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The term ‘group Signature’ is inherited element of ‘digital signature’ that permits any group members to sign the messages on behalf of its group which they belongs too. The identity of original signer is hidden by this resulting signature. Subsequently, the identity of the original signer can be reveal by the group manager, who is only responsible to, open the signatures respectively. The efficient approach that combines the revocation mechanism into group signature schemes based on the robust RSA assumption. The security is an essential factor for a secure group signature scheme, and the third party introduction make the scheme more practical and simple than the previous schemes of this kind.

Group signature scheme, Cloud computing, Revocation, RSA Algorithm

[1] J. Yuan and S. Yu, “Efficient public integrity checking for cloud data sharing with multi-user modification,” in Proc. of IEEE INFOCOM 2014, Toronto, Canada, Apr. 2014, pp. 2121–2129.
[2] B. Wang, L. Baochun, and L. Hui, “Public auditing for shared data with efficient user revocation in the cloud,” in Proc. Of IEEE INFOCOM 2013, Turin, Italy, Apr. 2013, pp. 2904–2912.
[3] C. Wang, Q. Wang, K. Ren, and W. Lou, “Privacy-preservingpublic auditing for data storage security in cloud computing,”inProc. of IEEE INFOCOM 2010, CA, USA, Mar. 2010, pp. 525–533.
[4] Pushkar Zagade, Shruti Yadav, Aishwarya Shah, Ravindra Bachate “ Group User Revocation and Integrity Auditing of Shared Data in Cloud Environment” International Journal of Computer Applications (0975 – 8887) Volume 128 – No.12, October 2015.
[5] Subhra Mishra and Tilak Rajan Sahoo” A Survey on Group Signature Schemes” Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela.
[6] Tao Jiang, Xiaofeng Chen, and Jianfeng Ma” Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation” 2015 IEEE.
[7] He Ge “An Effective Method to Implement Group Signature with Revocation”.
[8] Rupeng Li, Jia Yu, Jin Wang,Guowen Li, Daxing Li” Key-Insulated Group Signature Scheme with Verifier-Local Revocation” 2007 IEEE.
[9] Aayush Agarwal, Rekha Saraswat “A Survey of Group Signature Technique, its Applications and Attacks” International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 10, April 2013.
[10] J. Shen, J. Shen, X. Chen, X. Huang, and W. Susilo, “An efficient public auditing protocol with novel dynamic structure for cloud data,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 10, pp. 2402–2415, Oct. 2017.
[11] X. Liu, Y. Zhang, B. Wang, and J. Yan, “Mona: Secure multi-owner data sharing for dynamic groups in the cloud,” IEEE Trans. Parallel Distrib.Syst., vol. 24, no. 6, pp. 1182–1191, Jun. 2013.
[12] S. Yu, C. Wang, K. Ren, and W. Lou, “Achieving secure, scalable, and fine-grained data access control in cloud computing,” in Proc. Conf. Inf. Commun., 2010, pp. 1–9.
[13] T. Jiang, X. Chen and J. Ma, "Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation," in IEEE Transactions on Computers, vol. 65, no. 8, pp. 2363-2373, 1 Aug. 2016.
[14] Dan Boneh and Hovav Shacham. 2004. Group signatures with verifier-local revocation. In Proceedings of the 11th ACM conference on Computer and communications security (CCS `04). ACM, New York, NY, USA, 168-177.
[15] S. Cui, X. Cheng and C. W. Chan, "Practical group signatures from RSA," 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA`06), Vienna, 2006
[16] R. Li, J. Yu, J. Wang, G. Li and D. Li, "Key-Insulated Group Signature Scheme with Verifier-Local Revocation," Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), Qingdao, 2007
[17] X. Yi, “Identity-based fault-tolerant conference key agreement,” IEEE Trans. Depend. Sec. Comput., vol. 1, no. 3, pp. 170–178, Jul. 2004.
[18] Evgeny Milanov “The RSA Algorithm” published on 3 June 2009
[19] Ako Muhamad Abdullah “Advanced Encryption Standard (AES) Algorithm to Encrypt and Decrypt Data” 16 June 2017.
[20] Mr. Mangesh Nagarkar , Prof. Patole R.G “Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation” International Journal of Advanced Research in Computer and Communication Engineering. Vol. 5, Issue 11, November 2016.

Rohit Rai, Upasna Singh, "Survey on User Group Revocation and Integrity Auditing of Shared Data in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.481-487, 2018.

