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Cloud computing and social networks are providing real time data sharing by changing the way of healthcare in a cost-effective manner. However, data security issue is one of the main obstacles of mobile healthcare social networks (MHSN), since health information is considered to be highly sensitive and securable. In this paper, we introduce a mobile health care social networks in cloud computing based on profile matching and data sharing. The patients can outsource their encrypted health records to cloud storage with identity-based broadcast encryption (IBBE) technique, and share them with a group of doctors in a secure and efficient manner with domains and sub domains.

conditional proxy re-encryption, data security, encryption, health information management, profile matching

[1] M. Li, S. Yu, Y. Zheng, K. Ren and W. Lou, “Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption,” IEEE Trans on Parallel and Distrib. Syst., vol. 24, no. 1, pp. 131-143, Jan. 2013.
[2] M. Barua X. Liang, R. Lu and X. Shen, “ESPAC: Enabling Security and Patient-centric Access Control for eHealth in cloud computing,” International Journal of Security and Networks, vol. 6, no. 2/3, pp. 67-76, Nov. 2011
[3] S. Yu, C. Wang, K. Ren and W. Lou, “Achieving secure, scalable, and fine-grained data access control in cloud computing,” in Proc. 29th Conference on Information Communications, San Diego, CA, USA, 2010, pp. 534-542
[4] Y. Liu, Y. Zhang, J. Ling and Z. Liu, “Secure and fine-grained access control on e-healthcare records in mobile cloud computing,” Future Generat.Comput. Syst., vol. 78, pp. 1020-1026, Jan. 2017.
[5] Y. Yang, X. Liu, R. Deng and Y. Li, “Lightweight sharable and traceable secure mobile health system,” IEEE Trans. Depend. Sec Comput., Jul. 2017. [Online]. Available: https://doi.org/10.1109/TDSC.2017.2729556
[6] Y. Yang, X. Liu and R. Deng, “Lightweight break-glass access control system for healthcare internet-of-things,” IEEE Transactions on Industrial Informatics, Sept. 2017.[Online]. Available: https://doi.org/10. 1109/TII.2017.2751640
[7] M. Blaze, G. Bleumer and M. Strauss, “Divertible protocols and atomic proxy cryptography,” in Proc. Advances in Cryptology - EUROCRYPT` 98, Espoo, Finland, 1998, pp. 127-144.
[8] M. Green, G. Ateniese, “Identity-based proxy re-encryption,” in Proc. the 5th International Conference on Applied Cryptography and Network Security, Zhuhai, China, 2007, pp. 288-306.
[9] T. Matsuo, “Proxy re-encryption systems for identity-based encryption,” in Proc. 1st International Conference on Pairing-Based Cryptography, Tokyo, Japan, 2007, pp. 247-267.
[10] Y. Zhou, H. Deng, Q. Wu, B. Qin, J. Liu and Y. Ding, “Identity-based proxy re-encryption version 2: Making mobile access easy in cloud,” Future Generat. Comput. Syst., vol. 62, pp. 128-139, Sept. 2016.
[11] X. Wang, J. Ma, F. Xhafa, M. Zhang and X. Luo, “Cost-effective secure E-health cloud system using identity based cryptographic techniques,” Future Generat.Comput. Syst., vol. 67, pp. 242-254, Feb. 2017.
[12]J. Weng, R. Deng, X. Ding, C. Chu and J. Lai, “Conditional proxy re-encryption secure against chosen-ciphertext attack,” in Proc. 4th International Symposium on Information, Computer, and Communications Security, Sydney, Australia, 2009, pp. 322-332.
[13] P. Xu, T. Jiao, Q. Wu, W. Wang and H. Jin, “Conditional identity-based broadcast proxy re-encryption and its application to cloud email,” IEEE Transactions on Computers, vol. 65, no. 1, pp. 66-79, Jan. 2016.
[14] R. Zhang, J. Zhang, Y Zhang, J. Sun and G. Yan, “Privacy-preserving profile matching for proximity-based mobile social networking,” IEEE J. Sel. Areas Comm., vol. 31, no. 9, pp. 656-668, Sept. 2013.
[15]L. Zhang, X. Li, K. Liu, T. Jung and Y. Liu, “Message in a sealed bottle: Privacy preserving friending in mobile social networks,” IEEE Trans. Mob. Comput., vol. 14, no. 9, pp. 1888-1902, Sept. 2015.

A. Apoorva, K. Amoolya, K. Anil, M. Guruprasad, Vinodh H N, "A Mobile Health Care Social Networks In Cloud Computing Based On Secure Identity Based Data Sharing and Profile Matching", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.54-58, 2019.

Most of the defense organization now takes the help of robots to carry out many risky jobs that cannot be done by soldiers. These robots used in defense or usually employed with integrated system, including video screens, sensors, laser gun, metal detector and cameras. The defense robots also have different shapes according to various purposes. Here the new system is proposed with the help of camera through we can trace out the intruders and the robot will be employed with integrated system, including video camera, sensors, gripper and weapon. The intruders face detection by Haar Cascade Classifier and face recognition by LBPH (Local Binary Pattern Histogram). This is specially designed robotic system to protect the country from enemies and to save soldiers life. The proposed algorithm is implemented using Opensource Computer Vision (OpenCV) and image processing with python.

