Volume-6 , Issue-6 , Jun - 2018 Go Back

Machine Learning field has evolved from the field of Artificial Intelligence, in which machines aim to follow intellectual capabilities of human beings. Machine learning is a stream of study of computer algorithms that outperform through experience. These algorithms help in discovery of rules and patterns in sets of data. The paper presents architecture of a mobile intelligent system. It also presents machine learning algorithms useful for mobile intelligent system. Besides it also discusses performance indicators of machine learning algorithms for mobile intelligent system.

Machine Learning, SVM, CBR, CART

[1] Damopoulos, D., Menesidou, S.A., Kambourakis, G., Papadaki, M., Clarke, N., Gritzali, N., “Evaluation of Anomaly-Based IDS for Mobile Devices Using Machine Learning Classifiers”, Security Comm. Networks, Vol. 5, Issue. 1, pp. 3-14, 2011.
[2] Firdausi, I., Lim, C., Erwin, A., Nugroho, A.S. “Analysis of machine learning techniques used in behavior- based malware detection”, In: IEEE International Conference on Advances in Computing, Control, and Telecommunication Technologies, PP. 201-203, 2010.
[3] EI-Bendary, N., EI-Hariri, E., Hassanien, A.E., Badr, A.. “Using machine learning techniques for evaluating tomato ripeness. Expert Systems with Applications”, Vol. 42 Issue. 4, pp. 1892–1905, 2015.
[4] Phadikar, S., Sil, J., Das, A.K. “Rice diseases classification using feature selection and rule generation techniques”, Computers and Electronics in Agriculture, Vol. 90, Issue. C, pp. 76–85, 2013.
[5] Kundu, P.K., Panchariya, P.C., Kundu, M. “Classification and authentication of unknown water samples using machine learning algorithms”, ISA Transactions, Vol. 50, Issue.3, pp. 343-520, 2011.
[6] Sankaran, S., Mishra, A., Ehsani, R., Davis, C., “A review of advanced techniques for detecting plant diseases”, Computers and Electronics in Agriculture, Vol. 72, Issue. 1, pp. 1-13, 2010.
[7] Timmermans, A.J.M., and Hulzebosch, A.A., “Computer vision system for on-line sorting of pot plants using an artificial neural network classifier”, Computers and Electronics in Agriculture, Vol. 15, Issue.1, pp. 41–55, 1996.
[8] Butler, D.R., Wadia, K.D.R., Jadav, D.R., “Effects of leaf wetness and temperature on late leaf spot infection of groundnut”, Plant Pathology, Vol.43, Issue. 1 pp. 112–120, 1994.
[9] Chaudhary, A., Kolhe, S., Raj Kamal, “A Hybrid ensemble for classification in multiclass datasets: An application to Oilseed disease dataset”, Computers and Electronics in Agriculture, Vol. 124,pp. 65-72, 2016.
[10] Chaudhary, A., Kolhe, S., Raj Kamal, “An improved Random Forest Classifier for multi-class classification”, Information Processing in Agriculture, Elsevier Journal Information Processing in Agriculture, Vol. 3, Issue.4, pp. 215-222, 2016.
[11] Pujari, J.D., Yakkundimath, R., Byadgi, A.S., “Classification of fungal disease symptoms affected on cereals using color texture features”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 6, Issue. 6, pp. 321-330, 2013.
[12] Quinlan, J.R., “Generating Production Rules from Decision Trees”, In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 304–307, 1987.
[13] Zhao, Y., and Zhang, Y., “Comparison of decision tree methods for finding active objects”, Advances in Space Research, Vol. 41, Issue. 12, pp. 1955-1959, 2008.
[14] Quinlan, J.R., “Generating Production Rules from Decision Trees”, In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 304–307, 1987.
[15] Iba, W., and Pat, L., “Induction of One-Level Decision Trees”, In: Proceedings of the Ninth International Conference on Machine Learning, Aberdeen, Scotland, San Francisco, CA: Morgan Kaufmann, pp. 233–240, 1992.
[16] Breiman, L., “Random Forests”, Machine Learning, Vol. 45, Issue. 1,pp. 5–32, 2001.
[17] Seera, M., and Lim, C.P., “A hybrid intelligent system for medical data classification”, Expert Systems with Applications, Vol. 41, Issue. 5,pp. 2239–2249, 2014.
[18] Titapiccolo, J.I., Ferrario, M., Cerutti, S., Barbieri, C., Mari, F., Gatti, E., “Signorini, M.G., Artificial intelligence models to stratify cardiovascular risk in incident hemodialysis patients”, Expert Systems with Applications, Vol. 40, Issue. 11, pp. 4679–4686, 2013.
[19] Azar, A.T., Elshazly, H.I., Hassanien, A.E., Elkorany, A.M., “A random forest classifier for lymph diseases”, Computer Methods and Programs in Biomedicine, Vol. 113, Issue. 2,pp. 465–473, 2014.
[20] Austin, P.C., Tu, J.V., Ho, J.E., Levy, D., Lee, D.S., “Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes”, Journal of Clinical Epidemiology, Vol. 66, Issue. 4, pp 398-407, 2013.
[21] Tripoliti, E.E., Fotiadis, D.I., Argyropoulou, M., Manis, G., “A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data”,. Journal of Biomedical Informatics, Vol. 43, Issue. 2, pp. 307–320, 2010.
[22] Ramírez, J., Górriz, J.M., Segovia, F., Chaves, R., Salas-Gonzalez, D., López, M., Álvarez, I., Padilla, P., “Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification”, Neuroscience Letters, Vol. 472, Issue. 2, pp. 99-103, 2010.
[23] Singh, Y., Bhatia, P.K., Sangwan, O.M., “A Review of studies on Machine Learning techniques”, In: International Journal of Computer Science and Security, Vol. 1, Issue. 1, pp. 70-84, 2007.
[24] Chaudhary, A., Kolhe, S., Raj Kamal, “Machine Learning Techniques for Mobile Devices: A Review”, International Journal of Engineering Research and Applications, Vol. 3, Issue. 6, pp. 913-917, 2016.
[25] Oubaha, J., Habbani, A., Elkoutbi, M., “Mobile Intelligent System (MIS) and a multi-criteria in MPLS networks”, International Journal of Next Generation Networks (IJNGN), Vol. 2, Issue. 4, pp. 61-71, 2010.
[26] Jain, P.,Bhat, K., Kesharwani, H., Bhate, P., Khurana, K.P., “Stock Market Analysis and Prediction using Hadoop and Machine Learning”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue. 5, pp. 578–584, 2018.
[27] Sharmila, R., Chellammal, S., “A conceptual method to enhance the prediction of heart diseases using big data techniques”, International Journal of Computer Sciences and Engineering, Vol. 6, Special Issue. 4, pp. 21–25, 2018.

