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In this paper we present a handwritten Hindi character recognition system based on different Deep learning technique. Handwritten character recognition plays an important role and is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Hindi characters using deep learning approaches like Convolutional Neural Network (CNN) With Optimizer RMSprop (Root Mean Square Propagation) , Adaptive Moment (Adam) Estimation and Deep Feed Forward Neural Networks(DFFNN). The proposed system has been trained on samples of large set of database images and tested on samples images from user defines data set and from this experiment we achieved very high recognition results. Experimental results are compared with other neural network based algorithm.

DFFNN, CNN, Softmax classifier, RMSprop and Adam Estimation, Deep Learning

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R. Vijaya Kumar Reddy, U. Ravi Babu, "Handwritten Hindi Character Recognition using Deep Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1-7, 2019.

Secure code is one of the key parameters which must be taken care while software is being developed. Inspecting the source code at the earlier stages is always a better approach. Inspection involves carefully examining the source code for any flaws which may cause problems in the later stage of the software life cycle. The Vulnerability is a kind of weakness or security flaws in code that can be exploited by an attacker to perform unauthorized actions. A vulnerable code will lead to severe threats to the security of software. In this paper, we have investigated the source code of a well-known open source software (OSS) projects written in C and C++ programming language and figure out the presence of vulnerability in the software. The results also indicate that the vulnerabilities in the source code have shown an increasing trend with the lines of code (LOC). It pointed to the fact that addition of new features or change request into the OSS project will cause an increase in the vulnerability as well. It gives significant implication to the developers or project managers of OSS projects to not deny the existence of security flaws in the software as the software evolves. The obtained results will also help the project managers and developers to measure the state of software.

Open Source Software, Software Quality, Hits, Flawfinder, Vulnerability, Code Scanning tools

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[19] Viega, John, et al. "ITS4: A static vulnerability scanner for C and C++ code." Computer Security Applications, 2000. ACSAC`00. 16th Annual Conference. IEEE, 2000.
[20] Flawfindetr: https://dwheeler.com/flawfinder/flawfinder.pdf and A book entitled as “Secure Programming HOWTO” by David A. Wheeler.
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[22] GIT HUB, https://github.com/mysql/mysql-server.

Madanjit Singh, Munish Saini, Manevpreet Kaur, "Analyzing the Vulnerability in Open Source Software," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.8-15, 2019.

Low power and Lossy Networks now regarded as one of the most extensive study areas are comprised of a huge number of constrained nodes with restricted power, memory and processing facility. Besides, nodes in these networks consistently function on batteries and shows evidence of unpredictable communication often. Routing in such constrained networks is extremely tough since the available protocols were not suitable for low-power and lossy networks and thus the Internet Engineering Task Force (IETF) ROLL Working Group proposed an IPv6 based Routing Protocol for Low power and Lossy Networks called RPL capable of forming network routes issuing information about routing among nodes and adapting to network topologies. This paper mainly focuses on the simulation study of routing protocol for low power and lossy networks with Cooja simulator.

