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Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Recently, big data is widely available in information systems and data mining has pulled in a major thoughtfulness regarding analysts to transform such information into helpful learning. This implies the presence of low quality, questionable, excess and uproarious information which contrarily influence the way toward watching learning and helpful example. As follows, researchers require related big data utilizing feature selection methods. The process of feature selection is identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this paper, find out the result of different feature selection methods based on a recognized dataset (i.e., gens expression dataset) and classification algorithms were used to evaluate the performance of the algorithms. In this study revealed that feature selection methods are capable to improve the performance of learning algorithms. Still, there are no any single filter based feature selection method is the best. Taken as a whole, Classifier AttEval, CorrelationAttributeEval, Principal Components, and ReliefAttEval methods performed better results than the others.

Feature selection, Lung cancer, Gens expression, Classifier, Subset

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[19] Witten, I. H., Frank, E., & Hall, M. a. (2011). Data Mining: Practical Machine Learning Tools and Techniques (Google eBook). Complementary literature None.
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A. K. Shrivas, Prem Kumar Chandrakar, "Feature Selection on High Dimensional Big Data of Gens Expression Using Filter Based Feature Selection Methods", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.105-108, 2019.

Curcumin is an important phytochemical present in curcuminoids (rhizome plants) essential; for control of skin diseases. The phytochemicals are more in Zingiber officinale followed by Aloe barbadensis, Curcuma longa and Curcuma angustifolia. Amino acids, Proteins, carbohydrates, tanins and terpenoids were almost present in all the selected plants. The plant Curcuma angustifolia aqueous extract has good composition of terpinoids and analysed for in silico studies of Curcumin with Staphylococcus aureus, Streptococcus pyogenes and Trichophyton rubrum proteins. Curcumin has shown good binding affinity with streptococcal superantigen (SSA) from Streptococcus pyogenes (-90.62 KCal/Mol) followed by exfoliative toxin B in Staphylococcus aureus (-85.01 KCal/Mol) and Aspartate Semialdehyde Dehydrogenase from Trichophyton rubrum (-82.96 KCal/Mol). Curcumin has shown good in silico activities against proteins/ antigens for skin diseases. Further isolation and identification through wetlab studies provide good scope in the field of phytochemistry

Phytochemical screening, in silico studies, curcumin

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[9] R. Aly, “Microbial Infections of Skin and Nails. In: Baron S, editor. Medical Microbiology”, 4th edition. Galveston (TX): University of Texas Medical Branch at Galveston, Chapter 98, 1996.
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[11] C. Y. Hsieh, “Phase I clinical trial of curcumin, a chemopreventive agent, in patients with highrisk or pre-malignant lesions”, Anticancer Res, Vol. 21, pp. 2895-2900. 2001.
[12] P. Anand, A.B. Kunnumakkara, R.A. Newman, B. B. Aggarwal, “Bioavailability of curcumin: problems and promises”, Molecular pharmaceutics, Vol. 4, No. 6, pp. 807-818, 2007.
[13] B.B Aggarwal, A. Kumar, A.C. Bharti, “Anticancer potential of curcumin: preclinical and clinical studies”, Anticancer research, Vol. 23, No.1/A, pp. 363-398, 2003.
[14] S. A Jatoi, A. Kikuchi, S.A. Gilani, K. N. Watanabe, “Phytochemical, pharmacological and ethnobotanical studies in mango ginger (Curcuma amada Roxb.; Zingiberaceae)”, Phytotherapy Research: An International Journal Devoted to Pharmacological and Toxicological Evaluation of Natural Product Derivatives, Vol. 21, No. 6, pp. 507-516, 2007.
[15] S.V.G.K.D. Kaladhar, G.R. Duddukuri, K. Ramesh, V. Vadlapudi, N.S. Yarla, “In Vitro Protease Inhibition, Modulation of PLA2 Activity and Protein Interaction Studies on Calotropis gigantean”, Journal of Clinical & Cellular Immunology, Vol. 4, pp. 165, 2013.