Archana Thakur, Ramesh Thakur, "Machine Learning Algorithms for Intelligent Mobile Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1257-1261, 2018.

This paper gives a review about Google Blockly which is an open source programming library developed by Google to add block-based code to an application. It provides a graphical editor user interface and a code base for generating code in different programming languages. The paper also illustrates the working of Google Blockly with simple examples and reviews the applications of Google Blockly in various professional areas; focus is given on the innovative applications areas where Google Blockly can be used.

Google Blockly, Blockly, Ebot

[1] Innovative Methods, User-Friendly Tools, Coding, and Design Approaches In People - Oriented Programming Book, Accessed on : 11.03.2018.
[2] Todorka Glushkova, "Application of Block Programming and Game-Based Learning to Enhance Interest in Computer Science",Journal of innovations and sustainability, Vol.06,Issue.01,pp.1-5,2016, Accessed on : 10.02.2018.
[3] Neil Fraser, "Ten Things We’ve Learned from Blockly ", IEEE Blocks and Beyond Workshop, pp.1, 2015, Accessed on 10.04.2018.
[4] Ioana Culic, Alexandru RADOVICI, Laura Mihaela Vasilescu, "Auto-Generating Google Blockly Visual Programming Elements for Peripheral Hardware", Accessed on 27.02.2018.

Uttara Athawale, Krishnakumar Yadav, Vishal Yadav, "Review of Google Blockly and its Innovative Use," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1262-1266, 2018.

The quick progression in the trading of data through internet made it simpler to trade information correct and quickest to the receiver. The security of information is one of the immense parts of information technology and communication. Steganography is a term utilized for data hiding and it is a craft of concealed written work. In steganography we hide information with a multimedia carrier i.e. image, text, audio, video files, etc. Thus, the observers can`t locate the concealed data which we need to send to the recipient. Steganography principle objective is to give robustness, imperceptibility, and limit of hiding information because of which it contrasts from different methods, for example, watermarking and cryptography. In image steganography we shroud our secret information in image with the goal that the observer can`t feel its reality. Image steganography procedures are starting late been valuable to send any secret message in the secured image carrier to foresee dangers and assaults, however, it doesn`t give any kind of chance to programmers to discover the secret technique. Image steganography is efficient and better type than other types of steganography. The LSB method also faces the same challenge regarding the selection of which bits are used for hiding the data without effect the actual image pixels. This paper proposes a new technique used to hide information by image steganography using matrix multiplication in which we pick 6th , 7th , 8th bit of image pixel and add our message with it by applying some techniques and resultant will be added or placed at 6th , 7th , 8th bit of image. In this we made changes up to 3 bits, i.e. changes of seven in binary term, but we have to add +6 or -6 not the seven to form the stego image.

