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Core region of the sun is less hot due to absorption of thermonuclear fission (Demass) energy emitted from the Corona. Thermonuclear fusion (Enmass) process is going on the Core region continuously where Hydrogen is converted into Helium. Core region of the sun is gradually expanding. Due to the above cause the Corona region is hotter than Core.

Enmass, Demass, Corona, Core

[1] https://en.wikipedia.org/wiki/Corona.
[2] https://www.nasa.gov/feature/goddard/sounding-rockets/strong-evidence-for-coronal-heating-theory-presented-at-2015-tess-meeting/

Kisalaya Chakrabarti, "Explanation behind the less hot Core of the sun than its Corona," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.144-145, 2017.

Classification of skin cancer gives the best chance of being diagnosed early. Biopsy method for skin cancer detection is much painful. Human interpretation contains difficulty and subjectivity therefore automated analysis of skin cancer affected images has become important. This paper proposes an automatic medical image classification method to classify two major type skin cancers: Melanoma and Non-melanoma. In this paper, we have used the color and texture features in combination which give better results than using color or gray level information alone. We have used k-means clustering algorithm to segment the lesion. The features are extracted by four different color-texture feature extractors from the segmented images. DullRazor technique is used to eliminate surrounding hairs. Classification accuracy of our proposed system is evaluated on four different types of classifiers and their values are compared with one another. The results of the proposed system are computed on five different classification rates in order to perform better analysis of our proposed system. SVM outperforms among all other classifiers with accuracy of 76.69%.

Grey Level Co-occurrence Matrices, Support Vector Ma-chine, Local Binary Patterns, Texture features, color percentiles, K-means clustering, Cooccurence Matrix, Color Features, Integrative Cooccurence Matrix, Gabor Features, Linear Classifier, NN Classifiers, NMC Classifiers, Cross-validation.

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R. Maurya, A. Singh, V. Srivastava, R. Yadav, "A Comparative Review of Various Approaches for Skin Cancer Detection," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.146-152, 2017.

In this paper, we have studied the effect of AR (2) model and non-normality on the economic design of mean chart with known coefficient of variation under non normal population. We use the first four terms of an Edgeworth series for the production cycle time and cost parts. In AR (2) model three different situations arise as (i) Roots are real and distinct (ii) Roots are real and equal (iii) Roots are complex conjugate. The significant effects are seen on mean chart for the above three situation. We also develop the formula for the sample size (n) and sampling interval (h) for different combination of the skewness and kurtosis under AR (2) model.

AR (2) Model, Mean Chart, Autocorrelation, Non-Normality, coefficient of variation (CV), Sample Size, Sampling Interval

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J.R. Singh, A. Sanvalia, "Effect of AR (2) Model on the Economic Design Mean Chart with Known Coefficient of Variation Under Non Normal Population," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.153-169, 2017.

Data mining techniques are useful to extract the useful information and to support in the decision making process. There are too many application in the educational domain where we can apply the data mining. Right now data mining in Educational domain is rapidly developing technique. In this research paper we analyse the student’s result of every semester using data mining techniques. Data mining supports the too many techniques but here in the analysis we are using the various classification algorithm of data mining techniques. Here in this research analysis we worked on two model. Model A uses the dataset that contains all the student performance parameters and data mining classification techniques and generated the result based on Accuracy and Error Rate of the classifiers and Model B uses the dataset contains only statistically proved highly affected parameters on student performance and applied data mining techniques on this data sets and generate the results based on Accuracy and Error rate of the classifiers. This research work compare the result of both the model and check that which model is best. The comparison is done using the measurement of accuracy and measurements of Error Rate. This research work also shows that which algorithm is most suitable for predicting the performance of the students among the selected algorithms. The analysis work is done by considering various types of algorithm like decision tree algorithm, rule based algorithm, Bayesian algorithm and function based algorithms. This generic novel approach can be extended to other disciplines as well.

classification, error rate, data set, data mining, prediction.

