration of the cryptographic algorithms is a necessity.

NIDS, IDEA, Crptography, Mobile Communication, Modulo Multiplier

[1] Modugu.R, Yong-Bin Kim, Minsu Choi,“Design and performance measurement of efficient IDEA crypto-hardware using novel modular arithmetic components”, Instrumentation and Measurement Technology Conference (I2MTC), 2010 IEEE, 3-6 May2010,pp1222-1227.
[2] R. Zimmermann, A. Curiger, H. Bonnenberg, H. Kaeslin, N. Felber, and W. Fichtner,“A 177mb/s VLSI implementation of the international data encryption algorithm,”IEEE Journal of Solid-State Circuits, Vol. 29, 1994, pp. 303-307.
[3] Rahul Ranjan and I. Poonguzhali, “VLSI Implementation of IDEA Encryption Algorithm”, Mobile and Pervasive Computing (CoMPC–2008).
[4] Somayeh Timarchi, Keivan Navi, “Improved Modulo 2n +1 Adder Design”, International Journal of Computer and Information Engineering 2:7 2008.
[5] X.Lai and J.L Massey “A Proposal for a New Block Encryption Standard,” in advances in Cryptology – EUROCRYPT 90,Berlia,Germany: Springer Verlag pp. 389-404, 1990.
[6] Antti H¨am¨al¨ainen, Matti Tommiska, and Jorma Skytt¨, “6.78 Gigabits per Second Implementation of the IDEA Cryptographic Algorithm”, 2002 Springer-Verlag, pages 760-769.
[7] M.P. Leong, O.Y.H. Cheung, K.H.Tsoi and P.H.W.Leong “ABit Serial Implementation of the International Data Encryption Algorithm IDEA” ©IEEE 2000.
[8] P. Kitsos , N. Sklavos, M.D. Galanis, O. Koufopavlou , “64 Bit Blockciphers: Hardware Implementations and Comparison analysis”,593-604,3rd November,2004,Elsevier
[9] Thaduri,M.,Yoo,S. and Gaede,R, “ An Efficient Implementation of IDEA encryption algorithm using VHDL”, ©2004 Elsevier.
[10] Allen Michalski1, Kris Gaj, Tarek El-Ghazawi, “An Implementation Comparison of an IDEA Encryption Cryptosystemon Two General-Purpose Reconfigurable Computers”
[11] Sarang Dharmapurikar and John Lockwood, “Fast and Scalable Pattern Matching for Network Intrusion Detection Systems” IEEE Journal on Selected Areas in Communications: Oct. 2006, Volume: 24, pp. 1781- 1792 .
[12] Chiranth E, Chakravarthy H.V.A, Naga mohana reddy P, Umesh T.H, Chethan Kumar M., “Implementation of RSA Cryptosystem Using Verilog” International Journal of Scientific & Engineering Research Volume 2, Issue 5, May-2011.
[13] Rajashekhar Modugu, Yong-Bin Kim and Minsu Choi, “A Fast Low-Power Modulo 2n + 1 Multiplier”, Journal of IET Computers & Digital Techniques Jan-2011.

Sachin Singh, N. D. Sharma, "Optimised Design of Network Intrusion Detection System (NIDS) using HDL", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.4-8, 2019.

Cloud storage services enable individuals and organizations to outsource data storage to remote servers. Cloud storage providers generally adopt data de-duplication, a technique for eliminating redundant data by keeping only a single copy of a file, thus saving a considerable amount of storage and bandwidth. However, an attacker can abuse de-duplication protocols to steal information. At present, there is a considerable increase in the amount of data stored in storage services, along with dramatic evolution of networking techniques. In storage services with huge data, the storage servers may want to reduce the volume of stored data, and the clients may want to monitor the integrity of their data with a low cost, since the cost of the functions related to data storage increase in proportion to the size of the data. To achieve these goals, secure de-duplication and integrity auditing delegation techniques have been studied, which can reduce the volume of data stored in storage by eliminating duplicated copies and permit clients to efficiently verify the integrity of stored files by delegating costly operations to a trusted party, respectively. This paper present study and development of a prototype for role and attribute based de-duplication.

