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Today WSN are highly approached technology that used to interact several sensor nodes corresponding to at least common application. The WSN is affected by the problem of energy dissipation of the sensor node that collects and report the specific data to application monitoring node. The main reason to develop WSN network is to maximize the lifetime of the batteries that are constrained by the nodes during transmission. The clustering mechanism is the best and most efficient one to resolve the issue with the requirement of energy in WSN. In clustering the network is divided into smaller clusters and each cluster includes one cluster head and members. It is very much useful for reducing the energy dissipation and enhancing the lifetime of the network. In this paper we propose new clustering protocol Enhanced DEEC(Distributed Energy Efficient Clustering ) along with priority queue to balance the energy in the WSN network and prolonging the lifetime of the network. The simulation results revealed the performace of the proposed technique is better than existing protocol DEEC. Energy consumed during overall packet transmission, packet drop ratio, number of packets transmitted to the base station and cluster head are considered parameters.

WSN, DEEC, Energy Consumption, Packet Drop Ratio, Packets to base station, packet to cluster head

[1] B. Pati, J. L. Sarkar, and C. R. Panigrahi, “ECS: An Energy-Efficient Approach to Select Cluster-Head in Wireless Sensor Networks,” Arab. J. Sci. Eng., vol. 42, no. 2, pp. 669–676, 2017.
[2] S. Hasan, Z. Hussain, and R. K. Singh, “A Survey of Wireless Sensor Network,” vol. 3, no. 3, pp. 1–6, 2013.
[3] R. Grewal and P. G. Scholar, “A Survey on Proficient Techniques to Mitigate Clone Attack in Wireless Sensor Networks,” pp. 1148–1152, 2015.
[4] A. Preethi, E. Pravin, and D. Sangeetha, “Modified balanced energy efficient network integrated super heterogeneous protocol,” 2016 Int. Conf. Recent Trends Inf. Technol. ICRTIT 2016, 2016.
[5] T. Of, “A C OMPARATIVE S TUDY OF C LUSTERHEAD S ELECTION A LGORITHMS IN W IRELESS S ENSOR,” vol. 2, no. 4, pp. 153–164, 2011.
[6] R. Kumar, “Evaluating the Performance of DEEC Variants,” vol. 97, no. 7, pp. 9–16, 2014.
[7] B. Elbhiri, S. Rachid, S. El Fkihi, and D. Aboutajdine, “Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks,” 2010 5th Int. Symp. I/V Commun. Mob. Networks, ISIVC 2010, pp. 1–4, 2010.
[8] P. Saini and A. K. Sharma, “E-DEEC - Enhanced distributed energy efficient clustering scheme for heterogeneous WSN,” 2010 1st Int. Conf. Parallel, Distrib. Grid Comput. PDGC - 2010, pp. 205–210, 2010.
[9] D. Izadi, J. Abawajy, and S. Ghanavati, “An alternative clustering scheme in WSN,” IEEE Sens. J., vol. 15, no. 7, pp. 4148–4155, 2015.
[10] V. Midasala, S. Nagakishore Bhavanam, and P. Siddaiah, “Performance analysis of LEACH protocol for D2D communication in LTE-Advanced network,” 2016 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2016, pp. 2–4, 2017.
[11] R. Grewal, J. Kaur, and K. S. Saini, “A survey on proficient techniques to mitigate Clone attack in wireless sensor networks,” Souvenir 2015 IEEE Int. Adv. Comput. Conf. IACC 2015, pp. 1148–1152, 2015.
[12] L. Karim, N. Nasser, T. Taleb, and A. Alqallaf, “An efficient priority packet scheduling algorithm for Wireless Sensor Network,” 2012 IEEE Int. Conf. Commun., pp. 334–338, 2012.
[13] R. Mahidhar and A. Raut, “A Survey on Scheduling Schemes with Security in Wireless Sensor Networks,” Phys. Procedia, vol. 78, no. December 2015, pp. 756–762, 2016.

Updeep Kour, Sandeep Sharma, "Enhanced Distributed Energy Efficient Clustering Protocol:Using Priority Queue," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.126-134, 2019.

