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There is growing adoption of internet based services and mobility devices in last two decades. With more number of devices falling under internet of things, cloud based service developments are equally growing. Such cloud solution providers act as a service hub for the cloud service they publish. While lowering the maintenance of software and hardware infrastructure, the cloud solution provider faces challenges in increasing service availability, reducing network management time, minimizing risk of data breaches etc. For data security, both cloud service provider & service consumers rely on indispensable firewalls that use authentication and authorization mechanism for each login user. In spite of these measures, the security of sensitive data still remains a challenge that secure data is vulnerable to unauthorized access. This research paper proposes a new protocol called Secure Authentication Protocol (SAPC) for the authentication purpose which provides mutual authentication between client and remote cloud server. User and Server have to prove their identity to each other at the time of login phase to utilize the cloud resources. Whenever a user logs in, remote cloud server generates dynamic symmetric key and shares the key with client device over insecure channel using key agreement protocol for maintaining security. Proposed SAPC approach helps to stop illegitimate users/devices/things from accessing authorized services/data of cloud service provider.

Authentication scheme, Cloud services, Insider attack, Key agreement, Mutual authentication, Session key, User Anonymity, Waveform

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[12] A. U. S. Yogendra Shah, Vinod Choyi and L. Subramanian, “Multi-Factor Authentication as a Service,” in Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2015 3rd IEEE International Conference
[13] Arif Mohammad Abdul, Sudarson Jena, M Balraju and M. Kiran Sastry, “Enhanced Cipher Method for Cloud Authentication” in International Journal of Research in Engineering and Technology, eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 05 Special Issue: 05 | ICIAC-2016 | May-2016.

Arif Mohammad Abdul, Sudarson Jena, M Bal Raju, "Secure Authentication Protocol to Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1551-1557, 2019.

Educational Data Mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. Firstly, it introduces EDM and describes and why we Need of EDM then, how EDM support Students in Interactive Learning, Applications of EDM, then goes on to list the most typical/common tasks in the educational environment that have been resolved through data mining techniques, LMS Environment, Software used in EDM (MOODLE), Advantages of DM in Education System and what Challenges we are facing in the Field of EDM and finally some of the most promising future lines of research are discussed.

EDM, LMS, DM Techniques, Educational Systems

[1]. An Empirical Study of the Applications of Data Mining Techniques in Higher Education Dr. Varun Kumar Department of Computer Science and Engineering ITM University
[2]. Educational Data Mining: A Review of the State-of-the-Art By Cristóbal Romero, Member, IEEE, Sebastián Ventura, Senior Member, IEEE.
[3]. Educational Data Mining for Grouping Students in E-learning System Divna Krpan, Slavomir Stankov Faculty of Science, Teslina 12, Split, 21000 Croatia.
[4]. Importance of Data Mining in Higher Education System” 1Bhise R.B., 2Thorat S.S., 3Supekar A.K.
[5]. Use of Data Mining Methodologies in Evaluating Educational Data Thilina Ranbaduge Department of Information Technology, University of Moratuwa, Srilanka.

Gaurav Jindal, Sakshi Garg, "Educational Data Mining For Student Support in Interactive Learning Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1558-1565, 2019.

In the concept of data structures, the List plays a major role in the allocation of data. “A list in java is an interface that can extend to collection interface”. A list can be implemented in two ways: Array list and Linked list. Array list is a class which provides growable array of list ADT. Linked list provides different implementation of the List ADT. There are different kinds of linked lists support in data structures which could be singly linked list, doubly linked list and circularly linked list. This paper deals with the analysis of array list and linked list (i.e.) singly linked list by performing operations such as insertion, deletion, searching and provides results based on time complexity to decide which would be better and efficient for allocation of data.