Surbhi Dubey, Shweta Sao, "Phytochemical Screening and In Silico Analysis of Some Crude Stem Extracts against Skin Diseases", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.109-113, 2019.

The aim of the study is to predict significant features from dataset of Chronic Kidney Disease features. It represents the data in a tabular and graphical manner to form its clear understanding. This investigation helps in the crucial role of features and experimental features in the CKD dataset and their associations, their dependability for coming up with any classification system. It also shows that how CKD can be diagnosis by exploiting data mining techniques. The Data Mining algorithm is an inspirational force in detecting abnormalities in various data sets and with a good success utilized in various classification and feature selection task. The different kinds of Decision Tree-based classifiers like RF (Random Forest), J48 (C4.5), C5.0, and CART (Classification and Regression Tree) and their ensemble model have experimentally validated CKD dataset and our result is evaluated. Our result representation that the ensemble models classifier reached the most favourable performances on the identification of CKD dataset before and after the feature selection.

Chronic Kidney Disease, Feature Selection Techniques, Classification, Random Forest, J48(C4.5), C5.0, CART, Ensemble model, Genetic Search, Greedy Stepwise

[1] M. P. Webster A.C., Nagler E.V., R.L., “Chronic Kideny disease,” 2016. [Online]. Available: http://www.worldkidneyday.org/faqs/chronic-kidney-disease/. [Accessed: 28-Feb-2018].
[2] M. S. McManus and S. Wynter-Minott, “Guidelines for Chronic Kidney Disease: Defining, Staging, and Managing in Primary Care,” J. Nurse Pract., vol. 13, no. 6, pp. 400–410, 2017.
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[6] I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” J. Mach. Learn. Res., vol. 3, no. 3, pp. 1157–1182, 2003.
[7] A. Bhalla and R. K. Agrawal, “Microarray gene-expression data classification using less gene expressions by combining feature selection methods and classifiers,” Int. J. Inf. Eng. Electron. Bus., vol. 5, no. 5, pp. 42–48, 2013.
[8] A. K. Shrivas, S. K. Sahu, and H. S. Hota, “Classification of Chronic Kidney Disease with proposed Union Based Feature Selection Technique,” no. 2007, pp. 503–507, 2018.
[9] A. Subasi, E. Alickovic, and J. Kevric, “Diagnosis of Chronic Kidney Disease by Using Random Forest,” C. 2017 Proc. Int. Conf. Med. Biol. Eng. 2017, vol. 7, no. 1, pp. 589–594, 2017.
[10] B. Boukenze, A. Haqiq, and H. Mousannif, “Predicting Chronic Kidney Failure Disease Using Data Mining Techniques,” vol. 397, 2017.
[11] A. Charleonnan, T. Fufaung, T. Niyomwong, W. Chokchueypattanakit, S. Suwannawach, and N. Ninchawee, “Predictive analytics for chronic kidney disease using machine learning techniques,” 2016 Manag. Innov. Technol. Int. Conf., p. MIT-80-MIT-83, 2016.
[12] D. N. R. S.Ramya, “Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithms,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 4, no. 1, pp. 812–820, 2016.
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[16] R. Parimala and R. Nallaswamy, “A Study of Spam E-mail classification using Feature Selection package,” Glob. J. Comput. Sci. Technol., vol. 11, no. 7, pp. 45–54, 2011.
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A. K. Shrivas, Sanat Kumar Sahu, "Classification of Chronic Kidney Disease using Combination Feature Selection Techniques and Classifiers", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.114-117, 2019.

Consumer becomes a customer by a frequency of visits to shopping malls. The main objectives of the study are to identify and examine the perception of customer. Customer perception towards shopping malls in Chennai city is analyzed by the various factors such as ambience of the mall/shop, mall change the life style of consumers, mall enhances the consumer’s belief of want satisfaction, the physical facilities are visually appealing, perception way of shopping, identifying merchandise and convenience.

