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According to the estimation billionth person was born in 1804 and the second billionth was born about 123 years later in 1927. Since then it has taken humans 60 years to reach the 5 billion mark and now we are closer to population of 8 billion. India contributes about 20% of this population making it as the second most populous country in the world. Originally, most of the important predictions were made using the Malthusian growth models. The science of data analytics has opened up new possibilities in the creation of prediction graphs. Prediction graphs give useful information about tackling the problem of increasing population. R programming language is used to identify factors that impact the rate of change of population. Important factors such as literacy rate, death rate, religion and so on, deeply impact the rate of population growth. From the Kaiser-Meyer-Olkin test and Factor Analysis found that out of all factors that were considered, religious differences and migration rate were the most important factors affecting the rate of population growth.

Malthusian growth model,Data Analytics, Factors, R-Programmimg language, Kaiser-Meyer-Olkin

[1] Dr. Samir Vazidbhai Vohra, `Population Growth-India`s problem`, PARIPEX – Indian Journal of Research, Vol. 4 , Issue : 11 , November 2015, ISSN - 2250-1991.
[2] Suriani Hassan, Norlita Ismail, Wan Yonsharlinawati Wan Jaafar, Khadizah Ghazali, Kamsia Budin, Darmesah Gabda and Asmar Shahira Abdul Samad , `Using factor analysis on study of factors affecting students` learning style`, International journal of Applied Mathematics and Informatics, Vol. 6, 2012.
[3] Lucas Gren, Alfredo Goldman, `Useful statistical methods for human factors research in software engineering`, ACM, p.p 121-124, 2016.
[4] Sanchita Patil,` Big data analytics using R `, IRJET, Vol. 03, Issue: 07, July-2016.
[5] Huabin Wei, Yanqing Jiang and Yuxing Zhang, `A review of two population growth models`, Asian Journal of Economic Modelling, 3(1): 8-20, 2015
[6] Shilpa S Kulkarni et al,` Analysis of population growth in India and estimation for future`, IJIRSET, Vol. 3, Issue 9, Sep- 2014.
[7] Mohammed Yiha Dawed, Purnachandra Rao Koya, Ayele Taye Goshu,`Mathematical modelling of population growth: The case of logistic and Von Bertalanffy models`, Open Journal of Modelling and Simulation, 2, 113-126, 2014.
[8] A. Alexander Beaujean, `Factor analysis using R`, Practical Assessment, Research & Evaluation, Vol. 18, Number 4, Feb- 2013.
[9] Feng Zhiming et al, ‘Natural environment suitability for human settlements in China based on GIS`, Journal of Geographical Sciences, 19: 437-446 ,2009
[10] S,. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash “Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2 , pp.134-151, Apr-2018
[11] K. Sarmah, “Comparison Studies of Speaker Modeling Techniques in Speaker Verification System”, International Journal of Scientific Research in Network Security and Communication , Vol.5 , Issue.5 , pp.75-82, Oct-2017

Anupama Girish , Aditya Dey, Ankit Sharma, Ketan Jain, Kumar Sanket, Amutha S., Ramesh Babu. D.R, "Factor Analysis of Population Growth using Data Analytics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.422-425, 2018.

Lightweight cryptography is a very promising cryptographic technique which focuses on providing security to the smart devices in the IOT environment. The devices used in the IOT environment generate a large amount of data which can be very critical and sensitive to us. These IOT devices are generally very small and require low power to operate. So implementing strong cryptographic algorithms which need high computation is very difficult because of these limitations. Many lightweight ciphers are developed which focused on providing efficient encryption in these resource sensitive environment without taking much computational power. These ciphers are based on different approaches like AES, Feistel networks. In this paper it is proposed to make some change in the design of one such lightweight cipher i.e. PRESENT. The PRESENT cipher is based on the Substitution Permutation network and utilizes the S-box during the encryption. Here, the motivation is to improve the cipher technique and increase its efficiency by creating the dynamic S-boxes and comparing it with the static S-box on various factors.