In this world, where human beings and the other animals have access to every nook and corner, the existing wheeled vehicles are far behind. In this paper we present the design and development of a four legged walking robotic horse. The main vision of our work is to represent the four legged robotic horse prototype that can serve in much larger range of environment and most difficult terrain, where the wheeled vehicle fails. The design of the robotic horse is bio inspired and is analogous to the biological horse. Prior to the development of the quadruped robot, the gait pattern of a horse with walk, trot and gallop styles are analyzed. A robotic prototype with gait patterns walk, trot and gallop similar to its biological counterpart, has been developed which is actuated by servomotors and controlled by microcontrollers. This paper presents the various analysis of the gait patters and development of the robotic prototype.

Quadruped robot, walk, trot, gallop, legged robot, robotic horse

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[7] R. Williams, “Animating Horse Walk Cycle”,[Video file]( Published on Aug 25, 2009) Retrieved from https://www.youtube.com/watch?v=INQx-Lzs8mU&index=1&list =PLAEB453A1F1B39BB1
[8] R. Williams, “Richard Williams Horse Run”,[Video file]( Published on Sept 1, 2009) Retrieved from https://www.youtube.com/watch?v=lborw5Op84c

R. Chutia, S. Sharma, R. Kaushik, B.P. Tasa, "Design and Development of A Four Legged Robotic Horse," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.135-139, 2019.

In today’s computing world scheduling of real time task in a multiprocessor environment is very crucial. To do the scheduling, how the scheduler is implemented? what parameters are considered ? and how those parameters affect? Is also very important. Using the realistic parameters of the task the scheduling can be done and predict the resource requirement and analysis of the resource utilization factor can be done. Based on the tasks parameter it is necessary to classify them into dependent and independent, which is very important for the scheduler to assign them to the processors. For this prediction process machine learning algorithms are applied like logistic regression, decision tree, K-means and k-NN. In this paper initially classification of tasks into two categories dependent and independent is done later the same sets can be assigned to the processors for their execution.

Multiprocessor scheduling,Global scheduling, Partitioned scheduling, Machine Learning

[1] Mehdi Akbari, Hassan Rashidi, “A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems”, Expert Systems With Applications, Elsevier Vol.60, pp.234-248, 2016.
[2] Baruah Bipasa Chattopadhyay, Haohan Li Insik Shin, “Mixed-criticality scheduling on multiprocessors”, Vol 50, Issue 1, pp 1–4, 2014.
[3] Sanjaya K. Panda, Indrajeet Gupta, Prasanta K. Jana, “Allocation-Aware Task Scheduling for Heterogeneous Multi-Cloud System”, Procedia Computer Science, Elsevier Vol 50, pp.176-184, 2015.
[4] Yang Xin, Lingshuang Kong, Zhi Liu , Yuling Chen, Yanmiao Li, Hongliang Zhu, Mingcheng Gao,Haixia Hou, And Chunhua Wang, “Machine Learning and Deep Learning Methods for Cybersecurity”, IEEE Access, Vol.6, pp. pp.35365-35381, 2018.
[5] Panos Louridas and Christof Ebert, “Machine Learning”, IEEE Computer Society, Vol.4, Issue.11, pp.110-115, 2016.
[6] Chouhan Kumar Rath , Shashank Sekhar Suar ,Prasanti Biswal, “A Comparative Study On Dynamic Task Scheduling Algorithms”, Journal on Information Technology, Vol.71, Issue.1, pp.1-6, 2018
[7] Sri Raj Pradhan, Sital Sharma, Debanjan Konar, Kalpana Sharma, “A Comparative Study on Dynamic Scheduling of Real-Time Tasks in Multiprocessor System using Genetic Algorithms”, International Journal of Computer Applications Network Security and Communication, Vol.120, Issue.1, pp.0975-8887, 2015.
[8] Sakshi kathuria., “A Survey on Security Provided by Multi-Clouds in Cloud Computing”, IJSRNSC, Vol.6, Issue.1, pp.23-27, 2018.
[9] Pradeep K.Sharma, Vaibhav Sharma and Jagrati Nagdiya, “A proposed Method for Mining High Utility Itemset with Transactional Weighted Utility using Genetic Algorithm Technique ( -GA),” IJSRCSE, Vol.5, Issue.1, pp.31-35, 2017.
[10] Marko Bertogna, Michele Cirinei, Giuseppe Lipari Member “Schedulability analysis of global scheduling algorithms on multiprocessor platforms”, IEEE Transactions on Parallel and Distributed System,Vol X,No. X 2008.
[11] Rekha A Kulkarni ,Suhas H Patil, N.Balaji, “Fuzzy Real Time Scheduling on Distributed Systems to Meet the Deadline of Applications”, International Journal of New Technology and Research, Vol.2, Issue.4, pp.56-58, 2016.
[12] Wei Zhao and Krithi Ramamritham, “,” Simple and Integrated Heuristic Algorithms for Scheduling Tasks with Time and Resource Constraints”, Journal of Systems and Software,Vol 7, pp.195-205, 1987.
[13] Ashish Sharma and Mandeep Kaur,” An Efficient Task Scheduling of Multiprocessor Using Genetic Algorithm Based on Task Height”, JITSE, Vol 5, Issue 2 1000151, ISSN: 2165-7866,2015.
[14] Nirmala H, Girijamma H A,” Aperiodic task Scheduling Algorithms for Multiprocessor systems in Real Time environment”, International Journal of Engineering and Computer Science, ISSN 2319-7242, Vol 4, Issue 8, pp 13838-13841, 2015.
[15] Christos Gogos, Christos Valouxis, Panayiotis Alefragis , George Goulas, Nikolaos Voros ,Efthymios Housos,” Scheduling independent tasks on heterogeneous processors using heuristics and Column Pricing”, Future Generation Computer Systems, Issue 60, pp 48-66, 2016.