Perception, Shopping Malls

[1] (The mall story 2.0/THE HINDU Business Line/20/09/2017).
[2] J ay D. Lindquist /M.Joseph Sirgy “Consumer Behavior” 2009/Cengage Learning India Private Limited.
[3] Leon G.Schiffman, Leslie Lazar Kanuk, “Consumer Behavior”2009/Pearson Education, Inc.
[4] Shaphali Gupta (2015)” Effect of Shopping Value on Service Convenience, Satisfaction and Customer Loyalty: A Conceptual Framework” , SAMVAD: SIBM Pune Research Journal, Vol X, pp.78-85.
[5] Muzzafar Ahmad Bhat and AmitKumar(2016)” Customer Perception and its Implications in Modern Retail Sector: A Case Study of Big Bazaar”, International Journal of Research in IT, Management and Engineering, 6.123, Vol. 06, Issue 07, pp. 55-60.
[6] Shashikala.R.Mrs., Dr. A. M. Suresh Vishwakarma (2013)” Consumer Perception of Servicescape in Shopping Malls”, Vishwakarma Business Review, Volume III , 2, pp.68-75.
[7] IoanaNicoletaAbrudan, Dan-CristianDabijaa (2014) “ Emerging Market Queries in Finance and Business Measuring clients` satisfaction toward shopping centers - Empirical evidences from Romania” , Science Direct Procedia Economics and Finance 15, pp. 1243 –1252.
[8] Elangovan. D., Sangeetha. R (2016)” A study on customer’s perception and preferences towards shopping malls in Coimbatore”, International Journal of Multidisciplinary Research and Development, Vol. 3, Issue 3, pp.296-298.
[9] Mullick Naushadul Haque, “Enhancing the image & brand equity of a shopping mall”, Studies in Business and Economics, pp.72-81.
[10] NikamHarshalDnyandeo (2014) “A Study of Factors Affecting on Buying Decisions & Customer Preference towards Phoenix Market city Pune”, International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 12, pp. 60-67.
[11] ThitipannKernsom and Nopadon Sahachaisaeree (2011) “Compostional Design as Sale Strategy: Shopping mall window display”, Journal of ASIAN Behavioural Studies 1(1), January 2011 (Maiden Issue).
[12] Neelotpaul Banerjee (2012) “A Study on the attractiveness dimensions of shopping malls- An Indian perspective”, International Journal of Business and Social Science, Vol.3 No.2 [Special Issue] 102.
[13] Ala`Eddin Mohammad Khalaf Ahmad (2012)” Attractiveness Factors Influencing Shoppers’ Satisfaction, Loyalty, and Word of Mouth: An Empirical Investigation of Saudi Arabia -Shopping Malls”, International Journal of Business Administration Vol. 3, No. 6; pp.101-112.

K.Girija, G.Ravi, "Customer Perception towards Shopping Malls in Chennai", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.118-121, 2019.

An influential tool nowadays for any organization is Advertisement, as it not only helps in communicating the information to the customers about new product launched in the market but also induces them to purchase them. Companies usually follow unethical means to advertise their products, the term used for it is Misleading Advertisements. Misleading or false Advertisements is the use of wrong statements in advertisements for influencing customers to purchase the products, but it may negatively impact many customers. Many governments around the world use regulations and frame policies to control false and misleading advertisements, as advertisements have the potential to induce customers to buy products. The concept of the advertisements was that all the necessary information related to the product should be provided to customers using a global media that is the advertisement. This study is conducted to comprehend the impact of advertisements on customers using Medias and what can be done to improve the effectiveness.

Deceptive, Advertisement, Consumer Behaviour

[1] Becker GS, Murphy KM. “A simple theory of advertising as a good or bad, Quarterly Journal of Economics”.; 108:941-964. 1993.
[2] Friedman. “Endorser Effectiveness by product Type”, Journal of Advertising Research.; Vol. 19(5),pp 63-71. 2003.
[3] Daugeliene R., Liepinyte , M., “Interrelation of misleading advertising and solutions of consumers: legal regulation and institutional background in Lithuania”, Issn 1822–8402 ,European integration studies.Vol.6, pp-192-201. 2012.