Blockchain is one of the latest trends in Information Technology domain. It has changed a way of thinking for IT professional. Companies are focusing on implementation of blockchain with their services to ensure security and reliability. Still they are facing challenges and issues for development and implementation of blockchain based services. This paper discusses those issues and challenges to be considered in development and implementation.

Blockchain, Smart contracts, Issues and Challenges (key words)

[1] Iuon-Chang Lin, Tzu-Chun Liao, ”A Survey of Blockchain Security Issues and Challenges”, International Journal of Network Security, Vol 19, No. 5, pp. 653-659, 2017
[2] Jacek Bastin,” Blockchain technology issues and solutions: a complete overview”, 2018
[3] Jesse Yli-Huumo, Deokyoon Ko, Sujin Choi, Sooyong Park, Kari Smolander, ”Where Is Current Research on Blockchain Technology? -A Systematic Review “, PloS one Vol. 11, 10 ,2016
[4] Tobias Bamert, Christian Decker, Roger Wattenhofer, Samuel Welten,” Bluewallet: The secure bitcoin wallet”, International Workshop on Security and Trust Management. Springer, pp 65–80, 2014
[5] Joppe W Bos, J Alex Halderman, Nadia Heninger, Jonathan Moore, Michael Naehrig, Eric Wustrow, “Elliptic curve cryptography in practice”, International Conference on Financial Cryptography and Data Security, Springer, pp.157–175, 2014
[6] Melanie Swan, “Blockchain: Blueprint for a new economy”, O’Reilly Media, Inc., 2015
[7] Zibin Zheng ,Shaoan Xie, Hong-Ning Dai, “Blockchain challenges and opportunities: a survey”, International Journal of Web and Grid Services, Vol. 14, No. 4, pp. 352-375, 2018
[8] A. S. Elmaghraby and M. M. Losavio, “Cyber security challenges in smart cities: Safety,security and privacy”, Journal of Advanced Research, Vol. 5, pp. 491-497, 2014
[9] Archana Prashanth Joshi, Meng Han_ and Yan Wang, “A survey on security and privacy issues of blockchain technology”, Mathematical Foundations of Computing, Vol. 1, No. 2, pp. 121-147, 2018

Divyakant Meva, "Issues and Challenges with Blockchain: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.488-491, 2018.

Internet has made a significant impact on our economy and society. With the advances in information and communication technologies, Internet of Things (IoT) has emerged as one of the most powerful communication paradigms of the 21st century representing the trend of future networking and is leading the wave of the IT industry revolution. Advancement in technology related to data collection, such as embedded devices and RIFD technology had led to increase in number of devices that are connected to the net and transmit the data continuously. The continuation of this trend is poised to evolve as an “Internet of Things” where the web will provide a medium for objects to become interactive. IoT makes internet more pervasive by extending the concept of internet to accommodate each and every object existing in this world or likely to exist in the coming future. These objects continuously generate information about the physical world, communicate with other objects and the seamless interactions among them lead to many different applications such as home automation, smart grid, smart city, traffic management, etc. This paper addresses different perspectives, challenges, applications and current world wide activities related to IoT.

Internet of Things, IoT

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[19] Zanella, Andrea, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi. "Internet of things for smart cities", Internet of Things Journal, IEEE 1, vol. no. 1, pp. 22-32, 2014.
[20] Elyamany, Hany F., and A. H. AlKhairi, "IoT-academia architecture: A profound approach", International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015.
[21] D. Singh, G. Tripathi, A. J. Jara, “A survey of Internet-of-Things: Future Vision, Architecture, Challenges and Services”, IEEE World Forum on Internet of Things (WF-IoT), 2014.
[22] S. Ziegler, C. Crettaz , I. Thomas, “IPv6 as a global addressing scheme and integrator for the Internet of Things and the Cloud ”, 28th International Conference on Advanced Information Networking and Applications Workshops, 2014.
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[25] D. Christin, A Reinhardt, P. S. Mogre, R. Steinmetz ,"Wireless sensor networks and the internet of things: Selected challenges", Proceedings of the 8th GI/ITG KuVS Fachgespräch Drahtlose Sensornetze ,pp. 31-34,2009.
[26] Chen, Yen-Kuang. "Challenges and opportunities of internet of things." Design Automation Conference (ASP-DAC), 2012 17th Asia and South Pacific. IEEE, 2012.
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[28] Ma, H. Dong,"Internet of things: Objectives and scientific challenges",Journal of Computer science and Technology 26.6 , pp. 919-924, 2011.
[29] P. B. Gaikwad, V. K. Bairagi, “Hand Gesture Recognition for Dumb People using Indian Sign Language”, International Journal of Advanced Research in computer Science and Software Engineering, pp:193-194, 2014.
[30] P. Bohr, R. Gargote, R. Vhorkate, R.U. Yawle, V. K. Bairagi, "A No Reference Image Blur Detection Using Cumulative probability Blur Detection (CPBD) Metric", International Journal of Science and Modern Engineering, vol. 1, no. 5, April 2013.