Nirmala H, Girijamma H A, "A Novel Scheduler for Task scheduling in Multiprocessor System using Machine Learning approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.140-143, 2019.

Internet is the huge repository to provide a way to obtain the web documents in these recent years. Most of these documents are in a human understandable format that provides a bridge to ingress services from the web. These documents provide knowledge to retrieve information from the web. Information retrieval systems fail in providing a specific format for this knowledge representation. By considering all these criteria, Web 3.0 has stepped ahead to web entail peculiar format to obtain information from web documents called ontologies. Ontology is a knowledge-based conceptualization system that consists of concepts, relations and their properties that provides a semantic relationship between the concepts used for a specific domain. Diverse tools are implemented and introduced in the market to develop these ontologies in order to serve a particular purpose. This paper projects the workflow steps of ontology editors and shows the study of different ontology developments tools along with their comparison to one another.

Knowledge Representation System, Conceptualization, Ontology, Semantic tools, Uniform Resource Identifier(URI), extensible markup language(XML), Resource Description Framework (RDF)

[1] Berners-Lee, Tim, James Hendler, and Ora Lassila. "The semantic web." Scientific american 284, Issue. 5, pp. 28-37, 2001.
[2] Klyne, Graham, and Jeremy J. Carroll. "Resource description framework (RDF): Concepts and abstract syntax.",2006.
[3] Gunnam Swathi, S Mahaboob Hussain, Prathyusha Kanakam, D.Suryanarayana, "SPARQL for Semantic Information Retrieval from RDF Knowledge Base", International Journal of Engineering Trends and Technology (IJETT), Vol.41,Issue.7, pp. 351-354,2016.
[4] S Mahaboob Hussain, Prathyusha Kanakam, D. Suryanarayana, Gunnam Swathi, Sharmela Shaik, "Semantic Information Retrieval: An Ontology and RDF based Model", International Journal of Computer Applications, Vol.156, Issue. 9, pp 34-38, 2016.
[5] Ming, DENG Zhihong TANG Shiwei ZHANG, and YANG Dongqing CHEN Jie. "Overview of Ontology [J]." Acta Scicentiarum Naturalum Universitis Pekinesis Vol. 5,pp. 027,2002.
[6] Diana Man, "Ontologies in computer science". DIDACTICA MATHEMATICA, Vol. 31, Issue. 1, pp. 43–46, 2013.
[7] Kapoor, Bhaskar, and Savita Sharma. "A comparative study ontology building tools for semantic webapplications." International Journal of Web & Semantic Technology (IJWesT), Vol. 1,Issue. 3,pp. 1-13,2010.
[8] Alatrish, E. S. "Comparison some of ontology." Journal of Management Information Systems, Vol. 8, Issue. 2,pp.018-024, 2013.
[9] Noy, Natalya F., Michael Sintek, Stefan Decker, Monica Crubézy, Ray W. Fergerson, and Mark A. Musen. "Creating semantic web contents with protege-2000." IEEE intelligent systems 16, no. 2 (2001): 60-71.

