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Cloud computing in the present scenario is a developing and fast growing technology that is widely being adopted around the world. This is quite flexible, mutual, scalable and lucrative computational approach that has entered in various public domains. Cloud computing utilizes the Internet-based computing power and here the information, data, and other resources are provided to the user via a computer or device on-demand and is being charged on the basis of its consumption. This paper categorizes, presents the refined study of recently published articles on cloud computing, especially in the health care sector. It demonstrates an outline of the prime issues and challenges explores resources and discusses practical techniques and tools practiced in this context. It also provides an insight of various cloud computing applications in several scenarios, current enhancements and describes the possible future directions for a deeper understanding of cloud computing in healthcare. It proposes the recent state of the art used in cloud computing for healthcare.

Health, Medical, Hospital, Healthcare, Care Computing

[1]. Hamid Moghaddasi, Alireza Tabatabaei Tabrizi “Applications of Cloud Computing in Health Systems” Published by Canadian Center of Science and Education 9(6) ISSN 1916-9736. (2017).
[2]. Priyanshu Srivastava, R. Khan “Review paper on Cloud Computing” International Journals of Advanced Research in Computer Science and Software Engineering; 8(6), ISSN: 2277-128X. (2018)
[3]. Steven Mathew, SaritaGulia, Varinder Singh. “A Review Paper on Cloud Computing and its Security Concerns” Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, Vol. 10 pp. 245–250 ISSN 2300-5963 (2017).
[4]. Lidong Wang, Cheryl Ann Alexander “Medical Applications and Healthcare Based on Cloud Computing” International Journal of Cloud Computing and Services Science; 2(4), pp. 217~225 ISSN: 2089-3337 (2014).
[5]. Omotosho Folorunsho Segun, Fadiora Babatunde Olawale “Healthcare data breaches: Biometric technology to the rescue” International Research Journal of Engineering and Technology; 04(11), e-ISSN: 2395-0056 (2017).
[6]. Vaibhav Kamal Nigam, Shubham Bhatia “Impact of Cloud Computing on Health Care” International Research Journal of Engineering and Technology; 3(5) | p-ISSN: 2395-0072, e-ISSN: 2395 -0056 (2016).
[7]. Lingkiswaran Devadass, Sugalia Santhira Sekaran, Rajermani Thinakaran”Cloud computing in healthcare” International Journal of Students’ Research In Technology & Management; 5(1), pp 25-31, ISSN 2321-2543 (2017).
[8]. Mervat B., Sarfraz B., Suriayati C., Jamalul-lail Ab Manan “A Study on Significance of Adopting Cloud Computing Paradigm in Healthcare Sector” Proceedings of 2012 International Conference on Cloud Computing, Technologies, Applications & Management 978-1-4673-4416-61 12/$ 31.00 © 2012 IEEE
[9]. Tatiana Ermakova, Jan Huenges, Koray Erek “Cloud Computing in Healthcare – a Literature Review on the Current State of Research” Proceedings of the Nineteenth Americas Conference on Information Systems (2013).
[10]. Khyamling A. P., Naveen kumar, C. Patil, Shivananda R. Poojara, Tejaskumar , S. Kamble “Cloud-based Intelligent Healthcare Monitoring System” International Conference on Issues and Challenges in Intelligent Computing Techniques (2014).
[11]. Roma Chauhan, Amit Kumar “Cloud Computing for Improved Healthcare: Techniques, Potential and Challenges” The 4th IEEE International Conference on E-Health and Bioengineering (2013¬¬)
[12]. Vom Brocke, J. Simons, A. Niehaves, B. Riemer, K. Plattfaut, R. Cleven “Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process” In Proceedings of the 17th European Conference on Information Systems. (2009).
[13]. Lidong Wang, Cheryl Ann Alexander “Telemedicine-Based on Mobile Devices and Mobile Cloud Computing” International Journal of Cloud Computing and Services Science; 3(1), pp. 26~36 ISSN: 2089-3337. (2014).
[14]. Shaftab Ahmed, Azeen Abdullah “E-Healthcare and Data Management Services in a Cloud” 978-1-4577-1169-5/11/$26.00 ©2011 IEEE (2011).

S. K. Singh, Sapana Yadav, "Application of Cloud Computing In Healthcare: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.909-914, 2019.

