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Information has to face certain data security related issues like confidentiality, message integrity, authorization etc while transmitted over internet, therefore need to design appropriate mechanism and techniques that ensures secrecy of private information while it is communicated over public networks has turn out to be an urgent and desired research problem. Steganography has been widely used in historical times and the also currently used with intensive interest. The advantage of steganography over cryptography is that it doesn’t raise any suspicion and the message can be exchanged over a public communication channel. It is an ongoing research area having vast number of applications in distinct fields such as defense and intelligence, medical, on-line banking, on-line transactions handling, for various financial and commercial applications, to stop music piracy etc. In this paper a modified interpolation based method is used with adaptable range table to embed in pixels of interpolated image with secret bits. Proposed method can be used for applications (like for defense, and medical purposes etc) where exact retrieval of cover object at receiver side is required, due to use of reversible embedding approach. From experimental results it is observed that proposed technique achieves higher embedding capacity, acceptable value for visual quality and robustness against statistical and visual attacks.

Steganography, Data Security, Reversible Embedding, Interpolation

[1] R. J. Anderson and F. A. P. Petitcolas, "On the limits of steganography," in IEEE Journal on Selected Areas in Communications, vol. 16, no. 4, pp. 474-481, May 1998.
[2] Kodge B. G., “Information Security: A Review on Steganography with Cryptography for Secured Data Transaction”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.1-4, 2017.
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[8] L. Luo, Z. Chen, M. Chen, X. Zeng and Z. Xiong, "Reversible Image Watermarking Using Interpolation Technique," in IEEE Transactions on Information Forensics and Security, vol. 5, no. 1, pp. 187-193, March 2010.
[9] Wien Hong, Tung-Shou Chen "Reversible data embedding for high quality images using interpolation and reference pixel distribution mechanism," J ournal of Visual Communication and Image Representation, Volume 22, Issue 2, pp 131-140, 2011.
[10] Ya-Ting Chang, Cheng-Ta Huang, Chin-Feng Lee, Shiuh-Jeng Wang, "Image interpolating based data hiding in conjunction with pixel-shifting of histogram", The Journal of Supercomputing Volume 66, Number 2, pp 1093-1110. 2013.
[11] Lu, Tzu-Chuen, Chin-Chen Chang, and Ying-Hsuan Huang. "High capacity reversible hiding scheme based on interpolation, difference expansion, and histogram shifting", Multimedia Tools and Applications, volume 72, issue 1, pp 417–435, 2014.
[12] Yuan-Yu Tsai, Jian-Ting Chen, Yin-Chi Kuo, and Chi-Shiang Chan, "A generalized image interpolation-based reversible data hiding scheme with high embedding capacity and image quality", KSII Transactions on Internet and Information Systems (TIIS) VOL. 8, NO. 9, pp 3286-3301, Sep. 2014.
[13] Mingwei Tang, Shenke Zeng, Xiaoliang Chen, Jie Hu, Yajun Du, “An adaptive image steganography using AMBTC compression and interpolation technique,” Optik, Volume 127, Issue 1, pp 471-477, 2016.
[14] Tzu-Chuen Lu, “An interpolation-based lossless hiding scheme based on message recoding mechanism,” Optik, Volume 130, pp 1377-1396, 2017.
[15] Jun Tian, "Reversible data embedding using a difference expansion," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp 890-896, Aug. 2003.
[16] Chia-Chen Lin, Wei-Liang Tai, Chin-Chen Chang, “Multilevel reversible data hiding based on histogram modification of difference images,” Pattern Recognition, Volume 41, Issue 12, pp 3582-3591, 2008.

Sumeet Kaur, Savina Bansal, R.K. Bansal, "Reversible Image Steganography Based on Interpolation and Adaptive Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1394-1398, 2019.

Continuous evolution of CAPTCHA techniques is necessary to combat modern generation of AI enabled bots. Designing a new CAPTCHA scheme requires a careful review of existing CAPTCHA techniques. But existing reviews of current CAPTCHA techniques lack systematic evaluation of the current trends in CAPTCHA development. Existing reviews focus on mere enlisting of current CAPTCHA schemes in several categories and explaining their working schema. Hence systematic evaluation and analysis of existing CAPTCHA techniques in several categories is necessary. In this paper we highlight the facts and flaws of existing CAPTCHA techniques in order to provide insights for future improvements in current CAPTCHA techniques. We have focused on providing simple and clear understanding of existing CAPTCHA techniques in a systematic way. This will help researchers to overcome the drawbacks of current CAPTCHA schemes and work on improvement of weaker aspects of existing CAPTCHA techniques.

