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Everyone is moving from traditional services towards various cloud-based services at minimal cost. People are commonly using data storing and virtualization services because of their advantages. Increase in the use of these services is indeed increasing the challenges for its security in the cloud environment. Hence, security of cloud data and challenges related to it becomes the priority in the domain of cyber security. Many researchers have published different papers and have conducted different surveys related to security of cloud-based services, but researches show certain gap between cloud issue and their solution, few of them address data security issue and rest have virtualization problem. Here an effort is made to cover all factors related to security issue, and it has been derived properly and in clear way, which gives proper view of clouds security and challenges. Security of cloud platform is a major concern as the treats and attacks are increasing with advancement in the domain.

Cloud Computing, Security, Virtualization, Cloud services

[1] Dean, Jeffery, S. Ghemawat, “Map Reduce Simplified Data Processing on Large Clusters”, OSDI, 2008.
[2] Chen, Deyan, and Hong Zhao, "Data security and privacy protection issues in cloud computing", In the Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering, Vol. 1, pp. 647-651, 2012.
[3] T. Chou, ”Security threats on cloud computing vulnerabilitie”, International Journal of Computer Science & Information Technology 5, no. 3 ,p: 79, 2013.
[4] S. Nuno, K. Gummadi, and R. Rodrigues. "Towards Trusted Cloud Computing." HotCloud 9, no. 9, p:3, 2009.
[5] S. Basu, A. Bardhan, K. Gupta, P. Saha, M. Pal, M. Bose, K. Basu, S. Chaudhury, and P. Sarkar, "Cloud computing security challenges & solutions-a survey", In the Proceedings of the 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 347-356, 2018.
[6] Goyal, Sumit. "Public vs private vs hybrid vs community-cloud computing: a critical review." , International Journal of Computer Network and Information Security 6, no. 3, p: 20, 2014.
[7] J. Wayne, and T. Grance. "Guidelines on security and privacy in public cloud computing." , 2011.
[8] K. Hashizume, D. Rosado, E. Fernández-Medina, and E. B. Fernandez. "An analysis of security issues for cloud computing." Journal of internet services and applications 4, no. 1, p:5, 2013.
[9] Mahalakshmi, B., and G. Suseendran. "An Analysis of Cloud Computing Issues on Data Integrity, Privacy and Its Current Solutions." In Data Management, Analytics and Innovation, Springer, Singapore, pp. 467-482., 2019.
[10] W. Hanqian, Y. Ding, CH. Winer, and L. Yao, "Network security for virtual machine in cloud computing", In the Proceedings of the 2010 5th International Conference on Computer Sciences and Convergence Information Technology, Seoul, pp. 18-21.
[11] Q. Wang, C. Wang, L. Jin, R. Kui, and L. Wenjing. "Enabling public verifiability and data dynamics for storage security in cloud computing." In European symposium on research in computer security, Springer, Berlin, Heidelberg, pp. 355-370, 2009.
[12] Z. Kazi, and S. Vrbsky. "Security attacks and solutions in clouds." In Proceedings of the 2010 1st international conference on cloud computing, pp. 145-156.
[13] J. Sen, "Security and privacy issues in cloud computing." In Cloud Technology: Concepts, Methodologies, Tools, and Applications, IGI Global pp. 1585-1630., 2015.
[14] S. Aguru, and B.MadhavaRao “Data Security In Cloud Computing Using RC6 Encryption and Steganography Algorithms” In International Journal of Scientific Research in Computer Science and Engineering, Vol.07, Issue.01, pp.6-9, 2019.
[15] Y. Patil, and P. Deshmukh “A Review: Mobile Cloud Computing: Its Challenges and Security” In International Journal of Scientific Research in Network Security and Communication, Vol.06, Issue.01, pp.11-13, 2018.

Foram Suthar, Samarat V.O. Khanna, Jignesh Patel, "A Survey on Cloud Security Issues," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.120-123, 2019.