list, arraylist, linkedlist, data structure

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[3] Anshu Yadav, Aruna Bhat, Rajni Jindal, “Stack implementation of adjacency list for representation of graphs”, In the Proceedings of the 2008 IEEE International Conference on Advanced Learning Technologies, Noida, Uttar Pradesh, India, pp. 213-216, 2013.
[4] Shruti rishab panday, “A Heuristic Approach of Sorting Using Linked List”, In the Proceedings of the IEEE Second International Conference on Computing Methodologies and Communication, Erode,India, pp. 446-450, 2018.
[5] Karuna, Garina Gupta, “Dynamic Implementation Using Linked List”, International Journal of Engineering Research & Management Technology”, Vol.1, Issue.5, pp.44-48, 2014.
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[10] Nagendra Singh, “Role of Suffix Array in String Matching: A Comparative Analysis”, International Journal of Computer Sciences and Engineering, Vol. 3, Issue.6, pp.89-93, 2015.
[11] Sourabh Shastri, “A GUI Based Run-Time Analysis of Sorting Algorithms and their Comparative Study”, International Journal of Computer Sciences and Engineering, Vol. 5, Issue.11, pp.217-221, 2017.
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K. Renuka Devi , "Analysis of Arraylist and Linked list," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1566-1570, 2019.

"India now carries 20 percent of the global burden of diabetes. There is an immense need and progress to be made to identify the possible fluctuation of blood glucose before hand with minimal errors and thereby enabling proactive decision making. As per statistics one in 15 people in UK have diabetes, including one million people who have type 2, but haven`t been diagnosed. In this paper, focus is to use data science(An interdisciplinary field that uses skills from various fields such as statistics machine learning, artificial intelligence, visualization etc. ) algorithms like time series machine learning to derive meaningful and appropriate information from large volumes of blood glucose level and related data for precise forecasting of upcoming blood glucose level fluctuations. Not only can the patient and physician be informed beforehand, to avert complications, but it also aids in predicting response to certain medications with ease. In this case, time series machine learning algorithm is implemented on 15 days LIBRREPRO Continuous Glucose Monitoring (CGM) Sensor dataset of 10 different patients. A comparison of performance evaluation metrics of the different time series machine learning algorithms is drawn. Simple exponential smoothing(SES) Algorithm, with alpha and beta of 0.99 provided the least Root Mean Square Error (RMSE) of 7.98mg/dL for 15-minute prediction, 19.47mg/dL for 30-minute prediction. The Theil’s U coefficient was 0.12 for 15-minute, 0.39 for 30-minute prediction.

Glucose Prediction, Machine Learning, SES, MA, RMSE, Theil’s U, LIBREPRO, CGM Sensor, Data Science, Time Series Forecasting, Moving Window Walk Forward Validation

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[6] C. Marling, M. wiley, R. Buneseu, J. Shudrook and F. Schwartz, "Emerging Applications for intelligent diabetes management," AI MAgazine, vol. 67, 2012.
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[11] T. e. al., "Artificial neural network for blood glucose level prediction," in International conference on smart,monitored and controlled cities, 2017.
[12] S. Pappada, B. D. Cameron, P. M. Rosman, R. E. Bourey, T. J. Papadimos, W. Olorunto and M. J. Borst, "Neural Network based real time prediction of glucose in patients with insulin dependent diabetes," Diabetes technology and Therapeutics, pp. 135-141, 2011.
[13] MeriyanEren-Orukulu, A. Cinar, L. Quinn and D. smith, "Estimation of future glucose concentrations with subject specific recursive linear models," Diabetes technology and Theraupetics, pp. 243-253, 2009.
[14] V.Petridis, A. KehagiasL, PetrouA, BakirtzisS, Kiartzish, PanagiotouN and Masalaris, "A bayesian multiple models combination method for time series prediction," Journal of Intelligence and robotic systems, vol. 31, no. 1-3, pp. 69-89, may 2001.
[15] R. J, S. Rajaraman, A. Gribok and K. W. ward, "Predictive monitoring for improved management of glucose levels," Journal of diabetes science and technology, pp. 478-486.
[16] Lynn Kennedy, Adam Brown, Äbotts freestyle librepro professional CGM system.
[17] Freestyle Libre 14 days users manual
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Rekha Phadke, Varsha Prasad, H C Nagaraj, K P Nagesh, "Glucose Level Prediction of LIBREPRO CGM Sensor Data Using Machine Learning Algorithm for Enhanced Diabetes Mellitus Management," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1571-1582, 2019.