Data de-duplication, data reduction, de-duplication approaches, role based, attribute based de-duplication

[1] F. Durao, J. F. S. Carvalho, A. Fonseka, and V. C. Garcia, “A systematic review on cloud computing,” The Journal of Supercomputing, vol. 68, no. 3, pp. 1321–1346, 2014.
[2] P. Mell and T. Grance, “The NIST definition of cloud computing,”
National Institute of Standards and Technology, Information Technology Laboratory, Tech. Rep., 2009.
[3] “Google Drive,” 2017. [Online]. Available: https://www.google.
com/drive/
[4] “DropBox, a file-storage and sharing service.” [Online]. Available:
http://www.dropbox.com
[5] “Mozy, cloud backup solutions.” [Online]. Available: https:
//www.mozy.com
[6] D. T. Meyer and W. J. Bolosky, “A study of practical deduplication,”, ACM Transactions on Storage, vol. 7, no. 4, pp. 1–20, 2012.
[7] N. Mandagere, P. Zhou, M. A. Smith, and S. Uttamchandani,
“Demystifying data deduplication,” in Proceedings of the
ACM/IFIP/USENIX Middleware ’08 Conference Companion (Companion’08), 2008, pp. 12–17.
[8] D. Harnik, B. Pinkas, and A. Shulman-Peleg, “Side Channels in
Cloud Services: Deduplication in Cloud Storage,” IEEE Security & Privacy Magazine, vol. 8, no. 6, pp. 40–47, 2010.
[9] Nagapramod Mandagere, Pin Zhou, Mark A Smith, and Sandeep Uttamchandani. “Demystifying data deduplication”. In Proceedings of the ACM/IFIP/USENIX Middleware’08 Conference Companion, pages 12–17. ACM,2008.
[10] Mark W Storer, Kevin Greenan, Darrell DE Long, and Ethan L Miller. “Secure data deduplication”. In Proceedings of the 4th ACM international workshop on Storage security and survivability,pages 1–10. ACM, 2008.
[11]Ravindra Mahabaleshwar. “Effective data deduplication implementation”, Whitepaper 2011.
[12]Qinlu He, Zhanhuai Li, Xiao Zhang, “Data De-duplication Techniques”, International Conference on Future Information Technology and Management Engineering, IEEE 2010
[13] Dave Cannon. Data deduplication and tivoli storage manager. Tivoli Storage, IBM Software Group (September 2007), 2009
[14] AndrejTolic, AndrejBrodnik, “Deduplication in unstructured-data storae systems”, ELEKTROTEHNISKI VESTNIK 82(5): 233–242, 2015
[15]E. Manogar, S. Abirami, “A Study on Data Deduplication Techniques for Optimized Storage”, 2014 Sixth International Conference on Advanced Computing(lCoAC) IEEE
[16] Vruti Satish Radia, Dheeraj Kumar Singh, “ Secure deduplication Techniques: A Study”, International Journal of Computer Applications (0975 – 8887) Volume 137 – No.8, March 2016

Dinesh Mishra, Sanjeev Patwa, "A Prototype Of Role Based And Attribute Based De-duplication", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.9-12, 2019.

In this work, we give the related work of fundamental matrix decomposition techniques. The primary strategy that we talk about is known as Eigen value decomposition, which breaks down the underlying matrix into an authoritative shape. The second strategy is nonnegative matrix factorization (NMF), which factorizes the underlying grid into two littler matrixes with the imperative that every component of the factorized matrix ought to be nonnegative. The third strategy is singular value decomposition (SVD) that utilizations particular estimations of the underlying network to factorize it. The last technique is CUR decomposition, which faces the issue of high thickness in factorized matrixes (an issue that is confronted when utilizing the SVD strategy). This work concludes with a description of other state-of-the-art matrix decomposition techniques

Matrix Factorization, Non Negative Matrix Factorization, Singular Value Decomposition

[1] Pudil, P., Novoviˇ cová, J.: Novel methods for feature subset selection with respect to problem knowledge. In: Feature Extraction. Construction and Selection. Springer International Series in Engineering and Computer Science, vol. 453, pp. 101–116. Springer, US (1998).
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[7] Anand Rajaraman and Jeffrey David Ullman: Mining of Massive Datasets. Cambridge University Press, New York, NY, USA (2011).

