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Dct and Arnold transform is used for a procedure called encryption where encryption is a process of adding extra bits top the information which can be read by the end to end sender and receiver. Example of encryption can defined as let’s consider a sentence ‘’there code is 100052’’ this is very confidential which should be known only to two persons which is sender and receiver. Here sender adds some information using some algorithms and the sentence becomes ‘thereacodeaisa1a0a0a0a5a2’ or a by using a circuit called encoder. The same when the receiver uses the same encoded algorithm to remove the extra bits added here is the original data is obtained by using decoder circuit which performs the inverse operation of that of encoder. To hide the information or a string were a image consists of 4 parts by applying the 2nd level LWT. Low frequency sub bands LL2 and LH2 are then converted by DCT. And the 2 image are converted using DCT and again one of the converted image is the n again applied to ARNOLD. The output of Arnold is the water marked image.

RDWT and SVM, Watermarking, Encoding.

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Vinjamuri Roopaswi, V.V. Hari Babu, "Multiple Image Hiding Using Arnold Transformation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.295-299, 2019.

Privacy preserving data mining Extracts relevant knowledge from large amount of data and at the same time protect sensitive information from the data miners. People in business, hospitals, educational institutions, and banks need a secure and safe transaction of their data. To serve this need Privacy Preserving Data Mining (PPDM) was created. PPDM solves the problem related to designing accurate models about combined data without requiring the access to exact information in individual data record. PPDM is the most important research area for protecting the perceptive data or knowledge. The important technique of PPDM is Association rule hiding that protects the association rules generated by association rule mining. This study presents a survey of association rule hiding approach for preserving privacy of the user data. Association rule hiding methodology consists of five approaches namely Heuristic, Border, Exact, Cryptography and Reconstruction The study has briefly explained the Heuristic approach.

Privacy preserving Data Mining, Association Rule Hiding approaches. Heuristic Approach

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S. Sharmila, S. Vijayarani, "Heuristic Approach in Association Rule Hiding- A Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.300-305, 2019.

In an era of online social networking, the data privacy is becoming on important topic for researchers to explore. To regulate collection, use and processing of personal data, one needs to understand the behavioral attitude of users of internet so that their need for a law can be assessed. Several studies in this regard have been conducted in other parts of the world where data privacy law is in place, but in India the data privacy was not even a right till recently, when Supreme Court of India declared data privacy as part of the fundamental right of privacy in accordance with constitution of India. The present study is a maiden attempt in India to understand the privacy attitude of the Indian users of social networking sites.

Indobarometer, Data Privacy, Privacy Attitudes, Social Networking Sites, SNSs, Human behavior in Cyber Space

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Sandeep Mittal, Priyanka Sharma, "Indo-Privacy-Barometerv 1.0: Discerning Trends in the Privacy Attitude of Indian Users of Social Networking Sites," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.306-314, 2019.

The changes that occur are exciting and more challenging in our industry. There has been a massive increase in the amount of data in health informatics in the last decade. Over the years, deep learning is raising with its extraordinary success in the research areas of artificial intelligence. If we form larger neural network and then we train it with more available data and fast enough computers their performance then continues to arise. Earlier, machine-learning tools such as QSAR (quantitative structure-activity relationship) modeling have been used to identify potential biological active molecules from millions candidate compounds for drug discovery. But, in this era of big data machine learning approaches lack efficiency. Hence, deep learning evolved as a solution to the problem of big data. In this paper we discuss various deep learning approaches studied for various applications of health informatics with special reference to drug discovery.

Deep learning, Deep neural network, Convolutional neural network, Recurrent neural network, Deep Autoencoder, Health informatics

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Malvika Jasrotia, Prabhpreet kaur, "Deep learning aiding Health Informatics in Drug discovery," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.315-320, 2019.

With the demand in new technologies today internet of things has a big research area for the young researchers. It has a very vast area of field in almost every field of work today we are trying to implement the concept of internet of things. Researchers are focusing on the efficiency of the IOT networks. IOT’s provides a researcher or user to implement his/her own self-made sensor to the open environment to collect the necessary data and then to implement it accordingly with the help of wireless sensor networks. The Internet of Things is the decentralized kind of system where sensor hubs sense data and pass it to the base station. Because of self-designing and far organization of the system vitality utilization is a serious issue of IoT’s. The different methods have been planned by the creators to improve the lifetime of the system. In this paper, different vitality proficient systems are surveyed as far as specific parameters.

IoT, Sensors, Lifetime

[1] Navroop Kaur, Sandeep K.Sood, “An Energy-Efficient Architecture for theInternet of Things (IoT)” IEEE SYSTEMS JOURNAL,2015
[2]PallaviSethhi, SmrutiR.Sarangi, “Internet of Things: Architectures, Protocols, and Applications” 26 January 2017
[3]Kiat Seng Yeo, Mojy Curtis Chian, Tony Chon Wee Ng and Do Anh Tuan, “Internet of Things: Trends, Challenges and Applications”
[4] S.Santiago, Dr.L.Arckiam,“Energy Efficiency in Internet of Things: An Overview”21 December 2016.
[5]https://www.engineersgarage.com/Articles/Internet-of-Things-Architecture
[6]S.Nisha, Balakannan.S.P, “An Energy Efficient Self Orgamizing Multicast Routing Protcol for Internet of Things” 2017
[7]AlgimantasVenckauskas, NerijusJusas, EgidijusKazanavicius, VytautasStuikys, “AN ENERGY EFFICIENT PROTOCOL FOR THE INTERNET OF THINGS” Journal of ELECTRICAL ENGINEERING, VOL. 66, NO. 1, 2015, 47–52

Neha Sharma, Neeru Bhardwaj, "Analysis of Energy Efficient Techniques of IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.321-325, 2019.