WSN, RPL, Contiki, Cooja, LLN

[1] T. Winter, "RPL: IPv6 routing protocol for low power and lossy networks," Internet Eng. Task Force, Fremont, CA, USA, RFC 6550, accessed: Sep. 2017.
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[3] A Brandt and G. Porcu, "Home automation routing requirements in low-power and lossy networks," Internet Eng. Task Force, Fremont, CA, USA, RFC 5826, accessed: Sep. 2017.
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[7] P. Levis, T. Clausen, J Hui, O. Gnawali, and J. Ko, "The Trickle algorithm," Internet Eng. Task Force, RFC 6206.
[8] D. Barthel, J. Vasseur, K. Pister, M. Kim, and N. Dejean, "Routing metrics used for path calculation in low-power and lossy networks," Internet Eng. Task Force, RFC 6551.
[9] T. Zhang, L. Xianfeng, "Evaluating and analysing the performance of RPL in Contiki," Intenational workshop on mobile sensing, computing and communication," pp: 19-24, 2014.
[10] Olfa Gaddour, and Anis Koubaa, "RPL in a nutshell: A survey," 2013.
[11] J. Wang, and H. Li, "Researching and hardware implementation of RPL protocol based on Contiki operating system," International Journal of Future Computer and Communication, Dec. 2014.
[12] Xun Yang, "The Application of RPL routing protocol in low-power wireless sensor and lossy networks," Sensors and Transducers, Vol . 170, Issue 5, May 2014, pp. 107-111.
[13] Iova, O., et al. "The love-hate relationship between IEEE 802.15.4 and RPL," IEEE Communications Magazine, pp: 188-194, 2017.
[14] Olfa Gaddour, Anis Kaubaa and Mohammed A, "Quality-of-Service aware routing for static and mobile ipv6 based low-power and lossy networks using RPL," Adhoc Networks, pp: 233-256, 2015.
[15] J.P Vasseur, and Adam Dunkels, "Interconnecting smart objects with IP- The next Internet", 2010.
[16] J. P. Vasseur, N. Agarwal, J. Hui, Z. Shelby, P. Bertrand, and C. Chauvener, "RPL: The IP Routing protocol designed for low-power and lossy networks"", Internet Protocol for Smart Objects (IPSO) Alliance, April 2011.
[17] Contiki: The open source operating system for Internet of Things.
[18] Olfa Gaddour, Anis Koubaa, "Co-RPL RPL Routing for mobile low power wireless sensor networks using Corona mechanism," 9th IEEE International Symposium, pp: 200-209, 2014.
[19] Olfa Gaddour, et. al, "OF-FL: QoS-aware fuzzy logic objective function for RPL routing protocol," Proceedings of 12th International symposium on Modelling and Optimization in Mobile, Ad-Hoc and Wireless networks, pp. 365-372, May 2014
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Nithya Lakshmi N, M. Hanmandlu, "Simulation of Routing protocol for Low power and Lossy Networks with Cooja Simulator," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.16-20, 2019.

With nurturing reputation of the World Wide Web, large quantity of web usage data is collected by the web servers and stored in web access log files. Web usage mining is a technology to mine valuable knowledge from the World Wide Web. It intends to discover interesting user access patterns from web log files. Analysis of these user access patterns is used to determine that the information architecture of the web site can be reorganized to better facilitate information retrieval. Association rule mining is also used to find association relationships amongst large data sets. Mining frequent sequential patterns is a significant aspect in association rule mining. Based on this, the changes can be suggested to the web site by placing embedded hyperlinks on the home page to the frequently accessed sections of the web site. In this paper, a very efficient algorithm has been adopted for expert systems to mine frequent sequential patterns in web usage data

Data Mining, Web Mining, Association rule mining, Frequent sequential pattern mining, Web Log files, Web usage

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[27] Pradeep Chouksey, “Mining Frequent model Using mass-produced Approach”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.89-94, 2017.
[28] Sunil Joshi, “A Dynamic Approach for Frequent Pattern Mining Using Transposition of Database”, The IEEE 2010 International Conference on Communication software and Netweorks (ICCSN 2010) from 26 - 28 February 2010.
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A. Saravanan, S. Sathya Bama, "Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.21-26, 2019.

Sensor networks is widely used in real-time applications that have made emergent of Quality of Service (QoS) based communication schemes. Recently QoS in sensor network becoming an interesting topic among the research community. This paper proposes a Naïve Bayes based QoS mechanism, which is suitable for both real-time and non-real-time applications. The proposed mechanism achieves the desired QoS by selecting the neighboring nodes in a way to meet the required QoS. Performance of the scheme is evaluated through simulations. The results provide insights on the performance of the system based on different evaluation metrics such as end-to-end delay, packet delivery ratio and the node failure probabilities. The results demonstrate that the scheme is able to outperform the compared mechanisms such as Multi-constraint Multi-Path (MCMP) routing protocol and energy efficient QoS aware routing protocol (EQSR) using both real time traffic (EQSR-RT) and non-real time traffic(EQSR-NRT).

Sensor Network, Wireless, Quality of Service, Naive Bayes classifier, Real-time applications

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T. Beula Darling, G. Suganthi, "Naive Bayes Based QoS for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.27-33, 2019.