Steganography, Information hiding, LSB, Matrix multiplication, PSNR, MSE

[1] A. Cheddad and J. Condell, “Image Steganography survey and analysis”, Faculty of Computing and Engineering, Volume 90, Issue 12, pp. 727-752 , 2010
[2] T. Morkel and J.H.P. Eloff, “Exploring simple steganography”, Information and Computer Security Architecture Research Group, Department of Computer Science, University of Pretoria, Volume 87, Issue 2, pp. 26-34, 2005
[3] S. R. Yadav, A. Tiwari and N.K. Mittal, “Image Steganography technique”, International Journal of Engineering and Innovative Technology, Volume 3, Issue 7, pp. 19-23, 2014
[4] L. Zhi and S. A. Fen, “LSB Image Steganography”, Vehicular Technology Conference IEEE, Volume 3, Issue 5, pp. 2113-2117, 2004
[5] K. Muhammad and M. Sajjad, “Modification on LSB method”, Springer Science, Volume 22, Issue 1, pp. 647-654, 2015
[6] B. Siddiqui and S. Goswami, “A Survey on Image steganography using LSB substitution technique”, International Research Journal of Engineering and Technology, Volume 4, Issue 5, pp. 345-349, 2017
[7] K. Joshi, R. Yadav , “A new image steganography using three bit plane of gray level images”, International Research Journal of Engineering and Technology, Volume 10, Issue 38, pp. 1-8, 2017
[8] R. Tavoli and M. Bakhshi, “A new method for text hiding in the image using LSB”, International Journal of advanced computer science and applications, Volume 7, Issue 4, pp. 126-132, 2016
[9] S. Batra, R. Rishi and R. Yadav, “Insertion of message in 6th , 7th , 8th bit of pixel and its retrieval in cases changes the LSB of image pixels”, International Journal of security and its applications, Volume 4, Issue 3, pp. 1-10, 2010
[10] S. Arjun, A. Negi and C. Kranthi, "An approach to adaptive steganography based on matrix embedding”, Proceedings of the IEEE Conference, 2014
[11] S. K. Azad and S. K. Muttoo, “A Survey on Image steganography using LSB substitution technique”, International Journal of Electronic security and Digital Forensics, Volume X, Issue Y, pp. 1-21, 2014
[12] C. Zou, W.Mazurczyk and G.T. Leavens, “Image Steganography technique”, Computer Vision and Pattern Recognition Workshop and IEEE Conference, 2017
[13] A.F. Nilizadeh and A. R. Naghsh Nilchi, “Steganography on RGB Images based on matrix pattern using random blocks ”, International Journal of Modern Education and Computer Science, Volume 5, Issue 4, pp. 8-18, 2013
[14] A.F. Nilizadeh and A. R. Naghsh Nilchi, “A Novel Steganography method based on matrix pattern and LSB algorithms in RGB images”, Swarm Intelligence and Evolutionary Computation and IEEE Conference, 2016
[15] T. Yang and H. Chen, “Matrix embedding insteganography with binary Reed-Muller codes”, Institution of Engineering and Technology, Volume 11, Issue 7, pp. 522-529, 2017
[16] V. M. Potdar and E. Chang, “Gray level modification steganography for secret communication”, Proceedings of the International Conference on Industrial Informatics, pp. 223-8, 2004
[17] H.C. Wu, N.I. Wu, C.S. Tsai, and M.S. Hwang, “Image steganographic scheme based on pixel-value differencing and LSB replacement methods”, IEEE Proceedings of Vision, Image and Signal Processing, Volume 152, Issue 5, pp. 611-15, 2005
[18] D.C. Wu and W.H. Tsai, “A steganographic method for images by pixel-value differencing”, Pattern Recognition Letters, Volume 24, Issue 9/10, pp. 1613-1626, 2003
[19] C. C Chang and H.W. Tseng, “A steganographic method for digital images using side match”, Pattern Recognition Letters, Volume 25, Issue 12, pp. 1431-1437, 2004
[20] C. C. Chang, T. D. Kieu and Y. C. Chou, “Using nearest covering codes to embed secret information in grayscale images”, Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 315-20, 2008
[21] A. Faruq and H.S. Ghwanmeh, “An innovative information hiding technique utilizing cumulative peak histogram regions”, Journal of Systems and Information Technology, Volume 14, Issue 4, pp. 336-352, 2012
[22] K. Joshi, R. Yadav and R. Saini, “A new image steganography approach for information security using gray level images in spatial domain”, International Journal on Computer Science and Engineering, Volume 3, Issue 7, pp. 2679-2690, 2011
[23] K. Joshi, R. Yadav and S. Allwadhi, "PSNR and MSE based investigation of LSB”, Proceedings of the International Conference on Computational Techniques in Information and Communication Technologies, pp. 280-285, 2016
[24] K. Joshi, R. Yadav and G. Chawla, “An Enhanced method for data hiding using 2 bit XOR in image steganography”, International Journal of engineering and technology, Volume 8, Issue 6, pp. 3043-3055, 2017

Tanu Garg, Kamaldeep Joshi, Jyoti Pandey, Harkesh sehrawat, Rainu Nandal, "Enhancement of Efficiency in LSB Steganography Method Using Matrix Multiplication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1267-1278, 2018.

In the steganography, data are covered in terms that prevent mystery data. It`s for the most part intention is exchanging information starting with one area, then onto the next area. It implies disguised mystery information inside the other information in a way that a pundit can`t discover the presence of genuine topics. In steganography we shroud data with a mixed media bearer i.e. picture, content, sound, video documents, and so forth. In this way, that eyewitness can`t locate the concealed data which we need to send to the recipient. Steganography principle objective is to give heartiness, perceptibility, limit of shrouded information because of which it varies from different strategies, for example, watermarking and cryptography. In picture steganography we conceal our mystery information in the picture with the goal that the spectator can`t feel its reality. Steganography need is that the cover picture must be accurately picked. A characteristic picture should not to be used, it is better for stenographer to make their own specific pictures. Disguising the mystery information in the pictures, different sorts of techniques are utilized as a part of which some are more grounded than others to hide the information. In this paper a new method is used by using the gray code. Firstly, select the pixels in which message is embedded and then these pixels convert into ASCII code and apply the gray code method in the 7th bits of pixels and encoded pixels. The main advantage is only one bit is changed in each step. This method makes extraction of real message difficult. If an attacker come to know about the secret message in an image then will be not able to know the real message as it has to be added in the 7th bit by changing the binary code to gray code.