Ear biometric is considered as one of the most reliable and invariant biometrics characteristics. Ear recognition is an active area of research and automatic ear recognition is one of the challenging areas in biometric and forensic domains. Human ear contains large amount of unique features for recognition of an individual. The Ear biometric is unhurried as one of the majority unswerving and invariant biometrics approach. Ear appreciation is an active area of enquiry and instinctive ear recognition is one of the challenging areas in biometric and pathological provinces. When compared with the other biometric based recognition, human ear recognition system is universally accepted by various researchers. There are different approaches and descriptors that achieve relatively good results in ear biometric recognition. In this study, presents an overview of different local texture descriptors in the field of automatic ear recognition. Further, we have compared the various feature descriptor extraction techniques and discuss the recognition rate and accuracy for different problems.

Ear Biometric, Physiological and Texture Characteristics, Recognition, Local Descriptors

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[19] R. AnandhaJothi, V. Palanisamy, “Performance Enhancement of Minutiae Extraction Using Frequency and Spatial Domain Filters” International Journal of Pure and Applied Mathematics Volume-118 issue-7 page no-647-654 ISSN No-1314-3395.
[20] R.AnanadhaJothi and V.Palanisamy, "Analysis of Fingerprint Minutiae Extraction and Matching an Algorithm " International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Volume- 3, Special Issue 20, April 2016, PP: 398-410.
[21] R.AnandhaJothi ,V.Palanisamy and J.Nithyapriya ” Evaluation of fingerprint minutiae on ridge structure using Gabor and closed hull filter”, Computational Vision and Bio Inspired Computing. Springer (2018) (in press).
[22] P. Abirami, R. Anandhajothi V. Palanisamy, “A Survey on Biometric E-Voting System Using Retina.” International Journal of Pure and Applied Mathematics, 118 (7) 2018 647-654.
[23] R Suganya,R Anandha Jothi,V Palanisamy,”A Survey on Security Methodologies in E-Voting System.”,International Journal of Pure and Applied Mathematics, vol.118,no.7, 647-654,2018.
[24] AsmitaKamble , AbhijitPatil, VivekPatil, Priya More, AkshayShende, “Automated Human Identification Using Ear Imaging”, IJARIIE-ISSN(O)-2395-4396 Vol International Journal of Pure and Applied Mathematics, 118,(7) (2018) 647-654. -2 Issue-3 2016.
[25] AsmitaKamble , AbhijitPatil, VivekPatil, Priya More, AkshayShende, “Automated Human Identification Using Ear Imaging”, IJARIIE-ISSN(O)-2395-4396 Vol International Journal of Pure and Applied Mathematics, 118,(7) (2018) 647-654. -2 Issue-3 2016
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[28] AnikaPflug, Christoph Busch and Arun Ross, “2D Ear Classification Based on Unsupervised Clustering”.
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[30] David J. Hurley, Mark S. Nixon, John N. Carter, “Automatic Ear Regnition By Force Field Transformation”, IJARIIE-ISSN(O)-2395-4396 Vol-2 Issue-3 2016.
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S. Saranya, R. Anandha Jothi, V. Palanisamy, "An Review on Ear Recognition Techniques Based On Local Texture Descriptors," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1583-1587, 2019.

Grid computing technology can be seen as a positive alternative for implementing high-performance distributed computing. The goal of Grid computing is to create the illusion of virtual computer out of a large collection of connected heterogeneous nodes. Scheduling jobs on computational grids is identified as NP-hard problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. Today a highly secure or virtual grid is very demanding in which you can share any resource from any cluster even with existence of fault in system. In this paper, an algorithm named as UMAX is proposed. This method aims to improve the resource utilization with maximum efficiency and throughput

Meta task; Scheduling; Resource utilization; Grid task scheduling

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[7] D.C. Arnold, S.S. Vahdiyar, J. Dongarra, “On the Convergence of Computational and Data Grids”, Parallel Processing Letters, Vol. 11, pp. 187–202, 2001, ISSN 0129-6264.
[8] P. Sessini, “Scheduling in Grid Computing Systems”, University of Calgary, Alberta, Canada, 2015.
[9] C. Franke, U. Schwiegelshohn, R. Yahyapour, “Job Scheduling for Computational Grids”, University of Dortmund, Germany.
[10] M. Hemamalini, M.V. Srinath, “Memory Constrained Load Shared Minimum Execution Time Grid Task Scheduling Algorithm in a Heterogeneous Environment”, Indian Journal of Science & Technology, Vol. 8, 2015, ISSN (Print): 0974–6846, ISSN (Online): 0974–5645.
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[15] M. Hemamalini, M.V. Srinath, “Response Time Minimization Task Scheduling Algorithm”, International Journal of Computers and Applications, Vol. 145, pp. 9–14, 2016.
[16] J. Balasangameshwara, N. Raju, “A Hybrid Policy for Fault Tolerant Load Balancing in Grid Computing Environments”, Journal of Network and Computer Applications, Vol. 35, Issue. 1, pp. 412–422, 2012.