CAPTCHA, HCI, Web security, Human Interactive Proof (HIP), Bots

[1] A.L. Coates, H. S. Baird and R. J. Faternan, “Pessimal Print: A Reverse Turing Test,” In the Proceedings of the 6th International Conference on Document Analysis and Recognition, Seattle, WA, USA, pp. 1154-1158, 2001.
[2] M. Chew and H. S. Baird, “BaffleText: a Human Interactive Proof,” In the Proceedings of 10th SPIE/IS&T Document Recognition and Retrieval Conference (DRR2003), Santa Clara, CA, USA, pp. 305-316, 2003.
[3] G. Moy, N. Jones, C. Harkless, and R. Potter, “Distortion Estimation Techniques in Solving Visual CAPTCHAs,” In the Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04), vol. 2, pp. 23-28, 2004.
[4] K. Chellapilla, K. Larson, P. Y. Simard, and M. Czerwinski, “Building Segmentation Based Human-Friendly Human Interaction Proofs (HIPs),” In the Proceedings of HIP 2005, Bethlehem, PA, USA, pp. 1-26, May 19-20, 2005.
[5] H. S. Baird and J. L. Bentley, “Implicit CAPTCHAs,” In the Proceedings of SPIE/IS&T Conference on Document Recognition and Retrieval XII (DR&R2005), San Jose, pp. 191-196, 2005.
[6] J. Tam, J. Simsa, S. Hyde, and V. Ahn, “Breaking Audio CAPTCHAs,” In the Proceedings of 21st International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp. 1625-1632, December 2008.
[7] K.A. Kluever and R. Zanibbi., “Balancing usability and security in a video CAPTCHA,” In the Proceedings of the 5th Symposium on Usable Privacy and Security (SOUPS `09), ACM, New York, NY, USA, Article 14, pp. 1-11, 2009.
[8] M. Shirali-Shahreza and S. Shirali-Shahreza, “Question-Based CAPTCHA,” In the Proceedings of International Conference on Computational Intelligence and Multimedia Applications, Sivakasi, Tamil Nadu, India,pp. 54-58, 2007.
[9] C. J. Hernandez-Castro and A. Ribagorda, “Pitfalls in CAPTCHA design and implementation: The Math CAPTCHA, a case study,” Computers & Security, Vol. 29, No. 1, pp. 141-157, 2010.
[10] M. M. Tanvee, M. T. Nayeem, and M. M. Rafee, “Move & Select: 2 Layer CAPTCHA Based on Cognitive Psychology for Securing Web Services,” International Journal of Video & Image Processing and Network Security, IJVIPNS/IJENS, Vol. 11, No. 5, pp. 917, 2011.
[11] T. Yamamoto, T. Suzuki, and M. Nishigaki, “A Proposal of Four-Panel cartoon CAPTCHA,” In the Proceedings of International Conference on Advanced Information Networking and Applications 2011, Singapore , pp. 159–166, 2011.
[12] M. J. M. Chowdhury, N. R. Chakraborty, “CAPTCHA Based on Human Cognitive Factor,” International Journal of Advanced Computer Science and Applications, Vol. 4, No. 11, pp. 144-149, 2013.
[13] V. Dhaka, G. Gandhi, “Developing a CAPTCHA Utilizing Cognitive Ability of Human through PHP,” International Journal of Advanced Networking Applications, Special Issue, pp. 50–54, 2015.
[14] G. Goswami, B. M. Powell, M. Vatsa, R. Singh, and A. Noore., “FaceDCAPTCHA: Face Detection based Color Image CAPTCHA,” Future Generation Computer Systems, Vol. 31, pp. 59–68, February 2014.
[15] M. De Marsico, L. Marchionni, A. Novelli, and M. Oertel, “FATCHA: biometrics lends tools for CAPTCHAs,” Multimedia Tools Applications, Vol. 76, No. 4, pp. 5117-5140, February 2017.
[16] E. Uzun, S. P. H. Chung, I. Essa, and W. Lee, “rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System,” Network and Distributed Systems Security Symposium (NDSS), San Diego, CA, USA, 2018.
[17] Bin B. Zhu, Jeff Yan, Guanbo Bao, Maowei Yang, and Ning Xu, “Captcha as Graphical Passwords-A New Security Primitive Based on Hard AI Problems,” IEEE Transactions on Information Forensics and Security, Vol. 9, No. 6, pp. 891-904, June 2014.
[18] A. Bhalerao, L. Rade, “A Basic Survey of CAPTCHA :Application and Challenges”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 06, No. 01, pp.1-5, 2018.

