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Noise removal refers to the most vital process in image processing to remove the noise from a given image and reconstruct the original image. Among many denoising techniques, four types of extended versions of robust Annihilating filter-based Low-rank Hankel Matrix (r-ALOHA) approaches have been proposed in the previous researches. In those approaches, different kinds of transform domains like log-exponential, wavelet, generalized Hough, and gradient were considered separately in which that the image patch was considered as it was sparse in the considered transform domains independently to denoise the corrupted image. Even if gradient transform based denoising called e4-ALOHA achieves better performance than the other transform domains, it requires an automatic selection of Optimal Patch Size (OPS) to further improve the denoising performance. Hence in this article, an automatic selection of OPS is proposed with e4-ALOHA that searches similar image patches and selects an optimal patch size. In this technique, a Flower Pollination optimization Algorithm (FPA) is proposed to search similar patches and choose an optimal patch size adaptively according to the variance of similar patch groups. Once an optimal patch size is selected, e4-ALOHA is applied to perform the denoising process. Finally, the effectiveness of the proposed technique is evaluated through the experimental results.

Noise removal, r-ALOHA, e4-ALOHA, Optimal patch size, Flower pollination algorithm Formatting

[1] P. Kamboj, V. Rani, “A brief study on various noise model and filtering techniques”, Journal of Global Research in Computer Science, Vol.4, Issue.4, pp.166-171, 2013.
[2] A. Suganthi, M. Senthilmurugan, “Comparative study of various impulse noise reduction techniques”, International Journal of Engineering Research and Applications, Vol.3, Issue.5, pp.1302-1306, 2013.
[3] K. Pritamdas, Kh. M. Singh, L. L. Singh, “A summary on various impulse noise removal techniques”, International Journal of Science and Research, Vol.6, Issue.3, pp.941-954, 2017.
[4] K. H. Jin, J. C. Ye, “Sparse and low-rank decomposition of a hankel structured matrix for impulse noise removal”, IEEE Transactions on Image Processing, Vol.27, Issue.3, pp.1448-1461, 2018.
[5] L. B. Victoria, S. Sathappan, “A sporadic decomposition of hankel structured matrix in logarithmic and wavelet domain for impulse noise removal”, International Journal of Engineering & Technology, 2018.
[6] L. B. Victoria, S. Sathappan, “A sporadic decomposition of hankel structured matrix in generalized Hough and gradient transform domain for impulse noise removal”, In International Conference on Research Trends in Computing Technologies (ICRTCT18), 2018.
[7] C. A. Deledalle, J. Salmon, A. S. Dalalyan, “Image denoising with patch based PCA: local versus global”, In BMVC, Vol.81, pp.425-455, 2011.
[8] X. Zhang, X. Feng, W. Wang, “Two-direction nonlocal model for image denoising”, IEEE Transactions on Image Processing, Vol.22, Issue.1, pp.408-412, 2013.
[9] X. Chen, S. Bing Kang, J. Yang, J. Yu, “Fast patch-based denoising using approximated patch geodesic paths”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1211-1218, 2013.
[10] C. Kervrann, “PEWA: Patch-based exponentially weighted aggregation for image denoising”, In Advances in Neural Information Processing Systems, pp.2150-2158, 2014.
[11] K. Kundu, “Image Denoising using Patch based Processing with Fuzzy Gaussian Membership Function”, International Journal of Computer Applications, Vol.118, Issue.12, pp.35-40, 2015.
[12] X. Liu, Z. Yang, J. Wang, J. Liu, K. Zhang, W. Hu, “Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image”, Journal of Medical Imaging, Vol.4, Issue.1, pp.014002(1-12), 2017.
[13] M. Kaur, B. Jindal, “Improved sparse matrix denoising technqiues using affinity matrix for geographical images”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.51-57, 2017.

L. Baby Victoria, S. Sathappan, "An Optimal Patch Size based Sporadic Decomposition of Hankel Structured Matrix in Gradient Transform Domain for Impulse Noise Denoising," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.246-250, 2018.