[1] Bhise R.B, Thorat S.S, Supekar A.K, “Importance of Data Mining in Higher Education System”, 2013.
[2] Monika Goyal ,Rajan Vohra2, “Applications of Data Mining in Higher Education”, 2012.
[3] K.Shanmuga Priya, A.V.Senthil Kumar, “Improving the Student’s Performance Using Educational Data Mining”, 2013.
[4] Varun Kumar, Anupama Chadha, “Mining Association Rules in Student’s Assessment Data”, 2012.
[5] Mahendra Tiwari, Yashpal Singh (2012), “Performance Evaluation of Data Mining clustering algorithms in WEKA”, Global Journal of Enterprise Information System, vol 4, issue I
[6] Sonali Agarwal, G. N. Pandey, and M. D. Tiwari, “Data Mining in Education: Data Classification and Decision Tree Approach”, 2012.
[7] Surjeet Kumar Yadav, Brijesh Bharadwaj, and Saurabh Pal, “Mining Education Data to Predict Student’s Retention: A comparative Study”, 2012.
[8] Brijesh Kumar Baradwaj, Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, 2011.
[9] Pallamreddy.venkatasubbareddy, Vuda Sreenivasarao, “The Result Oriented Process for Students Based On Distributed Data Mining”, 2010.
[10] Tanuja S, Dr. U. Dinesh Acharya, and Shailesh K R (2011) , “Comparison of different data mining techniques to predict hospital length of Stay, Journal of Pharmaceutical and Biomedical Sciences (JPBMS)”, Vol. 07, Issue 07

B.R. Patel, "Comparative analysis of classification algorithm in EDM for improving student performance," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.171-175, 2017.

In recent years, Empirical software engineering is gaining popularity among the software engineering and research community to improve the software quality. However lack of clear understanding about how to do empirical research in software engineering poses threats of unreliable research results. |So to clear understand the process of doing research in empirical software engineering, Unified Modeling Language (UML) models are used. Unified Modeling Language in software engineering is used for designing and analysis of the software systems.

Unified Modeling Langugae, Activity diagram, Use Case diagaram, State diagram, Empirical Software engineering

[1] R. Mahrotra, “Empirical Research in Software Engineering Concepts Analysis and Applications”, CRC press Taylor and Francis Group, New York, 2016.
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[3] Aldowaison, TA, Cafar L. K. “Business process reengineering: an approach for process mapping”. Omega International Journal of Management Science, Vol. 27, pp. 515-524, 1999.
[4] Vipin Saxena, Ajay Pratap, “Performance comparison between relational and object-oriented databases”, International Journal of Computer Applications, 2013, Vol. 21, No. 22, pp. 6-9.
[5] W. Rhmann, V. Saxena, “Optimized and Prioritized Test Paths Generation from UML Activity Diagram using Firefly Algorithm”, International Journal of Computer Applications, 2016, Vol. 145, No. 6, pp. 16-22.
[6] T. Zaidi, V. Saxena, “A UML Frame Work for Town and Country Planning”, International Journal of Computer Applications, 2013, Vol. 73, No. 20, 2, pp. 23-25.
[7] Vipin Saxena, Deepak Arora, Manish Shrivastava, “Performance evaluation of network system through UML”, ACM SIGSOFT Software Engineering Notes, 2009, Vol. 34, No. 5, pp. 1-3.
[8] A. Bansal,“Empirical analysis of search based algorithms to identify change prone classes of open source software”, Computer Languages, Systems & Structures, 2017, Vol. 47, pp. 211–231.
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[10] W. Rhmann, V. Saxena, “Test Case Generation from UML Sequence Diagram for Aadhaar Card Number based ATM System”, International Journal of Applied Information System, 2016, Vol. 11, No. 4, pp. 37-43.
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[13] G. Ansari, W. Rhmann, V. Saxena, “Risk Based Test Case Prioritization using UML State Machine Diagram using Risk Information”, International Journal of Applied Information System, 2016, Vol. 11, No. 7, pp. 15-20.
[14] G. Ansari, V. Saxena, “Object Oriented UML modeling for traveler Management System”, Ubiquitous Computing and Communication Journal, 2008, Vol. 3, No. 3
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Wasiur Rhmann, "UML Models of Research Process in Empirical Software Engineering," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.176-180, 2017.

