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Security is the occasions of anyone unfastened from hazard. Safety has very crucial difficulty in conversation. To solve the difficulty of security we arrange those integrate techniques. Cryptography is the artwork and science of observe of generating the secret message of the original message. As Steganography is the art and technological know-how of hiding verbal exchange, into some other media type file consisting of image, text and video. The numerous cryptography and steganography methods to cover statistics like LSB, DCT etc. However these strategies are wounded by a few problems like reduce quality of image, lower covering capacity. To success over this difficulty the proposed strategies use Substitution Encryption method to similarly encrypt the data and steganography method uses the Improved LSB Steganography technique which uses the formats like bmp, jpg and so forth. Performance of the proposed system is better than existing system.

Cryptography, Steganography, Encryption, LSB, Security

[1] Amritpal Singh, Harpal Singh “ An improved LSB based Image Steganography Technique for TGB Images” , 978-1-4799-6085-9/15/$31.00 ©2015 IEEE.
[2] Amritpal Singh, Satinder Jeet Singh, “An Overview of Image Steganography Techniques”, International Journal of Engineering and Computer Science ISSN: 2319-7242 Volume 3 Issue 7 July, 2014 Page No. 7341-7345.
[3] Atish Jain, Ronak Dedhia, Abhijit Patil, "Enhancing the Security of Caesar Cipher Substitution Method using a Randomized Approach for more Secure Communication ", International Journal of Computer Applications (0975 – 8887) Volume 129 – No.13, November2015.
[4] Balvinder Singh, Sahil Kataria, Tarun Kumar, Narpat Singh Shekhawat, "A Steganography Algorithm for Hiding Secret Message inside Image using Random Key", International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 IJERTIJERT IJERTV3IS120844 www.ijert.org Vol. 3 Issue 12, December-2014.
[5] B.Ramesh Kumar, K.Suresh, S.K.Basheer, M. Raja Krishna Kumar, "Enhanced Approach to Steganography Using Bitplanes", International Journal of Computer Science and Information Technologies, Vol. 3 (6), 2012.
[6] Deepanshi Nanda, Sonia Sharma, “Security in Cloud Computing using Cryptographic Techniques”, IJCST Vol. 8, Issue 2, April - June 2017
[7] Dipti Kapoor Sarmah, Neha Bajpai, "Proposed System for data hiding using Cryptography and Steganography", https://www.researchgate.net/publication/46585135. 15 April 2016.
[8] Dr.M.Umamaheswari, Prof.S.Sivasubramanian, S.Pandiarajan, " Analysis of Different Steganographic Algorithms for Secured Data Hiding", IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.8, August 2010.
[9] Dr. Mohammed A. F. Al-Husainy, Dr. Diaa Mohammed Uliyan," Image Encryption Technique based on the Entropy Value of a Random Block", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No.7, 2017.
[10] E.Thmbiraja, G.Ramesh, Dr.R.Umarani, “A survey on various most common encryption techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 2, Issue 7, July 2012.
[11] Er. Munish Katoch, Reenu Jaswal, “Image Steganography: A Review”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 4, April 2016.
[12] Gurpreet Singh, Supriya, “A Study of Encryption Algorithm( RSA,DES,3DES and AES) for information Security” , International Journal of Computer application, Vol. 67- No 19, april 2013.
[13] G.Prashanti, B.V.Jyothirmai, K.Sai Chandana, " Data Confidentiality Using Steganography and Cryptographic Techniques", 978-1- 5090-4967- 7/17/$31.00 © 2017 IEEE
[14] Gurjeevan Singh, Ashwani Kumar Singla, K.S. Sandha “Performance Evaluation of Symmetric Cryptographic Algorithms”, International Journal of Electronics and Communication Technology Vol 2 Issue 3, Sep 2011.
[15] Hayfaa Abdulzahra, Robiah Ahmad1, Norliza Mohd Noor, " Combining Cryptography And Steganography for Data Hiding in Images", ISBN: 978-960-474-368.
[16] Jagdish Mali1, Viraj Sonawane, Prof. R.N.Awale, “Image Steganography Using Block Level Entropy Thresholding Technique”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 3, Issue 4, Jul-Aug 2013, pp. 412-415.
[17] K.Thangadurai and G.Sudha Devi, "An analysis of LSB Based Image Steganography Techniques", 2014 International Conference on Computer Communication and Informatics (ICCCI -2014), Jan. 03 – 05, 2014, Coimbatore, INDIA.
[18] K. Arora, G. Gandhi, "A Review of Approaches for Steganography", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.118-122, 2014.
[19] Mandeep Kaur Gill and Rupinder Kaur Randhawa , "Comparative Study of Multibit LSB Steganography with Cryptography", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.120-123, 2015.
[20] Manoj Kumar, Gursewak Singh, “Block based Image Steganography using Entropy with LSB and 2-bit Identical Approach”, International Journal of Computer Application (0975-8887) Volume 171-No. 8, August 2017.
[21] Mehdi Hussain and Mureed Hussain, “ A Survey of Image Steganography Techniques” , ”, International Journal of Advanceed Science and Technology Vol. 54, May,2013.
[22] Marwa E. Saleh, Abdelmgeid A. Aly, Fatma A. Omara, " Data Security Using Cryptography and Steganography Techniques" , (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 6, 2016.
[23] Navneet Kaur, Sunny Behal, "A Survey on various types of Steganography and Analysis of Hiding Techniques", International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 8 - May 2014.
[24] Priyanka Haridas, Gouri Shankar Prajapati, " A Combined Approach of Steganography and Cryptography Techniques for Information Security: A Survey", International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 4 Issue 12, December-2015.
[25] Shashi Mehrotra Seth, Rajan Mishra, “Comparative Analysis of Encryption Algorithms for Data Communication” IJCST Vol. 2, Iss ue 2, June 2011
[26] S Ushll, G A Sathish Kumal, K Boopathybagan, "A Secure Triple Level Encryption Method Using Cryptography and Steganography", 978-1-4577-1587-7/111$26.00 ©20111EEE.