P. Shukla, B.Karmakar, "A Review on Impact of Deceptive Advertisement on Consumer Behaviour", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.122-124, 2019.

Accurate recognition of optical handwritten numeral is still an open and demanding problem in present digital world. The basic objective of the present work is to develop a novel method for recognition of off line unconstrained handwritten Odia numeral using curvature feature and functional link artificial neural network (FLANN) based cat swarm optimization (CSO) technique. In this paper preprocessing and feature extraction steps are carried out before the recognition of numerals. For feature extraction curvature based approach is applied. For recognition of handwritten Odia numeral hybrid architecture has been proposed where the classification task is performed by FLANN classifier and cat swarm optimization is used for finding a suitable set of weights for the FLANN classifier. The proposed model is evaluated on database consisting of 4000 number of handwritten Odia numerals. The combined effect of curvature based feature extraction approach and FLANN based cat swarm optimization technique yielded a high accuracy which exhibits the effectiveness of CSO based FLANN optimization model (FLANN-CSO) for recognition of Odia handwritten numerals.

Odia numeral recognition, FLANN, CSO, Preprocessing, Feature extraction, classification

[1] M. Shia, Y. Fujisawab, T. Wakabayashia, F. Kimuraa, “Handwritten numeral recognition using gradient and curvature of gray scale image,” Journal of Pattern Recognition, Vol. 35, pp. 2051 – 2059, 2002
[2] A. Desai, “Gujarati handwritten numeral optical character reorganization through neural network” , The Journal of Pattern Recognition, Vol. 43, pp. 2582–2589, 2010.
[3] S. Mahmoud, “Recognition of writer-independent off-line handwritten arabic (indian) numerals using hidden Markov models,” Journal of Signal Processing, Vol. 88, pp. 844–857, 2008.
[4] Z. Kamranian, S.A. Monadjemi and N. Nematbakhsh, “A novel free format persian/arabic handwritten zip code recognition system,” Journal of Computers and Electrical Engineering, Vol. 39, pp:1970–1979, 2013.
[5] B. Majhi, J. Satpathy and M. Rout, “Efficient recognition of odia numerals using low complexity neural classifier,” In Proceedings of IEEE International Conference on Energy, Automation and Signal , pp. 140-143, 2011.
[6] T.K.Mishra, B. Majhi, S. Panda, “A comparative analysis of image transformations for handwritten Odia numeral recognition,” In Proceedings of IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, pp. 790-793.
[7] Prashant M. Kakde, S. M. Gulhane, “ A comparative analysis pf particle swarm optimization and support vector machines for Devnagri character recognition: An android application”, Procedia Computer Science, Proceedings of International conference on communication, computing and virtualization (ICCCV) 2016, volume 79, 2016, Pages 337-343
[8] Alejandro Baldominos , Yago Saez, Pedro Isasi, Evolutionary convolutional neural networks: An application to handwritten recognition”, Neuro Computing, Volume 283, 29 March 2018, Pages 38-52
[9] Adarsh Trivedi, Siddhant Srivastava, Aproova Mishra, Anupam Shukla, Ritu Tiwari,” Hybrid evolutionary approach for Devanagari handwritten numeral recognition using convolutional neural network”, Procedia Computer Science, Volume 125, 2018, Pages 525-532
[10] Joseph Tarigan, Nadia, Ryanda Diedan, Yaya Suryana, “ Plate recognition using backpropagation neural network and genetic algorithm”, Procedia Computer Science, Volume 116, 2017, Pages 365-372