IOT, AES, LightWeight Ciphers, PRESENT cipher, SP Network

[1] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang and W. Zhao, "A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications," in IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1125-1142, Oct. 2017.
[2] D. Makoshenko and I. Enkovich, "IoT development: Discovering, enabling and validation of real life IoT scenarios," 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), Valencia, pp. 159-164, 2017.
[3] M. Frustaci, P. Pace, G. Aloi and G. Fortino, "Evaluating Critical Security Issues of the IoT World: Present and Future Challenges," in IEEE Internet of Things Journal, vol. 5, no. 4, pp. 2483-2495, 2018.
[4] Sadiya Shakil and Vineet Singh, "Security of Personal Data on Internet of Things Using AES", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.35-39, 2016.
[5] Zheng Gong, Svetla Nikova, and Yee Wei Law. 2011. “KLEIN: a new family of lightweight block ciphers”. In Proceedings of the 7th international conference on RFID Security and Privacy (RFIDSec`11), Ari Juels and Christof Paar (Eds.). Springer-Verlag, Berlin, Heidelberg, pp. 1-18, 2011.
[6] Christof Beierle, Jérémy Jean, Stefan Kölbl, Gregor Leander, Amir Moradi, Thomas Peyrin, Yu Sasaki, Pascal Sasdrich, and Siang Meng Sim. 2016. “The SKINNY Family of Block Ciphers and Its Low-Latency Variant MANTIS”. In Proceedings, Part II, of the 36th Annual International Cryptology Conference on Advances in Cryptology --- CRYPTO 2016 - Volume 9815, Matthew Robshaw and Jonathan Katz (Eds.), Vol. 9815. Springer-Verlag, Berlin, Heidelberg, pp. 123-153, 2016.
[7] Jian Guo, Thomas Peyrin, Axel Poschmann, and Matt Robshaw. 2011. “The LED block cipher”. In Proceedings of the 13th international conference on Cryptographic hardware and embedded systems (CHES`11), Bart Preneel and Tsuyoshi Takagi (Eds.). Springer-Verlag, Berlin, Heidelberg, pp. 326-341, 2011.
[8] Shirai T., Shibutani K., Akishita T., Moriai S., Iwata T. (2007) “The 128-Bit Blockcipher CLEFIA (Extended Abstract)”. In: Biryukov A. (eds) Fast Software Encryption. FSE 2007. Lecture Notes in Computer Science, vol 4593. Springer, Berlin, Heidelberg, pp. 181-195, 2007.
[9] Deukjo Hong, Jung-Keun Lee, Dong-Chan Kim, Daesung Kwon, Kwon Ho Ryu, and Dong-Geon Lee. 2013. “LEA: A 128-Bit Block Cipher for Fast Encryption on Common Processors”. In Revised Selected Papers of the 14th International Workshop on Information Security Applications - Volume 8267 (WISA 2013), Yongdae Kim, Heejo Lee, and Adrian Perrig (Eds.), Vol. 8267. Springer-Verlag New York, Inc., New York, NY, USA, pp. 3-27, 2013.
[10] R. Beaulieu, S. Treatman-Clark, D. Shors, B. Weeks, J. Smith and L. Wingers, "The SIMON and SPECK lightweight block ciphers," 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), San Francisco, CA, pp. 1-6, 2015.
[11] Bogdanov A. et al. (2007) “PRESENT: An Ultra-Lightweight Block Cipher”. In: Paillier P., Verbauwhede I. (eds) Cryptographic Hardware and Embedded Systems - CHES 2007. CHES 2007. Lecture Notes in Computer Science, vol 4727. Springer, Berlin, Heidelberg, pp. 450-466, 2007.
[12] Albrecht M.R., Driessen B., Kavun E.B., Leander G., Paar C., Yalçın T. (2014) “Block Ciphers – Focus on the Linear Layer (feat. PRIDE)”. In: Garay J.A., Gennaro R. (eds) Advances in Cryptology – CRYPTO 2014. CRYPTO 2014. Lecture Notes in Computer Science, vol 8616. Springer, Berlin, Heidelberg, pp. 57-76, 2014.
[13] Christof Beierle, Jérémy Jean, Stefan Kölbl, Gregor Leander, Amir Moradi, Thomas Peyrin, Yu Sasaki, Pascal Sasdrich, and Siang Meng Sim. 2016. “The SKINNY Family of Block Ciphers and Its Low-Latency Variant MANTIS”. In Proceedings, Part II, of the 36th Annual International Cryptology Conference on Advances in Cryptology --- CRYPTO 2016 - Volume 9815, Matthew Robshaw and Jonathan Katz (Eds.), Vol. 9815. Springer-Verlag, Berlin, Heidelberg, pp. 123-153, 2016.
[14] Matthew Robshaw. 2008. “The eSTREAM Project. In New Stream Cipher Design”s, Matthew Robshaw and Olivier Billet (Eds.). Lecture Notes In Computer Science, Vol. 4986. Springer-Verlag, Berlin, Heidelberg, pp. 1-6, 2008.
[15] De Cannière C., Preneel B. (2008) “Trivium”. In: Robshaw M., Billet O. (eds) New Stream Cipher Designs. Lecture Notes in Computer Science, vol 4986. Springer, Berlin, Heidelberg, pp. 244-266, 2008.
[16] Wu W., Zhang L. (2011) “LBlock: A Lightweight Block Cipher”. In: Lopez J., Tsudik G. (eds) Applied Cryptography and Network Security. ACNS 2011. Lecture Notes in Computer Science, vol 6715. Springer, Berlin, Heidelberg, pp. 327-344, 2011.
[17] Deepanshu Mehta, "Internet of Things: Applications and Challenges", International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.289-293, 2018.