Sukhjeet kaur, Harpal Singh, "Enhanced Hybrid Techniques for Data Hiding," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.178-182, 2018.

In this paper, a two loop control strategy based on frequency domain measures is proposed for a PWM dc-dc buck converter to regulate the output voltage in the presence of input voltage disturbances and load variations. The parameters of inner PI controller and outer PI controller for a specified phase margin and gain cross over frequency are designed using the proposed algorithm. State space averaging technique is used for the modeling of a PWM dc-dc buck converter. The effectiveness of the proposed control strategy is validated using MATLAB/Simulink software for different input voltages and loads.

Two loop control, dc-dc buck converter, frequency domain measures, state space averaging technique

[1] A.W. Cristri, and R.F. Iskandar, “Analysis and Design of Dynamic Buck Converter with Change in Value of Load Impedance”, Procedia engineering, 170, pp.398-403, 2017.
[2] R.W. Erickson, and D. Maksimovic, “Fundamentals of power electronics”, Springer Science & Business Media, 2007.
[3] R.D. Middlebrook, and S. Cuk, “A general unified approach to modelling switching-converter power stages”, In Power Electronics Specialists Conference, June, 1976, IEEE,pp. 18-34, 1976.
[4] H. S. Thakur, R. N. Patel, "Assessment of Performance of Integrated Solar PV System with Hybrid Energy Storage System", International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.293-302, 2018.
[5] V. Vorpérian, “Simplified analysis of PWM converters using model of PWM switch. Continuous conduction mode”, IEEE Transactions on aerospace and electronic systems, Vol.26, Issue 3, pp.490-496,1990.
[6] F.L. Luo, and H. Ye, “Small signal analysis of energy factor and mathematical modeling for power dc–dc converters”, IEEE Transactions on Power Electronics, Vol.22, Issue 1, pp.69-79, 2007.
[7] V.S.C. Raviraj, and P.C. Sen, “Comparative study of proportional-integral, sliding mode, and fuzzy logic controllers for power converters”, IEEE Transactions on Industry Applications, Vol.33, Issue 2, pp.518-524,1997.
[8] M.M. Garg, Y.V. Hote, and M.K. Pathak, “Design and performance analysis of a pwm dc–dc buck converter using pi–lead compensator”, Arabian Journal for Science and Engineering, Vol. 40, Issue 12, pp.3607-3626, 2015.
[9] S. Choudhury, D. Panda, A. Choudhury and P. K. Rout, "A PV based autonomous microgrid performance analysis using PI and adaptive PI controller," 3rd International Conference on Electrical, Electronics, Engineering Trends, Communication, Optimization and Sciences (EEECOS 2016), Tadepalligudem, pp. 1-7, 2016.
[10] S. Das, M.S. Qureshi, and P. Swarnkar, “Design of integral sliding mode control for DC-DC converters”, Materials Today: Proceedings, Vol. 5, Issue 2, pp.4290-4298, 2018.
[11] K.M. Tsang, and W.L. Chan, “Non-linear cascade control of DC/DC buck converter”, Electric power components and systems, pp.977-989, 2008.
[12] D. Valério, and J.S. da Costa, “Tuning of fractional PID controllers with Ziegler–Nichols-type rules”, Signal processing, Vol. 86, Issue 10, pp.2771-2784, 2006.
[13] B.J. Patella, A. Prodic, A. Zirger, and D., “Maksimovic, High-frequency digital PWM controller IC for DC-DC converters”, IEEE Transactions on Power electronics, Vol. 18, Issue 2, pp.438-446, 2003.

R.D. Bhagiya, R.M. Patel, "Design and Performance Analysis of a Two Loop Control for a PWM dc-dc Buck Converter," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.183-188, 2018.

Due to advancement in technology and the features the mobile phone users are increasing with tremendous speed so the way of accessing information is changing day by day. Peoples are requiring the information or getting connected to their business when they are on move. The mobile computing applications have given rise to store data on mobile phones. Database developers have created light versions of databases for mobile devices. A trend of storing business-related data on mobile phones has already begun. Large data storage requires different frameworks. A framework for Distributed database system using Smart phone has been presented in this paper.

Distributed Database, Smart phone, SMS

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Nitin V. Wankhade, S.P.Deshpande, "Framework for Distributed Database System Using Smart Phone in Cellular Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.189-193, 2018.

Medical science is an important part segment of our day to day life. It generates medical report and other statistics in the form of multimedia format. The analysis and prediction of related disease can be done using the data analysis. Every prediction and analysis required image processing and its feature extraction. Processing a data with multiple feature aspect is still a challenging issue. Lung is the parts which carry multiple diseases as it’s a major body cycle process. Further which a proper classification and prediction is problem formulation with several research algorithm. Network based processing of data make use of its phases and help out to find more feature analysis and further detailed classification. In this paper an advance algorithm with semantic and ANN model is proposed for the lung cancer disease prediction from its image format. The analysis is compared with traditional SVM approach and proposed Semantic ANN approach. Implementation is performed using the images collected from web medical resources using MATLAB platform. The computed result shows the efficiency of proposed network layer based semantic solution for the lung cancer prediction and its feature analysis.