V.K.Bairagi, S.L. Joshi, S.H. Barshikar, "A Survey on Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.492-496, 2018.

Lung cancer is one of the dangerous and life taking disease in the world. However, early diagnosis and treatment can save our life. Although, CT scan imaging is best imaging technique in medical field, it is difficult for doctors to interpret and identify the cancer from CT scan images. Therefore computer aided diagnosis(CAD) can be helpful for doctors to identify the cancerous cells accurately. Many computer aided model using image processing and Machine Learning Technique(MLT) has been researched and developed. The main goal of this research work is to evaluate the various computer-aided model, analyzing the current best model and finding out their limitation and drawbacks and finally proposing the new model with improvements in the current best model. The model utilized that lung cancer detection model were sorted and arranged on the basis of their detection accuracy. The model were developed on each step and overall limitation, drawbacks were pointed out. It is found that some has low accuracy and some has higher accuracy, but not nearer to 100%. Therefore, this research targets to increase the accuracy towards 100%.

Image Processing, Data Mining, Segmentation, Classification, Lung Cancer, Prediction

[1] Krishnaiah V, Narsimha G, Chandra NS. “Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques”. International Journal of Computer Science and Information Technologies. 2013; 4(1):39-44.
[2] Ahmed K, Emran AA, Jesmin T, Mukti RF, Rahman MDZ, Ahmed F. “Early Detection of Lung Cancer Risk Using Data Mining”. Asian Pacific Journal of Cancer Prevention. 2013;14(1):595-97.
[3] Thangaraju P, Barkavi G, Karthikeyan T. “Mining Lung Cancer Data for Smokers and Non-Smokers by Using Data Mining Techniques”. International Journal of Advanced Research in Computer and Communication Engineering. 2014; 3(7):7622-5.
[4] Pandya R, Pandya J. “C5.0 algorithm to improved decision tree with feature selection and reduced error pruning”. International Journal of Computer Applications. 2015; 117(16):18-51.
[5] Er.Tapas Ranjan Baitharu, Dr.Subhendu Kumar Pani, “A Comparative Study of Data Mining Classification Techniques Using Lung Cancer Data”, International Journal Of Computer Trends And Technology (IJCTT) – Volume 22 Number 2–April 2015.
[6] Jaimini Majali, Rishikesh Niranjan, Vinamra Phatak, Omkar Tadakhe, “Data Mining Techniques For Diagnosis And Prognosis Of Cancer”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 3, March 2015
[7] Hilal Almarabeh, “A Study of Data Mining Techniques Accuracy for Healthcare”, International Journal of Computer Applications, Volume 168 – No.3, June 2017.
[8] J.Jamera banu, “A Study on Mining Lung Cancer Data for Increasing or Decreasing Disease Prediction Value by Using Ant Colony Optimization Techniques”, Special Issue Published in Int. Jnl. Of Advanced Networking and Applications (IJANA) Page 150.
[9] Neha Panpaliya, Neha Tadas, Surabhi Bobade, Rewti Aglawe, Akshay Gudadhe, “A Survey On Early Detection And Prediction Of Lung Cancer”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.1, January- 2015, pg. 175-184.
[10] .G. Krishnaveni, Prof. T.Sudha, “A Novel Technique To Predict Diabetic Disease Using Data Mining – Classification Techniques”, International Journal of Advanced Scientific Technologies, Engineering and Management Sciences (IJASTEMS, Volume.3,Special Issue.1,March.2017
[11] K. Arutchelvan, “Prognosis of Lung Cancer Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 3, March 2016
[12] N.V. Ramana Murty, “A Critical Study of Classification Algorithms for LungCancer Disease Detection and Diagnosis”, International Journal of Computational Intelligence Research, pp. 1041-1048
[13] Durairaj M, Deepika R, “Comparative Analysis of Classification Algorithms for the Prediction of Leukemia Cancer”, International Journal of Advanced Research in Computer science and Software Engineering, Volume 5, Issue 8, August 2015.
[14] P. Thamilselvan, “An Enhanced K Nearest Neighbor Method To Detecting And Classifying Mri Lung Cancer Images For Large Amount Data”, International Journal Of Applied Engineering Research Issn 0973-4562 Volume 11, Number 6 (2016) Pp 4223-4229
[15] Suganya, A., Mohanapriya, N. and Kalaavathi, B. 2015. “Lung Nodule Classification Techniques for Low Dose Computed Tomography (LDCT) Scan Images as Survey”. International Journal of Computer Applications, 131(14), pp.12-15.
[16] Zhang, F., Song, Y., Cai, W., Lee, M.Z., Zhou, Y., Huang, H., Shan, S., Fulham, M.J. and Feng, D.D. 2014. “Lung nodule classification with multilevel patch-based context analysis”. IEEE Transactions on Biomedical Engineering, 61(4), pp.1155-1166.
[17] Aggarwal, T., Furqan, A., & Kalra, K. (2015) “Feature extraction and LDA based classification of lung nodules in chest CT scan images.” 2015 International Conference On Advances In Computing, Communications And Informatics (ICACCI), DOI: 10.1109/ICACCI.2015.7275773.
[18] Jin, X., Zhang, Y., & Jin, Q. (2016) “Pulmonary Nodule Detection Based on CT Images Using Convolution Neural Network.” 2016 9Th International Symposium On Computational Intelligence And Design (ISCID). DOI: 10.1109/ISCID.2016.1053.
[19] Sangamithraa, P., & Govindaraju, S. (2016) “Lung tumour detection and classification using EK-Mean clustering.” 2016 International Conference On Wireless Communications, Signal Processing And Networking (Wispnet). DOI: 10.1109/WiSPNET.2016.7566533.
[20] Roy, T., Sirohi, N., & Patle, A. (2015) “Classification of lung image and nodule detection using fuzzy inference system.” International Conference On Computing, Communication & Automation. DOI: 10.1109/CCAA.2015.7148560.
[21] Ignatious, S., & Joseph, R. (2015) “Computer aided lung cancer detection system.” 2015 Global Conference On Communication Technologies (GCCT), DOI: 10.1109/GCCT.2015.7342723.
[22] Rendon-Gonzalez, E., & Ponomaryov, V. (2016) “Automatic Lung nodule segmentation and classification in CT images based on SVM.” 2016 9Th International Kharkiv Symposium On Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW).