Prathyusha Kanakam, Raghu Varma Edarapalli, S Mahaboob Hussain, "Collation of Diverse Ontology Tools," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.144-147, 2019.

Bitcoin has recently attracted lots of attention in various sectors like economics, computer science, and many others due to its nature of combining encryption technology and monetary units. Now-a-days social media is perfectly representing the public sentiment and opinion about Trending events. Especially, twitter has attracted a plenty of attention from analyst for studying the public sentiments. Bitcoin prediction on the basis of general public sentiments tweeted on twitter has been an intriguing field of research. This paper aims to see how well the change in Bitcoin prices, the ups and downs, is correlated with the public opinions being expressed in tweets. Understanding people’s opinion from a text tweet is the objective of sentiment analysis. Sentiment analysis and machine learning algorithms are going to be applied to the tweets which are captured from twitter and analyse the correlation between Bitcoin movements and sentiments in tweets. In an elaborate way, positive tweets in social media about a Bitcoin are expected to encourage people to invest in the crypto currency and as a result the Bitcoin price would increase.

Bitcoin, Long Short Term Memory , ARIMA, Deep Learning, Sentiment Analysis

[1] Mai, Feng and Bai, Qing and Shan, Zhe and Wang, Xin (Shane) and Chiang, Roger H.L., “From Bitcoin to Big Coin: The Impacts of Social Media on Bitcoin Performance,” (January 6, 2015).
[2] Hong Kee Sul, Alan R Dennis, and Lingyao Ivy Yuan.“Trading on twitter: Using social media sentiment to predict stock returns,”, Decision Sciences, 2016.
[3] Garcia D, Tessone CJ, Mavrodiev P, Perony N. “The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy,”, 2014.
[4] Stuart Colianni, Stephanie Rosales, and Michael Signorotti. “Algorithmic trading of cryptocurrency based on twitter sentiment analysis,”, 2015.
[5] Hong Kee Sul, Alan R Dennis, and Lingyao Ivy Yuan.“Trading on twitter: Using social media sentiment to predict stock returns,”, Decision Sciences, 2016.
[6] Dejan Vujičić, Dijana Jagodić, Siniša Ranđić. “Blockchain Technology, Bitcoin, and Ethereum: A Brief Overview.” 2018 17th International Symposium INFOTEH-JAHORINA (INFOTEH), 2018, doi:10.1109/infoteh.2018.8345547.
[7] Jang, Huisu, and Jaewook Lee. “An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information.” IEEE Access, vol. 6, 2018, pp. 5427–5437., doi:10.1109/access.2017.2779181.

Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav, "Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.148-152, 2019.

Automatic identification of plant is very useful for environmentalists, natural scientists, biologists, food engineers, amateur botanists, educators and doctors (Ayurvedacharya). In this paper a computer based application was developed to automatically identify herbal plant type by the photographs of plant leaves. The leaf image used for analysis can be either a database digital image or photograph recorded by camera. The image used was of single leaf with light and white background. The leaf image analysis has been performed with MATLAB 2016. The procedure comprised of analysis of leaf image segmentation, feature extraction from Shape, Color histogram and Cooccurrence Matrices respectively. Shape of leaf is the furthermost widespread feature used in identification. The Leaf analysis has been performed for 25 herbal medicinal plant leaves from Folio database. The Shape feature was extracted using edge detection operator Sobel; and to record the color statistical features, color histogram and co-occurrence matrices with statistical parameters. The results of this article will be useful to identify the leaves of different types of plants.

Leaf Analysis, Color Cooccurrence Matrix, Color Histogram, Statistical Features, Shape Features