Calcium Silicate acquires a higher luminous efficiency when it is doped with rare earth activated ions. The luminescence behaviour of unhoped CaSiO3 was investigated by a very few research groups. Effect of Tb3+ ion composition on the structural and phosphorescence properties from CaSiO3:Tb3+ nanocrystals has been evaluated using powders grown by the solution combustion technique. The surface morphology were analyzed by SEM and the chemical composition of phosphor characterized by EDX analysis. The XRD study indicates that as the terbium concentration increases the phase changes from CaSiO3 to Ca3Si2O7. Broad band emissions peaking between 280 - 360 nm derived from excited states of Tb3+ ions were observed for all powders grown from various Tb compositions. The green emission transition at 550nm due to an electronic transition of 5D4-7F5 was found to be more prominent and intense. Intensity of afterglow phosphorescence was greatly influenced by the composition of the activator ions. It is found that the composition shows optimum PL properties at 7% of terbium Tb3+ ions concentration. The impedance spectroscopic study shows that the present phosphor shows electrical behavior like dielectric material in AC field. The activation energy of phosphor analysed by Chem’s imperials formula in thermoluminescence analysis found to be.0.86eV. It is found that the prepared luminesce material is a new alternative for making cheap green organic light emitting diodes(OLED’s).

Impedance spectroscopic, Thermoluminescence, Photoluminescence, Energy Dissipative spectroscopic

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Ohm Prakash Verma, Shailendra Verma, M R Meshram, Nirbhay K Singh, "Luminescence and impedance analysis of CaSiO3:Tb3+ nanophosphors material prepared by combustion method for OLED," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.34-39, 2019.

Rubik`s cube is a 3-dimensional mechanical puzzle. The aim of this research paper is to describe a new algorithm which is state based to solve the Rubik`s cube. There 40 quintillion possibilities to solve this problem. Hence without knowing the principles behind cube it is nearly impossible to solve in proper manner. It will take you through everything in order to solve the Rubik`s cube. It is really simple, you just need to follow the algorithm and you will be solving Rubik`s cube in less than two minutes. This paper on how to do the Rubik’s Cube will take about 45 minutes to understand, but once you get, you can impress all your friends with how you can solve one of life’s great mysteries: how to do a Rubik’s Cube.

Flowchart, Algorithm of Rubik’s Cube, Artificial Intelligence, Group theory

[1] James G. Nourse, “The Simple Solution to Rubik`s”, McGraw-Hill Publication, India
[2] Dan Harris, Robert Steimle” Speed solving the Cube”,H&C Publications
[3] Daniel Ross,” Rubik`s cube best algorithms”,pearl publications
[4] Hill main,“Group Theory” H & C publicaions
[5] Kevin Warwick,” Artificial Intelligence”,Hill publications
[6] https://en.wikipedia.org/wiki/Rubik%27s_Cube
[7] https://www.computerhope.com/jargon/f/flowchar.html
[8] http://erikdemaine.org/papers/Rubik_ESA2011/paper. pdf
[9] https://www.quora.com/What-are-the-best-papers-Rubiks-cube-theory
[10] https://study.com/academy/lesson/flowchart-symbols
[11] http://www.genetic-programming.org/hc2010/7-Borschbach
[12] http://news.mit.edu/2011/rubiks-cube-0629
[13] http://www.divaportal.org/smash/get/diva2:816583/FULLTEXT01.pdf
[14] http://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group3

Jeslin Jery, "New State Based Algorithm For Rubik’s Cube," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.40-45, 2019.

Biometrics is the new technology for body measurements and calculations that is use to identifying a person. It signifies to metrics related to human physiological or behavioral characteristics. Many specific physiological and behavioral parts, personal characteristics have been suggested and used for biometric security scheme [1]. Any Biometric system comprises of four modules: sensor module, feature extraction module, database module and matching module. Out of all these module feature extraction module of any recognition system plays an important role in recognizing the particular objects with same set of images [3]. This paper presents an analysis on the use of the newly introduced modern and popular key-points feature extracting tools and methodologies that can be applicable in the biometric domain. The implementation is carried out using MATLAB programming environment and tested on CASIA database for Iris and FVC2004 DB3_A for Fingerprint.

Biometric; Iris; Fingerprint; Feature;Templte; Matching

[1] A. K. Jain, Fellow, IEEE, A. Ross, Member, IEEE, and S. Prabhakar, Member, IEEE, “An Introduction to Biometric Recognition”, IEEE transactions on circuits and systems for video technology, vol. 14, no. 1, january 2004.
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[11] Chinese Academy of Sciences Institute of Automation (CASIA) iris database [http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp]
[12] R. Kabbani,“Selecting Most Efficient Arabic OCR Features Extraction Methods Using Key Performance Indicators”, 2nd International Conference on Communications, Computing and Control Applications (CCCA), 2012.
[13] Yakubu Ajiji Makeri, “The role of Cyber Security and Human-Technology Centric for Digital Transformation”, International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol.6, Issue.6, pp.53-59, December (2018).