This paper reviews the current technology & trends of electrodeless lamps, videlicet, fluorescent induction lamps & sulphur lamps. Induction lamps utilize the principle of electromagnetic induction & visible light is generated by phosphor coating in case of fluorescent lamps and by sulphur molecules in case of sulphur lamps. The methodologies of light generation, lighting characteristics, application areas, advantages & disadvantages in their utilization & the future scope of improvement are hereby duly expounded & analysed.

Induction Lamp, Sulphur Lamp, Energy Efficient Lighting, Plasma Lamp

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Sourin Bhattacharya, Nirab Majumder, "On Electrodeless Induction Lamps," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.181-184, 2017.

Software Transactional Memory (STM) is a new alternative approach to locks which solves the problem of synchronization in parallel programs. In STM users have to identify the critical sections in the program and enclose them within transactions by using appropriate STM function calls. Then STM automatically by its internal constructs ensures synchronization in the program. This paper shows how to solve the problem of synchronization in parallel programs by using a hybrid programming approach using both locks and STM. Locks use pessimistic approach to solve the problem of synchronization in parallel programs. STM uses optimistic approach to solve the problem of synchronization in parallel programs. Both the optimistic and pessimistic approaches have some advantages and disadvantages. The disadvantage of optimistic approach is that transactions are aborted when validation cannot be done. This approach works well when there are no conflicts (hence the term optimistic) but wastes work when there are conflicts. Aborting of transactions is a severe problem when the transactions are long and interactive. The disadvantage of pessimistic approach is that large number of locks in the program will lead to very slow execution speed which may cancel out the gains made by solving the problem in parallel. The hybrid approach combines the advantages of the optimistic and pessimistic approaches removing their disadvantages without any degradation of performance.

Multiprocessing, Parallel Processing, Locks, Software Transactional Memory, Hybrid Parallel Programming

[1] Ryan Saptarshi Ray,“STM:Lock-Free Synchronization”, Special Issue of IJCCT, ISSN (ONLINE): 2231 – 0371, ISSN (PRINT): 0975 – 7449, Volume- 3, Issue-2, pp. 19-25, February 2012
[2] Ryan Saptarshi Ray and Utpal Kumar Ray, “Writing Lock-Free Code”, In the Proceedings of International Conference on Computer Science and Engineering(ICCSE),Kolkata, pp. 19-25, 24th March 2012
[3] Pascal Felber, Christof Fetzer, Torvald Riegel, “Dynamic Performance Tuning of Word-Based Software Transactional Memory” In the Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, pp. 237-246 ,2008
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[5] Sang H. Son, Junhyoung Lee, “A New Approach to Real-Time Transaction Scheduling”, In the Proceedings of the Fourth Euromicro workshop on Real-Time Systems, pp. 177-182, June 1992
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[8] Jan Lindstrom, “Hybrid Concurrency Control Method in Firm Real-Time Databases”
[9] Debendranath Das, Ryan Saptarshi Ray, Utpal Kumar Ray, “Implementation and Consistency Issues in Distributed Shared Memory”, International Journal of Computer Sciences and Engineering (IJCSE) E-ISSN:2347-2693 Volume- 4, Issue-12, pp 125-131, 2016

Ryan Saptarshi Ray, Parama Bhaumik, Utpal Kumar Ray , "Hybrid Parallel Programming Using Locks and STM," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.185-192, 2017.