steganography, LSB, gray code, ASCII code, PSNR, MSE

[1]. S. Shahreza and M. Shahreza, “Steganographyin Textiles”, 4th International Conference on Information Assurance and Security, Volume 3, Issue 6, pp. 5661, 2008.
[2]. S. Batra, R. Rishi and Rajkumar , ”Insertion of Message in 6th, 7th and 8th bit of pixel values and its retrievals in case intruder changes the least significant bits of image pixels‏”, International Journal of security and its applications, Volume 4,Issue 3, pp. 1-10, 2010.
[3]. Parvinder, S. Batra and H. Sharma, “Evaluating the Performance of Message Hidden in First and Second Bit Plane”, W SEAS Transaction on Information International Journal of Computer Applications, Volume. 2, Issue. 86, pp. 1220- 1222, 2005.
[4]. K. Bailey, and K. Curran, “An evaluation of image based steganography methods”, Multimedia Tools, Volume 2, Issue 2, pp. 55–88, 2006.
[5]. S. Manaseer, A. Aljawawdehand and D. Alsoudi, “A new Image steganography depending on reference & LSB”, International Journal of Applied Engineering Research ISSN 0973-4562, Volume 12, Issue 9, pp. 1950-1955, 2017.
[6]. K. Joshi, R. Yadav and G. Chawla, “An Enhanced method for data hiding using 2 bit XOR in image steganography”, International Journal of engineering and technology, Volume 8, Issue 6, pp. 3043-3055, 2017.
[7]. K. Qazanfari, R. Safabakhsh, “A new steganography method which preserves histogram: generalization of LSB”, International Journal of Engineering, Volume 277, Issue 7, pp. 90-101, 2017.
[8]. P. Li and A. Lu ,“LSB-based Steganography Using Reflected Gray Code for Color Quantum Images”, International Journal of Theoretical Physics, Volume 57, Issue 5, pp. 1516-1548, 2018.
[9]. A. Kumar Bairagi, “ASCII based Even-Odd Cryptography with Gray code and Image Steganography: A dimension in Data Security”, International Journal of Engineering, Volume 01, Issue 02, pp.2078-5828, 2011.
[10]. C. Chen, C. Chang, “LSB-Based Steganography Using Reflected Gray Code”, IEICE - Transactions on Information and Systems, Volume E91-D, Issue 4, pp. 1110-1116, 2008.
[11]. Chang and Tsang ,“Performance Evaluation of a Steganographic Method for Digital Images Using Side Match”, Conference on Innovative Computing, Information and Control, Volume 25, Issue 12, pp.1431-1437, 2006.
[12]. H.Wu, Tsai, N. Wu, Hwang, “Image steganographic scheme based on pixel-value differencing and LSB replacement methods”, IEE Proceedings - Vision, Image and Signal Processing, Volume 152, Issue 5, pp.611-615, 2005.
[13]. K. Joshi and R. Yadav , “A New Method of Image Steganography using Last Three Bit Plane of Gray Scale Images”, Indian Journal of Science and Technology, Volume 10,Issue 38, 2017.
[14]. K. Joshi and R. Yadav, “A new LSB-S image steganography method blend with Cryptography for secret communication”, Conference: Third International Conference on Image Information Processing, pp. 86-90, 2015.
[15]. M. Chaudhary, K. Joshi, R. Yadav, R. Nanda, “Survey on Image Steganography and its Techniques”, International Journal of Engineering and Technology, 2017.
[16]. A Saini, K Joshi, K Sharma, R Nandal, “An Analysis of LSB Technique in Video Steganography using PSNR and MSE”, International Journal, Volume 8, Issue 5, 2017.
[17]. A. Faruq and H.S. Ghwanmeh, “An innovative information hiding technique utilizing cumulative peak histogram regions”, Journal of Systems and Information Technology, Volume 14, Issue 4, pp. 336-352, 2012.

Sangeeta, Jyoti Pandey, Kamaldeep Joshi, Rainu nandal , "A Novel Gray Code based Image Steganography Model for Covert Communication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1279-1288, 2018.

This paper presents an efficient approach for sentiment and domain analysis of text sentences using POS tagging and random forest classifier machine learning approach. POS tagging technique is used for sentiment analysis while Random forest classifier is used for domain analysis of the text sentences. Various categories of sentiments are defined as positive, neutral, and negative while the domain’s categories are defined on various real & professional life sentences to train the system like education & research, personal, marketing & advertizement, security of nation, political, religious, sports and legal issues. Every text sentence always reflects the domain’s categories along with its sentiments. Therefore, Analyzing domain of text sentences along with sentiments is a challenging task and can be useful for various applications based on human computer interaction. The experimental result shows that the proposed method works effectively, efficiently and can be applied on real life applications where obligatory actions are taken automatically through sentences.

Random forest classifier, Machine Learning, Domain Analysis,Sentiment Analysis, Human computer interaction

[1] S. Rathor, R. S. Jadon, "Text independent speaker recognition using wavelet cepstral coefficient and butter worth filter", 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-5, 2017. doi:10.1109/ICCCNT.2017.8204079.
[2] A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, "Current State of Text Sentiment Analysis from Opinion to Emotion Mining," ACM Computing Surveys, 2017.
[3] A. Abbasi, H. Chen, S. Thoms, and T. Fu, "Affect analysis of web forums and blogs using correlation ensembles," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 9, pp. 1168-1180, 2008.
[4] S Dahl, George E., Dong Yu, Li Deng, and Alex Acero. "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition", IEEE Transactions on Audio, Speech and Language Processing , vol 20, no. 1, pp 30-42, 2012.
[5] I. Szoke, P, Schwarz, P.Matejka, L. Burget, M. Karafiat and J. Cernocky, “Phoneme Based Acoustics Keyword Spotting in Informal Continuous Speech” Speech and Dialogue. Springer, Berlin, Heidelberg vol 3658. pp. 302–309,
[6] V. Krakovna and F. Doshi-Velez, “Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models”, ICML Workshop on Human Interpretability in Machine Learning (WHI 2016).
[7] D.Stowell, D. Giannoulis, E. Benetos, M. Lagrange, and M. D. Plumbley, "Detection and Classification of Acoustic Scenes and Events," IEEE Transactions on Multimedia, 2015.
[8] Z. J. Chuang, and Wu. Chung-hsien, "Multi-modal emotion recognition from speech and text." Journal of Computational Linguistics and Chinese, Vol. 9, no. 2, pp. 45-62, 2004.
[9] D. N. Agrawal and D. Kapgate, “Face Recognition Using PCA Technique”, International journal of Computer science and Engineering, Vol. 2, Issue 10, pp. 59-61, 2014.
[10] A. D Kulkarni and B. Lowe, "Random Forest Algorithm for Land Cover Classification”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 4, Issue 3, pp.58- 63,2016.