K. Narmada, G.Prabakaran, R.Madhan Mohan, "A Study on Lung Nodule Segmentation and Classification using Supervised Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.497-503, 2018.

Data mining and knowledge discovery in databases have been considered as an important research area in education and industry. This survey presents an overview, a description and future direction which denotes a standard for knowledge discovery using dynamic data mining process model. The paper mentions particular real-world applications, data mining techniques, challenges incorporated in real-world application of knowledge discovery, current and future research concepts in the field. The applications to both academic and industrial concerns are discussed. The major target of the survey is the integration of the research in this particular area and thereby assisting in improving the existing model by using dynamic data mining. The bonding between the knowledge discovery and dynamic data mining in real world is reviewed with appropriate examples. The survey critically evaluates the area of knowledge discovery database to inform users about various models and to develop various models using dynamic data mining. The knowledge discovery database management standards will help in promoting the industry growth and pushing the industry beyond the edge.

Knowledge Discovery Database, Data Mining, Dynamic Data Mining Real World Application

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D. Ramana Kumar, S. Krishna Mohan Rao, "A Comprehensive Survey of Dynamic Data Mining Process in Knowledge Discovery from Database," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.504-509, 2018.

Forecasting Power load is a prime function of power system planning and management. However, it has proved to be a complicated task because of many unstable factors. There are a substantial growth rate and application levels of neural network (NN) in the power system. This review paper explores the forecasting methodology derived from the specific neural network. The electric power industries are presently undergoing transformations and extraordinary reforms. The most exciting and probably profitable developments in recent times are growing Artificial Intelligence (AI) usage of techniques. Therefore, this paper takes an overview of NN techniques and their application in power sectors. According to NN growth rate statistics, in certain power system issues, this paper considers the load forecasting, security assessment, economic dispatch, fault diagnosis, and harmonic analysis. The various disadvantages and advantages of NN applications in this aspect, envisaging the major challenges in this field are explained while considering NN applications in the power system operations and control. The comparison is made regarding several published IEEE papers from 1990 onwards until the present date, which clearly showed that this subject has attracted the maximum awareness in the last one decade, concerning; Load forecasting; Fault diagnosis and fault location; Economic Dispatch; Security Assessment; and Transient Stability.

