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A new plant cell image segmentation algorithm is presented in this paper. The difficulty of the segmentation of plant cells lies in the complex shapes of the cells and their overlapping, often present due to recent cellular division. The algorithm presented tries to imitate the human procedure for segmenting overlapping and touching particles. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue.

Segment, tissue, watershed, cellular

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Sachin Singh, J.P. Upadhyay, Shivangi Singh, "Analysis of Disease in plant leaves by image segmentation method", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.46-49, 2019.

The proliferation of social media in the recent past has provided end users a powerful platform to voice their opinions. Businesses (or similar entities) need to identify the polarity of these opinions in order to understand user orientation and thereby make smarter decisions. One such application is in the field of politics, where political entities need to understand public opinion and thus determine their campaigning strategy. Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Sentiment analysis is the computational study of opinions, sentiments, evaluations, attitudes, views and emotions expressed in text. It refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Sentiment analysis over Twitter offers people a fast and effective way to measure the public’s feelings towards their party and politicians. The primary issues in previous sentiment analysis techniques are classification accuracy, as they incorrectly classify most of the tweets with the biasing towards the training data. We performed data (text) mining on thousands of tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2018. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Random Forest algorithm as the main algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The result of the analysis was for the Bhartiya Janta Party. Proposed algorithm predicted a chance that the BJP would win more elections in the general election. Therefore, here we adopt a lexicon based sentiment analysis method, which will exploit the sense definitions, as semantic indicators of sentiment. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy.

Negation Handling; Sentiment Analysis; WordNet; SentiWordNet; Word Sense Disambiguation

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[11] H.T. Le, G.R. Boynton, Y. Mejova, Z. Shafiq, and P. Srinivasan, Revisiting The American Voter on Twitter Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017, pp. 4507-4519.
[12] A. Pak and P. Paroubek, Twitter as a corpus for sentiment analysis and opinion mining Proceedings of the 7th International Conference on Language Resources and Evaluation, 2010, pp.1320-1326.
[13] B.O. Connor, R. Balasubramanyan, B.R. Routledge, and N.A. Smith,” From tweets to Polls: Linking Text Sentiment to public opinion time series” Proceedings of the 4th ICWSM, 2010, pp 122 129.
[14] Rui Xia, FengXu, ChengqingZong, QianmuLi, Yong Qi, and Tao Li, August 2015,” Dual Sentiment Analysis: Considering Two Sides of One Review”, IEEE transactions on knowledge and data engineering, Vol.27, AnNo.8.
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[21] Amitava Das, SivajiBandopadaya, SentiWordnet for Bangla, Knowledge Sharing Event -4: Task, Volume 2, 2010.
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Kirti Chouksey, Amit Ranjan, "Analysis of Indian Election using Random Forest Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.50-57, 2019.

Use of Data mining techniques for managing Virtual Machine migration has been used in the past. Many researchers have applied different data mining techniques for the same and has been proven to be encouraging. Specially in cloud environment where thousands of virtual machines are employed at a time. For this proactive evaluation methods has been used by many researchers In this paper, such methods have been studied and improved k-median technique has been proposed for such migration. The results obtained discussed are showing better accuracy and performance.

Cloud Computing, Fault Tolerance, Virtual Machines Migration, Resource Management

[1] R. Aluvalu, M. A. J. Vardhaman and J. Kantaria, "Performance evaluation of clustering algorithms for dynamic VM allocation in cloud computing," 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), Bangalore, 2017, pp. 1560-1563. doi: 10.1109/SmartTechCon.2017.8358627
[2] M. R. Desai and H. B. Patel, "Efficient Virtual Machine Migration in Cloud Computing," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, 2015, pp. 1015-1019. doi: 10.1109/CSNT.2015.263
[3] Noshy, M., Ibrahim, A., & Ali, H. A. (2018). Optimization of live virtual machine migration in cloud computing: A survey and future directions. Journal of Network and Computer Applications, 110, 1–10. doi: 10.1016/ j.jnca.2018.03.002
[4] Anita Choudhary1, Mahesh Chandra Govil2, Girdhari Singh1, Lalit K. Awasthi3, Emmanuel S. Pilli1* and Divya Kapil4 A critical survey of live virtual machine migration techniques Journal of Cloud Computing: Advances, Systems and Applications Choudhary et al. Journal of Cloud Computing: Advances, Systems and Applications (2017) 6:23 DOI 10.1186/s13677-017-0092-1
[5] Nitishchandra Vyas1 , Prof. Amit Chauhan2 A Survey on Virtual Machine Migration Techniques In Cloud Computing International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: editor@ijaiem.org Volume 5, Issue 5, May 2016 ISSN 2319 – 4847
[6] Sandeep Kaur, Prof. Vaibhav Pandey A Survey of Virtual Machine Migration Techniques in Cloud Computing Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol.6, No.7, 2015

Palash Soni, Ankur Mudgal, "Application of Improved K-Medians for VM Migration", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.58-63, 2019.