S. Rathor, R. S. Jadon, "Sentiments and Domain Analysis of Text Sentences Using POS Tagging & Machine Learning Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1289-1292, 2018.

In the modern era of technology, Multiprogramming (Multi-Tasking) became a major issue for a system because every user wants to run many applications at a time. In time shared-environment, system resources distributed among the available application in main memory. Improper uses of system resources may degrade the system performance, so we need proper utilization of resources. Hence scheduling is a mechanism by which we can utilize the system resources efficiently. We have many CPU scheduling algorithms but in contrast to time shared environment, round robin is the best choice. In this algorithm, processes get equal opportunity to execute its task. System performance solely depends on scheduling algorithm and round robin performance only depends on the choice of time quantum. Thus selecting the optimal time quantum is the main problem in this algorithm because if it very small then CPU will spend lots of time context switches which will degrade the system performance and if it is too large then response time processes will be increased which cannot be tolerated in time shared environment. Therefore this shows an optimal approach for selecting time quantum and comparison shows that the OASRR algorithm performance better than all variants of round robin algorithms and this algorithm having minimum avg. turnaround time, waiting time and less no. of context switches.

CPU Scheduling, Round Robin, Time Quantum, Waiting Time, Turnaround Time, Context Switches

[1] Saroj hiranwal and D.r. K.C.Roy"Adaptive Round Robin Scheduling using Shortest Burst Approach Based on Smart Time Slice”.volume 2,issue 3.
[2] Sanjay Kumar Panda and Saurav Kumar Bhoi, “An Effective Round Robin Algorithm using Min-Max Dispersion Measure” ISSN: 0975-3397, Vol. 4 No. 01, January 2012.
[3] "Tannenbaum, A.S., 2008" Modern Operating Systems. 3rd Edn., Prentice Hall, ISBN: 13:9780136006633, pp: 1104.
[4] “Silberschatz, A., P.B. Galvin and G. Gagne, 2008”Operating Systems Concepts. 7th Edn., John Wiley and Sons, USA., ISBN: 13: 978-0471694663, pp: 944.
[5] H. S. Behera, Rakesh Mohanty, Sabyasachi Sahu and Sourav Kumar Bhoi.”Comparative performance analysis of multi-dynamic time quantum round robin (mdtqrr) algorithm with arrival time”, ISSN: 0976-5166, Vol. 2, No. 2, Apr-May 2011.
[6] “Rakesh Mohanty, H.S. Behera and et. al, Design and Performance Evaluation of a new proposed Shortest Remaining Burst Round Robin(SRBRR) scheduling algorithm, Proceedings of the International Symposium on Computer Engineering and Technology(ISCET), March 2010.
[7] "Yaashuwanth .C & R. Ramesh" Intelligent time slice for round robin in a real-time operating system, IJRRAS 2 (2), February 2010
[8] Milan Milinkovic, “Operating Systems Concepts and Design”, McGraw-Computer Science Series, second edition.
[9] A. Dhore “Operating Systems”, Technical Publications.
[10] M. Dietel, “Operating Systems”, Pearson Education, Second Edition.
[11] “Real Time Task Scheduling “, NPTEL, IIT Kharagpur
P. Balakrishna Prasad, “Operating Systems” Second Edition

Sumit Mohan, Rajnesh Singh, "An Optimal Approach to Selecting the Time Quantum for Dynamic Round Robin," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1293-1296, 2018.

A lot of resources and computing facilities are afforded by Cloud computing through the Internet. It attracts many users with its advantageous features. Despite of this, Cloud system experience several security issues. Distributed Denial of Service (DDoS) attacks is the most dangerous attack in the cloud computing environment. Hence, it is important to develop an Intrusion Detection System (IDS) to detect the attacker with high detection accuracy in the cloud environment. This work proposes an anomaly detection system named Ant Agent Rule Based Multiclass Support Vector Machine (AA-RB-MSVM) Algorithm at the hypervisor layer which is a hybrid approach of various algorithms like Ant Colony Algorithm, Rule based Approach and Support Vector Machine Algorithms to progress the precision of the detection system. The DARPA’s KDD cup dataset 1999 is used for experiments. The proposed algorithm shows high detection accuracy and low false positive rate based on the experimental observation when compared with the existing algorithms.