K. Padma Priya, M. Hemamalini, "UMAX Meta Task Scheduling Algorithm in Grid Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1588-1592, 2019.

This paper proposes architecture for a Smart helmet using the IOT technology. Regular bike riding requires various parameters to be handled by human beings. These parameters change in real time while driving. Even slightly neglecting these parameters can cause accidents. The Proposed system collects the data sensed from various sensors in real time, processes that data and helps the rider to be alert about the surrounding and inform him in advance through actuators. It will reduce the manual effort required to find the routes by automating the process. This System can largely reduce the number of accidents by alerting the rider in advance.

Actuators, internet of things, IoT, sensors, Smart Helmet

[1] Mohammad Abdur Razzaque, Marija Milojevic-Jevric, Andrei Palade, and Siobhán Clarke, Middleware for Internet of Things: A Survey,IEEE INTERNET OF THINGS JOURNAL, VOL. 3, NO. 1, FEBRUARY 2016,70-95.
[2] Jianli Pan, Raj Jain, Subharthi Paul, TamVu, Abusayeed Saifullah, andMo Sha, An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype and Experiments, IEEE INTERNET OF THINGS JOURNAL, VOL. 2, NO. 6, DECEMBER 2015,527-537.
[3] Matthew Edel, Road Profile Sensor: A Detection Method for Active Suspension Systems, SEATTLE PACIFIC UNIVERISTY HONORS PROJECT, MAY 2014,1-17.
[4] Drinking and Driving: a road safety manual for decision-makers and practitioners. Geneva, Global Road Safety Partnership, 2007,22-23.
[5] Gamini Bulumulle and Ladislau Boloni, Reducing Side-Sweep Accidents with Vehicle-to-Vehicle Communication,Journal of Sensor and Actuator Networks,NOVEMBER 20,6.

[6] Edel, Matthew, "Road Profile Sensor: A Detection Method for Active Suspension Systems" (2014). Honors Projects OCTOBER 2017, 1-18.

[7] P. Manickam, V. Muthu Ganeshan , M. Girija “Comprehensive Approach in Studying the Behaviour of Contiki RPL Protocol in Diverse Data Transmission Ranges” ,International Journal of Scientific Research in Network ,Volume-6, Issue-3, June 2018
[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 , June (2018)

Ameya Deshpande, Sudeshna Roy, "Modern Helmet with Smart Utility Features," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1593-1595, 2019.

Data visualization calls to mind the old saying: “a picture is worth a thousand words.” Data visualization techniques exploit this fact: they are all about turning data into visual form by presenting data in pictorial or graphical format. This makes it easy for decision-makers to comprehend the information contained within vast amounts of data at a glance to understand and draw inferences from it. In the present paper the authors have presented techniques and approaches for how huge quantities of data can be represented visually. The authors have designed an intuitive interface to make it easier for an end user to plot data and interact with it. It also demonstrated how data presented visually can be used to draw meaningful inferences from datasets representing real-world scenarios.

Big data, Data visualisation, Interactive data representation, Data analytics

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[4] Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.
[5] Fang, X. & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data.
[6] 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
[7] Rakesh. S.Shirsath, Vaibhav A.Desale, Amol. D.Potgantwar, "Big Data Analytical Architecture for Real-Time Applications", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.4, pp.1-8, 2017
[8] Oluigbo Ikenna V., Nwokonkwo Obi C., Ezeh Gloria N., Ndukwe Ngoziobasi G., "Revolutionizing the Healthcare Industry in Nigeria: The Role of Internet of Things and Big Data Analytics", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.1-12, 2017

Asoke Nath, Tejash Datta, Faisal Ahmed, Nitin Gupta, "Scope and Challenges in Data Visualisation: Presentation of Data in a Graphical Format," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1596-1601, 2019.