[11] W-D Weng, C-S Yang and R-C Lin, A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks, Information Sciences, Elsevier, vol.177, issue 13, pp.2642-2654, July 2007. (FLANN)
[12] B. Naik, J. Nayak and H. S. Behera, A global-best harmony search based gradient descent learning FLANN (GbHS-GDL-FLANN) for data classification, Egyptian Informatics Journal, Elsevier,21 October 2015(In press). (FLANN)
[13] C.M.Anish and B. Majhi, Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis, Journal of the Korean Statistical Society, July 2015 (in press). (FLANN)
[14] R. Majhi, G. Panda and G. Sahoo, Development and performance evaluation of FLANN based model for forecasting of stock markets, Expert Systems with Applications, Elsevier, vol.36, pp. 6800-6808, June 2012. (FLANN)
[15] B. Majhi and P. K. Sa, FLANN-based adaptive threshold selection for detection of impulsive noise in images, AEU- International Journal of Electronics and Communications, Elsevier, vol. 61, issue 7, pp.478-484, July 2007. (FLANN)
[16] J. C. Patra and A. Bos, Modelling of an intelligent pressure sensor using functional link artificial neural networks, ISA transactions, Elsevier, vol.39, issue 1, pp.15-17, February 2000. (FLANN)
[17] S. K. Behera, D. P. Das and B. Subudhi, Functional link artificial neural network applied to active noise control of a mixture of tonal and chaotic noise, Applied soft computing, Elsevier, vol. 23, pp. 51-60, October 2014. (FLANN)
[18] Chu, S.-C., & Tsai,P.-W, Computational intelligence based on the behavior of cats. International journal of innovative computing, Information and control, 3, pp.163-173
[19] G. Panda, P.M.Prahan, B. Majhi, IIR system identification using cat swarm optimization, Expert system with applications, 38(2011), pp.12671-12683
[20] A.D. Parkins, A.K. Nandiembers, “Genetic programming techniques for hand written digit recognition”, Signal Processing, Volume 84, Issue 12, pp: 2345-2365, December 2004.
[21] Moayad Yousif Potrus, Umi Kalthum Ngah, Bestoun S. Ahmed, “An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition”, Ain Shams Engineering Journal, Volume 5, Issue 4, pp. 1129-1139, December 2014.
[22] Saurabh Shrivastava, Manu Pratap Singh, “Performance evaluation of feed-forward neural network with soft computing techniques for hand written English alphabets”, Applied Soft Computing, Volume 11, Issue 1, pp: 1156-1182, January 2011.
[23] C. De Stefano, F. Fontanella, C. Marrocco, A. Scotto di Freca, “A GA-based feature selection approach with an application to handwritten character recognition”, Pattern Recognition Letters, Volume 35, Pages 130-141, 1 January 2014
[24] M. Kherallah, F. Bouri, A.M. Alimi, “On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm”, Engineering Applications of Artificial Intelligence, Volume 22, Issue 1, pp: 153-170, February 2009.

Puspalata Pujari, Babita Majhi, "Application of Cat Swarm Optimization for Recognition of Handwritten Numerals", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.125-130, 2019.

Big data science is the new emerging term in the field of computer technology. During the emergence of computer science the database is used to store the data but with the emergence of social networking sites the data is generated exponentially that is not able to store in the conventional method of storage so the new technology is derived that is used to store data in cloud technology. Cloud technology is a technology that is used to store data in a safe and secure manner. And it also gives the opportunity to use the data when required. We can mine the data with data mining algorithm and take decisions on the basis of that data.