DDoS attack, Resource Availability, Cloud Computing, Soft Computing

[1] Md. Tanzim Khorshed, A.B.M Shawkar Ali, Saleh A. Wasimi, “ A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing”, Future Generation Computer Systems, Vol 28, 2012, pp 833-851.
[2] Hai J, Guofu X, Deqing , “AVMM-based intrusion prevention system in cloud computing environment”, J Supercomputer Springer Science, Bus Media 66(3):1133–1151. 2013
[3] Vishnu Patidar, Makhan Kumbhkar, “Analysis of Cloud Computing Security Issues in Software as a Service”, International Journal of Scientific Research in Computer Science and Engineering, Vol.2, Issue 3, pp 1-5, June 2014.
[4] Pandeeswari.N, Ganesh kumar, “Anomaly detection system in Cloud Environment using Fuzzy Clustering based ANN”, Mobile Network and Applications, Vol 21 (3), pp 494-505, August 2015.
[5] A Bala, Y Osais, “Modelling and simulation of DDOS Attack using SimEvents”, International Journal of Scientific Research in Network Security, Vol 2(1), pp 39–45,2013.
[6] Vinothina V, Sridaran R, Padmavathi G,” A survey on resource allocation strategies in cloud computing”, International Journal of Advanced Computer Science Applications, Vol 3(6), pp 97–104, 2012
[7] W Wang, S Gombault, W Wang, S Gombault, Proceedings of the 3rd International conference on Risks and Security of Internet and Systems (CRiSIS’08), in Efficient detection of DDoS attacks with important attributes(IEEE, Tozeur, 2008), pp. 61–67
[8] V Bolon-Canedo, N Sanchez-Marono, A Alonso-Betanzos, Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst Appl 38(5), 5947–5957 (2011)
[9] Z Baig, S Sait, A Shaheen, GMDH-based networks for intelligent intrusion detection. Eng Appl Artif Intel 26(7), 1731–1740 (2013)
[10] S Lin, K Ying, C Lee, Z Lee, An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Appl Soft Comput 12(10), 3285–3290 (2012)
[11] Y Li, J Xia, S Zhang, J Yan, X Ai, K Dai, An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst Appl 39(1), 424–430 (2012)
[12] S Sindhu, S Geetha, A Kannan, Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst Appl 39(1), 129–141 (2012)
[13] F Zhang, D Wang, Proceedings of the 8th International Conference on Networking, Architecture and Storage (NAS), in An effective feature selection approach for network intrusion detection (IEEE, Xi’an, 2013), pp. 307–311
[14] S Bhattacharya, S Selvakumar, Multi-measure multi-weight ranking approach for the identification of the network features for the detection of DoS and Probe attacks. Compt. J. 1-21 (2015)
[15] A Olusola, A Oladele, D Abosede, in Proceedings of the World Congress on Engineering and Computer Science. Analysis of KDD’99 intrusion detection dataset for selection of relevance features (San Francisco, USA, 2010), pp.1-7.http://www.iaeng.org/publication/WCECS2010/WCECS2010_pp162-168.pdf
[16] G Geng, N Li, S Gong, The Proceedings of International Conference on Industrial Control and Electronics Engineering (ICICEE), in Feature Selection Method for Network Intrusion Based on Fast Attribute Reduction of Rough Set (IEEE, Xi’an, 2012), pp. 530–534
[17] Akramifard, H., et al. "Intrusion Detection in the Cloud Environment Using Multi-Level Fuzzy Neural Networks." Proceedings of the International Conference on Security and Management (SAM). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2015.
[18] Bhat, Amjad Hussain, Sabyasachi Patra, and Debasish Jena. "Machine learning approach for intrusion detection on cloud virtual machines." International Journal of Application or Innovation in Engineering & Management (IJAIEM) 2.6 (2013): 56-66.
[19] Sondhiya, Richa, Maneesh Shreevastav, and Mahendra Mishra. "To improve security in cloud computing with intrusion detection system using neural network." Int. J. Soft Comput. Eng 3.2 (2013).
[20] Ramteke, Swati, Rajesh Dongare, and Komal Ramteke. "Intrusion Detection System for Cloud Network Using FC-ANN Algorithm." International Journal of Advanced Research in Computer and Communication Engineering 2.4 (2013).
[21] Mahmoudpour, Saeid, and Seyed Javad Mirabedini. "Diagnosis of Distributed Denial of Service Attacks using the Combination Method of Fuzzy Neural Network and Evolutionary Algorithm." Indian Journal of Science and Technology 8.28 (2015): 1.
[22] Anitha, R., et al. "A Real Time Detection of Distributed Denial-of-Service Attacks Using Cumulative Sum Algorithm and Adaptive Neuro-Fuzzy Inference System." Advances in Computer Science, Engineering & Applications. Springer Berlin Heidelberg, 2012. 773-782.
[23] Chirag NM, Dhiren RP, Avi P, Muttukrishnan R (2012) Integrating Signature Apriori based Network Intrusion Detection System (NIDS) in Cloud Computing. In: Proceedings of 2nd International Conference on Communication, Computing & Security, Procedia Technology, 6:905–912. doi:10.1016/j.protcy.2012.10.110
[24] Bakshi A, Yogesh B (2010) Securing cloud from DDOS attacks using intrusion detection system in virtual machine. In: Proceedings of second International Conference on Communication Software and Networksp260–264. doi:10.1109/ICCSN.2010.56
[25] Amjad HB, Sabyasachi P, Debasish J (2013) Machine learning approach for intrusion detection on cloud virtual machines. Int J Appl Innov Eng Manag 2(6):57–66

A. Manimaran, "Anomaly Detection System using Ant Agent Rule Based Multiclass Support Vector Machine (AA-RB-MSVM) Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1297-1305, 2018.