Kumar Anurupam, "Dynamic S-box implementation in PRESENT Cipher," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.426-431, 2018.

Clustering mixed datasets is one of the challenging task. Traditional algorithms like k-prototype algorithm is used for mixed dataset, but is limited to only complete datasets. In any dataset missing values are common. To handle such missing values or incomplete mixed datasets we use extended squeezer algorithm, which includes the new dissimilarity measure ISMD that is incomplete set mixed dissimilarity for numerical and categorical attribute values. In this method we consider dissimilarities in the missing values and in this extended squeezer algorithm it not only cluster the incomplete dataset, it also need not to input the missing values and need not to initialize any clusters at the beginning. This method is compared with traditional k-prototype algorithm on benchmark datasets. The experimental results shows that the ISMD using extended squeezer algorithm gives better accuracy than the traditional k-prototype algorithm and also it overcomes the limitation of initial clusters. This method is implemented by using Python programming. The results shows that there is significant improvement in the clustering results.

Incomplete set mixed dissimilarity, k-prototype, extended squeezer algorithm, Python programming

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[8] W. Fengmei and H. Lixia, “A Missing Data Imputation Method Based on Neighbor Rules”, Computer Engineering, vol. 38, no. 21, (2012).
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[10] Takashi Furukawa, Shin-ichi Ohnishi, and Takahiro Yamanoi “On a Fuzzy c-means Algorithm for Mixed Incomplete Data Using Partial Distance and Imputation” Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I, IMECS 2014, March 12 - 14, 2014, Hong Kong.
[11] Vaishali H. Umathe, Prof. Gauri Chaudhary. “A Review on Incomplete Data And Clustering” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, pp 1225-1227
[12] J. Twisk, M. de Boer, W. de Vente and M. Heymans, “Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis”, Journal of Clinical Epidemiology, vol. 66, no. 9, (2013), pp. 1022-1028.
[13] Wu Sen, Chen Hong and Feng Xiaodong “Clustering algorithm for incomplete data sets with mixed numeric and categorical Attributes” IJDTA, vol. 6 No. 5 2013, pp 95-104.
[14] W. Guoyin, “Expansion in the theory of rough set in incomplete information system”, Journal of computer research and development, vol. 33, no. 10, (2002), pp. 1239-1240.
[15] M..V.Jagannatha Reddy, Dr.B.Kavitha “Clustering Incomplete Mixed Numerical and Categorical Datasets using Modified Squeezer Algorithm International Journal of Computer Science and Engineering, E- ISSN:2347-2693, Vol-4, issue-5 pp-36-41 may-16

M.V. Jagannatha Reddy, D. Ramachandra Reddy, M. Mahesh Kumar, "Clustering Incomplete Mixed Datasets by using Extended Squeezer Algorithm and Finding Incomplete Set Mixed Dissimilarity (ISMD)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.432-437, 2018.