S.S. Kulkarni, H.S. Fadewar, "Systematic Evaluation of Existing CAPTCHA Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1399-1402, 2019.

the paper entitled as “Scraping and Visualization of Product Data from E-commerce Websites”. Extracting data from websites is called as web scrapping. The main advantages of the scrapping are inexpensive, easy to implement, low maintenance and speed. The main objective of the work is to scrap the data from websites and store the extracted data in Comma-separated values (CSV) format for analysis. The data available in the websites are in the form of unstructured information. Web scraping helps to collect these unstructured data and store it in a structured form. The process of Web scraping is to extract the data using various methods from the internet. Millions of people consider universally accessible resource as internet. The rise in the usage of internet has commonly been increased day by day and there is high growth in competition between the organizations in their business. This work consists with three phases. The first phase of the work is web scrapping. In this phase, the extracted data will be stored as a csv file. The second phase of the work is data analysis. In this phase, the data is imported from the csv format and analyzed using statistical analysis. The third phase of the work is visualization and in this the extracted data has been visualized with the help of different charts.

Web scrapping, Data Analysis, Visualization, data mining, websites

[1] Eloisa Vargiu, Mirko Urru, “Exploiting web scraping in a collaborative filteringbased approach to web advertising”, Artificial Intelligence Research, Vol. 2, Issue 1, pp. 44-54, 2013.
[2] Vasani Krunal A., “Content Evocation Using Web Scraping and Semantic Illustration”, IOSR Journal of Computer Engineering (IOSR-JCE) Vol. 16, Issue 3, pp. 54-60, 2014
[3] Jose Ignacio Fern ´ andez-Villamor, Jacobo Blasco-Garc ´ ´ıa, Carlos A. Iglesias, Mercedes Garijo, “A Semantic Scraping Model for Web Resources-Applying Linked Data to Web Page Screen” in the Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Volume 2 - Agents, Rome, Italy, 2011.
[4] Emilio Ferraraa, Pasquale De Meob, Giacomo Fiumarac, Robert Baumgartner, “Web Data Extraction, Applications and Techniques: A Survey”, Elsevier, knowledge-Based Systems, pp. 301-323, 2014.
[5] Faustina Johnson and Santosh Kumar Gupta. “Web Content Mining Techniques: A Survey”, International Journal of Computer Applications Vol. 47, Issue 11, pp. 44-50, 2012 .
[6] Rahul Dhawani, Mrudav Shukla, Priyanka Puvar, Bhagirath Prajapati, “A Novel Approach to Web Scraping Technology”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5, Issue 5, pp.747-750, 2015
[7] Anand V.Saukar, Kedar G.Pathare, Shweta A. Gode, “An Overview on Web Scrapping Techniques and Tools”, International Journal on Future Revolution in Compuer Science & Communication Engineering, Vol. 4, Issue 4, 2018.
[8] Riya Shah, Karishma Pathan, Anand Masurkar, Shweta Rewatkar, Prof. (Ms.) P.N.Vengurlekar, “Comparison of E-commerce Products using web mining”, International Journal of Scientific and Research Publications, Vol. 6, Issue 5,pp.640-644, 2016.
[9] Nakash, J., Anas, S., Ahmad, S. M., Azam, A. M., Khan, T. “Real Time Product Analysis using Data Mining” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 4, Issue 3, pp. 815–820, 2015.
[10] Rehman S.U. ‘Smart agent for automated E-commerce’, 2011 World Congress on Sustainable Technologies (WCST), UK IEEE pp.124-128, 2011.
[11] Shikha Mahajan, Nikhit Kumar , “A Web Scraping Approach in Node.js”, International Journal of Science, Engineering and Technology Research (IJSETR) Vol. 4, Issue 4, pp. 909-912, 2015.
[12] Sneh Nain, Bhumika Lall, “Web Data Scraper Tools: Survey”, International Journal of Computer Science and Engineering, Vol. 2, Issue 5, pp. 39-44, 2014.
[13] Sarah Swain, Shriya Mishra,” Prolego: A Data Science Approach to Predict the Outcome of a Football Match “, Vol. 6, Issue 4, pp. 132-136, 2018

V. Srividhya, P. Megala, "Scraping and Visualization of Product Data from E-commerce Websites," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1403-1407, 2019.