In the world of information, the data has to be stored in a large amount where the compression plays a vital role. Compression is beneficial because it reduces the resources required to store and transmit data. The paper discusses the different vision on BWT transformation algorithm, BWT works on data in memory and files too big to process in one go. The first section concentrates on complete Burrow Wheeler Transformation algorithm compression and decompression mechanism. This paper also examines the various Modification on BWT. The primary objective of this study is investigating the different approaches using BWT transformation. The comparison and performance analysis of second step algorithm in BWT is highlighted in this survey. Various Search algorithm using BWT is discussed briefly. A brief on recent work using this algorithm in different application Suffix Array, Suffix Sort on small space. Therefore this paper examines comparative analysis performance in compression ratio is carried out on various techniques.

Move to Front, Frequency Count, Suffix Sort, Inversion Frequencies

[1] David Salomon, “Data Compression the Complete Reference” 3rd Edition, Spring Verlag, New York, pp.777, 2004
[2] Peter Fenwick, “Burrows–Wheeler compression: Principles and reflections“, Theoretical Computer Science 387 (2007) pp. 200–219, 2007 Elsevier.
[3] M. Burrows, D.J. Wheeler. A Block-sorting Lossless Data Compression Algorithm. DEC Systems Research Center Research Report 124, May 1994.
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[6] Tim Bell1, Matt Powell1, Amar Mukherjee, Don Adjeroh, “Searching BWT compressed text with the Boyer-Moore algorithm and binary search”, Proceeding in 2002 Data compression Conference, Snowbird, UT, USA, pp. 112-121, 2002.
[7] Andrew Firth, Tim Bell, Amar Mukherjee, and Don Adjeroh “Ä comparison of BWT Approaches to String Patterns Matching”, Journal of Software-Practice and Experience, Volume 35, Issue 13, pp. 1217-1258,10 November 2005.
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[9] Juha K¨arkk¨ainen∗,” Fast BWT in small space by blockwise suffix sorting “, Theoretical Computer Science 387 (2007) pp. 249–257, 2007 Elsevier

S.Ranjitha, L. Robert, "A Survey on Diverse Vision and Varied Application Zone of BWT Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.124-135, 2019.

This paper presents a novel smart water management system which provides facility for water level control, water consumption prediction and water usage analysis in a cost-effective manner. The proposed system uses a NodeMCU Wi-Fi module to achieve wireless communication. A HC-SR04 Ultrasonic Ranging Module is used to detect the water levels and automate the pump operation, in coordination with DS18B20 waterproof temperature sensor probes to detect anomalies in water inflow and close off the water supply on assertion of undesirable water characteristics. The data provided by the system is processed via optimized machine learning and neural network algorithms to provide critical analytics to users.

Water management, NodeMCU, HC-SR04, DS18B20, Firebase, Analytics, Data aggregation

[1]. S. Wadekar, V. Vakare, R. Prajapati, S. Yadav and V. Yadav, "Smart water management using IOT," 2016 5th International Conference on Wireless Networks and Embedded Systems (WECON), Rajpura, 2016, pp. 1-4.
[2]. P. Verma et al., "Towards an IoT based water management system for a campus," 2015 IEEE First International Smart Cities Conference (ISC2), Guadalajara, 2015, pp. 1-6.
[3]. B. N. Getu and H. A. Attia, "Automatic water level sensor and controller system," 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), Ras Al Khaimah, 2016, pp. 1-4.
[4]. Ocampo-Martinez, C.; Puig, V.; Cembrano, G.; Quevedo, J. “Application of predictive control strategies to the management of complex networks in the urban water cycle [Applications of Control]”. IEEE conference on Control Systems, (Volume:33, Issue: 1) Date of Publication: Feb. 2013 Page(s): 15 – 41 ISSN :1066-033X
[5]. J. A. Napieralski, "Statistical methods for data prediction," 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, 2016, pp. 120-123.
[6]. Verma, S., Prachi, “Wireless Sensor Network application for water quality monitoring in India”. Publisher IEEE. National conference on Computing Communication Systems (NCCCS), 2012 Date of Conference: 21-22 Nov. 2012.
[7]. Beza Negash Getu Hussain A. Attia "Remote Controlling of Light Intensity Using Phone Devices" Research Journal of Applied Sciences Engineering and Technology (RJASET) vol. 10 no. 10 pp. 1206-1215 2015.
[8]. Mantripatjit Kaur, Anjum Mohd Aslam, “Big Data Analytics on IOT: Challenges, Open Research Issues and Tools”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.81-85, 2018.
[9]. K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi, "Challenges and Opportunities with Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.16-20, 2017
[10]. Yogita G. Patil, Pooja S. Deshmukh, "A Review: Mobile Cloud Computing: Its Challenges and Security", International Journal of Scientific Research in Network Security and Communication, Vol.06, Issue.01, pp.11-13, 2018
[11]. L. Shaikh, K. Sawlani, "A Rainfall Prediction Model Using Articial Neural Network", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.1, pp.24-28, 2017