Face detection; Haar Cascade Classifiers; Face recognition; LBPH; OpenCV

[1] “Context-Aware local binary feature learning for face recognition” Yueqi Duan, Jiwen Lu, Jianjiang Feng,Jie Zhou,IEEE Transactions on pattern analysis and machine intelligence, vol 40, no 5, May 2018.
[2] “Simultaneous Local Binary Feature Learning and encoding for homogenous and heterogenous face recognition”,Jiwen Lu, Venice Erin Liong,Jie Zhou, IEEE Transactions on pattern analysis and machine intelligence,vol 40,no 8, August 2018.
[3]”Trunk- Branch Ensemble Convulational neuaral network for video based face recognition” Changxing Ding,” Dancheng Tao, IEEE Transcations on pattern analysis and machine intelligence, vol 40, no 4,April 2018.
[4]”Panoric Face Recogntion” Yun-Fu liu, Jing-Ming Guo,Po-Hisen Liu, Jiann-Der Lee, Chen-ChichYao, IEEE Transactions on circuits and systems for video Technology, Vol 28,No 8,August 2018.
[5]”Real-time face detection based on YOLO” Wang Yang,Zheng Jiachun,1st IEEE International conference on knowledge Innovation and Invention 2018
[6] “Face Recogntion based door lock system using OpenCv and C# with Remote Access and Security Features”Prathamesh Timse,Pranav Aggarwal, Prakhar Sinha, Neel Vora, Prathamesh Timse et al Int. Journal of Engineering Research and Applications, ISSN: 2248-9622,Vol 4 ,Issue 4(Version 6),April 2018,pg.52-57
[7] “Face Recogntion and Tracking System based and Embedded Platform” Chen Zhang, Tianygue Li, Boquan Li,Xi Ye, 10th International conference on modelling Identification and control,July,2-4,2018, Guiyang, China
[8]”Multi- Faces Recognition Process Using Haar Cascades and EigebFace Methods” Teddy Mantoro,Suhendi, 10th International Conference, august ,2018
[9] “Automatic Door Access System Using Face Recogntion” Htelk Htar Lwin,Aung Soe Khaing,Hla Myo Tun,International Journal of Scientific and technology Research volume 4, Issue 06,June 2015

Kavya R, Keerthana A, Keerthana H N, Meena K N, Chetana Srinivas, "TerrorBot- Python Based Cascade Classifier to Detect Terrorists and Soldiers", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.59-64, 2019.

Machine learning can be referred as discovery of relationships in larger datasets and in some cases it is used for predicting relationships based on the results discovered. Nowadays machine learning is achieving widespread in various fields such as healthcare industry, scientific and engineering. In healthcare industry, machine learning is mainly used for disease prediction. The main objective of our work is to predict heart disease using Naïve Bayes classifier. Naïve Bayes are the probabilistic classifiers used to classify the data using attributes. It retrieves the trained data and compares the attribute values with test data sets and predicts the result.

Machine learning, prediction, healthcare industry, Naïve Bayes Classifier, Heartdisease

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Lavanya L, Megha V, Nagashree H, Pavithra S, Anusha K L, "Prediction of Heart Disease with Claims Data using Machine Learning Method", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.65-68, 2019.

Information and data has always been consideredas the lifeline to run any business. The stability of an organization, the growth of the business, the profit gained or the loss suffered, all these factors depend on the information gained about the market trends and also most importantly public opinion. The sentiments of the people are therefore considered as the most crucial data that the organizations use in order to take effective decisions with minimum risk.In this paper we gather data from a type of social media that is twitter. This is because the public nowadays express their opinions and their feedbacks largely to through social media. In this paper we attempt to perform sentiment analysis using text classification by Naïve Bayes and text Clustering by K-means.

Twitter, Sentiment Analysis, Social Media, Naïve Bayes, K-means

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Kumari Ashmita Sinha, Maanasa VKP, Nikhitha SP, Ramya S, Mangala CN, "Sentiment Analysis of Twitter Data Using Text Classification And Clustering", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.69-72, 2019.

Many experiments have been conducted for attitude estimation with low cost micro electro-mechanical system (MEMS), even though it was of low cost and less weight it causes certain noise and errors over time. The main objective of the paper is to introduce accelerometer portion of basic attitude and heading reference system for aerospace application using Internet of Things (IoT) which can be used to find the optimal weight value such error of the Attitude and Heading Reference (AHRS) is minimised. The proposed system also assures the body rate by using 2 axis gyro and it provides distance rate measurement and angular rotation. This system consumes low power and easy to handle.

ArdiunoUno, Accelerometer, Gyroscope, Magnetometer

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Kirthana B, Madhushree K, Madhushree M S, Megha P H, Dhanraj S, "Attitude and Heading Reference System for Aerospace Application", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.73-77, 2019.