Everything in our digital life requires an authentication mechanism to establish the identity of the user and protect his/her privacy. Since passwords are the most common form of authentication, our aim is to provide an alternative form that is not susceptible to the security risks and problems associated with passwords. However, the password may be foreseeable because it should be easy for users to memorize. Thus, an rival could get the passwords of users by brute-force attack in a short period of time. Two-Factor Authentication (TFA) can be used as a antidote to this weakness. One Time Password is mostly used authentication method now days. Our proposed idea is to enhance the security level of One Time Password by using improved and more secure image based OTP. In this method text fields are encrypted with image as key string to produce OTP. Proposed method keep resistance against token theft, man in the middle attack, reply attacks etc.

Authentication, TFA, MFA, OTP, Image OTP

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[2] Davi, L., A. Dmitrienko, et al.,“Privilege Escalation Attacks on Android Information Security,” M. Burmester, G. Tsudik, S. Magliveras and I.Ilic, Springer Berlin /Heidelberg. 6531: 346-360, 2011.
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[4] Ausitn, Charles Frederick, Xingsheng Wan, and Andrew Wright. ”Two factor authentication”, U.S. Patent Application 13/748, 153
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[14] Changsok Yoo, Byung-Tak Kang, Huy Kang Kim, ”Case study of the vulnerability of OTP implemented in internet banking systems of South Korea”, An International Journal Springer Science+Business Media New York 2014, 10.1007/s11042-014-1888-3, 2014.

Shweta Gupta, Prateek Gupta, Amit Ranjan, "Authentication Using Improved Image Based OTP", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.64-67, 2019.

The critical challenge of cloud storage or cloud computing is the organization of the continuously growing volume of data. Data de-duplication fundamentally submits to the exclusion of redundant data. However, indexing of all data is quiet maintained should that data ever be required. In general the data de-duplication eradicates the duplicate copies of duplicate data. Data De-duplication mechanism attain popularity from the academics and industrial as well, because the storage utilization in cloud storage is more efficient to store the data with the help of the cloud service providers. In this method redundant data is replaced with a pointer to the unique data copy. This reduces the hardware used to store data and the bandwidth costs required for transmitting and receiving purposes. This paper represents study of de-duplication as well as proposes a method for saving bandwidth and storage.

Cloud computing, de-duplication, saving storage, bandwidth reduction

[1] Yukun Zhou, Dan Feng, Wen Xia, Min Fu, Fangting Huang, Yucheng Zhang, Chunguang Li,“SecDep: A User-Aware Efficient Fine-Grained Secure Deduplication Scheme with Multi-Level Key Management”, IEEE Mass Storage Systems and Technologies (MSST) 2015 31st Symposium, Year – 2013.
[2] B.Tirapathi Reddy, U.Ramya, Dr.M.V.P.Chandra Sekhara Rao, “A comparative study on data deduplication techniques in cloud storage”, IJPT| Sep-2016 | Vol. 8 | Issue No.3 | 18521-18530.
[3] Wen Xia, Hong Jiang, Dan Fen, “A Comprehensive Study of the Past, Present, and Future of Data De-duplication”, Vol. 104, No. 9, IEEE 2016
[4] Heidi Biggar. Experiencing data de-duplication: Improving efficiency and reducing capacity requirements. White paper February, The Enterprise Strategy Group, 2007
[5] Heidi Biggar. Experiencing data de-duplication: Improving efficiency and reducing capacity requirements. White paper February, The Enterprise Strategy Group, 2007.
[6] Benjamin. Zhu, Kai Li, and Hugo Patterson. Avoiding the disk bottleneck in the Data Domain deduplication file system. In Proceedings of the 6th USENIX Conference on File and Storage Technologies (FAST). USENIX, 2008.
[7] Xiang Zhang, Zhigang Huo, Jie Ma, and Dan Meng. Exploiting data deduplication to accelerate live virtual machine migration. In Proceedings of the 2010 IEEE International Conference on Cluster Computing (CLUSTER), pages 88–96. IEEE, September 2010
[8] Avani Wildani, Ethan L. Miller, and Ohad Rodeh. HANDS: A heuristically arranged non-backup in-line deduplication system. Technical Report UCSC-SSRC-12-03, University of California, Santa Cruz, March 2012
[9] Yoshihiro Tsuchiya and Takashi Watanabe. DBLK: Deduplication for primary block storage. In Proceedings of the 27th IEEE Symposium on Mass Storage Systems and Technologies (MSST), pages 1–5. IEEE, May 2011
[10] Ms. P. Minisha priya, Dr. S.Maheswari, “Performance Analysis of Cloud Storage Using Chunking Algorithm”, IEEE-2018
[11] Haonan Su, Dong Zheng, Yinghui Zhang, “An Efficient and Secure Deduplication Scheme Based on Rabin Fingerprinting in Cloud Storage”, IEEE-2017
[12] Huijun Wu, Chen Wang, Kai Lu, Yinjin Fu,” One Size Does Not Fit All: The Case for Chunking Configuration in Backup Deduplication” Liming Zhu, IEEE-2018.