Economic dispatch, Fault diagnosis, Harmonic is analyzing, Load forecasting, Neural network, Power system

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Anamika Singh, M.K. Srivastava, "Application of Artificial Neural Network in Power System with Examples A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.510-516, 2018.

In dynamic data mining situations, the attribute decrease issue has three issues: variety of protest sets, variety of trait sets and variety of property estimations. For the initial two issues, a couple of accomplishments have been made. For variety of the property estimations, current characteristic decrease approaches are not productive, in light of the fact that the strategy turns into a non-incremental or wasteful one sometimes. With the end goal to address this, we initially present the idea of an irregularity degree in a deficient choice framework and demonstrate that the property decrease dependent on the irregularity degree is proportional to that dependent on the positive area. At that point, three refresh procedures of irregularity degree for dynamic fragmented choice frameworks are given. At long last, the system of the incremental attribute decrease calculation is proposed.

DIDS, mechanism in DIDS

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D. Ramana Kumar, S. Krishna Mohan Rao, K. Rajeshwar Rao, "A Survey on Incremental Attribute Reduction Method for Dynamic Data mining Decision Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.517-519, 2018.

Data Mining plays a major role in the decision making process of any application as in Health Care, Artificial Intelligence, military and weather forecasting. In particular, Classification is used to implement the real time Clinical Decision Support System (CDSS) in health care industry. Thus the CDSS can be viewed as if it predicts the decisions through the supervised learning instances from the training dataset. Here a discrete set of algorithms and techniques are in vogue in the backdrop of classification through supervised learning and hence termed as classification algorithms. Among these classification algorithms, Naïve Bayes is the most familiar which uses the historical data as supervised learning instances. This paper surveys the application of Naïve Bayes classification in health care with specific pertinence to analyzing Diabetic Mellitus disease. It also focuses on the implementation of this specific algorithm in the Diabetic domain to expertise an application.

Classification, Text Classification, Naïve Bayes, Semantic Analysis, Health Care

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S. Sankaranarayanan, T. Pramananda Perumal, "Analysis of Naïve Bayes Classification for Diabetes Mellitus," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.520-524, 2018.

Data mining and knowledge discovery in databases have been considered as a significant research area in industry. This survey presents an overview, description and future directions which depict a standard for knowledge discovery and data mining process model. The paper mentions particular real-world applications, specific data mining techniques, challenges involved in real-world application of knowledge discovery, current and future research ideas in the field. The applications to both academic and industrial problems are discussed. The main target of the review is the consolidation of the research in this particular area and thereby helping in enhancing the existing model by embedding other current standards.

Knowledge discovery database, data mining, real world application

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D. Ramana Kumar, S. Krishna Mohan Rao, K. Rajeshwar Rao, "A Comprehensive Survey of Dynamic Data Mining Process in Knowledge Discovery Database," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.525-530, 2018.

In this paper, the work carried out to design the gradient feature based static sign language is presented. Sign languages are the gestures used by the hearing and speaking impaired people for communication. The sign languages are classified into static or dynamic or both static and dynamic sign languages. In static sign languages, still hand postures are used to convey information. In the dynamic sign languages, sequence of hand postures is used to convey information. In the present work, efforts have been made to design the computer vision based static sign language recognition system for the American Sign Language alphabets. The images that represent the static sign language alphabets are grouped into training and test images. The training sign language images are subjected to preprocessing. From the preprocessed images, magnitude and direction gradient features are extracted. These features are used to train the recognition system. The test images are subjected to preprocessing and feature extraction. The extracted features from the test sign language images are used to test the designed sign language recognition system. To classify the static sign language hand gestures, nearest neighbor classifier has been used. Independent experiments are carried out to evaluate the performance of the gradient magnitude and the gradient direction features. The average recognition accuracy of 95.4% for magnitude gradient feature and 80.3% for direction gradient feature are obtained.

Sign Language Recognition System; American Sign Language; Static Sign Language; Gradient Features

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M. Mahadeva Prasad, "Gradient Feature based Static Sign Language Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.531-534, 2018.