R. Mishra, S. Choudhary, "A Review on Various Matrix Factorizaton Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.13-15, 2019.

The appearance of computer aided technologies image handling procedures have turned out to be progressively essential in a wide assortment of restorative applications. Mediation between the insurance of helpful indicative data and noise concealment must be cherished in therapeutic images. Image de-noising is an appropriate issue found in differing image handling and computer vision issues. There are different existing techniques to denoise images. The imperative property of a decent image de-noising model is that it ought to totally evacuate noise beyond what many would consider possible just as save edges. This paper shows a survey of some real work in region of image de-noising. The target in all control is to extricate data about the scene being imaged. The quick advancement in automated therapeutic image recreation and the related improvements in investigation strategies and computer helped determination has supported medicinal imaging into a standout amongst the most vital sub-fields in logical imaging Ultrasound, MRI, CT-Scan are the restorative procedures basically utilized by the radiologist for representation of inside structure of the human body with no medical procedure. These give sufficient data about the human delicate tissue, which helps in the finding of human sicknesses.

Medical Image Processing, Medical Image Enhancement, Mammogram

[1] A.K. Jain, Fundamentals of Digital Image Processing.
[2] B. Zhang, Computer Vision vs. Human Vision.
[3] R.C. Gonzalez, Digital Image Processing, Pearson Education India, 2009.
[4] N. Patel, A. Shah, M. Mistry, K. Dangarwala. "International Conference on Convergence of Technology-2014". IEEE-2014.
[5] R. Sumalathaand M. V. Subramanyam, "Hierarchical Lossless Image Compression for Telemedicine Applications" Sciene Direct IMCIP-2015.
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[11] P.H. Tsui, C.K. Yeh, C.C. Huang, "Noise-Assisted Correlation Algorithm for Suppressing Noise-Induced Artifacts in Ultrasonic Nakagami Images". IEEE Trans Information Technology in Biomedicine. Vol. 16, No. 3, May 2012.
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[18] S. Chen, A.R. Ramli, "Contrast enhancement using recursive meanseparate histogram equalization for scalable brightness preservation", IEEE Trans. Consum. Electron., 2003, 49, (4), pp. 1301–1309
[19] M. Tiwari, B. Gupta, M. Shrivastava, "High-speed quantile-based histogram equalization for brightness preservation and contrast enhancement", IET Image Processing, vol. 9(1), 2014, pp. 80-89.
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K. Ojha, A. Khurana, "A Review on Various Medical Image Preprocessing Methods", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.16-19, 2019.

This work deals with the channel estimation and channel equalization for OFDM system. In present scenario every communication system demands high data rate wireless access and larger bandwidth is. This is a challenging task to develop such wireless communication system. The major challenge in the OFDM system is to achieve better channel estimation and channel equalization with lower values of BER (Bit Error Rate) and MSE (Mean Square Error). In this work a low complexity modified iterative linear minimum mean square error (MI -LMMSE) channel estimation algorithm with modified iterative NLMS (MI-NLMS) channel equalization algorithm integrated with CC-OFDM system is proposed.

OFDM, NLMS,BER,Robust,Equalization

[1]. Anil Kumar Pattanayak: “Channel Estimation In Ofdm Systems” NIT Rourkela thesis 2007.
[2]. Charles U. Ndujiuba et.al : “Dynamic Differential Modulation of Sub-Carriers in OFDM” Journal of Wireless Networking and Communications 2016, 6(1): 21-28.
[3]. Pallavi Suryawanshi et.al : “Underwater communication by using OFDM system” International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013.
[4]. Alessandro Tomasoni, Member et.al: “Efficient OFDM Channel Estimation via an Information Criterion” IEEE transactions on wireless communications, vol. 12, no. 3, march 2013.
[5]. Hardeep Kaur et.al : “ Analyzing the Performance of Coded OFDM based WiMax System with different Fading Conditions” International Journal of Advanced Science and Technology Vol.68 (2014), pp.01-10.
[6]. Sanjana T et.al : “Comparison Of Channel Estimation And Equalization Techniques For Ofdm Systems” Circuits and Systems: An International Journal (CSIJ), Vol. 1, No. 1, January 2014.
[7]. Sunho Park : “”Iterative Channel Estimation Using Virtual Pilot Signals for MIMO-OFDM Systems” IEEE transactions on signal processing, vol. 63, no. 12, june 15, 2015.
[8]. Petros S. Bithas et.al : “An Improved Threshold-Based Channel Selection Scheme for Wireless Communication Systems” IEEE transactions on wireless communications, vol. 15, no. 2, february 2016.
[9]. Dimitrios Katselis et.al: “Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study” IEEE transactions on signal processing, vol. 58, no. 5, may 2010.
[10]. Yuan Ouyang et.al : “Performance Analysis of the Multiband Orthogonal Frequency Division Multiplexing Ultra-Wideband Systems for Multipath Fading and Multiuser Interference Channels” Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2015, Article ID 190809, 9 pages.
[11]. Leonardo Rey Vega et.al: “A New Robust Variable Step-Size NLMS Algorithm” IEEE transactions on signal processing, vol. 56, no. 5, may 2008.