Roshni Jaiswal, Nagendra Kumar, "Bandwidth Saving Approach For De-duplication", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.68-71, 2019.

In the current scenario, Cloud computing carved itself as an emerging technology which enables the organization to utilize hardware, software and applications without any upfront cost over the internet. A very efficient computing environment is provided by cloud computing where the customers or several tenants are in need of multiple resources to be provided as a service over the internet. The challenge before the cloud service provider is, how efficiently and effectively the underlying computing resources like virtual machines, network, storage units, and bandwidth etc. should be managed so that no computing device is in under-utilization or over-utilization state in a dynamic environment. A good task scheduling technique is always required for the dynamic allocation of the task to avoid such a situation. A Particle Swarm Optimization (PSO) package is integrated in our simulator so as to achieve an effective result where PSO will randomly find the suitable Physical host in heterogeneous environment so as to transfer the load. Through this paper we are going to present the new Algorithm based on task scheduling technique, which will distribute the load effectively among the virtual machine so that the overall cost should be minimal. A comparison of this proposed Algorithm is performed on our simulator which shows that, this will outperform the existing techniques like EFT.

Cloud Computing, Task Scheduling, Resource Optimization, EFT, PSO, QoS

[1]. W A. Karthick, E. Ramaraj, and R. Subramanian, “An efficient multi queue job scheduling for cloud computing,” in proc. IEEE world congress on computing and communication technologies (wccct), Trichirappalli, India, pp. 164-166, Mar. 2014.
[2]. P. Samal, and P. Mishra, “Analysis of variants in round robin algorithms for load balancing in cloud computing,“ International Journal of Computer Science and Information Technologies (IJCSIT), vol. 4 (3) , pp 416-419, 2013
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[6]. G. Gan, T. Huang, S. Gao, “Genetic simulated annealing algorithm for task scheduling based on cloud computing environment,” in Proc. IEEE International Conference on Intelligent Computing and Integrated Systems (ICISS), pp. 60–63 ,2010.
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[8]. Singh, S., Chana, I.: Q-aware: quality of service based cloud resource provisioning. Comput. Electr. Eng. Elsevier. doi:10.1016/j.compeleceng.2015.02.003
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[10]. Neeta Patil, Deepak Aeloor,” A REVIEW – DIFFERENT SCHEDULING ALGORITHMS IN CLOUD COMPUTING ENVIRONMENT”, IEEE 2017.
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[12]. Shaobin Zhan,Hongying Huo Shenzhen “Improved PSO based Task Scheduling Algorithm in Cloud Computing”, Institute of Information Technology, Shenzhen,China Journal of Information and Computational Science 9:13 (2012) 3821–3829.
[13]. Ali Almaamari and Fatma A.Omara, “Task Scheduling Using PSO Algorithm in Cloud Computing Environments”, International Journal of Grid Distribution Computing.Vol. 8 No.5,(2015),pp.245-256.
[14]. Sandeep Rana,Sanjay Jasola,Rajesh Kumar, ”A review on Particle Swarm Optimisation algorithms and their applications to data Clustering”, Artif Itell Rev(2011) Springer(2010) 35:211-222.
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[17]. J.C.Bansal,P.K.Singh,Mukesh Saraswat,Abhishek Verma,Shimpi Singh Jadon,Ajith Abraham,”Inertia Weight Strategies in Particle Swarm Optimisation”,978-1-4577-1123-7 IEEE 2011.
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Garima Singh Thakur, Sapna Choudhary, "Cloud Resource Cost Minimization using PSO Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.72-77, 2019.

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modelling biological processes at the molecular level and making inferences from collected data. As an emerging discipline, it covers a lot of topics from the storage of DNA data and the mathematical modelling of biological sequences, to the analysis of possible mechanisms behind complex human diseases, to the understanding modelling of the evolutionary history of life, etc. It studies the DNA and genetic information.