In this time of technological advancement everything is online and so is the voting. The online voting is time saving, efficient, reliable and fast. The online voting requires safety for the communication setup between client and server so for that purpose Kerberos protocol is used in the voting process for the authentication purpose which uses DES cryptography but DES is not secure enough to handle highly valuable data used in voting so in order to make it more secure we need to replace DES with some other cryptography technique. Kerberos uses DES by default, but it easy to decrypt so to enhance its security we need to replace it with AES cryptography. So here in this paper, we will discuss AES cryptography as the replacement of DES and its implementation with Kerberos. As AES is mathematically more efficient than DES other than that it allows choosing a 128-bit, 192-bit or 256-bit key as compared to the 56-bit key of DES thus making it exponentially stronger.

DES, AES, NIST, IDEA, BDB, HMAC, CBC

[1] Kerberos Overview- An Authentication Service for Open Network Systems, Document ID:16087
[2] S. P. Everett, M. D. Byrne, and K. K. Greene, "Measuring the usability of paper ballots: Efficiency, effectiveness, and satisfaction", Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, (2006) October 16-20; Santa Monica, USA
[3] S. P. Everett, K. K. Greene, M. D. Byrne, D. S. Wallach, K. Derr, D. Sandler, and T. Torous, "Electronic Voting Machines versus Traditional Methods: Improved Preference, Similar Performance", CHI Proceedings: Measuring, Business, and Voting, (2008) April 5-10; Florence, Italy.
[4] M. Patil, V. Pimplodkar, A. R. Zade, V. Vibhute and R. Ghadge, “A Survey on Voting System Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 1, (2013).
[5] Diffie, Whitfield; Hellman, Martin (8 June 1976). "Multi-user cryptographic techniques". AFIPS Proceedings. 45: 109–112
[6] Jindal, S., & Sharma, M. (2016). Design and Implementation of Kerberos using DES Algorithm, 92–95.
[7] Robert Sugarman (editor) (July 1979). "On foiling computer crime". IEEE Spectrum
[8] Voting methods in Estonia: Statistics about Internet Voting in Estonia VVK.
[9] Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
[10] Lalit Kumar Gupta, Utkarsh Tiwari, Manoj Kumar Chaudhary, Kuldeep Kasaudhan, "Secure Voting Using Bio-metric Authentication", International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.731-735, 2019.

Lalit Kumar Gupta, Utkarsh Tiwari, Ajay Kumar, Saumya Jaiswal, "AES Based Online Voting System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.915-918, 2019.

Gesture is a way of communication which involves body language and can be defined with or without spoken words. The primary goal of gesture interaction applied to Human-Computer Interaction (HCI) is to create systems, which can identify specific human gestures and use them to convey information or control devices. Gesture interaction is already a promising input modality in modern gaming, augmented and virtual reality. Gestural input makes computing more “natural” by enabling communication with a computer the same way we also communicate with one another. Computer users face challenges when engaging and implementing gesture interactions, that is: complex coordination of all gestures regularly, mastering approachability to different gestures, muscle fatigue and a lack of supporting data input that is seamless with gesture interaction. In combination with a lack of tracking success or failure, end users often struggle to execute gestures correctly. Most gesture interaction approaches are designed to control discrete events one by one given gesture till all are complete To compensate this, users require a design thinking framework that supports them during execution of gestures.

Design Thinking, Modalities, Interaction Design

[1]. Muser S. Gestures in Human-Computer-Interaction. 2015.
[2]. Sharma RP, Verma GK. Human Computer Interaction using Hand Gesture. Procedia Comput Sci. 2015;54:721-727. doi:10.1016/j.procs.2015.06.085
[3]. Dalka P, Czyzewski A. Human-computer interface based on visual lip movement and gesture recognition. 2010;7(3):124-139.
[4]. Butler AG, Roberto MA. When Cognition Interferes with Innovation: Overcoming Cognitive Obstacles to Design Thinking: Design thinking can fail when cognitive obstacles interfere; appropriate cognitive countermeasures can help disarm the traps. Res Technol Manag. 2018;61(4):45-51. doi:10.1080/08956308.2018.1471276
[5]. Razzouk R, Shute V. What Is Design Thinking and Why Is It Important? Rev Educ Res. 2012;82(3):330-348. doi:10.3102/0034654312457429
[6]. Zimmerman J, Forlizzi J, Evenson S. Research Through Design as a Method for Interaction Design Research in HCI design research in HCI. 2007.
[7]. Owen CL. Design Thinking : Driving Innovation. BPM Strateg Mag. 2006:1-5. BPMInstitute.org.
[8]. Nehaniv CL. Classifying types of gesture and inferring intent. Companions Hard Probl Open Challenges Robot Interact. 2005:74. doi:ng
[9]. Murphy K. Building Meaning in Interaction: Rethinging Gesture Classifications. Crossroads Lang Interact Cult 2003 UC Regents CA Vol 5 p 27-47 2005. 2005;5(1972):1-10. doi:10.1007/s13398-014-0173-7.2