Badari Nath K, Suhas Poornachandra, Tanmay S H, Yatish H R, Vishal Gowda, Venkatesh D N, "Novel Smart Water Metering and Management System for Smart Cities," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.136-143, 2019.

Induction motors are performing very critical roles in modern industries. Their reliable and efficient operation has great impact on performance of concerned industry. This work is intended to check feasibility for the use of micro electro mechanical accelerometers for the purpose of electric motor’s vibration signature analysis. Here Lab-VIEW software is used for analysis and user interface. Vibration signal of motor is observed for various speeds and speed is changed by SPWM technique. The complete assembly of MEMS (Micro Electro Mechanical Systems) accelerometer ADXL 203 and interfacing devices (PCI 6221 and BNC 2120) is also verified with standard vibration signal generating laboratory equipments. This experimental work has confirmed the resemblance of electrical and mechanical fault in induction motors in terms of observable change in motor vibrations.

Vibration signature, MEMS, “g” scale, FFT, Accelerometer

[1] Steve Goldman, Vibration Spectra Analysis
[2] R. Keith Mobley, Vibration Fundamentals
[3] Vibration Analysis Handbook
[4] ADXL 203 Datasheet
[5] www.analog.com
[6] IS 11726

Viral R Avalani, Takshak V Rabari, "Induction Motor’s Health Diagnosis Based on Vibration Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.144-148, 2019.

Heart-related disease or cardiovascular disease is the main reason for a huge number of deaths in the world. Machine learning techniques help health care professionals in the diagnosis of heart disease. This aims a better understanding and application of machine learning in the medical domain, an automated system in medical diagnosis would enhance medical efficiency and reduce costs. In order to decrease the number of deaths by heart diseases there have to be a quick and efficient detection technique, the use of multiple machine learning algorithms for heart disease, models based on supervised learning algorithms such as: Decision tree, Neural Networks, Logistic Regression, and then implement them to predict heart disease based on patients’ medical records. Find the accuracy of the models, Choose the best output with the highest accuracy. These machine learning algorithms and techniques have been applied to various medical data sets. The implementation of work is done on heart disease data set from the University of California Irvine (UCI) machine learning repository, it contains several instances and attributes. By using the data set we test on different machine learning techniques and predict the best model which is computationally efficient as well as accurate for the prediction of heart disease.