An attacker will break cloud data confidentiality by abusing cryptographic keys utilizing secondary passages in cryptographic code. The main plausible measure is restricting assaulter from getting to the cipher text, when cryptography mystery key is uncovered. Existing cryptography plans can`t protect cloud information classification underneath key presentation as despite everything they bargain at one figure square. Bastion, a proficient system is suggested that jam cloud data confidentiality against an assaulter who knows about the cryptography key and approaches the encoded information. We have a tendency to dissect Bastion`s security and we survey its execution with existing plans in parts of security, stockpiling and calculation.

Assaulter, key exposure, confidentiality

[1] Zissis, Dimitrios, and Dimitrios Lekkas. “Addressing cloud computing security issues”. Future Generation computer systems 28.3 (2012): 583-592.
[2] Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li, “Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing”, IEEE Transactions on Parallel and Distributed Systems (TPDS).
[3] C. Wang, Q. Wang, K. Ren, and W. Lou, “Ensuring Data Storage Security in Cloud Computing”, The 17th IEEE International Workshop on Quality of Service (IWQoS`09), July 13-15, (2009), Charleston, South Carolina.
[4] Swapnali More, Sangita Chaudhari “Third Party Public Auditing scheme for Cloud Storage” Proc.7th International Conference on Communication, Computing and Virtualization 2016.
[5]” The Security of All-Or-Nothing Encryption: Protecting Against Exhaustive Key Search” , Anand Desai adesai@cs.ucsd.edu
[6] K. Yang and X. Jia, “Data Storage Auditing Service in Cloud Computing: Challenges, Methods and opportunities,” World Wide Web, vol. 15, no. 4, pp. 409-428, 2012.
[7] Boyang Wang, Ming Li, Sherman S. M. Chow “Computing encrypted cloud data efficiently under multiple keys”, 2013 IEEE Conference on Communications and Network Security (CNS)
[8]S. Swaminathan, A. Karthick, S. Suganya, "A secure and robust crypto system based on unique dynamic key generation scheme", Computing Communication and Networking Technologies (ICCCNT) 2014 International Conference on, pp. 1-7, 2014.[9]Jitender Grover, Shikha, Mohit Sharma “Cloud computing and its security issues — A review “ Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) 2014
[10]A. Desai, “The security of all-or-nothing encryption: Protecting against exhaustive key search,” in Advances in Cryptology (CRYPTO), 2000, pp. 359–375.
[11]Sneha singha and S.D Satav “An Effective Approach for Key Exposure Resistance in Cloud using De Duplication and Tile Bitmap Method” International Journal of Computer Applications (0975 – 8887) Volume 146 – No.9, July 2016.
[12] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, “Provable Data Possession at Untrusted Stores,” Proc. 14th ACM Conf. Computer and Comm. Security, pp. 598-609, 2007.
[13] G. Timothy and M. M. Peter, “The NIST definition of cloud computing,” Vol. NIST SP - 800-145, September (2011).
[14] A. Juels, and B. Kaliski, “PORs: Proofs of Retrievability for Large Files,” Proc. 14th ACM Conf. Computer and Comm. Security, pp. 584-597, 2007.
[15] H. Shacham and B. Waters, “Compact Proofs of Retrievability,” Advances in Cryptology-Asiacrypt’08, pp. 90-107, 2008.
[16] V. Boyko, “On the Security Properties of OAEP as an Allor-nothing Transform,” in Advances in Cryptology (CRYPTO), 1999, pp. 503–518.
[17]S. Micali and L. Reyzin, “Physically observable cryptography,” in Theory of Cryptography Conference (TCC), 2004, pp. 278–296.
[18] D. Cash, A. K¨upc¸ ¨u, and D. Wichs, “Dynamic proofs of retrievability via oblivious ram,” Advances in Cryptology-Eurocrypt’13, pp. 279-295, 2013.
[19]R. L. Rivest, “All-or-Nothing Encryption and the Package Transform,” in International Workshop on Fast Software Encryption (FSE), 1997, pp. 210–218.

G. Sravani, G.V. Padma Raju, "A Novel Approach for Achieving Cloud Data Confidentiality Under Key Exposure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1306-1310, 2018.

Visually impaired persons use Braille language as they can’t access digital or print media. Braille document is mostly embossed on Braille plate and are captured and processed using Optical Braille Recognition (OBR) system. OBR converts the Braille document into natural text. The quality of converted documents largely affected by the skew generated during the document scanning. Skew angle detection is one of the most important requirements in preprocessing stage of OBR. The introduction of skew is unavoidable in digitized document. Skew may get introduced while scanning the Braille plate due to human error. In this paper, two different algorithms based on Sobel edge detector, Gaussian filter method and another histogram and blob properties are used to estimate skew angle and its correction. The preprocessing, skew detection and skew correction algorithms are evaluated on MATLAB. The obtained results are compared to identify the efficient algorithm

Braille, Gaussian Filter, Histogram Equalization, image segmentation, Linear regression, Skew angle, OBR, preprocessing, sobel, skew detection