Machine Learning Algorithm; Artificial Neural networks; MATLAB; Data Sets; Genetic Algorithm

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[3]. M. Gomathi, Dr. P. Thangaraj, "A Computer Aided Diagnosis System for Detection of Lung Cancer Nodules Using Extreme Learning Machine", International Journal of Engineering Science And Technology Vol.2(10), 2010.
[4]. Jia Tong, Zhao Da-Zhe, Wei Ying, Zhu Xin-Hua, Wang Xu, "PC Aided Lung Nodule Detection Based On CT Images", IEEE/ICME International Conference on Complex Medical Engineering, 2007.
[5]. Taher F, Sammouda R, ―Lung Cancer Detection By Using Artificial Neural Network and Fuzzy Clustering Methods‖, IEEE GCC Conference and exhibition(GCC), 2011, 295-298.
[6]. Tong J, ZHAO D, Ying W, ZHU X and Xu W, ―Computer-Aided Lung Nodule Detection Based on CT Images ― IEEE-International Conference on Complex Medical Engineering, 2007, 816 – 819.
[7]. Tseng L, Huang L, ―An Adaptive Thresholding Method for Automatic Lung Segmentation in CT Images ― IEEE AFRICON, September 23-25, 2009, pp 1-5
[8]. Chaudhary A, Singh S, ―Lung Cancer Detection Using Digital Image Processing‖, IJREAS vol 2, Issue 2, 2012, 1351-1359.
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[14]. Ye X, Lin X, Dehmeshki J, Slabaugh G and Beddoe G, ―Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images‖, IEEE Transactions on Biomedical Engineering, , vol. 56, no. 7, 2009, 1810-1820.
[15]. Maini R and Aggarwal H ―A Comprehensive Review of Image Enhancement Techniques‖, Journal of Computing, Vol 2, Issue 3, 2010, 8-13.

Chandni Kumari, Kamlesh Chandravanshi, Gaurav Soni, "Lung Cancer Detection Using Semantic Based ANN Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.194-199, 2018.

Data mining is a result of advancement in information technology. It is a progression of discovering knowledge from large databases. Earthquake prediction is one of the major issue in seismology. The intention of the prediction is to make possible emergency measures to reduce death and demolition, breakdown by giving forewarning about earthquake. Now a day’s neural network plays a vital role in the prediction of earthquake. Back propagation is a neural network learning algorithm is used to analyze the relationship between the earthquakes. This analysis is performed with the parameters such as date, time of the event, latitude, longitude, depth and magnitude of past earth quake events. This set of data converted into seismic indicators by doing mathematical calculations and given to the input layer of neural network. The output of neural network is used for prediction. In this paper, various research articles deals with earthquake which are using neural network algorithms are studied and the accuracy rate of the prediction is compared.