[10]. Ingulkar CS, Gaikwad AN. Hand Data Glove: A wearable real time device for human computer Interaction. Int J Sci Eng. 2013;1(2):99-104.
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[12]. Palacios JM, Sagués C, Montijano E, Llorente S. Human-computer interaction based on hand gestures using RGB-D sensors. Sensors (Switzerland). 2013;13(9):11842-11860. doi:10.3390/s130911842
[13]. 13. Visell Y, Cooperstock J. Modeling and Continuous Sonification of Affordances for Gesture-Based Interfaces. Proc ICAD 2007. 2007;(January 2007):423-429.
[14]. Wobbrock JO, Morris MR, Wilson AD. User-Defined Gestures for Surface Computing. 2009. https://www.microsoft.com/en-us/research/wp-content/uploads/2009/04/SurfaceGestures_CHI2009.pdf.
[15]. Ruiz J. Motiongestures-Chi2011.Pdf. 2011.
[16]. Razzouk R, Shute V. What Is Design Thinking and Why Is It Important? Rev Educ Res. 2012;82(3):330-348. doi:10.3102/0034654312457429
[17]. Plattner. Design thinking. 2009:240. doi:10.1145/2535915
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Kelvin Wambani Siovi, Cheruiyot Willison Kipruto, Agnes Mindila, "Design Thinking for Gesture-based Human Computer Interactions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.919-925, 2019.

Soft computing techniques and its applications extends its wings in almost all areas which includes data mining, pattern discovery, industrial applications, robotics, automation and many more. Soft computing comprises of the core components such as fuzzy logic, genetic algorithm, artificial neural networks and probabilistic reasoning. In spite of these, recently many bio – inspired computing attracted attention for the researchers to work in that area. Machine learning plays an important role in the design and development of decision support systems, applied soft computing and expert systems applications. Attribute selection is conducted by Rhopalocera optimization algorithm which mimic the features of butterfly optimization algorithm. After that an improved fuzzy logic based artificial neural network classifier for predicting coronary artery heart disease among diabetic patients is developed. Real time data are obtained and the built ROA - IFANN classifier is tested for performance in terms of prediction accuracy, sensitivity, specificity and Mathew’s correlation coefficient. The significance of MCC is that to test the ability of the machine learning classifier in spite of other performance metrics. Implementations are done in Scilab and from the obtained results it is inferred that the built ROA - IFANN outperforms that that of other classifiers.

soft computing, fuzzy logic, machine learning, CAHD, diabetes, artificial neural network, applications of soft computing

[1] D. Gupta, S. Sundaram, A. Khanna, A. E. Hassanien, V. H. Albuquerque, Improved diagnosis of Parkinson`s disease using optimized crow search algorithm, Computers & Electrical Engineering, Vol 68, pp 412-424, 2018.
[2] P. Samanta, A. Pathak, K. Mandana, G. Saha, Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal, Biocybernetics and Biomedical Engineering, 2019.
[3] Z. Zhang, J. Dong, X. Luo, K. Choi, X. Wu, Heartbeat classification using disease-specific feature selection, Computers in Biology and Medicine, Vol 46, pp 79-89, 2014.
[4] M. Esmaelian, H. Shahmoradi, M. Vali, A novel classification method: A hybrid approach based on extension of the UTADIS with polynomial and PSO-GA algorithm, Applied Soft Computing, Vol 49, pp 56-70, 2016.
[5] V. M. Guerra, J. Novo, J. Rouco, M. G. Penedo, M. Ortega, Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers, Biomedical Signal Processing and Control, Vol 47, pp 41-48, 2019.
[6] L. Hu, C. Yin, S. Ma, Z. Liu, Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol 205, pp 574-581, 2018.
[7] G. Sannino, G. D. Pietro, A deep learning approach for ECG-based heartbeat classification for arrhythmia detection, Future Generation Computer Systems, Vol 86, pp 446-455, 2018.
[8] M. H. Vafaie, M. Ataei, H. R. Koofigar, Heart diseases prediction based on ECG signals’ classification using a genetic-fuzzy system and dynamical model of ECG signals, Biomedical Signal Processing and Control, Vol 14, pp 291-296, 2014.
[9] H. H. Carvalho, R. L. Moreno, T. C. Pimenta, P. C. Crepaldi, E. Cintra, A heart disease recognition embedded system with fuzzy cluster algorithm, Computer Methods and Programs in Biomedicine, Vol 110, pp 447-454, 2013.
[10] Z. Arabasadi, R. Alizadehsani, M. Roshanzamir, H. Moosaei, A. A. Yarifard, Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm, Computer Methods and Programs in Biomedicine, Vol 141, pp 19-26, 2017.
[11] S. Arora, S. Singh, "Butterfly optimization algorithm: a novel approach for global optimization", Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 23, no. 3, pp - 715 - 734, 2019.
[12] B. Narasimhan, A. Malathi, "Artificial Lampyridae Classifier (ALC) for coronary artery heart disease prediction in diabetes patients", International Journal of Advance Research, Ideas and Innovations in Technology, vol. 5, no. 2, pp - 683 - 689, 2019.
[13] B. Narasimhan, A. Malathi, " Improved Fuzzy Artificial Neural Network (IFANN) Classifier for coronary artery heart disease prediction in diabetes patients", Indian Journal of Advanced Research, vol. 9, no.4, 2019.