[1] Mzoughi, I. Yahiaoui, N. Boujemaa and E. Zagrouba, “Advanced tree species identification using multiple leaf parts image queries”, IEEE ICIP 2013.
[2] Bhardwaj, M. kaur, “A review on plant recognition and classification techniques using leaf images”, International Journal of Engineering Trends and Technology, Vol. 4, Issue 2, 2013.
[3] Wang, D. Brown, Y. Gao and J. L. Salle, “Mobile plant leaf identification using smart-phones”, IEEE ICIP 2013.
[4] S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, and Q. L. Xiang, “A leaf recognition algorithm for plant classification using probabilistic neural network”, IEEE ISSPIT, pp. 11– 16, 2007.
[5] J. Chaki, and R. Parekh, “Plant leaf recognition using Gabor filter”, International Journal of Computer Applications Vol. 56 issue10.
[6] Ghasab MAJ, Khamis S, Mohammad F, Fariman HJ, “Feature decision-making ant colony optimization system for an automated recognition of plant species”, Expert Systems with Applications , Vol. 42, issue 5, pp 2361-2370, 2015.
[7] Stricker MA, Orengo M., “Similarity of color images”, IS&T/SPIE`s Symposium on Electronic Imaging: Science & Technology, pp 381–392, 1995.
[8] Chaki J, Parekh R, Bhattacharya R., “Plant leaf recognition using texture and shape features with neural classifiers”, Pattern Recognition Letters, Vol 58, pp 61-68, 2015.
[9] Kadir A, Nugroho LE, Susanto A, Santosa PI, “Experiments of Zernike moments for leaf identification”, Journal of Theoretical and Applied Information Technology, Vol 41, issue 1, pp 83-93, 2012.
[10] Rashad MZ, el-Desouky BS. Khawasik MS, “Plants images classification based on textural features using combined classifier”, International Journal of Computer Science & Information Technology , Vol. 3, issue 4, pp 93-100, 2011.
[11] Wang F, Liao DW, Li JW, Liao GP, “Two-dimensional multifractal detrended fluctuation analysis for plant identification”, Plant Methods , Vol. 11, issue 1, pp 12-18, 2015.
[12] Asha Patil, Kalyani Patil, Kalpesh Lad, “Leaf Disease detection using Image Processing Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.33-36, 2018.
[13] C.T. Lin, M. Thomous, “Study and Overview of Venation of leaf using Image Processing”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.5, pp.25-30, 2016.

Renuka R. Londhe, "Plant Leaf Analysis Based on Color Histogram and Cooccurrence Matrices," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.153-157, 2019.

Users of Online Social Network (OSN) generate their own post by using texts, images, videos and resources like emojis, stickers etc., Among the different types of posts, the text content can easily be interpreted by other and exposes the full thoughts of a user towards a topic. This paper attempts to group similar users, who produced the same text posts towards a set of pre-defined topics. The similarity among users with their posts is calculated with the aid of linear Support Vector Machine (LinearSVM) classifier and the performance is evaluated.

OSN, Similar Users, LinearSVM, Text Classification, text posts, tf-idf vectorizer

[1] Reshma. M and Raji.R.Pillai, “Semantic Based Trust Recommendation system for Social Networks using Virtual Groups”, published in International Conference on Next Generation Intelligent Systems (ICNGIS),India, September 2016.
[2] Mariam Adedoyin-Olowe, Mohamed Medhat Gaber et al, ”A Survey of Data Mining Techniques for Social Network Analysis”, Journal of Data Mining and Digital Humanities, December 2013.
[3] Georgios Lappas, “From Web Mining to Social Multimedia Mining”, published in International Conference on Advances in Social Networks Analysis and Mining, Taiwan, July 2011.
[4] Guoxin Li, Xue Yang et al,” Customer-Generated Content in Company Social Media Platform: How Social Network Works?”, published in IEEE International Conference on Management of Innovation and Technology (ICMIT), Thailand, September 2016.
[5] Shankar Setty, Rajendra Jadit et al, “Classification of Facebook News Feeds and Sentiment Analysis”, IEEE, September 2014.
[6] Harsh Namdev Bhor, Tushar Koul et al,” Digital Media Marketing using Trend Analysis On Social Media “, ICSCI, January 2018.
[7] Waseem Ahmad and Rashid Ali, “A Framework for Seed User Identification across Multiple Online Social Networks”, IEEE, September 2017.
[8] Soufiene Jaffali, Salma Jamouss et al, ”Clustering and Classification of Like-Minded People from Their Tweets”, IEEE International Conference on Data Mining, 2014.
[9] Mohammed H. Abd El-Jawad et al, “Sentiment Analysis of Social Media Networks Using Machine Learning”, published in 14th International Computer Engineering Conference (ICENCO), IEEE, December 2018.
[10] B. Vamshi Krishna, Ajeet Kumar Pandey et al, ”Feature Based Opinion Mining and Sentiment Analysis Using Fuzzy Logic”, Springer Link, Cognitive Science, and Artificial Intelligence, pp 79 – 89, December 2017.
[11] Bernardus Ari Kuncoro, Bambang Heru Iswanto et al, “TF-IDF Method in Ranking Keywords of Instagram Users’ Image Captions”, ICITSI,2015.
[12] Devi Munandar, Andria Arisal et al,” Text Classification for Sentiment Prediction of Social Media Dataset using Multichannel Convolution Neural Network”, published in International Conference on Computer, Control, Informatics and its Applications (IC3INA), November 2018.
[13] Irina Stefanova and Andrey Kiryantsev, “Analysis of User Groups in Social networks to Detect Socially Dangerous People”, International Scientific Practical Conference Problems of Info Communications, Science and Technology (PIC S &T), October 2018.
[14] Macro Di Givvanni, Macro Brambilla et al, “Content-based Classification of Political Inclinations of Twitter Users” IEEE International Conference on Big Data, December 2018.
[15] S.Geetha, Visnukumar et al, “Tweet Analysis Based On Distinct Opinion of Social Media Users’”, IEEE, December 2018.
[16] Vibhuti Gupta and Rattikorn Hewett, “Unleashing the Power of Hashtags in Tweet Analytics with Distributed Framework on Apache Strom”, IEEE International Conference on Big Data Conference, December 2018.