Handwriting is a tool to understand partially the unknown world of subconscious mind. The motor nerves come into play while writing. Personality trait identification can be done successfully with accuracy through handwriting. A research is done to show new avenues of application of handwriting analysis. Diseases like strokes, Alzheimer’s disease, Parkinson, Dyslexic disorders can be very easily diagnosed well in advance before the onset of the disease. A novel work is carried out to enlighten that, hand writing analysis not only identifies a person’s characteristic traits but also identifies many diseases including brain disorders like Alzheimer’s disease, suicidal tendency and pessimism etc.

Behavior Recognition; Segmentation; SVM Classifier;Drop Fall Algorithm; Zernike Moments

[1] Syeda Asra, Dr.Shubhangi DC, “Personality Trait Identification – A Survey”, International Journal of Computer Science (IJCSN) , Vol 3, Issue 2 , pp.2277-5420, 2014.
[2] SyedaAsra, Dr.Shubhangi D.C ,”Personality Trait Identification Using Unconstrained Cursive and Mood Invariant Handwritten Text”I.J. Education and Management Engineering, 2015, 5, 20-31 Published Online October 2015 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2015.05.03
[3] Syeda Asra, Dr.Shubhangi DC ,” Specific Trait Identification in Margins Using Hand Written Cursive”, International Journal Of Engineering And Computer Science (IJECS) ISSN: 2319-7242, Volume 6 Issue 1 Jan. 2017, Page No. 19963-19964 Index Copernicus Value (2015): 58.10, DOI: 10.18535/ijecs/v6i1.19.
[4] Syeda Asra, Dr.Shubhangi D.C,” Human Behavior Recognition based on Hand Written Cursives by SVM Class/ifier, ”, in ICEECCOT Mysuru,2017.
[5] Syeda Asra, Dr.Shubhangi D.C,”Behaviour Recognition Based on Hand Written T-Letter Using SVM Classifier “ International Journal of Computer Science (IAENG) Scopus Indexed
[6] Jinyin Yang et.al.” A Novel Drop-fall Algorithm Based on Digital Features for Touching Digit Segmentation” IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON),2016.
[7] Tomoki Watanabe, Satoshi Ito, and Kentaro Yokoi,“ Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection”, Springer-Verlag Berlin Heidelberg 2009.
[8] Michael Vorobyov Notes on, Topic: “Shape Classification Using Zernike Moments”, iCamp at University of California Irvine August 5, 2016.

Syeda Asra, Shubhangi D.C, "Handwriting Analysis for Disease Identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.251-254, 2018.

Glaucoma detection is one of the most recent researches in medical field. There are several researches which mainly focus on optic cup to disc ratio to efficiently identify glaucoma. The objective of this paper is to identify glaucoma by creating a binary mask for optic cup and disc of glaucomatous eyes. The query image is segmented using these masks and identified as either normal or glaucomatous eyes. The proposed method is tested on RIM-ONE r3 database. The experimental results substantially proved that the proposed method achieved 95.29% specificity at 94.59% sensitivity with AUC of 0.869. The proposed method is also compared with existing methods and proved to work better than them.

Fundus image, glaucoma, optic disc, optic cup, mask

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[14] PriyankaVerma, “Segmentation of Cup And Disc For Glaucoma Detection”, International Journal Of Current Engineering And Scientific Research, Vol. 2, Iss. 4, pp. 43-48, 2015.
[15] Hanamant M. Havagondi, Mahesh S. Kumbhar, “Optic cup and disc localization for Detection of glaucoma using Matlab”, International Journal of Electrical, Electronics and Computer Systems, Volume -2, Issue-7, pp. 13-166, 2014.
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[18] M. Arulmary, S. P. Victor, “Geometrical Feature Extraction for Glaucoma Detection”, International Journal of Computer Applications (0975 – 8887) Vol. 180, – No.27, pp. 1-5, March 2018.
[19] M. Arulmary, S.P. Victor, “Block Based Probability Intensity Feature Extraction for Automatic Glaucoma Detection”, International Journal of Pharmaceutical Research, Vol. 10, Issue 2, pp. 87-93, April-June 2018.

M. Arulmary, S.P. Victor, "Binary Mask Pattern Segmentation in glaucoma detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.255-259, 2018.