B. Narasimhan, A. Malathi, "Rhopalocera Optimization Algorithm Based Attribute Selection With Improved Fuzzy Artificial Neural Network (ROA - IFANN) Classifier for Coronary Artery Heart Disease Prediction in Diabetes Patients," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.926-935, 2019.

In today’s world, images have been generated from various sources like camera, Satellites, CCTV, and X-rays etc. The images which are collected shall provide lot of information if processed properly. It is the crucial task of segregating the data from an image, especially when working with large data sets. The image should be pre-processed and categorized through clustering algorithms. In image analysis, the clustering and classification are the two fundamental tasks. In this paper the DBSCAN algorithm has been applied on aerial digital images to categorize them accordingly for flight runway detection. Detection of snowfall in airport runway is the crucial task. The aerial images are gathered from various flight run way occurrence with snowfall as background situations.

DBSCAN, Aerial image, Clustering, Machine Learning

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[2] Safaa O. Al-Mamory and Zahraa Mohammed Ali, “Using DBSCAN Clustering Algorithm in Detecting DDoS Attack”, Journal of Babylon University, Pure and Applied Sciences, No. 4, Vol.23, 2015.
[3] Grace L. Samson, and Joan Lu, “PaX-DBSCAN: a proposed algorithm for improved clustering”, Studia Ekonomiczne. Zeszyty Nauykowe, Uniwersytetu Ekonomicznego w Katowicach, Nr. Vol.296 No. 6, 2016, ISSN 2083-8611.
[4] Abdellah IDRISSI and Altaf ALAOUI, “A Multi-Criteria Decision Method in the DBSCAN Algorithm for Better Clustering”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, 2016 pg. 377 – 384.
[5] Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman and Angela Y. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, July 2002, pp. 881 -892.
[6] Karuna Kant Tiwari, Virendra Raguvanshi, Anurag Jain, “DBSCAN: An Assessment of Density Based Clustering and It’s Approaches”, International Journal of Scientific Research & Engineering Trends, Volume 2, Issue 5, Sept.-2016, ISSN (Online): 2395-566X
[7] Shaily G. Langhnoja, Mehul P. Barot, and Darshak B. Mehta, “Web Usage Mining to Discover Visitor Group with Common Behavior Using DBSCAN Clustering Algorithm”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 7, January 2013, pp.169 – 173.
[8] Muralidharan, R., Chandrasekar, C, “3D object recognition using multiclass support vector machine—K-nearest neighbor supported by local and global feature”. J. Comput. Sci. Vol. 8, pp.1380–1388, 2012.
[9] Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).
[10] Li Ma, Lei Gu, Bo Li, Sou yi Qiao, Jin Wang, “ G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid”, Advanced Science and Technology Letters Vol.74 (ASEA 2014), pp.23-28.
[11] R Manavalan, K Thangavel, "TRUS image segmentation using morphological operators and DBSCAN clustering", Information and Communication Technologies (WICT) 2011 World Congress on, pp. 898-903, 2011
[12] Christian Bodenstein, Markus Götz, Annika Jansen, Henrike Scholz, Morris Riedel, "Automatic Object Detection Using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay", Machine Learning and Applications (ICMLA) 2016 15th IEEE International Conference on, pp. 746-751, 2016.
[13] M. Mayilvaganan, R. Manickam, “Geometrical Analysis Of Snow Fall Region In Aerial View Using Image Scaling Factor & Geometrical Focal Length”, International Journal of Advanced Computer Technology (IJACT), Vol. 6, No. 4, pp. 25-29, 2017.