K.Mohankumar, B.Srinivasan, "Formation of Similar Users group by using Support Vector Machine with Facebook Posts," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.158-163, 2019.

Cloud Computing is today’s exciting technology. Cloud computing means delivering various computer resources over the internet. Main examples of cloud services are webmail, social networking sites and business applications. Depending upon the services clouds can be divided into three categories SaaS, PaaS and IaaS. Depending upon the location clouds can be divided into four parts public, private, hybrid and community. Cloud security is very important to organization because data is very important to organizations as well as for the individuals. Depending upon the type of cloud and types of services, security is the two way responsibility. Security is responsibility of both the cloud service providers and users. Three major organizations which provide the cloud service are Azure, Google and AWS. This study discusses various features of Google Cloud and AWS with respect to security. Both these organizations give high priority to the security of user’s data. Both these organizations follow layer wise approach to protect user’s data. These organizations also provide their own build tools for security and privacy of the data.

Cloud Security, Iaas, PaaS, SaaS, Data Encryption, DDoS

[1] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski,G. Lee, D. Patterson, A. Rabkin, I. Stoica et al., “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010.
[2] Z. Xiao and Y. Xiao, “Security and privacy in cloud computing,” IEEE Communications Surveys & Tutorials, vol. 15, no. 2, pp. 843–859, 2013.
[3] https://azure.microsoft.com/en-in/overview/what-is-cloud-computing/
[4] https://en.wikipedia.org/wiki/Cloud_computing
[5] http://en.wikipedia.org/wiki/Cloud_computing_architecture
[6] Hashizume, Keiko, et al. "An analysis of security issues for cloud computing." Journal of internet services and applications 4.1 (2013): 5.
[7] http://searchcloudcomputing.techtarget.com/definition/public-cloud
[8] Padhy, Rabi Prasad, Manas Ranjan Patra, and Suresh Chandra Satapathy. "Cloud computing: security issues and research challenges." International Journal of Computer Science and Information Technology & Security (IJCSITS) 1.2 (2011): 136-146.
[9] https://www.interoute.com/what-hybrid-cloud
[10] http://www.globaldots.com/cloud-computing-types-of-cloud/
[11] http://en.wikipedia.org/wiki/Cloud_computing_security
[12] https://www.linkedin.com/pulse/cloud-computing-security-sandesh-h-n
[13] https://aws.amazon.com/security/
[14] https://cloud.google.com/security/infrastructure/design/
[15] https://cloud.google.com/security/overview/whitepaper
[16] Sun, Yunchuan, et al. "Data security and privacy in cloud computing." International Journal of Distributed Sensor Networks 10.7 (2014): 190903.
[17] https://www.esds.co.in/blog/cloud-computing-security-questions/#sthash.JIZjE8JG.dpbs

Rajinder Singh, "Cloud Computing and Data Security : AWS and Google Case Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.164-171, 2019.