Usage of internet applications has increased in this world of technology. Growing technology has both advantages and disadvantages. On the one hand, it provides lots of comfort and ease, on the other hand, it provides it poses a security threat. To secure our communication over the internet we use cryptography. Cryptography is done using asymmetric cryptography and symmetric cryptography. Asymmetric cryptography uses two keys, one is used for encryption and other is used for decryption. The key used for encryption is public and it is not required to keep it secret. While key used for decryption is always kept secret. In symmetric cryptography both the keys are same and both need to be secret. RSA is a popular cryptography algorithm. It is an asymmetric-key algorithm. Diffie-Hellman is an algorithm which uses symmetric keys. This paper presents a novel modified hybrid RSA-Diffie Hellman algorithm, in order to utilize the key benefits of both.

decryption, public key, prime number, RSA algorithm, Diffie Hellman algorithm

[1] Mahalakshmi and Suseendran G., (2018). "An analysis of Cloud Computing issues on data Integrity, privacy, and its current solutions".
[2] K David Raju, L Vijay Kumar, K Anthony Rahul Showry, B LhoitKrishn ,(2018) ." techniques of providing data integrity in cloud computing".
[3] Joseph Selvanayagam1, Akash Singh2, Joans Michael3, Jaya Jeswani4 (2018) ."secure file storage on cloud using cryptography".
[4]https://searchsecurity.techtarget.com/definition/asymmetric-cryptography
[5] Shreen Nisha, Mohammed Farik (2017), " RSA public key cryptography algorithm - a review", international journal of scientific and technology research volume 6.
[6] Prabhat Kumar Panda, (2017). "A hybrid security algorithm for RSA cryptosystem".
[7] Sultan Aldossary, William Allen, (2016). "Data Security, Privacy, Availability, and Integrity in Cloud Computing: Issues and Current Solutions".
[8] Ayan Roy (2016)," brief comparison of RSA and Diffie-hellman (public key) algorithm"
[9] Israt Jahan, Mohammad Asif, Liton Jude Rozario (2015),
Improved RSA cryptosystem based on the study of number theory and public key cryptosystems. American Journal of Engineering research.
[10]https://www.ibm.com/support/knowledgecenter/en/SSB23S_1.1.0.14/gtps7/s7symm.html
[11] Miss. Renushree Bodkhe , Prof. Vimla Jethani , (2015) "Hybrid encryption algorithm based improved RSA and Diffie - Hellman ".
[12] Gaurav R. Patel, Prof. Krunal Panchal (2014), "Hybrid encryption Algorithm".
[13] Mahima Joshi, Yudhveer Singh Moudgil,"secure Cloud Storage."
[14]Sarthak R Patel, Prof. Khushbu Shah, Gaurav R Patel, "Study on Improvements in RSA algorithm"
[15] P. Anusha, . R. Maruthi ” A survey paper on data integrity for cloud”
[16] Shraddha Saxena , Manish Sharma “Secure technique to achieve data privacy and data integrity in cloud computing “

Shivani kaushik, Anirudh tripathi, Pankaj Pratap Singh, Amit kishor, "Review Paper on Data Integrity for Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1408-1411, 2019.

Machine learning is very important technology that can support people in different disciplines (Agriculture, health centers, household, transportation, etc) and different levels of life. Machine learning increases accuracy. It uses various types of data (image, video, audio and text) for different purposes and applications. Our work mainly focuses on cactus diseases detection to early prevent the reduction of productivity (quantitatively and qualitatively) of the cereal. To do this, the researchers have used 500 unhealthy and 72 healthy cactus images. The images were enhanced, noises were removed and images were segmented to create good model using imadjust, guided filter and K-means clustering techniques respectively. These image preprocessing techniques were selected from many techniques after implementing each technique and measuring their performances. As part of creating the model, feature extraction techniques (Color histogram, Bag of features and GLCM) were applied to extract color, bag of features and texture and respectively. After testing the model applying these features, bag of features were found to be best for creating better model and they were selected as features of our model. We created our machine learning model using bag of features applying linear SVM. Other machine learning algorithms were used to train and test the model for detecting the diseases, but linear SVM was found with best performance (97.2%). In this task, 75% of each class were used for training and 25% were used for testing the model. Finally, the similarity for classification was checked using linear kernel, RBF kernel and Polynomial kernel and an average accuracy of 94% was achieved though linear kernel is the best classifying method with an accuracy of 98.951%.

Machine learning, supervised learning, unsupervised learning, training, classification, feature, bag of features, algorithm, k-means, MSE, PNSR, and linear SVM

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Hailay Beyene, Narayan A.Joshi, "Classification of Healthy and Diseased Cactus plants using SVM," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1412-1426, 2019.