Cloud Technology, Big data, Virtualization

[1] Vouk A. Mladen.” Cloud Computing-Issues Research and Implementation”, Journal of Computing and Information Technology- CIT 16(2008).
[2] Fan Wei et al. “, Mining Big Data: Current Status, and Forecast to the Future”, SIGKDD Explorations, Vol 14, Issue 2,(2012).
[3] Jha Anupama et al. ,” A Review on the Study and Analysis of Big Data using Data Mining Techniques”, “ International Journal of Latest Trends in Engineering and Technology (IJLTET). Vol (6), Issue 3 January (2016) ISSN: 2278-621X
[4] Sivarajah Uthayasankar,” Critical Analysis of Big Bata challenges and analytical methods”, “Journal of Business of Research”, (2017).
[5] SAGIROGLU Seref,” Big Data: A Review”, IEEE (2013).
[6] V. Spoorthy et al., “A Survey on Data Storage and Security in Cloud Computing”,” InternationalJournal of Computer Science and Mobile Computing”, ISSN 2320-088X ,(2014), pg.306 – 313.
[7] Kumar Santosh et al.,” Cloud computing- Research Issues, Challenges, Architecture, Platforms and Applications: A Survey”, International Journal of Future Computer and Communication, Vol 1 No-4 (2012).
[8] Sivarajah Uthayasankar et al. ,” Critical Analysis of Big Data challenges and analytical methods”, “ Journal of Business Research” pg 263286 (2017).

R. Milan, K.K. Pandey, D. Shukla, "Review of Big Data Science and its relation with Cloud Technology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.131-133, 2019.

This study is designed to investigate the acceptance of Learning Management Systems (Moodle) in Higher Educational Institutions (HEI’s) of Oman. Due to the technological developments the importance of using learning Management system in Higher Educational Institutes (HEI’s) are increasing day by day. This study is to investigate the acceptance of Learning Management Systems in different HEI’s of Sultanate of Oman. To investigate the factors that influence the use of LMS’s in HEI’s the 5-point likert questionnaire has been made and distributed electronically through e-mail and Social media applications. The questionnaire sent to the students of different HEI’s of Sultanate. Data were collected from 137 respondents belongs to various HEI’s in which 132 are the valid responses. The questionnaire consists of 5 demographic question and 13 survey questions. The variables used in this research are perceived playfulness, perceived usefulness, and perceived ease of use, attitude, and intention to use. The research is based on the Technology Acceptance Model (TAM). The Structural equation modelling (SEM) technique is used to evaluate the causal model and to extreme the validity of model. This research paper tested the 7 hypotheses.

Moodle (Modular Object Oriented Dynamic Learning Environment), Higher Educational Institutions, learning Management system, E-mail, Technology acceptance model, Structural equation modelling

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Ashish, "A Study on The Factors That Influence the Acceptance of LMS’s In Higher Educational Institution’s", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.134-140, 2019.

The proposed work is based on questionnaire base primary data and its analysis through numerically and linguistically terminology. The resultant is found as banking is an optimized principle known by the public as per essential but not as generalized. This paper proposed a new model of banking for the future prospective. We presented a new function based on economical & transaction application. There are some variants in banking found in the paper. These e variants will be formulated for the future banking is suggested as the resultant.

questionaire, banking, numerically, linguistically

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Ruchi Gupta, Sunil Kumar Kashyap, "Current System of Banking: An Analysis in Reference of Capital Raipur of Chhattisgarh State", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.141-144, 2019.

Swarm robotics is based on the characteristics displayed by the insects and their colony and is applied to solve real world problems utilizing multi-robot systems. Research in this field has demonstrated the ability of such robot systems to assemble, inspect, disperse, aggregate and follow trails. A set of mobile and self-sufficient robots which has very restricted capabilities can form intricate patterns in the environment they inhabit. However, coordination of multiple robots to accomplish such tasks remains a challenging problem. Pattern formation is one of typical problems in the field of multi-robot cooperation. Compare to traditional multi-robot coordination algorithm, the method based on swarm robots to solve the issue of pattern formation has better scalability and dynamic adaptability and robustness. The swarm robots have only local perception and very limited local communication abilities, so one of the challenging tasks while designing swarm robotic systems with desired collective behaviour is to understand the effect of individual behaviour on the group performance. So fault tolerance is another issue to deal with. This paper reviews the background knowledge and some noticeable achievements in the field of pattern formation and fault tolerance.

Swarm robotics, pattern formation, swarm intelligence, particle swarm intelligence, fault tolerance

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S.K. Rakesh, M. Shrivastava, "A Fault Tolerant Model for Geometric Patterns of Swarm Agents", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.145-151, 2019.