Data mining technology is a procedure for analyzing large amounts of available data and extracting useful information and knowledge to support critical decision-making processes. Data mining can be applied to various applications in the field of education to improve student performance. Educational data mining is developing rapidly and is an important technology for data analysis in the field of education. This paper shown different classification algorithms of data mining those are used for development of a data mining model for predictions of performances of students, on the basis of their personal demographic and academic information. This paper analyze and evaluate the students‟ performance by applying data mining classification algorithms in weka tool.

classification, data mining, prediction, performance, knowledge, information

[1] Mahendra Tiwari, Yashpal Singh, “Performance Evaluation of Data Mining clustering algorithms in WEKA”, Global Journal of Enterprise Information System, vol 4, Issue I, pp.24-29, Nov. 2012.
[2] Tanuja S, Dr. U. Dinesh Acharya, and Shailesh K R “Comparison of different data mining techniques to predict hospital length of Stay, Journal of Pharmaceutical and Biomedical Sciences (JPBMS)”, Vol. 07, Issue 07, Oct. 2011.
[3] Sonali Agarwal, G. N. Pandey, and M. D. Tiwari, “Data Mining in Education: Data Classification and Decision Tree Approach”, 2012.
[4] Monika Goyal ,Rajan Vohra2, “Applications of Data Mining in Higher Education”, 2012.
[5] Surjeet Kumar Yadav, Brijesh Bharadwaj, and Saurabh Pal, “Mining Education Data to Predict Student’s Retention: A comparative Study”, 2012.
[6] Brijesh Kumar Baradwaj, Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, 2011.
[7] K.Shanmuga Priya, A.V.Senthil Kumar, “Improving the Student’s Performance Using Educational Data Mining”, 2013.
[8] Bhise R.B, Thorat S.S, Supekar A.K, “Importance of Data Mining in Higher Education System”, 2013.
[9] Varun Kumar, Anupama Chadha, “Mining Association Rules in Student’s Assessment Data”, 2012.
[10] Pallamreddy.venkatasubbareddy, Vuda Sreenivasarao, “The Result Oriented Process for Students Based On Distributed Data Mining”, 2010.

B.R. Patel, "Data Mining Technique for Improving Student Performance," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.194-197, 2017.

In automatic gate entry system security of vehicle numbers stored in the computer system is a crucial issue because in some parking areas only few important vehicles are permitted. The numbers of permitted vehicles are stored in computer systems. Cryptography based security systems are used to secure these numbers, but in modern environment this type of secure data can also be hacked and altered by the unauthorized users. In order to solve these vulnerable problems, in this paper, we try to create a security mechanism by using Artificial Neural Network (ANN) to protect the data stored on a computer device against unauthorized access. In place of saving vehicle numbers in actual form or in form of alphanumeric data into a text file, we store them in the form of network parameters and these parameters are generated by the back propagation algorithm of ANN using neural network toolbox of MATLAB. This type of security approach is the newest form of cryptography and also cracking of these types of parameters is not possible till today.

Artificial Neural Network; Back Propagation; Cryptography; Automatic Gate Entry System

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E. Narwal, S. Gill, "Securing Vehicle Numbers using Artificial Neural Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.195-199, 2017.

The huge amount of data produced and captured by the Internet of Things (IoT) in current situations. IoT data created by several applications such as from smart cities, monitoring system, health care, object detecting systems and smart systems etc which all produce the continues data streams.But data of IoT recognized with RFID signal their size is near to 20 bytes. So that it is requiring of special procedures for analyzing, managing and mining the IoT stream data which is identified by RFID. Several software tools have been developed for mining the stream data relatively Big Data tools. While most current research looking into data mining and relative big data tools which are on machine learning of distributed and non-distributed systems with respect to IoT stream data.This paper focused on IoT data format, different kind stream data mining tools according to mining steps and few Big Data tools relative with data mining for handling the stream data of IoT.

IoT, Data mining ,Big Data , RFID, Stream Data

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Sallauddin Mohmmad, Syed Nawaz Pasha, Dadi Ramesh ,Shabana, "A Relative Study of Data Mining and Big Data on IoT `s Data Streams," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.200-209, 2017.