Clustering is a significant task of research in data mining and analysis of statistics, which is initiate in many areas, including health care, social networking, image analysis, object recognition, etc. volume, quantity and speed. To effectively manage large-scale data sets and clusters, public cloud infrastructure plays an important role in both performance and economy. However, the use of public cloud services inevitably involves confidentiality issues. Indeed, not only a large scale of data mining applications but also deals with sensitive data such as personal healthcare information, location data, financial data, etc. In this paper we proposed Novel Secured MapReduce Based K-Means Clustering in Big Data Framework. This scheme achieves clustering speed and accuracy that are comparable to the K-means clustering without privacy protection. Furthermore we design securely integrated MapReduce framework and make it extremely suitable for parallelized processing in cloud computing environment.

K-means clustering, Data encryption, Privacy-preserving, MapReduce

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D. Saidulu, V. Devasekhar, V. Swathi, "Secured MapReduce Based K-Means Clustering In Big Data Framework," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1427-1430, 2019.

This paper contains the analysis of Queuing systems for data of sales check out operation at a busy mall as an example. Queuing theory is a most important area of applied mathematics which pacts with observable fact of waiting and take place from the employ of potent mathematical study to explain busy mall sales and this work provides signifies the data in the form of queueing models to evaluate the performance measures. The paper describes a queuing simulation for a multiple server process as well as for single queue models. This study requires an empirical data which may include the variables like, arrival time in the queue of checkout operating unit , departure time, service time, etc More over study of future behavior of the busy mall, using monte carlo simulation method to reduce minimum waiting time of the customers and length of the queues for M/M/1 and M/M/C so that to provide better service to the customers in the busy mall.

Queue model, multi channels queuing models (M/M/C), probability distributions, average waiting time, average queue length

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V.P. Murugan, P. Sekar, "Queueing Models Using Simulation Method in Busy Mall," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1431-1435, 2019.

Healthcare systems have been using data mining to predict disease in recent years. Early prediction of liver diseases is important to save human life, mainly to decrease mortality rates by taking appropriate disease control measures. This paper explores early predictions of liver disease through various classification techniques. The liver disease dataset selected for this study consists of 15 CT scan images of the liver. The images were segmented with GLCM features. The main purpose of this paper is to propose a hybrid classifier algorithm for predicting liver diseases involving multiple techniques. The proposed technique is also compared with existing classifiers like Naïve Bayes, K nearest neighbor and support vector machine (SVM) on the scales of sensitivity, specificity and classification accuracy. Experimental results of the proposed hybrid classifier algorithm were found to better in predicting liver diseases.

Classification, GLCM, liver disease, prediction, standard deviation

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R. Malathi, S. Ravichandran, "Dual Threshold Based Classification Technique (DTBCT) For Assessing Liver Abnormalities from Medical Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1436-1439, 2019.

There are many incidents of Lung Cancer in the globe. This Cancer is curable if diagnosed early stage, where screening plays an important role in prevention of the disease. Computed Tomography (CT) scans can provide medical information, but its access is limited in rural areas. Computer-aided diagnosis (CAD) which can assist in screening of cancer from medical images can also provide help to doctors in remote areas. Previous studies have promoted and proposed CAD based systems for predicting lung cancer. Their findings have laid the foundation of promise lung cancer diagnosis using the deep learning approaches. This paper proposes and demonstrates a novel Automated CNN Based Lung Cancer Prediction Technique (CNNLCPT), a set of unique steps in image processing for predicting lung cancer from medical CT scans. The accuracy of predictions is also demonstrated in the paper.

Automation, Deep Learning, Image processing, Lung cancer, Prediction

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M. Muthuraman, S. Ravichandran, "A Novel Automated CNN Based Lung Cancer Prediction Technique (CNNLCPT) for CT scan images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1440-1444, 2019.

Bio inspired algorithm plays a major role in data mining. The scope of the bio-inspired algorithm is very enormous, it provides major advantages to solve many computational problems. Bio-inspired and frequent path mining is embedded to solve critical problems in data mining. Frequent patterns in a data stream can provide an important basis for decision making and applications. This survey paper represents the applications bio-inspired algorithms, comparative study of Swarm based algorithms, which includes Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search (CS), Artificial Bee Colony (ABC), and Firefly algorithm, which enhance the performance to predict their competent frequent paths.

Bioinspired, Swarm algorithms, Evolutionary programming, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search(CS), Artificial Bee Colony (ABC), Firefly algorithm

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S.Kiruthika, A. Malathi, "A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1445-1452, 2019.