ECG (Electrocardiogram), non-stationary biomedical signal measures the electrical activity of the human heart. This ECG signal helps the professional as a diagnostic tool to predict the cardiac disorder and the function of the human heart. According to the report of WHO (World Health Organization), most of the humans suffer from the cardiac disorder and passed due to the cardiac illness.ECG signal analysis is an important factor in this prediction and this work proposes an automatic classification of the signal which identifies the normal and abnormal signal. The ECG signals are taken from the MIT BIH database. The ECG signal is identified as normal and abnormal with the deep learning classifier CNN. The CNN is a convolution neural network which requires minimal pre-processing compared with traditional machine learning classifier. This work focus on the prediction of the cardiac disorder with automatic classification.

ECG, Arrhythmia, MIT-BIH, CNN

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[3] z.Deng, M. Zhar, et.al., "Deep Structured models for group activity recognization", British Machine Vision Conference 2015.
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[5] K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, vol. 36, no. 4, pp. 193–202, 1980.
[6] M. Abo-Zahhad, S. M. Ahmed, and S. N. Abbas, "Biometric authentication based on PCG and ECG signals: present status and future directions," Signal, Image and Video Processing, vol. 8, no. 4, pp. 739– 751, 2014.
[7] R.Kavitha, Dr.T.Christopher " Predicting Accuracy in ECG signal classification: A comparative method for feature selection", Journal of advanced research and dynamic control systems, vol.10 pp:273-281,2018.

R. Kavitha, T.Christopher, "ECG Signal Classification Based On Deep Learning Classifier," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.438-441, 2018.

This paper introduces an approach to be adopted for the detection of iris from the medical image of human eye. The microscopic image of human eye is taken which consists of number of features such as pupil, retina, iris etc. Firstly image pre-processing is done on the input image so as to remove unwanted noise from it and then various image segmentation techniques such as edge detection, Hough transform etc. are applied for the efficient identification of the inner and outer boundary of iris. To efficiently detect iris boundary, accurate evaluation of circumference of iris in the human eye is required.

Segmentation,Edgedetection,Houghtransform,etc

[1] Navita Kamboj, Preeti Gupta,“A Review on Segmentation techniques for
Iris Recognition System”, International Journal of Engineering and
Management Research, Vol.5, pp. 14-16, 2015.
[2] Mahmoud Mahlouji and Ali Noruzi,” Human Iris Segmentation for Iris Recognition in Unconstrained Environments “, International Journal of Computer Science Issues, Vol. 9, pp.149-152, 2012.
[3] M. Karpaga Kani, Dr.T. Arumuga MariaDevi, “Iris Segmentation and Recognition System” International Journal of Advanced Research in Computer Engineering & Technology,Vol. 3, pp. 1-5, 2014.
[4] D.Anitha,M.Suganthi and P.Suresh, “Image Processing of Eye to Identify the Iris Using Edge Detection Technique based on ROI and Edge Length”, International Conference on Signal, Image Processing and Applications, Vol. 21,pp.1 -5,2011.
[5] Surjeet Singh, Kulbir Singh, “Segmentation Techniques for Iris Recognition System” ,International Journal of Scientific & Engineering Research Vol. 2, pp.1-8, 2011.
[6] Nidhi Manchanda, Oves Khan, Rishita Rehlan and Jyotika Pruthi, ”A Survey: Various Segmentation Approaches to Iris Recognition”, International Journal of Information and Computation Technology,Vol. 3,pp.419-424,2013.

Neeti Taneja, Mohammad Shabaz, Vinayak Khajuria, "Iris Detection Using Segmentation Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.442-444, 2018.

Cloud based software-as-a-service (SaaS) is a new paradigm where services are offered over the internet. The service providers develop and deploy services that consumers can use to build their applications of high quality. As such, consumers can use these services in various applications and in various combinations. The main concern for every consumer is the quality of the service being provided by the service provider. There are number of quality attributes available to assess the quality of service. Some of the attributes are reliability, scalability, availability, reusability and coupling etc. After reviewing number of QoS attributes, we have found that Reusability and coupling of services, is a key QoS attribute to measure the value and Return on Investment (ROI) of the cloud based software service. In this paper we have proposed a reusability framework for cloud based SaaS and constructed their metrics scores. We have also done empirical evaluation on three different cloud based software services to show applicability of our proposed framework.