Backpropagation, Neural network, Earthquake, Prediction

[1]. Mohammed J. Zaki, Wagner Meira Jr., “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Cambridge University Press, USA, pp. 4, 2014.
[2]. Pang-Ning Tan, Michael Steinbach, Vipin Kumar., “Introduction to data mining”, Pearson new International Edition, pp. 3-8, 2014.
[3]. N.S. Soleimani Zakeri, S. Pashazadeh , “Classification of databases and methods for seismic data analysis and earthquake prediction”, International Journal of technical and physical problem of engineering, Vol.6, No.1, pp.18-26, 2014.
[4]. Shaganpreet kaur, Chinu, “Comparative Analysis of Data Mining Techniques”,International journal of Computer Science and Engineering, Vol.6, Issue.4, pp.301-304, 2018.
[5]. Arjun singh, Amit Saxena, “A hybird Data Model for prediction of disaster using Data Mining Approaches”, International Journal of Engineering Trends and Technology, Vol 41, No.7, pp.384-392, 2016.
[6]. Dr.K.MohanKumar, K.Sangeetha , “Performance analysis on Decision Tree and SVM Classification with reference to flood occurrences in India”, International Journal of Advanced Research in computer Science, Vol.9, No.2, pp.478-483, 2018.
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[8]. R. Sukanya, K. Prabha, “Comparative Analysis for Prediction of Rainfall using Data Mining Techniques with Artificial Neural Network”, International journal of Computer Science and Engineering, Vol.5, Issue.6, pp.288-292, 2017.
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[10]. David Kriesel, “A Brief Introduction to Neural networks”, dkriesel.com, pp. 33, 2005.
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[13]. Reni Suryanita, Hendra Jingga “Application of Backpropagation Neural Networks in Predicting Story Drift of Building”, In the Proceedings of the International MultiConference of Engineers and Computer Scientists(IMECS 2017), Hong Kong, pp.20-23, 2017.
[14]. S. Narayanakumar, K. Raja, “A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India”,Circuits and Systems,Scientific research publishing,Vol.7, pp.3456-3468, 2016.
[15]. Gutenberg, B. and Richter, C.F., “Earthquake Magnitude, Intensity, Energy and Acceleration.” Bulletin of the Seismological Society of America , Vol.46, No.2, pp.105-146, 1956.
[16]. Maria Moustra,Marios Avraamides,Chris Christodoulou, “Artificial neural network for earthquake prediction using time series magnitude data or Seismic Electric Signals ”,Exprert Systems with application,Elesvier, Vol.38,No.12,pp.15032-15039, 2011.
[17]. Asim K.M, Idris.A, Iqbal.T,Martinez.Alvarez.F,“Earthquake prediction model using support vector regressor and hybrid neural networks”,PLOS ONE, Vol.13,No.7, pp.1-22, 2018.
[18]. Abdulrahman S.N. Alarifi, Nassir S.N. Alarifi, Saad Al-Humidan, “Earthquake magnitude prediction using artificial neural network inNorthern Red sea area”,Journal of King Saud University –Science,Vol.24, pp.301-313, 2012.
[19]. Ashif Panakkat, Hojjat Adeli, “Neural network models for Earthquake magnitude prediction using multiple seismicity indicators”, International Journal of Neural Systems, Vol.17, No.1, pp.13-33, 2007.
[20]. R.S. Kamath, R.K. Kamat, “Earthquake Magnitude Prediction for Andaman-Nicobar Islands: Adaptive Neuro Fuzzy Modeling with Fuzzy Subtractive Clustering Approach”, Journal of Chemical and Pharmaceutical Sciences, Vol.10,No.3, pp.1228-1233, 2017.
[21]. Fangzhou Xu, Xianfeng Song, Xinhong Wang, Juexiao Su “Neural Network Model for Earthquake Prediction using DMETER Data and Seismic Belt Information”, Second WRI Global Congress on Intelligent Systems(CGIS) Conference , China, pp.180-183, 2010.
[22]. Khawaja M. Asim, Muhammad Awais. F, Martı´nez–A´lvarez, “Seismic activity prediction using computational intelligence techniques in northern Pakistan”, Springer Acta Geophysica, Vol.65, No.5, pp.919-930, 2017.
[23]. G. Asencio-Corte´s, F. Martı´nez-A´lvarez, A.Troncoso,A. Morales-Esteban, “Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks”,Springer Neural computing and Applications,Vol.28, No.5, pp.1043-1055, 2015.
[24]. Emad Amar,Tawfik Khattab,Fatma Zada, “Intelligent Earthquake prediction System Based on Neural network”,World Academy of Science,Engineering and Technology International Journal of Civil and Environmental Engineering,Vol.8, No.12, pp.874-878, 2014.
[25]. J.Rayes, A.Morales-Esteban, F.Martínez-Álvarez, “Neural networks to predict earthquakes in chile”,Elsevier - Applied Soft Computing,Vol.13, Issue.2, pp.1314-1328, 2013.
[26]. Asim, K. M. ,Martínez-Álvarez, F., Basit, A., Iqbal, T., “Earthquake magnitude prediction in Hindukush region using machine learning techniques”,Springer Natural Hazards, Vol.85, Issue.1, pp.471-486, 2017.

K. Mohankumar, K. Sangeetha, "A Study on Earthquake Prediction Using Neural Network Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.200-204, 2018.