In this study, Random Forest Regression machine learning rule is applied to predict salary levels of individuals supported their years of expertise. Random forest regression is employed since it gave higher accuracy compared to decision tree regression and Support Vector Regression classifier. Choosing the foremost effective machine learning rule so as to unravel the problems of classification and prediction of data is the most vital part of machine learning which depends on dataset likewise. The predictive accuracy of the Random forest regression on test data is 97%, while the accuracy of Decision tree regression and Support Vector Regression is 85% and 90% respectively. The model has been used on the training data to predict dependent variables and to extract features with highest impact on salary prediction.

Machine learning, Random forest regression, Decision tree regression, Support vector regression, salary prediction

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Navyashree M, Navyashree M K, Neetu M, Pooja G R, Arun Biradar, "Salary Prediction in It Job Market", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.78-84, 2019.

A principled method to manage, uncover the structure of visual data by understanding significant learning task initiated by visual stage learning is presented. The target of this task is to find the phase that recovers the structure of data from revamped types of it. By virtue of standard pictures, this task comes down to recovering the primary picture from patches improved by a dark change and organised. Stage grids are discrete in this way introduces inconveniences for slant based streamlining techniques. To this end, we resort to a perpetual gauge using doubly-stochastic cross sections and define a novel bi-level streamlining issue on such systems that makes sense of how to recover the change. Such a plan prompts costly inclination calculations. We go around this issue by further proposing a computationally shoddy pattern for producing doubly stochastic frameworks dependent on PCA and DWT. The utility is exhibited on three testing PC vision issues, to be specific, relative traits learning, managed figuring out how to rank and self-directed portrayal learning. Our outcome shows condition of the craftsmanship execution on the open figure and osr benchmarks for relative qualities.

visual permutation learning, PCA, DWT, CNN

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Nisha Prakash, Niveditha G, Poojitha M, Rakshitha H D, Swetha N, "An Innovative Dashboard to Order Images with Respect to Shuffled and Smile Frequencies", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.85-89, 2019.

Along with the development of cloud computing more and more applications are moved to the cloud. With cloud storage services, users can remotely store their data to the cloud and realize the data sharing with others on the condition that the sensitive information is hidden in order to guarantee the integrity and confidentiality of the data stored in the cloud. Remote data integrity auditing is proposed to guarantee the integrity of the data stored in the cloud. In some common cloud storage systems such as the electronic health records system, the cloud file might contain some sensitive information. The sensitive information should not be exposed to others when the cloud file is shared. Encrypting the whole shared file can realize the sensitive information hiding but will make this shared file unable to be used by others. How to realize data sharing with sensitive information hiding in remote data integrity auditing scheme with time seal management still has not been explored In order to address this problem, we propose a remote data integrity auditing scheme with appropriate time management thereby providing limited access that realizes data sharing with sensitive information hiding in this paper. In this scheme, a sanitizer is used to sanitize the data blocks corresponding to the sensitive information of the file and transforms these data blocks’ signatures into valid ones for the sanitized file. These signatures are used to verify the integrity of the sanitized file in the phase of integrity auditing. As a result, our scheme makes the file stored in the cloud able to be shared and used by others on the condition that the sensitive information is hidden, while the remote data integrity auditing is still able to be execute efficiently. Meanwhile, the proposed scheme is based on Identity-based cryptography, which simplifies the complicated certificate management and also solves key exposure problem. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient.

Cloud storage, key management, Remote data Integrity, Confidentiality, Sensitive Information Hiding

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Divya. U, Nagaveni. S, Pooja. S, Ramya. R, Supritha. N, "Integrity Auditing and Data Sharing With Sensitive Information Hiding for Secure Cloud Storage", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.90-98, 2019.

In the era of information age, due to different electronic, information & communication technology devices and process like sensors, cloud, individual archives, social networks, internet activities and enterprise data are growing exponentially .The most challenging issues are how to effectively manage these large and different type of data .Big data is one of the term named for this large and different type of data .Due to its extraordinary scale, privacy and security is one of the critical challenge of big data.Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements.

Privacy-preserving,data-encryption-strategy,BigData,mobile-cloudcomputing

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Praveen G, Pratheek R, Rahul Mogar Y, Patil G B, Prasanna G, "Advanced Encryption Standard Strategy for Big Data in Cloud", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.99-104, 2019.

With millions of users tweeting around the world, real time search systems and different types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate traffic and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL shortness, that lead users to completely unrelated websites. This kind of spam can contribute to de-value real time search services unless mechanisms to fight and stop spammers can be found. To solve this issue, we propose to take tweet text features along with user-based features. We have evaluated our approach with natural language processing and the naïve-Bayes machine learning algorithm.

Twitter, tweets, spam, navie bayes, natural lanugage processing

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Himank Gupta, Mohd. Saalim Jamal, Sreekanth Madisetty and Maunendra Sankar Desarkar Department of Computer Science and Engineering,Indian Institute of Technology Hyderabad, India,2018.

Pooja Naik, Monisha S, Supritha Shetty, Pooja NR, Anoop N Prasad, "A Framework for Detection of Accuracy of Spam in Twitter", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.105-110, 2019.