J. P. Upadhyay, Rakesh Mishra, Mohsin Khan, "A Robust Multi Carrier Frequency Domain Equalization With Proper Channel Estimation", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.20-23, 2019.

Internet has become a major part for every individual. More and more users are inclined to share their reviews on internet. This lead to a massive extent of data on web which require analysis so as to become useful. Extracting user’s perception from a large dataset of reviews is a difficult task. Sentiment analysis deals at analyzing user’s perception from this huge amount of reviews. The idea behind sentiment analysis aims at finding the polarity of text data and classify it into positive or negative. Machine Learning techniques proves to be very helpful in performing sentiment analysis task. This paper presents the survey of main techniques used for sentiment analysis and sentiment classification

Sentiment Analysis, Sentiment classification, machine learning, user review’s

[1] Pang B, Lee L. , “Opinion mining and sentiment analysis” FoundTrends Inform Retriev:1- 135, 2008
[2] Upma Kumari, et al.,” Sentiment analysis of smart phone product review using SVM classification technique”, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)
[3] F.M Takbir Hossain, Md. Ismail Hossain and Ms. Samia Nawshin.”Machine Learning Based Class Level Prediction of Restaurant Reviews” 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)
[4] Pang, Bo, Lillian Lee, Shivakumar Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques", Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, 2002
[5] Abinash Tripathy, Ankit Agrawal, Santanu Kumar Rath, ClassiÞcation of Sentiment Reviews using N-gram Machine Learning Approach, Expert Systems With Applications (2016)
[6] B. Gokulakrishnan, P. Priyanthan, T. Ragavan, N. Prasath, and A. Perera, “Opinion mining and sentiment analysis on a twitter data stream,” in Advances in ICT for Emerging Regions (ICTer), 2012 International Conference on, 2012, pp. 182–188.
[7] Ghose, Anindya, and Panagiotis G. Ipeirotis. "Designing novel review ranking systems: predicting the usefulness and impact of reviews." Proceedings of the ninth international conference on Electronic commerce. ACM,
[8] S. M. vohr et al., “A Comparative study of Sentiment Analysis Techniques”, issn: 0975 – 6760| nov 12 to oct 13 | volume – 02, issue – 02.
[9] Introduction to Machine Learning, Second Edition, Ethem Alpaydn, The MIT Press Cambridge, Massachusetts London, England
[10] Christian Bodenstein et. al.” Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay” 2016 15th IEEE International Conference on Machine Learning and Applications
[11] Chetan Dharni and Meenakshi Bnasal. “An improvement of DBSCAN Algorithm to analyze cluster for large datasets” 2013 IEEE International Conference in MOOC, Innovation and Technology in Education (MITE)

Riya Jain, Siddharth Dutt Choubey, "A Survey of Sentiment Analysis based on Machine Learning Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.24-28, 2019.

In the past few years weblogs have become a major channel for publishing content over the Internet. With the popularity of social media as a medium to communication, everyone around the world has started using weblogs as part of their communication strategy. However there remains a void of literature on mining information from blogging, and users still do not have a solid understanding of how and why people are using this tool. This is an exploratory study into the world of blogging, and it aims to add some insight as to what is going on in the blogosphere. As data mining is an important tool for gathering information in any field. Applying this tool in the field of blogosphere is somewhat we are here to discuss about. The thesis aims at gathering information related to the users and documents being published over the internet. We wish to know the documents and the users that are highly active in the blogosphere. This study of our can be conducted by mining high utility documents and users in the blogosphere. This study we have conducted on a new blogging website created by us by using ASP.Net 4.0 as the tool and then applying the code for mining and reporting of the data.