Steganography is a sort of cryptography in which the mystery message is covered up in an advanced picture. While cryptography is engrossed with the security of the substance of a message or data, Steganography focuses on hiding the specific presence of such messages from recognition. In the proposed system, maximum motion and high intensity between every consecutive frame is evaluated. The frame must be found in which maximum motion and intensity is there and this frame is marked as the target frame. This technique provides a large amount of security to the stego video as it is very difficult for the attacker to guess or find the target frame in which secret message is hidden. The proposed system hides the image in video frames that is indistinguishable. Video is collection of frames that contains multiple redundancies which provide high security to transfer data from one location to another. Video steganography gives emphasis on hiding the data in such a way so that it cannot be even detected by naked eyes. The unauthorized person may conceal data that is travel from one location to another location .A secure method is needed to safe information from unauthorized person. In the proposed work, Least Significant Bit (LSB) along with motion estimation is used to hide the message into a video in such way that does not raise suspicion. The proposed system generates the better results in terms of PSNR, MSE as compared to existing system.

Video Stegnography, Data hiding, Securing Data, LSB Technique, Maximum motion stegnography technique

[1] Amritpal Singh, Harpal Singh “An Improved LSB based Image Steganography Technique for RGB Images”, IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT),March 2015.
[2] Achmad Solichin and Painem “Motion-based Less Significant Frame for Improving LSB-based Video Stegnography”,International Seminar on Application for Technology of Information and Communication (ISemantic), Semarang, 2016, pp. 179-183.
[3] Anush Kolakalur, Ioannis Kagalidis, and Branislav Vuksanovic“Wavelet Based Color Video Steganography”, International Journal of Engineering and Technology(IACSIT ), Vol. 8, No. 3, March 2016.
[4] Dipak A. Mashe “Data hiding in motion vectorss of compressed video” International Advanced Research Journal in Science, Engineering and Technology Vol. 3, Issue 4, April 2016.
[5] Hitendra Donga, Kishor Atkotiya, "STEGO: A Tool for Implementing Text-Audio-Video Steganography", International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.159-162, 2017.
[6] Kasra Rezagholipour, Mohammad Eshghi, “Video Steganography Algorithm based on motion vector of moving object”, IEEE Eighth International Conference on Information and Knowledge Technology (IKT), Hamedan, Iran 2016.
[7] K.Rosemary Euphrasi, M. Mary Shanthi Rani, “A Comparative Study On Video Steganography in Spatial and IWT Domain”, IEEE International Conference on Advances in Computer Applications (ICACA),Oct2016.
[8] K. Steffy Jenifer , G. Yogaraj , K. Rajalakshmi “LSB Approach for Video Steganography to Embed Images”, International Journal of Computer Science and Information Technologies, (IJCSIT), Vol. 5 (1) , 2014, 319-32.