Bio-informatics, Prokaryotic, Eukaryotic, Peptides, genomics, pretomics, HGP

[1] Y. Lin () Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China e-mail: ylin@math.tsinghua.edu.cn R. Jiang MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084,
[2] M.Q. Zhang () Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 West Campbell Rd, RL11, Richardson, TX, USA 75080 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China e-mail: mzhang@cshl.edu A.D. Smith Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
[3]Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Mach Learn 21(1–2):51–80 4. Bairoch A (1992) PROSITE: a dictionary of site and patterns in proteins. Nucl Acids Res 20:2013–2018
[4] R. Jiang et al. (eds.), Basics of Bioinformatics: Lecture Notes of the Graduate Summer School on Bioinformatics of China, DOI 10.1007/978-3-642-38951-1 3, © Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg 2013
[5] Zvelebil M, Baum J (2007) Understanding bioinformatics. Garland Science, New York, NY. ISBN 978-0815340249
[6] J. Chen and S. Lonardi, Biological Data Mining. Chapman & Hall/ CRC, 2010
[7] Mitton, Jeffery, B (2002). "Heterozygous Advantage". eLS. doi:10.10.38/npg.els.0001760.
[8] Keane MG; Pyeritz RE (May 2008). "Medical management of Marfan syndrome". Circulation. 117 (21): 2802–13. doi:10.1161/CIRCULATIONAHA.107.693523. PMID 18506019.
[9] Walker FO (2007). "Huntington`s disease". Lancet. 369 (9557): 218–28 [221]. doi:10.1016/S0140-6736(07)60111-1. PMID 17240289.
[10] Williams T. N.; Obaro S. K. (2011). "Sickle cell disease and malaria morbidity: a tale with two tails". Trends in Parasitology. 27 (7): 315–320. doi:10.1016/j.pt.2011.02.004.
[11] Kumar A* and Chordia N, School of Biotechnology, Devi Ahilya University, Khandwa Rd, Indore, Indore 452001,India,
Tel: +21674241888; Fax: 07312527532; E-mail:ak_sbt@yahoo.com

Shubham Naga, Sapna Choudhary, "Computation on Bio-Informatics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.78-81, 2019.

Now a day, most of people are using twitter, face book and micro blogging sites. They share their opinion, feeling for particular topic through comment, review. The volume of data generated daily is very large. So, it is important to analyse the data for gaining information from that. Sentimental analysis is used for mining various types of data for opinion through text analytics. It can be negative, positive or impartial. Twitter became one of the largest platform for people to show their opinion, share their thoughts and consistently updated about any organization, events etc. So, data collected is huge somewhat called big data. To process such a big data we need framework that manages this entire thing. In this paper, we attempt to perform cyber bullying detection in a supervised way by proposing a learning framework. More specifically, we first investigate whether sentiment information is correlated with cyber bullying behavior.

Cyber bullying, social media, attacks, security, cyber crime

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Anurag Upadhyay, Manish Maheshwari, "Cyber Bullying Detection In Hinglish Language On Social Media", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.82-86, 2019.

In this paper, the design of a 4-Bit Arithmetic Logic Unit (ALU) using Modified Gate Diffusion Input technique is being done which is implemented using minimum transistor full adder and also adapts hardware reuse method which has advantages of minimum transistors requirement, more switching speed and low power consumption with respect to the conventional CMOS techniques. 4-Bit Arithmetic Logic Unit (ALU) is being implemented with MGDI technique in DSCH 3.5 and layout generated in Microwind tool. The Simulation is done using 65 nm technology at 1.2 v supply voltage The results show that the proposed design consume less power uses less number of transistors, while achieving full swing operation compared to previous work

MGDI, PTL, CMOS, Switching Delay, Power dissipation

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Nitin Singh , "Design and Implementation of Low Power and Area Efficient 4 Bit ALU Using MGDI Technique", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.87-90, 2019.

Social media today is the most popular medium of communication, due to its immediacy. According to Statista, the number of social media users in India is 226 million (2018) and this is expected to go up to 336 million by 2021. The 2014 Lok Sabha elections witnessed a significant usage of social media by political parties and leaders, especially the BJP and their then PM designate Narendra Modi to disseminate their ideology, policies and programmes and highlight the shortcomings / corruption-related scandals of the previous regime. All this helped in creating what is called the ‘Modi wave’, and led to BJP sweeping the 2014 polls. After 2014, most political parties realised the importance of social media and registered their presence on platforms like Facebook, Twitter, Instagram. About 65 percent of India’s population is within the age group 18-35. This group spends almost 4 hours on the internet. Political parties are therefore targeting this group of voters for mobilisation, as most of them use Twitter / Facebook to consume news. This paper represents various issues, methodologies, techniques and research work carried out for election prediction.

Social media, performance indicators, sentiment analysis, prediction

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Praveen Kumar Singh, Anurag Seetha, "Election Prediction On Social Media", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.91-96, 2019.