Wireless Sensor Network (WSN) is one of the merging network technologies. It is an extensively distributed network equipped with low powered and lightweight wireless sensor nodes. WSN are being deployed to monitor system and environment. WSN are being used in health sectors, military operations, automation industries, traffic monitoring, oil refineries etc. But with the emerging applications, WSN is prone to attacks and threats. These attacks and threats must be studied thoroughly in order to counter them. Today security issues are the major challenges faced by WSN. The deployment of wireless sensor nodes in hostile environments makes them the subject of lethal attacks, also due to the limitations of resources processing capability, power consumption & communication range WSN are vulnerable to many types of threats/attacks. There are serious consequences if security of WSN gets compromised by any means such as information theft, lack of privacy, etc. Thus it must be our utmost priority to save WSNs from malicious attacks. In this study we have highlighted the various fatal attacks that can destruct WSN with their impacts and consequences.

Wireless Sensor Network (WSN), Network Security, Cryptography, Confidentiaity, Authenicity, Blackhole, Jammming.

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[10] Sheela D. Naveen K,C and Mahadevan G, A non cryptographic method of sink hole attack detectionin wireless sensor networks, Recent Trends in Information Technology (ICRTIT), 2011 International Conference IEEE.
[11] Hamid, M. A., Rashid, M-O., and Hong, C. S., “Routing Security in Sensor Network: Hello Flood Attack and Defense”, to appear in IEEE ICNEWS 2006.
[12] Choong Seon Hong. "Security in wireless sensor networks: issues and challenges", 2006 8th International Conference Advanced Communication Technology, 2006.
[13] Wang Xin-sheng, Zhan Yong-zhao, Xiong Shu-ming, Wang Liangmin, “Lightweight Defense Scheme against Selective Forwarding Attacks in Wireless Sensor Networks’ pp 226-232, IEEE 2009.
[14] Ahmad Salehi, S., M.A. Razzaque, Parisa Naraei, and Ali Farrokhtala. "Security in Wireless Sensor Networks: Issues and challanges", 2013 IEEE International Conference on Space Science and Communication (IconSpace), 2013.

Shafiqul Abidin, "WSN – An Emerging Technology and its Security Meaures," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.260-264, 2018.

The number of the computations and feature transformations along with the normalization and automatic categorization is required by the object classification algorithms. In this paper, the robust feature descriptor used with the active object detection method (AODM) along with the probabilistic equation enabled deep neural networks (DNN). The multi-category DNN (mDNN) has been described with the repetitious phases so that it is simple to do job with the multi-category dataset. In every iterative phase mDNN shows the training data of main class as primary class and remaining all other training data are divided as the secondary class for the supervised classification. In the object image dataset, designed model is proficient of working with the variations which are observed in the configuration of the color, texture, light, image orientation, and occlusion and color illuminations. Certain analysis has been organized over the designed model for the performance calculation of the object identification system in the designed model. The results which we collected are in the shape of the various performance parameters of statistical errors, precision, recall, F1-measure and overall accuracy. In the terms of the overall accuracy the designed model has clearly outperformed the existing models. The designed model growth has been recorded higher than ten percent for all of the evaluated parameters against the existing models based upon SURF, FREAK, etc.

Deep neural network, active object model, object recognition, SIFT, SURF

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R. Kapila, H. Wadhwa, "Active Object Detection Model with Deep Neural Network for Object Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.265-269, 2018.

A data warehouse is a centralized repository of information from one or more data sources. The amount of big data that arrives in data warehouse typically comes from transactional systems and other relational databases. Often the data is stored in the form of materialized views in order to improve the performance of query execution in data warehouse. One of the most important techniques for improving query optimization performance is the selection of views to materialize. In this paper, the views selection problem is modelled as constraint satisfaction and optimization problem. The exact method standard may take a considerable amount of time in order to find an optimal solution. To address this limitation of the exact method, we proposed an approach based on consistency techniques and systematic search techniques to select an optimal set of views for materialization. This proposed approach improves the quality of execution time for selecting an optimal set of views to materialize.

Data warehouse, view selection problem, constraint satisfaction and optimization problem, hybrid approach, exact method

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Mohammed El Alaoui, Karim El Moutaouakil, Mohamed Ettaouil, "A Hybrid Approach To Solving The View Selection Problem In Data Warehouse," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.270-275, 2018.