[9] Kousik Dasgupta, J.K. Mandal and Paramartha Dutta,“Hash Based Least Significant Bit Technique For Video steganography(HLSB)”, International Journal of Security, Privacy and Trust Management ( IJSPTM), Vol. 1, No 2, April 2012.
[10] K.Vidyavathi, Dr.R.S.Sabeenian, “Estimation and Compensation of Video Motion - A Review” Journal of Convergence Information Technology (JCIT),Volume 9, Number 6, November 2014.
[11] Ms.Pooja Vilas Shinde,Dr.Tasneem Bano Rehman, “A Survey: Video Steganography techniques” International Journal of Engineering Research and General Science ,Volume 3, Issue 3, May-June, 2015 ISSN 2091-2730.
[12] Mukesh Dalal, Mamta Juneja, "H.264/AVC Video Steganography Techniques: An Overview", International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.297-303, 2018.
[13] Paramjit kaur,Vijay laxmi, “An Upgraded approach for robust Video Watermarking Technique Using Stephens Algorithm”,International Journal of Computer Science and Mobile Computing” Vol.3,Issue.11,Nov 2014,pg. 612-622.
[14] Paramjit kaur,Vijay laxmi, “Review on different video watermarking techniques”,International Journal of Computer Science and Mobile Computing” Vol.3,Issue. 9,Sept. 2014,pg. 190-195
[15] Ramadhan J. Mstafa,Khaled M.Elleithey and Eman Abdelfattah “Video Steganography Techniques: Taxonomy, Challenges, and future directions”,IEEE Long Island Systems, Applications and Technology Conference (LISAT), May 2017.
[16] Ramadhan J. Mstafa, Khaled M. Elleithy,Eman Abdelfattah, “A Robust and Secure Video Steganography Method in DWT-DCT Domains Based on Multiple Object Tracking and ECC”, IEEE(2017).
[17] Ramadhan J. Mstafa, Khaled M. Elleithy,Eman Abdelfattah, “A New Video Steganography Algorithm Based on Multiple Object Tracking and Hamming Code”, 14th International Conference on Machine Learning and Applications,IEEE(2015).
[18] Ramandeep Kaur,Pooja,Varsha, “A Hybrid Approach for Video Steganography using Edge Detection and Identical Match Techniques” IEEE International Conference on Wireless Communications Signal Processing and Networking (WISPNET-2016).
[19] R. Shanthakumari and Dr.S. Malliga, “ Video Steganography Using LSB Matching Revisited Algorithm”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 6, Ver. IV(Nov – Dec. 2014), PP 01-06.
[20] Saravanan Chandran, Koushik Bhattacharyya,“Performance Analysis of LSB, DCT, and DWT for Digital Watermarking Application using Steganography”, International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) - 2015
[21] Sheng Dun Hu, KinTak U, “A Novel Video Steganography based on Non-uniform Rectangular Partition” 14thIEEE International Conference on Computational Science and Engineering CSE/I-SPAN,2011.
[22] Swetha V,Prajith V,Kshema V,“Data Hiding Using Video Steganography- A Survey” IJCSET, June 2015 Vol 5, Issue 6,206-213.
[23] Venkat P. Patil, Umakant Bhaskar Gohatre, R.B. Sonawane,“An Enhancing PSNR, Payload Capacity and Security of Image using Bits Difference Base on Most Significant Bit Techniques”, International Journal of Advanced Electronics & Communication Systems,21 March, 2017.
[24] Xijian Ping, Changyong Xu, Tao Zhang,“Steganography in Compressed Video Stream”, International Conference on Innovative Computing, Information and Control (ICICIC`06) IEEE 2006.

