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Parallel and distributed computing becomes critical in the heavy workload environment. In such situations, job partitioning becomes need of the hour. Smaller junks known as task has limited complexity and hence overall execution speed increased considerably as these allotted to the processors. In case of parallel computing, there exist several distinct tasks that may belong to single or multiple jobs having resource requirements. Assigning resources to tasks need strategies to reduce execution time and prevent starvation. This literature put a light on strategies used to allocate resources optimally to tasks meant to execute on distributed environment. Highlights of distinct literature presented through parameters in the form of comparative table so that useful feature can be extracted for future enhancements.

Parallel and distributed computing, execution speed, starvation

[1] A. Vasudevan, “Static Task Partitioning Techniques for Parallel Applications on Heterogeneous Processors,” Trinity Coll. dublin, no. December, 2015.
[2] N. Saranya and R. C. Hansdah, “Dynamic partitioning based scheduling of real-time tasks in multicore processors,” Proc. - 2015 IEEE 18th Int. Symp. Real-Time Distrib. Comput. ISORC 2015, pp. 190–197, 2015.
[3] M. Y. Alzahrani, “Discovering Sequential Patterns from Medical Datasets,” 2016.
[4] S. Alamanda, S. Pabboju, and N. Gugulothu, “An Approach to Mine Time Interval Based Weighted Sequential Patterns in Sequence Databases,” 2017 13th Int. Conf. Signal-Image Technol. Internet-Based Syst., pp. 29–34, 2017.
[5] F. Ahmed, “A Simple Acute Myocardial Infarction ( Heart Attack ) Prediction System Using Clinical Data and Data Mining Techniques,” pp. 22–24, 2017.
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[9] Y. CHENG, Y.-F. Lin, K.-H. Chiang, and V. Tseng, “Mining Sequential Risk Patterns from Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease,” IEEE J. Biomed. Heal. Informatics, pp. 1–1, 2017.
[10] B. R. M. Eenan, “Non-homogeneous Markov models for sequential pattern mining of healthcare data,” IEEE, pp. 327–344, 2009.
[11] J. Wei, Y. Yin, and F. Liu, “Multi-model LPV approach to CSTR system identification with stochastic scheduling variable,” Proc. - 2015 Chinese Autom. Congr. CAC 2015, pp. 303–307, 2016.
[12] Y. Sun and P. Jiang, “A Novel Bottleneck Identification Based Differential Evolution Algorithm for Scheduling Complex Manufacturing Lines,” Proc. - 2016 3rd Int. Conf. Inf. Sci. Control Eng. ICISCE 2016, pp. 774–778, 2016.
[13] J. Dai, B. Hu, L. Zhu, H. Han, and J. Liu, “Research on dynamic resource allocation with cooperation strategy in cloud computing,” 2012 3rd Int. Conf. Syst. Sci. Eng. Des. Manuf. Informatiz. ICSEM 2012, vol. 1, pp. 193–196, 2012.
[14] A. Mohtasham, R. Filipe, and J. Barreto, “FRAME: Fair resource allocation in multi-process environments,” Proc. Int. Conf. Parallel Distrib. Syst. - ICPADS, vol. 2016-January, pp. 601–608, 2016.

Jasleen Kaur, Anil kumar, "Effective Strategy Identification for Parallel Job Execution Job Partitioning, Requirement Gathering and Allocation Strategies," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.192-197, 2019.

Environment of Microsoft Excel integrates storage, analysis and visualization of data. After the data is stored in a precise structured format, further analysis and visualization of data is essential to discover the hidden valuable insight from the large dataset. Visualization supports extracting and understanding the information as it is represented in a graphical format. Visualization plays a vital role in decision making at various levels in the organization. There are numerous techniques of visualization. However, the most extensively used technique is to present the data in a chart format. Various charting techniques such as Column Chart, Pie Chart, Line Chart and Bar Chart are existing in Microsoft Excel application. In addition to these conventional charts, Microsoft Excel 2016 presents six advanced chart types named, Box and Whisker chart, Funnel chart, Histogram chart, Sunburst chart, Treemap chart and Waterfall chart to present the data differently. This paper describes illustrations of new chart types of Microsoft Excel 2016 along with their elements and respective attributes. The purpose of this research paper is to present how advanced charting techniques can be used for visualizing varied data types in the engineering manufacturing industries.