To increase the utilization and development of solar energy which is Eco friendly. Weather Predication is done in order to improve the performance and maintain its consistency for long term to deliver secure and reliable power while managing uncertainties. In order to enhance and improve the performance, preventive maintenance of solar power plant is done by implementing Operation & Maintenance (O&M)activities using predictive analytics and supervisory control and data acquisition (SCADA). With the help of internet(cloud)along with IOT devices, Operation and maintenance, Supervisory Control And Data Acquisition the preventive maintenance can be improved. Many industries in India are working towards increasing the performance ratio of solar power plants in large scale. This paper presents a comparative study to improve the performance of solar power plants through IOT and predictive analytics.

IOT, solar energy, scada, O&M

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[2] S. Chinnammai, ‘An Economic Analysis of Solar Energy’, Journal of Clean Energy Technologies, Vol. 1, No. 1, January 2013
[3] A. Garro, ‘Reliability Analysis of Residential Photovoltaic Systems’, International Conference on
Renewable Energies and Power Quality (ICREPQ’11)
[4] International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 7 (2016) pp 4803-4806 © Research India Publications. http://www.ripublication.com
[5] EMMVEE PV Pvt Ltd (EPPPL PVT LTD) New International Airport Road Bangalore.
[6] “Smart Grid Security Issues and Challenges for IOT”, NCRATCE-2016, National Conference at AIT Chickmangalore.
[7] “A Review on Biologically Inspired Approaches to Security of Internet of Things to ICEEOT 2016, International Conference at Chennai with IEEE catalog 978-1-4673-9939-/16/$31.00
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[11] “Analyzing and Comparison of Marking Mod RED Active Queue Management Scheme on TCP
Applications”, International Conference on Communications and Signal Processing (ICCSP-2012)
Sponsored by AICTE- on 2nd- 4th April, 2012at QIS College of Engineering , Ongole,(A.P).
[12] “Performance Comparison of Active Queue Management Techniques”, IJCSNS International Journal of Computer Science and Network Security,2009

Shahida Begum, Reshma Banu , "Improving the Performance of Solar Power Plants through IOT and Predictive Data Analytics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.276-279, 2018.

This is the time of web, in which whole world is associated with one another, thus significance of security builds step by step. To protect the data from unauthorized user, it is the new challenge for us so that we developed a very simple technique to transfer the data from one party to other by secure channel. As we know that Steganography is a digital technique for hiding information in some form of media, such as image, audio or video. Steganography has advanced routine with regards to hide information in bigger document so that others can`t associate the nearness with a shrouded message. In this paper, we outline a framework, which utilizes highlights of both cryptography and in addition steganography, where TCP/IP header is utilized as a steganographic transporter to hide encrypted data. [1] Steganography is a valuable apparatus that permits secretive transmission of data over the correspondences channel. [2] In this paper we have use the MKA algorithm [4] to embed the data inside the image so that capacity of data will be large.

LSB,Steganography, Cryptography, Encryption, TCP/IP Header, Fragmentation, MKA algorithm