Karamjit Kaur, Vijay Laxmi, "A SECURE VIDEO STEGANOGRAPHY TECHNIQUE BASED ON MOTION DETECTION," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.205-210, 2018.

Image Steganography is an emerging field in computer science. There is a need of secured data transmission system to protect the data and maintain the privacy. This work is based on multilevel security method. The reversible data hiding has been implemented using a robust method. The secret image is encrypted using Reversible Image transformation, followed by AES (Advanced Encryption Standard) algorithm and finally the UES( Unified Encryption and Scrambling) is applied for data embedding into the carrier image. The secret key is also added to the encrypted image and it further makes the frame work more robust. The frame work is simulated using MATLAB and the statistical results like PSNR, MSQE and SSIM confirm the proposition.

AES, UES, public channel, , image encryption, reversible image transformation, privacy protection, imperceptible , secret key, stego, public channel, scrambling, PSNR, MSQE, SSIM

[1] K. Hwang, D. Li, “Trusted cloud computing with secure resources and data coloring,” IEEE Internet Computing, vol. 14, no. 5, pp. 14-22, Sept.-Oct. 2010.
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[5] V. Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y. Q. Shi, “Reversible watermarking algorithm using sorting and prediction,” IEEE Trans. on Circuits and Systems for Video Technology, vol.19, no.7, pp. 989-999, Jul. 2009.
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[9] J. Tian, “Reversible data embedding using a difference expansion,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, no.8, pp. 890-896, Aug. 2003.
[10] X. Hu, W. Zhang, X. Li, N. Yu, “Minimum rate prediction and optimized histograms modification for reversible data hiding,” IEEE Trans. on Information Forensics and Security, vol. 10, no. 3, 653-664, Mar. 2015.
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[15] BossBase image database, [Online]. Available: http://agents.fel.cvut.cz/stegodata/RAWS

Akanksha Bansal, Manoj Ramiya, Nirupma Tiwari, "Reversible Data Hiding using RIT, AES and UES Frame Work," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.211-215, 2018.