Blog Network, Blogs, Content Power User (CPU), Power User, Document Content Power

[1]. Seung-Hwan Lim, Sang-Wook Kim, Sunju Park, and Joon Ho Lee“Determining Content Power Users in a Blog Network: An Approach and Its Applications” in September 2011.
[2]. N. Agarwal and H. Liu, Modeling and Data Mining in Blogosphere. San Rafael, CA: Morgan and Claypool, 2009.
[3]. C. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008.
[4]. X. Song, Y. Chi, K. Hino, and B. Tseng, “Mining in social networks information flow modeling based on diffusion rate for prediction and ranking,” in Proc. Int. Conf. WWW, 2007, pp. 191–200.
[5]. R. Kumar, J. Novak, and A. Tomkins, “Structure and evolution of online social networks,” in Proc. Int. Conf. Knowl. Discov. Data Mining, ACM SIGKDD, 2006, pp. 611–617.
[6]. D. Gruhl, R. Guha, D. Nowell, and A. Tomkins, “Information diffusion through blogspace,” in Proc. Int. Conf. WWW, 2004, pp. 491–501.
[7]. D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in Proc. ACM Int. Conf. Knowl. Discov. Data Mining, SIGKDD, 2003, pp. 137–146.
[8]. M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” in Proc. ACM Int. Conf. Knowl. Discov. Data Mining,SIGKDD, 2002, pp. 61–70.
[9]. J. Goldenberg, B. Libai, E. Muller, 2001 “Talk of the network pp 211-223.

Shashank Khare, Sapna Choudhary, "Algorithm for Mining above and Below Average Utility Blogosphere Users in a Blog Network", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.29-34, 2019.

Cloud computing gives different sorts of administrations to its clients. storage as-a-service is one of the administrations gave by cloud framework in which extensive measure of electronic information is put away in cloud. As profitable and critical information of undertakings are put away at a remote area on cloud we should be guaranteed that our information is protected and be accessible whenever. In circumstances like flood, fire, quakes or any equipment breakdown or any coincidental erasure our information may never again stay accessible. To keep up the information well being there must be a few data backup procedure for cloud stage to recoup profitable and critical information productively in such circumstances said above. This paper gives a audit on different backup systems utilized for cloud computing stage in regards to this worry.

cloud computing, data security, data recovery

[1] K. Sharma, K.R. Singh, "Seed Block Algorithm: A Remote Smart Data Back-up Technique for Cloud Computing", International Conference on Communication Systems and Network Technologies IEEE.
[2] C. Song, S. Park, D. Kim, S. Kang, 2011, "Parity Cloud Service: A Privacy-Protected Personal Data Recovery Service", International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11.
[3] Y. Ueno, N. Miyaho, S. Suzuki, M. Gakuendai, C.K. Ichihara, "Performance Evaluation of a Disaster Recovery System and Practical Network System Applications", Fifth International Conference on Systems and Networks Communications, pp 256-259.
[4] G. Pirrro, P. Trunfio, D. Talia, P. Missier and C. Goble, 2010, "ERGOT: A Semantic-based System for Service Discovery in Distributed Infrastructures", 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.
[5] V. Javaraiah, "Backup for cloud and disaster recovery for consumers and SMBs," 2011 Fifth IEEE International Conference on Advanced Telecommunication Systems and Networks (ANT

P. Dubey, S. Choudhary, "An Introduction to Methods of Backup and Disaster Recovery for Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.35-40, 2019.

This work investigates a technique of watermarking for secured communication. Now a day’s authorized access of multimedia data has been increasing rapidly and it demands higher security. A new modified LSB watermarking embedding technique is presents in this regards. The main objective of this technique is to provide highest robustness against different types of attacks like rotation, cropping, noise and filtering. Simulation result of proposed work has been shown to claim the better robustness.

Watermarked, PSNR, MSE, DWT, IDWT, RGB

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