Machine Learning, Supervised Learning, Classification Techiniques

[1] Sanjay Kumar Sen, “Predicting and Diagnosing of Heart Disease Using Machine Learning Algorithms”, International Journal of Engineering and Computer Science, ISSN: 2319-7242, Volume.6, Issue.6, June 2017, Page No. 21623-21631.
[2] Jaymin Patel, Prof. TejalUpadhyay, Dr. Samir Patel, “Heart disease prediction on using machine learning and data mining technique.”, IJCSC, Volume.7, Number.1, pp.129-137, 2016.
[3] Tülay Karayilan, izkan Kiliç, “Prediction of Heart Disease Using Neural Network”, IEEE, Vol.978, Issue.1, pp.5386-0930, 2017.
[4] Theresa Princy. R, J. Thomas, “Human Heart Disease Prediction System using Data Mining Techniques” 2016 International Conference on Circuit, Power and Computing Technologies [ICCPCT] , Vol.978, Issue.1, pp.5090-1277, 2016 IEEE.
[5] Ashok Kumar Dwivedi, “Performance evaluation of different machine learning techniques for prediction of heart disease”, Springer, DOI 10.1007/s00521-016-2604-1. 2016.
[6] Akash Mukherjee, Raj Manjrekar, Ashish Marde, Prof. Rajesh Gaikwad, “Heart Disease Prediction Using Artificial Neural Networks”, IJSRD, National Conference on Technological Advancement and Automatization in Engineering, January 2016, ISSN: 2321-0613.
[7] A. Sankari karthiga, M. Safish Mary, M. Yogasini “Early Prediction of Heart Disease Using Decision Tree Algorithm” , International Journal of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), Vol.3, Issue.3, March 2017.
[8] Avni Sharma, Deeksha Tyagi, Dr. Tarun Kumar Gupta, “Comparative Analysis of Machine Learning Techniques in Heart Disease Prediction by R Language”, IJSRD - International Journal for Scientific Research & Development|, Vol. 5, Issue 02, 2017.

Nukala V V Pravallika, P Suresh Varma, "Prediction of Heart Disease Using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.149-155, 2019.

Cognitive skills (CS) are the basic processing functions that enable to learn. These include attention, memory, auditory processing, visual processing, logic, and reasoning ability. It play a vital role in performance of any individual. Performance of students can be predicted by knowing the level of cognitive skill. The proposed method consists of three stages quantization, simulation and prediction. Finally, we analyzed the simulated data using deep learning algorithms. The learning algorithm Convolutional Neural Network (CNN) is used for our study. The proposed method is tested on the students` performance data sets in UCI repository. The results shows that CNN achieve higher accuracy than other the traditional approach.

Cognitive skills, Study related characteristics, quantization, Deep learning algorithms, Convolutional Neural Network.

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J.Suganya, T. Chakravarthy, "Analyzing Cognitive Factor to Enhance Student Performance using Deep Learning Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.156-160, 2019.

Sentiment Analysis is one of the major areas in text analytics. It primarily focuses on the recognition and categorization of opinions. Sentiment analysis is the way by which we mine the reviews given by people on different events, products, movies and many more. People rely on the reviews provided by the users of the product before shopping. Likewise people depend on the reviews of a movie before watching it. In this work, we have shown how regression algorithm work on the sentiment analysis of movie reviews and we also which regression algorithm is better for sentiment analysis. The regression algorithm which we have implemented is Random Forest, Ridge, Linear and ElasticNet. The dataset which we used for sentiment analysis is based on movie reviews also known as IMDB dataset and the parameters which we have used for analysis is mean square error and R squared error. From the result, it can be easily concluded that regression analysis with the best accuracy can be considered as a benchmark for all the other algorithms.

Sentiment Analysis, Regression, Naïve Bayes, Random forest, Features

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Rajit Nair, Vaibhav Jain, Amit Bhagat, Ratish Agarwal, "An Efficient Approach for Sentiment Analysis Using Regression Analysis Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.161-165, 2019.

This paper presents the Reinforced Dynamic Clustering (RDC) for optimal selection of cloud packages which enable effective package allocation for users. This model operates on four major phases. The initial phase identifies the QoS requirements of customers and clusters them effectively. The second phase identifies the average QoS requirements based on each of the clusters. Decision Tree model is used to train on the data from the clusters and to predict packages that are most suitable for each of the clusters. The next phase handles the real-time resource requirements from the users and allocates packages. The final phase aggregates the user requirements, which are then used in the clustering phase to incorporate the latest user requirements. Experiments were performed with the access log data and comparisons were performed with state-of-the-art models. Results indicate highly effective performances of the proposed model.