[1] R.M. Goudar, Prashant N. Patil, Aniket G.. “Secure Data Transmission by using Steganography” ISSN 2224- per) ISSN 2224-896X Vol 2, No.1, 2012
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[4] . H.B.Kekre, Archana Athawale, and Pallavi N.Halarnkar ,” Increased Capacity of Information Hiding in LSB’s Method for Text and Image” World Academy of Science, Engineering and Technology Vol:2 2008-05-22.
[5] Udit Budddia and Deepak Kundur, “Digital video steganalysis exploiting collusion Sensitivity”,IEEE, 1(4):502- 516, 2006.
[6] Furuta, T,.Noda, H., Niimi, M., Kawaguchi E,”Bit-plane decomposition steganography using wavelet compressed video”, Joint Conference of the Fourth International Conference, 2(5): 970 - 974, 2003.
[7] V.Karthekayani and kammalakan, “Conversion grayscale image to color image with and without texture synthesis”, International journal of computer science and network security, 7(4):11-16, 2007.
[8] Eiji Kawaguchi and Richard O. Eason, “Principle and applications of BPCS- Steganography”, Proc. SPIE ,3528: 464-473 , 1999.
[9] Nameer N. EL-Emam, “Hiding a Large Amount of Data with High Security Using Steganography Algorithm” Jordan Journal of Science publications, 3 (4): 223-232, 2007.
[10] K B Raja, C R Chowdary K R Venugopal, “A Secure Image Steganography using LSB, DCT and Compression Techniques on Images”, IEEE, 170-176, 2005.
[11] Naofumi,” Technique of lossless steganography”, IEICE Transactions on Communications, 90(11):1-4, 2007.
[12] .Nan jiang and wan jiang, “Random oracle model of information modeling”, World academy of science, 18:1307-6884, 2006
[13] Steganography [tG. Pulcini, Stegotif,"http://www.geocities.com/SiliconValley/ 9210/gfree. Html, 10/28/2008
[14] K. Sullivan, U. Madhow, S. Chandrasekaran, and B. S. Manjunath, “Steganalysis of Spread Spectrum Data Hiding Exploiting Cover Memory," The International Society for Optical Engineering, Electronic Imaging, San Jose, CA, USA, 2005.
[15] G. Xuan, Y. Q. Shi, D. Zou, J. Gao, C. Yang, Z. Zhabg, P. Chai, W. Chen, C. Chen, “Steganalysis based on multiple features formed by statistical moments of wavelet characteristic Functions," IH 2005 LNCS 3727, pp. 262-277, Springer-Verlag Berlin Heidelberg 2005.
[16] H. Noda, J. Spaulding, M. Shirazi, M. Niimi and E. Kawaguchi, “BPCS Steganography Combined with JPEG2000 Compression”, Proceedings of Pacific Rim Workshop on Digital Steganography , pp. 98-107, 2002.
[17] J. Huang and Y. Shi, “Adaptive image watermarking scheme based on visual masking”, Elect. Lett., vol. 34, no. 8, pp. 748-750,1998.
[18] A. Piva, M. Barni, F. Bartolini and V. Capellini, “DCT-Based watermark recovering without restoring to the uncorrupted original image”, in Proc. of IEEE ICIP, pp. 520-523, 1997. 438
[19] Rahul Jain,Naresh kumar .” Efficient data hiding scheme using lossless data compression and image steganograpgy “, International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462 Vol. 4 No.08 August 2012
[20] Swami Vivekanand Mahavidhyalaya etl,” Information Security: A Review on Steganography with Cryptography for Secured Data Transaction “,Int. J. Sc. Res. in Network Security and Communication , ISSN: 2321-3256 , Volume-5, Issue-6, Dec 2017 .
[21] A. Sudha1, A. Basheer Ahamed, ” Img-Protect : Privacy Protection of Images in Online Social Networks Using Watermarking Scheme “, International Journal of Scientific Research in Computer Science, Engineering and Information Technology , Volume 3 | Issue 7 | ISSN : 2456-3307

Basant Sah, V .K Jha, "Secure Approach To Data Transmission Using Steganography and RSA Technique Through TCP/IP Header," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.280-284, 2018.

Mobile phones has become the integral part of human life in today’s world. The data shows that the total number of mobile phone users has been increased to 4.68 billion and is expected to reach 5 billion mark in 2019.Indians are second highest user 1,183,408,611 in numbers after China in may 2018. But every coin has two sides similarly here also one negative side of mobile phones is on human brain. Radiation of electromagnetic waves created a negative impact on human’s brain and their immune system. In recent years numerous ways such as (earphones, bluetooth, headset) has been investigated to reduce this harmful effect of EM wave .These frequencies are reducing human health exponentially. In this paper effect of EM waves with or without devices has been discussed.