Microsoft Excel 2016, Visualization, Charting Techniques, Structured Data

[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] Michael Alexander and Dick Kusleika, “Excel 2016 Formulas”, Wiley India Pvt. Ltd., India, pp. 425-467, 2016.
[4] Getting started with Excel 2016, Microsoft IT Showcase, January 2017.
[5] Greg Harvey "Excel® 2016 For Dummies®", John Wiley & Sons, Inc., pp. 295-329, ISBN 978‐1‐119‐07701‐5 (pbk), ISBN 978‐1‐119‐07702‐2 (ePub), 978‐1‐119‐07703‐9 (ePDF), 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, "Advanced Charting Techniques of Microsoft Excel 2016 Aiming Visualization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.198-207, 2019.

Handwritten Devanagari Script Recognition has a lot of applications in the field of document processing, automation of postal services, automated cheque processing and so on. Several approaches have been proposed and experimented in the past depending on the type of features extracted and the ways of extracting them. In this paper, we proposed the use of Capsule Neural Networks (CapsNet) for the recognition of Handwritten Devanagari script, which is an advancement over the Convolutional Neural Networks (CNN) in terms of spatial relationships between the features. Capsule Neural Networks follow the principle of equivariance unlike the convolutional neural networks which follow the invariance property. CapsNet uses the dynamic routing by agreement method for passing data to higher capsules. CapsNet uses vector format for data representation. It can recognize similar characters in a more efficient manner as compared to CNN. Thus by using the advantages of CapsNet we are aiming to achieve better classification rate. We collected 100 samples of each of the 48 Devanagari characters and 10 Devanagari digits, and performed scaling, rotation and mirroring operations on these images. Hence, our dataset consists of total 29000 images.

Capsule Networks, Dynamic routing, Devanagari Script Recognition, Convolutional Neural Networks

[1] Sara Sabour, Nicholas Frosst, Geoffrey Hinton , “Dynamic Routing Between Capsules”, In the Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2017.
[2] Paul Gader, Magdi Mohamed, “Handwritten Word Recognition with Character and Inter-Character Neural Networks”, IEEE Transactions On Systems, Man, And Cybernetics-Part B: Cybernetics-Part B: Cybernetics, Vol.27, Issue.1, pp. 158-164, 1997.
[3] M. Blumenstein, B. Verma, H. Basli, “A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters”, In the Proceedings of ICDAR, pp. 137-141, 2003.
[4] Abdelhak Boukharouba, Abdelhak Bennia, “Novel feature extraction technique for recognition of handwritten digits”, Applied Computing and Infomatics, pp. 20-26, 2017.
[5] Shalaka Deore, Leena Ragha, “Moment Based Online and Offline Handwritten Character Recognition”, CiiT International Journal of Biometrics and Bioinfomatics, Vol.3, Issue.3, pp. 111-115, 2011.
[6] Punam Ingale, “The importance of Digital Image Processing and its applications”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp. 31-32, 2018.
[7] P. Umorya, R. Singh, “A Comparative Based Review on Image Segmentation of Medical Image and its Technique”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.71-76, 2017.

U.M. Sawant, R.K. Parkar, S.L. Shitole, S.P. Deore, "Devanagari Script Recognition using Capsule Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.208-211, 2019.

This paper focused to green system of the small data centers with sustainable energy power generation. The estimation must start with a baseline view of power consumption in every part of the data center. The primary vital issues of data centers are increasingly under intervention of their energy consumption and operations. This paper takes first step toward exploring green data centers powered by renewable energy system that include base load power supply, intermittent power supply and backup energy storage systems. The required energy assists in an embryonic attitude of data centers being saved energy with a "greener" consequence. Data centers are the factories of the digital world and the cloud is coming back to earth that ethereal place where data is being stored. The convention of renewable energy resources has been the vital role in our future development of modern technology and cloud computing is the fastest evolving paradigm of the modern age of computers. This requires more and more remote host machines such as servers. Naturally, data centers are required large amounts of energy to power the growing demand. Now, data centers are turning to energy-efficient data facilities to cut costs and green power for their operations. This paper presents small-scale routing algorithm used energy consumption by instead of using conventional energy, thus increasing the effective performance up gradation of the virtual data center. We bring new thought in energy saving of the data centers. This presents the technological way of power consumption in the records of data center.