Resource provisioning, Cloud resource allocation, Clustering, Package Selection, Reinforcement

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[23] Li, Z., Chu, T., Kolmanovsky, I. V., Yin, X., & Yin, X., “Cloud resource allocation for cloud-based automotive applications”. Mechatronics, Vol. 50, pp.356-365, 2018.
[24] C., Madhumathi, and Gopinath Ganapathy. "Cloud Package Selection for Academic Requirements using Multi Criteria Decision Making based Modified Ant Colony Optimization Technique." International Journal of Engineering and Technology (IJET) Vol. 8 pp. 1205-1211, 2016.

K Mani, R Mohana Krishnan, "Reinforced Dynamic Clustering (RDC) for Optimal Selection of Cloud Packages," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.166-172, 2019.

Big data is not only about mammoth volume of data along with volume velocity i.e. data generating speed like more than a speed of cheetah and also verity of data like a verity of vegetables in market, which we cannot process using our traditional system, processing is nothing but storing and analyzing the generated huge amount of verity of streaming and non- streaming data. Around us each and every device generates huge amount of structured and unstructured data. From many years many devices and organizations generates the data, generated data is not used by organizations for many years, now a day’s organizations thinking of using the generated data for analysis and enhance the performance of organizations. Different data generation sources generate variety of data, i.e. Not of same in nature variety of data like structured whose features (fields) and features types are known, semi structured whose features types are unknown but features are known and unstructured whose features types and features are not known. To process big data Hadoop is developed by Benn cutting of yahoo later enhanced by google and amazon. Now amazon is number one company in the world because of analyzing the generated data. To process big data many tools and software frame work have been developed by many companies like Amazon, Google and Yahoo. Hadoop basically had two components like HDFS and Map Reduce one for storing and other one for processing, later stages YARN is added as recourse manager, before Yarn HDFS takes care of Recourse management which leads poor performance so YARN additional frame work added on top of Hadoop to manage recourse, along with Yarn later stages many other components like H-base-Hive, Sqoop are added to process only structured data and to process unstructured data. Pig and Flume are added to process unstructured data. Main work of Sqoop is to import and export structured data from database to Hadoop and vice versa. whereas flume is to import unstructured data generated from web server, twitter and face-book to Hadoop for analysis. The ecosystem of recent Hadoop are H-base, PIG, hive, Zoo-keeper, Oozie, flume, mahout machine learning tool and many more to make user friendly and to improve the performance of data analysis. Similar spark and flink are also competitors of hadoop spark which overcome limitations of Hadoop and flink which overcome the limitations of spark. In this we wanted to highlight the map- reduce applications for word-count bench mark examples, in our research we executed the bench mark word count program using pig and hive and achieved hive is much faster than PIG.

Hadoop;Map-Reduce;Hive;Pig;wordcount;cloudxlab;flink;spark

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J. Santosh Kumar, B. K. Raghavendra, S. Raghavendra, "Big data Processing Comparison using Pig and Hive," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.173-178, 2019.

Communication (from Latin communicare, meaning "to share") is the act of conveying messages from one entity or group to another through the use of mutually understood signs. It not only facilitates the process of sharing information and knowledge, but and also helps people to develop relationships with others. Security matters to people differently. However, it is always required. Same is with our shared information. The main purpose of this paper is to introduce security of the text messages that people share among them. In this paper, we are presenting Secure SMS System for Android users, thus providing an End-to-End Encryption (E2EE). RSA algorithm of Cryptography and a Key generation algorithm have been used. Whenever a message is sent, it is not sent as a plain text; rather it is encrypted using the public key of the receiver to get the cipher text, which is transmitted to the receiver. At the receiver side, that cipher text is decrypted using the private key of the receiver, to get the plain text, the actual message that is sent to him. This would prevent the third parties from interfering into the text messages shared. This implementation can then be used by people in general as well as all the intelligence agencies.

Cryptography, Encryption, Key, Cipher text

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[10] Pelzl & Paar (2010). Understanding Cryptography. Berlin: Springer-Verlag.

Lalit Kumar Gupta, Ananya Gupta, Akshay Singh, Abhishek Kumar, "Secure SMS System for Android," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.179-183, 2019.