R. Manickam, M. Mayilvahanan, "Airport Runway Snow Fall Detection using Density Based Spatial Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.936-941, 2019.

The development of social networks such as Twitter, Facebook, Sina weibo etc, online information sharing is becoming ubiquitous every day. Spreading information through social networks includes both positive and negative sides. Rumor propagation is a major problem in large scale social networks such as twitter, Chinese weibo. Propagating positive information may produce better result such as new ideas, innovations and recent research topics. On the other side propagating negative information may create chaos among the crowd. Malicious rumors could serious issue in society; hence it needs to be blocked after being detected. Most of the previous research focused on influence maximization. In contrast this work focuses on minimizing the propagation of malicious rumor by blocking of certain nodes. This paper includes the basics of rumor influence minimization and some methods to minimize the rumor influence.

Greedy, dynamic blocking, Survival theory, User experience

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[7]. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins, “Information diffusion through blog space,” in Proc. 13th Int. Conf. World Wide Web, 2004,
[8]. Shrija Madhu, “ An approach to analyze suicidal tendency in blogs and tweets using sentiment analysis” International journal of Scientific Research in Computer Sciences and Engineering, Vol 6,Issue 4,Aug 2018.
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[10]. Ketan Sarvakar, Urvashi K Kuchara “Sentiment Analysis of Movie Reviews: A new feature based sentiment classification” in International Journal of Computer Sciences and Engineering, Vol 6, Issue 3,June 2018.
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[12]. Biao Wang, Ge Chen, Luoyi Fu, Li Song, and Xinbing Wang, “DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks” in IEEE Transactions on Knowledge and Data Engineering, Vol 29, July 2017.

R. Amutha, D. VimalKumar, "Detection and Minimization of Rumor Influence in Social Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.942-945, 2019.

Poverty is a classic problem in every region. It is rooted in various causes like corruption, lack of education, political instability, geographical characteristics. The success of a region is strongly influenced how big this poverty can be overcome. So that poverty reduction becomes a priority for both central and local government. There are also multiple ways to do away with it, various programs and policies began to be formulated to reduce and minimize the problem. It is extremely difficult for social programs such as this to gauge the right amount of aid that needs to be given to the right people. This problem is made exponentially more difficult when that program is dealing with the least fortunate portion of the population. This is because they cannot provide the necessary details of their income, asset or expense records to justify that they need the aid to qualify. Hence, this paper’s defining question is: how to determine a method to effectively gauge the right amount of aid to be given to each household given the multitude of variables present in the vast dataset? In our work we will use supervised machine learning algorithms to a dataset to train a model which will predict the poverty based on the household level.

Machine Learning, Random Forest, Supervised Learning, XGBoost

[1]. Alwin Yaoxian Zhanga, Sean Shao Wei Lamb,c, Nan Liub,c,Yan Panga Ling Ling Chanc,d, Phua Hwee Tangc,e Development of a Radiology Decision Support System for the Classification of MRI Brain Scans IEEE 2018 Conference
[2]. Zhao, Yandong, and Xiao Ma. Study on Credit Evaluation of Electricity Users Based on Random Forest 2017 Chinese Automation Congress (CAC), 2017, doi:10.1109/cac.2017.8243614.
[3]. www.kaggle.com/c/costa-rican-household-poverty-prediction.
[4]. Amanpreet Singh, and Nanita Thakur.A review of Supervised Machine Learning Algorithm 2018 IEEE Conference.

Ajay Sharma, Jatin Rathod, Rushikesh Pol, Swati Gajbhiye, "Poverty Prediction Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.946-949, 2019.

Document summarizers create a shorter compressed version of a text document automatically. Summaries are created to give the gist of the entire document covering the key points of the document with improved readability while avoiding redundancy. Abstractive summarization synthesizes summary statements of a given document and is presently limited to single document summarization. The proposed model extends the applicability of abstractive summarization for multi-document texts by proposing a new Deep Learning architecture as a cascade of Abstractive and Extractive summarization. The proposed hybrid architecture is used to generate compact and comprehensive summaries from multiple news articles published on specific topics. The architecture was evaluated using DUC 2004 data and its performance is found to be better compared to traditional Multi Document Extractive Summarization methods in terms of ROUGE scores.