the intent of this paper is prediction of the salt content in agriculture soil. Soil salinity is a process which affects the quality of soil and reduces the agriculture production. The soil salt content adversely affects the soil physical property including soil water content. The Visible and Near-Infrared Reflectance Spectroscopy provides improved estimation of soil salinity as fast approach to the characterization of soil salt content with spectral resolution of 350-2500 nm. The Partial Least Square Regression Method (PLSR) is frequently used to determine Soil Salt Content (SSC) obtains from the spectral data. The Result shows that the 550nm, 850 nm, 1430nm, 1918nm, 2052nm wavelength which are sensitive to salt content and model based on Partial Least Square Regression PLSR can only make approximate predictions for First Derivative RMSE (Root Mean Square Error) = 0.0282-0.0365, R2 (Coefficient of Determination) = 0.9313-0.9051 and for Continuum Remove RMSE (Root Mean Square Error) = 0.0280-0.0386, R2 (Coefficient of Determination) = 0.9313-0.8939.

Spectral Data, Visible-NIR, Soil Salt Content (SSC), Partial Least Square Regression (PLSR).

[1] Jun Wang, Zhoujing Li, XuebinQin, XiuchengYang, ZhonglingGao, Qiming Qin, “Hyperspectral predicting model of soil salinity using partial least square regression’’ © IEEE 2014.
[2] Ashwini D. Padmbhai, R. R. Deshmukh “Hyperspectral Analysis of Soil Total nitrogen,” International Journal of Advance Resesrch in Computer Science Vol 8, No. 7, July – August 2017.
[3] DongZhang, TashpolatTiyip, JianliDing, FeiZhang, “Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data” Hindawi Publishing 14Sept 2016.
[4] YaLiu, XianzhangPan, ChangkunWang “predicting of soil salinity with VIS-NIR spectra,” journal.pone.0140688 Oct 15, 2015.
[5] Shibin Liu,Yi Li and chansheng, “Spectral estimation of soil salt content,” soilsci April 2, 2013.
[6] Farifteh J., F. Van der Meer, C. Atzberg, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra A comparison of two adaptive methods (PLSR),” Remote Sensing Environment, vol. 110, pp. 59-78, 2007.
[7] Yogling Weng., P. Gong, and Z. Zhu, “Reflectance spectroscopy for the assessment of soil salt content in soils of the Yellow River Delta of China,” International Journal Of Remote Sensing, vol. 29, pp. 5511-5531, Feb2, 2008.
[8] Metternicht G. and J. Zinck, “Remote sensing of soil salinity potentials and constraints,” Remote Sensing Of Environment, vol. 85, pp.Jan 1, 2003.
[9] S. Nawar, H. Buddenbaum, J. Hill, “Estimation of soil salinity using three quantitative methods based on Vis-NIR reflectance spectroscopy a case study from Egypt,” Arabian Journal of Geosciences, vol. 8, no. 7, pp. 5127–5140, 2015.
[10] Amal Allbed. “ Soil Salinity Mapping and Monitoring,” Scip journal oct 2013.
[11] Harris J.K. “Spectral identification and Quantification of Salt”, Remote sensing 2016.
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Tejas U. Padghan, Ratnadeep R. Deshmukh, Jaypalsing N. Katye, Anita G. Khandizod, Pooja V. Janse, "Prediction of Salt in Soil by PLS Regression Using Hyperspectral Laboratory Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.172-175, 2019.

In today’s era the information is represented with the help of digital system. With the advancement of technology , now a days data is transmitted from source to destination with the help of images. Images are the source of data transmission. Data transmission through images is more secure as compared to the transmission through networked medium. In networked medium, the text can be easily sent and sometimes affected or may be harm by some third party or others. In this paper, we have provided two way security mechanism. Firstly, we have encrypted the text in the image and then in second phase we have placed one image on another image. Hence it is a two way security mechanism. Proposed system uses the hybridization of SVD-SLT techniques to secure the image and achieve better results in terms of accuracy. Two phases of digital image watermarking are used one of which is encryption and another is decryption. SVD (Singular Value Decomposition) will divide the image into parts and SLT (Slant Let Transformation) will merge two images overall resulting in watermarking. The result improvement is indicated through performance analysis with parameters PSNR, SNR, SSIM and through proposed mechanism 15% improvement is observed.

Truncated SVD, Modified SLT, Watermark, Encryption , Decryption, PSNR, SNR, SSIM

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Deepakshi Mohal, Sandeep Sharma, "Enhanced Security Mechanism Using Hybrid Approach of Watermarking," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.176-189, 2019.