Cloud technology has gained fabulous popularity in recent years. By outsourcing the confidential resources to the public providers and paying for the provision used, the users can bliss upon the advantages of this new paradigm. However, the archive which backups the user’s sensitive data may not be fully trustworthy and introduces new challenges from the perspectives of data correctness and security. The users may also concern much about data intactness. Bountiful attempts have been espoused and many technological implementations have been established to remove insecurities. This paper aims to enhance the importance of the data integrity scheme and proposes a remote data possession checking based on the Luhn’s approach. The main idea is to design the tags computed from cipher blocks can be used to check the integrity of the resources in deposited in the archive. The security and performance analysis illustrates the computational, storage and communication efficiency of this scheme. Finally, it performs unbounded data possession checking which provides confidentiality of archived sensitive data

Cloud storage, remote data possession checking, provable data possession, proof of retrievability

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[3] Hema. V, and Dr M. Ganaga Durga. “An Improved Novel Hill Cipher Using RCLT “, International Journal of Engineering & Technology,vol.7,no.3.3,2018,p.209.
[4] Deswarte Y, Quisquater J J. “Remote integrity checking”. In: Proc. of IICIS ’03, 2003, pp.1–11.
[5] Ateniese G, Kamara S, Katz J. “Proofs of storage from
homomorphic identification protocols”. In: Proc. of
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large files”, In: Proc. of ACM-CCS ’07, 2007, pp.584–597.
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Quisquater J. “Efficient remote data possession checking in critical information infrastructures”, IEEE Trans.
on Knowl. and Data Eng., 2007, pp.1034–1038.S.
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using algebraic signatures to check remotely administered storage”, In: Proc. of ICDCS ’06, 2006, pp.12.
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V. Hema, M. Ganaga Durga, "Remote Integrity Auditing Scheme (RIAS) based on Luhn’s Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1602-1607, 2019.

Road accidents are dreaded incidents which are known to take about 146 thousand lives in a year in India itself This system proposes the solution, where the users phone itself initiates a communication to report the accident to the respected authorities. Here an application is being installed in the respective device which will send messages to the emergency numbers those are added at the time of installation of application in the smartphone including the location where the accident occurred. At the time of accident, the smartphone will detect the accident strike and by using accelerometer measurements are being taken, an alert message will send to the emergency numbers including the relatives, hospital and to the police station automatically. Here a threshold value is setted for an accelerometer reading, if the threshold value of accelerometer exceeds, the automatic alert message will send to the respective numbers. This message consists not only the alert of accident but also the exact location of the accidental spot. The alert message is sent using SMS module and the exact location of accident is spotted by using the GPS. This system is taken place using an android application and the user must to carry the smartphone while driving. Now that is not a mandatory case because all are with the smartphone with them always. Proposed system is user friendly and it don`t force any user to carry any other devices but only the device which is already with them.

GSM,GPS,Accelerometer,Threshold

[1] Indranil Nikose1 , TusharRaut1 , Reena Bisen1 , Varsha Deshmukh1 , Ashwini Damahe1 , Pranoti Gho ,” Smart helmet using GSM and GPS technology”, International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 6, Issue 2, February 2017
[2] Prajitha Prasad A1, Reshma Mohan2, S Lekshmi Raj3, Sreelekshmi S4, Divya R Pillai5”, smart helmet and intelligent bike system” international journal of current engineering and scientific research, Volume-5, ISSUE-5, 2018
[3] Manjesh N “Smart Helmet Using GSM & GPS Technology for Accident Detection and Reporting System” Internal Journal for Electrical and Electronics Research, Vol:2, Issue:4, October 2014
[4] Nitin Agarwal, Anshul Kumar Singh, PushpendraPratap Singh, Rajesh Sahani “Smart Helmet” International Research Journal of Engineering and Technology, Vol:2, Issue:2, May 2015
[5] Mohd Khairul, Afiq Mohd Rasli, Nina Korlina Madzhi, Juliana Johari,” smart helmet with sensors for accident detection”,2013 International Conference on Electrical, Electronics and System Engineering, 978-1-4799-3178-1/13/$31.00 ©2013 IEEE

Christy Jose, Bejoy Mathew, Giffin M George, Renu V Rajan, Anish George, "Automatic Accident Detection and Reporting Using Life Saver Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1608-1610, 2019.