Document Summarization, Abstractive, Extractive, ROUGE

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[10]. Lin, Chin-Yew. "Rouge: A package for automatic evaluation of summaries." Text Summarization Branches Out (2004).
[11]. Hermann, Karl Moritz, et al. "Teaching machines to read and comprehend." Advances in Neural Information Processing Systems. 2015.
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[13]. Carbonell, Jaime G., and Jade Goldstein. "The Use of MMR and Diversity-Based Reranking for Reodering Documents and Producing Summaries." (1998).

Anita Kumari Singh, M Shashi, "Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.950-954, 2019.

As one important technique of fuzzy clustering in data mining and pattern recognition, the possibility c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogeneous data, since it is initially designed for only small structured data set. To tackle this problem, the paper proposes a high-order PCM algorithm for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on Map Reduce for very large amount of heterogeneous data. Experimental results indicate that PPHOPCM can effectively cluster numerous heterogeneous data using cloud computing without disclosure of private data.

Clustering Big data ,Cloud Computing, possibilistic -means algorithm, Privacy preserving , Tensor space

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[3] Q. Zhang, L. T. Yang, and Z. Chen, ”Deep Computation Model for Unsupervised Feature Learning on Big Data,” IEEE Transactions on Services Computing, vol. 9, no. 1, pp. 161-171, Jan. 2016.
[4] N.SoniandA.Ganatra,”MOiD (Multiple Objects Incremental DBSCAN) - A Paradigm Shift in Incremental DBSCAN,” International Journal of Computer Science and Information Security, vol. 14, no. 4, pp. 316-346, 2016.
[5] Z. Xie, S. Wang, and F. L. Chung, ”An Enhanced Possibilistic c-Means Clustering Algorithm EPCM,” Soft Computing, vol.12,no.6,pp.593-611, 2008.
[6] Q. Zhang, C. Zhu, L. T. Yang, Z. Chen, L. Zhao, and P. Li, ”An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things,” IEEE Transactions on Industrial Informatics, 2015. DOI: 10.1109/ TII.2017. 2684807
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V. Prasathkumar, K. Senthil, S.Vignesh, P. Ranjith Roshan, E. Prakash, "Effective Data Clustering and Efficient Security scheme in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.955-960, 2019.

Urban air pollution rate has grown to alarming state across the India. Most of the cities are facing issue of poor air quality which fails to meet standards of air for good health. An air quality index(AQI) is a numeric representation used by government agencies to tell the people that how polluted the air they are breathing or how polluted it is forecast to become. As the AQI increases, an large percentage of the population is likely to experience increasingly severe adverse health effects. Number of various methods and algorithms are used by various agencies across the world to compute the AQI requires and the air pollutant concentration over a specified averaging period, the algorithms used are bsed on EPA’s (Environmental Protection Agency) method for relating hourly data to the AQI or using technologies like the big data analytics used to process terabytes of data every hour-along with a dispersion model powered by machine learning techniques to get validated, reliable information. World Air Quality Index Project is another social enterprise project started in 2007 providing AQI info for more than 80 countries, covering more than 10,000 stations in 1000 major cities via those two websites: acqin.org and waqi.info which involved in understanding, accessing and verifying those new technologies, which can replace the more traditional setups at a affordable cost. This paper aims to study the various methods used for the calculation and the forecast of the various air pollutants which can help the officials to take necessary actions against the increase of the air pollutants.

Air Quality, Real Time AQI

[1]. Wikipedia
[2]. Rukmini S “ How to read India’s new colour-coded Air Quality Index” a blog of The Hindu published on April 09 ,2015 09:08 IST
[3]. Hansa Rajput and Snehlata Barde,”An idea to design a system to detect air pollution in different area”,International Journal of Computer Sciences and Engineering, volume-6, issue-5,pp. 1034-1036,May-2018
[4]. SAFAR(System Of Air Quality and Weather Forecasting And Research) website
[5]. The World Air Quality Project (aqicn.org)
[6]. Research Paper published by : PR Division on behalf of Dr. A.B. Akolkar, Member Secretary, CPCB, Delhi-110032

Harshita Raj, Suhasini Vijaykumar, "A Study on Air Quality Index," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.961-966, 2019.