Cloud data center, Small scale algorithm, Energy consumption, Renewable energy

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M. R. Sudha, C. P. Sumathi, "A Smart Green Data Center of Energy Conservation in Cloud and Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.212-220, 2019.

In the present paper, generalized differential transform method (GDTM) is used for obtaining the approximate analytic solutions of non-linear dispersive partial differential equations of fractional order. The fractional derivatives are described in the Caputo sense.

Fractional differential equations; Caputo fractional derivative; Generalized Differential transform method; Analytic solution. Mathematical Subject Classification (2010) — 26A33, 34A08, 35A22, 35R11, 35C10, 74H10

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Deepanjan Das, "Approximate Series Solution of Non Linear Fractional Dispersive Equations Using Generalized Differential Transform Method," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.221-228, 2019.

Digital image are used in several areas. Digital image includes large amount of data. So transmission of such large amount of data require large storage space. Hence to deal which such problems, image compression is used. Image compression is a technique in which redundant information of image is removed, such that only essential information remain. Image compression technique is also helpful in reduce storage size, transmission bandwidth and transmission time. This paper provides review and comparison of different image compression techniques like DCT ( Discrete Cosine Transform ) , DWT ( Discrete Wavelet Transform) and Hybrid (DCT and DWT) and Fractal Image compression by using Affine Transformation and Iterated function system ( FIS). Research finding of this paper helps to build new and more effective image compression technique.

DCT (Discrete Cosine Transform), DWT (Discrete Wavelet Transform), Fractal image compression (FIC), Affine Transformation, Iterated function system (FIS)

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Nitu, Yogesh Kumar, Rahul Rishi, "Fractal Image Compression Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.229-233, 2019.

Classification techniques in Machine Learning are implemented on datasets. In this work, the cancer datasets are used for the classification purpose and collected from UCI Machine Learning repository. There are two types of datasets of breast cancer. Both the datasets are varying by their number of features available across the datasets. This paper presents the implementation and comparative study of major and popular classification techniques such as Decision Tree, k-Nearest Neighbour, Support Vector Machine, Bayesian Network and Naïve Bayes under WEKA environment for accuracy based on evaluation of performance metrics. This paper evaluates that the Bayesian Network gives the best accuracy with less featured dataset while Support Vector Machine gives best accuracy for more featured dataset.

Classification Techniques, Feature Selection, k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), Bayesian Network (BN), WEKA

[1] B. Nithya, V. Ilango, 2017, “Comparative Analysis of Classification Methods in R Environment with two Different Datasets.”, Intl J Scientific Research and Computer Science, Engineering and Information Technology (IJSRCSEIT), vol 2, Issue 6, ISSN: 2456-3307.
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Ajay Kumar, R. Sushil, A. K. Tiwari, "Comparative Study of Classification Techniques for Breast Cancer Diagnosis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.234-240, 2019.

Sarcasm is a nuanced form of speech broadly utilized in different online platforms such as social networks, micro-blogs and sarcasm recognition refers to anticipate whether the content is sarcastic or not. Identifying sarcasm in content is among the significant issues confronting sentiment analysis. In sarcasm, individuals express their negative feelings by utilizing positive or strengthened positive words in the content. While talking, individuals regularly utilize intense tonal force and certain gestural pieces of information like rolling of the eyes, hand development, and so forth to reveal sarcasm. Due to these challenges, in the last few decades, researchers have been working rigorously on sarcasm recognition so as to amend the performance of automatic sentiment analysis of data. In this paper, a supervised learning approach, which learns from four different categories of features and their combinations, is presented. These feature sets are employed to classify instances as sarcastic and not-sarcastic using four different classifiers, namely – Naïve Bayes, SVMs, Random Forest and k-Nearest Neighbor classifiers. In particular, it has been tried to explore the impact of sarcastic patterns based on POS tags and the outcomes demonstrate that they are not useful as a feature set for recognizing sarcasm when compared to content words and function words. Using the finest feature set i.e. the combination of content words and function words, a precision and AUC of approximately 85% and 87%, respectively, were achieved. Additionally, the Naïve Bayes classifier gives better results over every single other classifier that has been utilized.