Om Prakash Sharma, Jitendra Sheetlani, Praveen Shrivastava, "Recent Advancement in Feature Extraction tools for Biometric System: Comparative Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.46-50, 2019.

Exchanging data over the system is generally utilized quick and solid hotspot for correspondence. Clients from wide devotion utilize this component for exchanging and getting to data. Portability and between operability inside cloud framework through disconnected and online mediums are persistently alluring yet the issue of security emerges amid the transmission process. Security and unwavering quality is the key issue during the exchange process which is considered in this exploration. Data security is given utilizing the public and private key block level de-duplication. The analysis is inferred at disconnected information as well as an online information, for example, googledocs. Redundancy handling mechanism of the component is utilized to guarantee that space at information storage supplier is slightest utilized since taken a cost in DSP is went with a measure of capacity utilized. Overall space necessity if there should arise an occurrence of heavy documents is decreased and security of online data getting to is improved by the utilization of Byte level de-duplication.

de-duplication, redundancy handling mechanism, cryptosystem

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[5] T. H. Noor, Q. Z. Sheng, L. Yao, S. Dustdar, and A. H. H. Ngu, “CloudArmor: Supporting Reputation-Based Trust Management for Cloud Services,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 2, pp. 367–380, Feb. 2016.
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Sunil Gupta, Rajeev Bedi, Amandeep Kaur, "Design of an Improved Data Deduplication Technique for Cloud Storage," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.51-56, 2019.

The voyage of data begins with collection of data, followed by storing this data in precise format, further traveling through the process of Data Analysis and Data Visualization, ultimately concluding the journey by reaching to valuable knowledge. Thus, this journey originates from Data, reaching to Knowledge. This original data has various dimensions, several logical formats like text, numbers and also physical structures such as structured, semi-structured and unstructured. The complexity of the data increases with the increased number of dimensions of data. During this entire journey, the data has to travel through various phases including Data Analysis and Data Visualization. However, the outcome of data analysis may not be adequate to provide the knowledge. Visualization Process involves seven steps, Acquire, Parse, Filter, Mine, Represent, Refine and Interact. Acquiring refers to obtaining the data, Parsing structures the data, Filtering allows to select the precise data, Mining supports in uncovering the patterns, Representing provides visual data, Refining allows to enhance the presentation of data, Interacting develops an interaction with the gained knowledge. Each phase makes data more meaningful as the process of data visualization contributes in enhancing the quality of the analysed data. Thus, role of visualization in this voyage is significant as it transforms data into knowledge. The purpose of this research paper is to describe the various phases in the process of data visualization along with several formats of original data and also presents comparison between data and information, before and after visualization.

Data Visualization, Data Transformation, Visualization Process, Information

[1] Kirti Nilesh Mahajan and Leena Ajay Gokhale, “Comparative Study of Static and Interactive Visualization Approaches”, International Journal on Computer Science and Engineering (IJCSE), e-ISSN: 0975-3397 p-ISSN: 2229-5631, Vol. 10 No.03, DOI: 10.21817/ijcse/2018/v10i3/181003016 Vol. 10 No.03 Mar 2018 85, pp.85-91, March 2018.
[2] Kirti Mahajan and Leena Ajay Gokhale, “Significance of Digital Data Visualization Tools in Big Data Analysis for Business Decisions”, International Journal of Computer Applications (IJCA), Volume 165 – No.5, pp.15-18, May 2017.
[3] Kirti Mahajan and Leena Ajay Gokhale, “Advanced Charting Techniques of Microsoft Excel 2016 Aiming Visualization”, International Journal of Computer Sciences and Engineering (IJCSE), Vol.-7, Issue-1, E-ISSN: 2347-2693, Jan 2019.
[4] Robert Radburn, Jason Dykes, Jo Wood, "vizLib: Using The Seven Stages of Visualization to Explore Population Trends and Processes in Local Authority Research".
[5] Michael Alexander and Dick Kusleika, “Excel 2016 Formulas”, Wiley India Pvt. Ltd., India, pp. 99-123, 2016.
[6] Mantripatjit Kaur, Anjum Mohd Aslam, “Big Data Analytics on IOT: Challenges, Open Research Issues and Tools”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.81-85, 2018.
[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, Volume-5, Issue-4, ISSN: 2321-3256, 2017.

Kirti Nilesh Mahajan, Leena Ajay Gokhale, "Process of Data Visualization: Voyage from Data to Knowledge," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.57-63, 2019.