The present study examines the use of Remote Sensing and GIS techniques in the mapping of urban sprawl (2001-2011) and land use/land cover change detection. The Year of 2001 and 2011 has been taken to detect the urban sprawl. An urban area is the most powerful territory on the earth. As we know in the last decade, the size of the urban areas has been continuously increased, and it will go for in the future. In the present day, urban growth is a big problem. For better livelihood, the urban areas are expanding rapidly. Remote Sensing and GIS techniques can define the process of urban sprawl. In the present study, Landsat ETM Plus satellite imageries and Google Earth Pro has been used to identify the urban sprawl of the study area.

Remote Sensing, GIS, Urban Sprawl, Land Use Change Detection

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S. Fatema, A. Chakrabarty, "Urban Sprawl Monitoring with the help of Remote Sensing & GIS Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.216-221, 2018.

The mobility of the nodes is an important factor in Mobile Ad hoc Networks (MANETs). The reactive routing protocols are very useful to deal with the mobility of nodes. In the Reactive routing or On Demand protocol the transmission of data is always preceded by the process of finding the route. So these protocols can effectively deal with the stale routes arising due to the mobility of nodes. Examples of such protocols include Dynamic source routing protocol (DSR), Ad-hoc On demand Distance Vector Routing protocol (AODV) etc. The reactive protocols can further be categorized as Unipath and Multipath routing Protocols. The AODV is Unipath routing protocol whereas multipath variant of AODV is Ad hoc On demand Multipath Distance Vector Routing Protocol (AOMDV). Here the efficiency of both AODV and AOMDV has been tested using NS2 simulator for different number of nodes moving at different speeds with respect to different performance metrics.

MANET, Unipath, Multipath, Reactive Routing, Mobility, Stale routes, AODV, AOMDV

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[8] A. A. Chavan, Prof. D. S. Kurule, and Prof. P. U. Dere, "Performance Analysis of AODV and DSDV Routing Protocol in MANET and Modifications in AODV against Black Hole Attack," in Procedia Computer Science, 2016, pp. 835 – 844.
[9] S Gowrishankar, Subir Kumar Sarkar, and T.G. Basavaraju, "Performance Analysis of AODV, AODVUU, AOMDV and RAODV over IEEE 802.15.4 in Wireless Sensor Networks," in 2nd IEEE International Conference on Computer Science and Information Technology, 2009, pp. 59-63.

Jaideep Atri, Shuchita Upadhyaya, "ANALYSING EFFICIENCY OF MULTIPATH ROUTING ON REACTIVE ROUTING PROTOCOLS IN MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.222-225, 2018.

News channels established a 24-hour news habit which gets updated virtually in every second. Archiving becomes a challenging process since the news production is huge. Viewers are interested in news stories as it delivers useful and detailed information in short form. The news story created based on the history and the latest news updates The journalists access news archives to get details about the news happened related to the new happenings. Searching archives, fetching and linking related news is a tedious job for a reporter. In this work, a system is suggested which uses ontology and vanilla recurrent neural network to create news automatically for a query. The framework is evaluated using BLEU method and correlated with human evaluation. Ontology completeness decides the quality of the news generated.

Ontology, Deep learning, recurrent neural network, news generation, Personalization

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Shine K George, Jagathy Raj V. P, "Ontology based News Extraction System using Vanilla Recurrent Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.226-230, 2018.