GSM, CDMA, Electroencephalogram, EM waves

[1]. Aruna Tyagi, Manoj Duhan, Dinesh Bhatia (2011) Effect of Mobile Phone Radiation on Brain Activity GSM Vs CDMA. IJSTM.
[2]. Scarella O Clatz, Lanteri S, Beaume G, Oudot S, Piperno S, et al. (2006) Realistic numerical modeling of human head tissue exposure to electromagnetic waves from cellular phones.
[3]. Robert Leeb, Felix Lee, Claudia Keinrath, Reinhold Scherer, Horst Bisch, et al. (2007) Brain-Compute Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment Ieee Transactions On Neural Systems And Rehabilitation Engineering
[4]. Vijay Kr Garg (2000) IS-95 CDMA and cdma: Cellular/pcs system implementation
[5]. Riadh W Y Habash (2011) Non-Invasive microwave hyperthermia PhD Thesis, ECE Deptt, IISc, Bangalore, India.
[6]. Aruna Tyagi, Vaishali Jain, Dinesh Bhatia, Manoj Duhan (2010) Review of Effect of Electromagnetic Radiations of Mobile Phone on Human Health Proceeding of Control Instrumentation System Conference
[7]. Delgado JM, Leal J, Monteagudo JL, Gracia MG (1982) Embryological changes induced by weak, extremely low frequency electromagnetic fields. Journal of Anatomy.
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[9]. Rahim Mohammed AL-Imari, Muhammad Ali Imran, and Tafazolli “Low Density Spreading for Next Generation Multicarrier Cellular Systems”.
[10]. Muhammad Mokhlesur Rahman; Shalima Binta Manir “Performance Analysis of Sc-FDMA and OFDMA in LTE Frame Structure”2012.

Rahul Ohlan, "Effect of Electromagnetic Radiations On Human Body," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.285-288, 2018.

Real world analysis the data based on the realistic approach to deal any objectives in online environment. Specifically social remedies have various approach of projective comments to share the information about products, innovative technologies etc. in this thing tweeter is a main platform to provide communication mammon the sharable users. In this provision most used by the people have the onions to make sentimental key terms to notify the originality. The sentiments about specific data be pointed by the comments in content format with sort text opinions. The opinions are extracted from the comments statement to analyst the tweeter data. By the fact of analyzing tweets have the hidden sentimental approach the problem arise due to right choice of sentimental extraction to classification is difficult. To overcome the problematic issue to propose a selective feature based case content extraction using maximum entropy classifier (SFCETR) to rank the tweets. This initially preprocess the tweets data the content reason from the comments statement. to aim the case reasons of relational features observed from the tweet contents are key term as contents .the comment case sentimental relation keyword terms are extracted synonmically to classify the data base on the reference key variable . Finally the rank case resultant categorize the sentimental case reasoning onion s about the predicative approach are classifies as class. This improves the tweets case opinions extraction are carried with the high performance sentimental research.

Opinion mining, rank analysis, tweeter analysis, sentiment classification, features election

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[9]. S.Lohmann, M. Burch, H. Schmauder and D. Weiskopf, “Visual Analysis of Microblog Content Using Time-Varying Co-occurrence Highlighting in Tag Clouds,” Annual conference of VISVISUS. Germany: University of Stuttgart, 2012.
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[12]. A.Pak and P. Paroubek. „Twitter as a Corpus for Sentiment Analysis and Opinion Mining". In Proceedings of the Seventh Conference on International Language Resources and Evaluation, pp.1320-1326.1010
[13]. Po-Wei Liang, Bi-Ru Dai, “Opinion Mining on Social MediaData", IEEE 14th International Conference on Mobile Data Management,Milan, Italy, June 3- 6, pp 91-96, ISBN: 978-1- 494673-6068-5, 2013.
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[18]. Shi Yuan, Junjie Wu, Lihong Wang and Qing Wang, “A Hybrid Method for Multi-class Sentiment Analysis of Micro-blogs”, ISBN- 978-1-5090-2842-9, 2016.
[19]. Halima Banu S and S Chitrakala, “Trending Topic Analysis Using Novel Sub Topic Detection Model”, (IEEE) ISBN- 978-1-4673-9745-2, 2017.
[20]. David Zimbra, M. Ghiassi and Sean Lee, “Brand-Related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks”, IEEE 1530-1605, 2016.

P. Kavitha, M. Prabakaran, "An Efficient Tweeter Sentiment Analysis Sfcetr: Selective Feature Based Case Content Extraction Using Maximum Entropy Classifier To Rank The Tweets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.289-299, 2018.