The Data analysis inspires many applications in the field of Computing. It may be a phase either in design or on-line operation. The procedures of Data analysis are dichotomized as exploratory and confirmatory. Irrespective of these two types, the primary component for both procedures is grouping or classification. It can be done based on either (i) goodness-of-fit to a postulated model or (ii) natural groupings (clustering) revealed through analysis. Clustering is a process of partitioning a set of data or objects into a set of meaningful sub-classes, called clusters based on similarity. Obliviously, clustering has its own impact in solving complex real world problems. This paper addresses the impact of clustering algorithms for one such problem i.e. for the prediction of absenteeism at work place. The proposed method will draw predictions about absenteeism at work place by decision cluster based rule generation.

Computing, Data Analysis, Clustering, Classification

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[12] A.Deepa , E. Chandra Blessie, “Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm”, Int. J. Sci. Res. in Network Security and Communication, Vol.6(3), pp. 23-27, E-ISSN: 2321-3256, Jun 2018.
[13] T.SenthilSelvi , R.Parimala, “Improving Clustering Accuracy using Feature Extraction Method”, Int. J. Sci. Res. in Computer Science and Engineering, Vol-6(2), pp. 15-19 , E-ISSN: 2320-7639, April 2018.
[14] Gagandeep Kaur , Harmanpreet Kaur, “Ensemble based J48 and random forest based C6H6 air pollution detection”, Int. J. Sci. Res. in Computer Science and Engineering, Vol-6(2), pp 41-50, E-ISSN: 2320-7639, April 2018.

S. Adaekalavan, "Enhancing The Prediction of Absenteeism By Decision Cluster Based Rule Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.326-330, 2019.

The information system audit is an important review process for any organization. Information system audit is essential to identify any venerability in information technology infrastructure. In this work, information system audit process was studied in Indian context and proposed a refine framework for the information system audit process to overcome the deficit in present auditing process. The proposed framework covers important aspect of information system audit such as security environment, website security auditing, software code security, network security, physical security, organization and administration aspects etc. Further, a case study on information security audit in the small a small business enterprise was performed. The systematic review and the report of audit process are reported based on the case study. Finally, some sensitive managerial issues and findings of an awareness survey of information security were presented.

Information security management, information system security audit, data framework and applied model

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D M Chudasama, L. K. Sharma, N. C. Sonlanki, Priyanka Sharma, "Refine Framework of Information Systems Audits in Indian Context," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.331-345, 2019.

Automation testing is a methodology that uses an application to implement entire life cycle of the software in less time and provides high efficiency and effectiveness to the software. In automation testing the tester writes scripts by own with the help of any suitable application software in order to automate any target software application. Automation is basically an automated process that comprises lots of manual activities. In other words, Automation testing uses automation tools like Selenium, Sikuli, Appium etc. to write test script and execute test cases, with no or minimal manual involvement while executing an automated test suite. Usually, automation testers write test scripts for any test case using any of the automation tool and then group several test cases into test suites. Here, we will discuss a neat case study explaining the automation testing using hybrid test script.

Automation testing, Selenium, Sikuli, ROI (Return on Investment), Hybrid automation testing

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Ashish, Nishu, "Automation Testing Using Selenium+Sikuli Scripting," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.346-351, 2019.

Face Recognition is an exciting task in the field of machine learning. Various techniques and methods have been used to solve the problem of face recognition. In this paper, we have shown that how K Nearest Neighbors algorithm along with Principal Component Analysis can be used to recognize a face efficiently. K nearest neighbor algorithm is a non parametric learning algorithm that works on target values of K nearest data points of the query point and finalize the value of the query point. PCA uses the concept of Eigen vectors. An Eigen vector represents an image. PCA finds K Eigen vectors corresponds to K higher Eigen values. So PCA algorithm is an efficient method for feature extraction in face recognition. Implementation is done using python programming language. This paper shows the effect of combination of above mentioned technologies and their edge cutting results.

Face Recognition, KNN, PCA, Eigen vectors

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Nitin Kumar, Gaurav, Deepak Kumar, "Face Recognition Using K-NN Algorithm Along With PCA," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.352-354, 2019.

Cloud computing is a potent field with an on-demand resource provisioning and “Pay Per Use” facility to the customers. Virtualization is the most centered technology used in cloud computing. The virtual machine live migration is an important feature of virtualization which provides virtual machine migration from one host to another host when the services are still running. It provides fault tolerance, load balancing, power saving in data centers. The most efficient technique will have least total migration time and downtime. With an increasing data, we need data centers to store and process it, which leads to huge environmental destruction. Introduction of Virtual machines steps towards Green Computing. Virtual machine migration is of high importance for the uninterrupted and high-quality services. This paper reviews` various techniques for virtual machine migration with their merits and demerits.

Virtualization, Virtual Machines, Live Migration, Downtime, Total Migration Time

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Annie Pathania, Kiranbir Kaur, "Comparative Analysis of Various Techniques of VM Live Migration in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.355-359, 2019.