Sentiment analysis, Sarcasm, Supervised learning, Feature-sets

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Sakshi Thakur, Sarbjeet Singh, Makhan Singh, "Sarcasm Recognition in Twitter," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.241-248, 2019.

SAC matrices have been implemented for S-boxes of DES and AES to implement a higher order differential analysis, known as truncated differentials. This new approach will help us to find the vulnerability to attacks. After getting the original outputs corresponding to the input strings, inputs to s-boxes of DES and AES are then truncated in two parts, strings (a, b), of equal bit length Then each bit of both a and b is changed one after the other to get the new input and its corresponding output. Using all outputs of every possible input, SAC matrices are generated for statistical and truncated differential analysis to reach the conclusion.

Truncated Differential; S-box; SAC; Higher order differential; Cryptanalysis; Cryptology; Differential Cryptanalysis

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Avijit Datta, Dipanjan Bhowmik, Sharad Sinha, "A New Approach towards Confusion Analysis of S-boxes using Truncated Differential Cryptanalysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.249-256, 2019.

Big data is the complete data that is generated by human beings in daily life. Data is gathered from various sources and thus made useful for people by preprocessing it. For processing such huge amount of data (i.e. in terabytes and petabytes), specialized hardware and software is required. Thus, to store, manage, and process the increasing amount of data is really a challenging area of research and development in big data analysis. The objective of this paper is to explore the impact, challenges, architecture of big data and various tools associated with it. This paper also provides a comparative study of Hadoop distributed file system and traditional database and a platform to explore it at various stages

Hadoop, HDFS, Challenges, Big Data Analysis

[1] International Journal of Engineering Technology, Management and Applied Sciences www.ijetmas.com March 2017, Volume 5 Issue 3, ISSN 2349-4476 19 AnjanaRaviprolu The Big Data and Market Research Anjana Raviprolu, Dr.Lankapalli Bullayya.
[2] International Journal of Scientific and Research Publications, Volume 4, Issue 10, October 2014 1 ISSN 2250-3153 www.ijsrp.org A Review Paper on Big Data and Hadoop Harshawardhan S. Bhosale1 , Prof. Devendra P. Gadekar.
[3] Jimmy Lin “MapReduce Is Good Enough?” The control project. IEEE Computer 32 (2013)
[4] Shilpa Manjit Kaur,” BIG Data and Methodology- A review” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.
[5] D. P. Acharjya, S. Dehuri and S. Sanyal Computational Intelligence for Big Data Analysis, Springer International Publishing AG, Switzerland, USA, ISBN 978-3-319-16597-4, 2015.
[6] Jyoti Kumari, Mr. Surender, Statically Analysis on Big Data Using Hadoop,IJCSMC, Vol. 6, Issue. 6, June 2017, pg.259 – 265
[7] C.L. Philip Chen, Chun-Yang Zhang, “Data intensive applications, challenges, techniques and technologies: A survey on Big Data” Information Science 0020-0255 (2014), PP 341-347, elsevier.
[8] Gayathri Ravichandran ,Big Data Processing with Hadoop : A Review , International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02,Feb -2017 .
[9] Abdelladim Hadioui!!", Nour-eddine El Faddouli, Yassine Benjelloun Touimi, and Samir Bennani Machine Learning Based On Big Data Extraction of Massive Educational Knowledge,iJET ‒ Vol. 12, No. 11, 2017.
[10] Rahul Beakta , Big Data And Hadoop: A Review Paper Volume 2, Spl. Issue 2 (2015) e-ISSN: 1694-2329 | p-ISSN: 1694-2345
[11] ]Kache, F., Kache, F., Seuring, S., Seuring, S.,Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management 37, 10–36, (2017)https://doi.org/10.1108/IJOPM-02-2015-0078
[12] Zhou, L., Pan, S., Wang, J., Vasilakos, A.V., Machine Learning on Big Data: Opportunities and Challenges. Neurocomputing, (2017).

Priya, "A Review Paper on Big Data with comparative analysis of Hadoop," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.257-261, 2019.