[1] H. Zhang, J. Li , "A Research on Paper-Mediated Braille Automatic Extraction Method," 2010 International Conference on Intelligent Computation Technology and Automation, Changsha, China, 2010, pp. 328-331. doi:10.1109/ ICICTA.2010.145.
[2] J. Subur, T. A. Sardjono and R. Mardiyanto, "Braille character recognition using find contour method," 2015 International Conference on Electrical Engineering and Informatics (ICEEI), Denpasar, 2015, pp. 699-703. doi: 10.1109/ ICEEI. 2015. 7352588.
[3] J. Mennens, L. Van Tichelen, G. Francois and J. J. Engelen, "Optical recognition of Braille writing using standard equipment," in IEEE Transactions on Rehabilitation Engineering, vol. 2, no. 4, pp. 207-212, Dec 1994. doi: 10.1109/86.340878.
[4] Jie Li and Xiaoguang Yan, "Optical Braille character recognition with Support-Vector Machine classifier," 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 2010, pp. V12-219-V12-222.
doi: 10.1109/ICCASM.2010.5622245
[5] L. Nian-feng and W. Li-rong, "A kind of Braille paper automatic marking system," 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), Jilin, 2011, pp. 664-667. doi: 10.1109/MEC.2011.6025553.
[6] M. Hanumanthappa and V. V. Murthy, "Optical Braille recognition and its correspondence in the conversion of Braille script to text — A literature review," 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, 2016, pp. 297-301. doi: 10.1109/CSITSS.2016.7779374
[7] Padmavathi.S, Aishwarya.A.V, Iswaryah,. “Geometric Correction for Braille Document Images.”, 2016 International Conference on Pedagogic Advances in Computer Science (ICPACS-2016), Computer Science & Information Technology (CS & IT), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India. (2016).
[8] T. Shreekanth and V. Udayashankara, "A two stage Braille Character segmentation approach for embossed double sided Hindi Devanagari Braille documents," 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, 2014, pp. 533-538.
doi: 10.1109/IC3I.2014.7019590.
[9] Wang Changjie, Nian Hua. "Algorithm of remote sensing image matching based on corner-point", 2017 Int ernat ional Workshop on Remote Sensing with Intelligent Processing (RSIP), 2017, doi:10.1109/RSIP.2017.79588

Vishwanath Venkatesh Murthy, M. Hanumanthappa, "Enhancing Skew Detection and Correction methods to Improve Optical Braille Recognition.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1311-1315, 2018.

Scene content area and division are two indispensable and testing research issues in the field of PC vision. This paper proposes a novel strategy for scene content revelation and division in light of fell convolution neural Networks (CNNs). In this system, a CNN based substance careful cheerful substance district (CTR) extraction show (named recognizable proof orchestrate, DNet) is arranged and arranged using both the edges and the whole territories of substance, with which coarse CTRs are recognized. A CNN based CTR refinement show (named division organize, SNet) is then created to section the coarse CTRs into substance to get the refined CTRs. With DNet and SNet, numerous less CTRs are removed than with regular philosophies while all the more bona fide content areas are kept. The refined CTRs are finally requested using a CNN based CTR game plan illustrate (named gathering framework, CNet) to get the last substance locale. This paper proposes a novel scene content area procedure by using distinctive convolution neural frameworks. This technique contains three phases including content careful CTR extraction, CTR refinement, and CTR course of action.

Scene Text detection, scene text segmentation, text-aware candidate text region extraction, candidate text region refinement, candidate text region classification

[1] A. Shahab, F. Shafait, and A. Dengel, “ICDAR 2011 robust reading competition challenge 2: Reading text in scene images,” in Proc. Int. Conf. Document Anal. Recognit., Sep. 2011, pp. 1491–1496.
[2] D. Karatzas et al., “ICDAR 2013 robust reading competition,” in Proc. Int. Conf. Document Anal. Recognit., Aug. 2013, pp. 1484–1493.
[3] D. Karatzas et al., “ICDAR 2015 competition on robust reading,” in Proc. Int. Conf. Document Anal. Recognit., Aug. 2015, pp. 1156–1160.
[4] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 2963–2970.
[5] J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide-baseline stereo from maximally stable extremal regions,” Image Vis. Comput., vol. 22, no. 10, pp. 761–767, 2004.
[6] C. Yi and Y. Tian, “Text string detection from natural scenes by structure-based partition and grouping,” IEEE Trans. Image Process., vol. 20, no. 9, pp. 2594–2605, Sep. 2011.
[7] C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, “Detecting texts of arbitrary orientations in natural images,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 1083–1090.
[8] L. Neumann and J. Matas, “Real-time scene text localization and recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 3538–3545.
[9] W. Huang, Z. Lin, J. Yang, and J. Wang, “Text localization in natural images using stroke feature transform and text covariance descriptors,” in Proc. Int. Conf. Comput. Vis., Dec. 2013, pp. 1241–1248.
[10] L. Neumann and J. Matas, “Scene text localization and recognition with oriented stroke detection,” in Proc. Int. Conf. Comput. Vis., Dec. 2013, pp. 97–104.
[11] C. Shi, C. Wang, B. Xiao, Y. Zhang, and S. Gao, “Scene text detection using graph model built upon maximally stable extremal regions,” Pattern Recognit. Lett., vol. 34, no. 2, pp. 107–116, Jan. 2013.
[12] X.-C. Yin, X. Yin, K. Huang, and H.-W. Hao, “Robust text detection in natural scene images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 5, pp. 970–983, May 2014.
[13] L. Neumann and J. Matas, “A method for text localization and recognition
in real-world images,” in Proc. Asian Conf. Comput. Vis., 2010, pp. 770–783.
[14] W. Huang, Y. Qiao, and X. Tang, “Robust scene text detection with convolution neural network induced MSER trees,” in Proc. Eur. Conf. Comput. Vis., 2014, pp. 497–511.
[15] Y. Li, W. Jia, C. Shen, and A. van den Hengel, “Characterness: An indicator of text in the wild,” IEEE Trans. Image Process., vol. 23, no. 4, pp. 1666–1677, Apr. 2014.

K. V. Mounika, N. K. Kameswara Rao, "Scene Content Classification and Segmentation using Convolution Neural Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1316-1320, 2018.