Reusability, cloud based Software-as-a-Service, Quality attributes, Metrics, Cloud computing

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Monika Sethi, Jatender Kumar, "Evaluation framework of reusability QoS metrics for Cloud based SaaS: an empirical study," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.445-450, 2018.

Convolutional neural network (CNN) based image classifiers always take input as an image, automatically learn its feature and classify into predefined output class. If input image resolution varies, then it hinders classification performance of CNN based image classifier. This paper proposes a methodology (training testing methods TOTV, TVTV) and presents the experimental study on the effects of varying resolution on CNN based image classification for standard image dataset MNIST and CIFAR10. The experimental result shows that degradation in resolution from higher to lower decreases performance score (accuracy, precision and F1 score) of CNN based Image classification.

Varying Resolution, Convolution Neural Network, Image Classification, Feature Learning, Classification

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Suresh Prasad Kannojia, Gaurav Jaiswal, "Effects of Varying Resolution on Performance of CNN based Image Classification: An Experimental Study," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.451-456, 2018.

The code cleaning requires the incorporation of the various processes to remove the clones from the source code as well as the programming irregularities, which improve the overall design of the code. In this paper, the proposed model has been designed for the purpose of code cleaning by using the multi-factor code cleaning algorithm. The proposed model is entirely based upon the elimination of the source code irregularities, which contains the bad smells, code clones and other such problems. The proposed model is designed to work in the three primary components, which includes the code clone and smell detection and marking algorithm, which is followed by the refactoring method estimation and then the application of the refactoring application in the final phase for the act of cleaning the source code. The proposed model utilizes the divide and conquer method, which is concerned with the extraction of the methods from the class files. Also the proposed model analyzed and extracts the independent statements from the extracted methods, which incorporates the common statement elimination, which removes the common statements from the duplication removal process. The proposed model has been designed to refactor the code on the basis of the bad smell detection and elimination with the appropriate method. The proposed model has been analyzed under the various kinds of the datasets for the experimental evaluation, where it has been found better. The proposed model has been recorded with the significant values of the parameters of the accuracy, precision and recall.

Code clone detection, Code cleaning, Duplication detection, Overlapping shifting method

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Pooja Kapila, A. Sharma, N. Kaur, "Multilevel Code Cleaning using Root Extract Method for Java Programs," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.457-461, 2018.

Wireless sensor network is the emerging area where we are largely depending on it, in our day to day life. The major application of the wireless sensor network is the emergency navigation service during the emergency situations, and the main goal of this service is to save the people from emergency situations by guiding them to reach a safe with small congestion and fewer detours. Wireless Sensor Network will be the future of communication and it plays the vital rule of super internet in the future were all data are powered by wireless communication for transmission. This research paper discuss the techniques, key challenges and classification applications.

Classification, Mechanism, Adaptive, Fault Tolerance

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Jayakeerthi M., "Analysis on Wireless Sensor Networking Applications, Classification and Challenges Mechanisms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.462-466, 2018.

Today, encryption is considered as a basic security measure to ensure protection of sensitive data contained within storage devices from external physical threats (such as people on-site) as well as network threats (such as malicious users over the internet or intranet). Today, since encryption techniques are freely and commercially available at ease to computer users all over the world, they have far reaching effects when utilized by malicious users to hide their data for the purpose of avoiding to get caught by lawful authorities. This research work essentially takes the case of encrypted disks/volumes that could cause problems in digital forensic investigations, since they provide criminal suspects with a range of opportunities for deceptive anti-forensics and a countermeasure to legislation written to force suspects to reveal decryption keys. This research work also covers techniques using which decryption keys could be found out so that encrypted data could be obtained in decrypted form to uncover artifacts of evidentiary value. This could also help the lawful authorities to bring cyber-criminals to justice and aid digital forensic analysts with a technique in their hands for retrieving data out of encrypted storage devices especially HDDs and SSDs.

Attack vector, BitLocker, Decryption, Disk, Encryption, Forensics, Hackers, HDD, Lawful, Malicious, SSD, Volume

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Jay Parag Mehta, Digvijaysinh Rathod, "Towards Enablement Of Efficient Forensics Of Encrypted Storage Devices Such As HDDs and SSDs," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.467-473, 2018.