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Fish species recognition is a challenging task for research. Great challenges for fish recognition appear in the special properties of underwater videos and images. Due to the great demand for underwater object recognition, many machine learning and image processing algorithms have been proposed. Deep Learning has achieved a significant results and a huge improvement in visual detection and recognition. This paper mainly reviews some techniques proposed in past years for automatic fish species detection and classification.

Fish Recognition;Fish Classification;Feature ExtractionImage Processing;Neural Network;Deep Learning

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[14] .Nour Eldeen M. Khalifa , Mohamed Hamed N. Taha and Aboul Ella Hassanien,”Aquarium Family Fish Species Identification System Using Deep Neural Networks “
[15] .Jonas Jager,Erik Rodner,Joachim Denzler,Viviane Wo and Klaus Fricke-Neuderth ,”SeaCLEF 2016: Object proposal classification for fish detection in underwater videos”
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[19] .Dhruv Rathi, Sushant Jain and Dr. S. Indu,” Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning “

R. Fathima Syreen, K. Merriliance, "A Survey on Underwater Fish Species Detection and Classification", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.95-98, 2019.

The revolution of document analysis provides the handwritten palm leaf manuscripts and historical documents, epigraphic into digital. Digital document is available as auto recognition of those historical documents which is the second revolution in document research. Many algorithms are available from the period of the 19th century to the 20th century with the researchers across the world. In order to achieve auto recognition, the line segmentation is a foremost process. Though automatic segmentation of text lines is still a burning research, many technical issues remain unsolved yet. The present survey has been carried out as the survey of newly proposed and modified methods of text line segmentation in palm leaf manuscripts and handwritten historical documents published during the period 2008 – 2018. It could benefit the researchers who do research in handwriting recognition.

pre-processing, path finding approach, performance measure

[1] Made Windu Antara Kesiman, Dona Voly., “Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia”,JImaging,4,43;doi:10.3390/jimaging 4020043, 2018.
[2] Himanshu Jain, Archana Praveen Kumar, “A Bottom Up Procedure for Text Line Segmentation of Latin Script”, IEEE, pp 1182 – 1187, 2017.
[3] Payal Jindal, Dr. Balkrishnan Jindal, “Line and Word Segmentation of Handwritten Text Documents written in Gurmukhi Script using Mid Point Detection Technique”, IEEE, Proceedings of 2015 RACES UIET Panjab University Chandigarh 21-22nd December2015, 2015.
[4] Dona Valy, Michel Verleysen, Kimheng Sek, “Line Segmentation for Grayscale Text Images of Khmer Palm Leaf Manuscripts”, IEEE, 2017.
[5] Rapeeporrn Chamchong, Chum Che Fung, “Text Line Extraction Using Adaptive Partial Projection for Palm Leaf Manuscripts from Thailand”, IEEE, 2012 International Conference on Frontiers in Handwriting Recognition, IEEE, PP 586-5591, 2012.
[6] Youbao Tang, Xiangqain Wu, Wei Bu, “Text Line Segmentation Based on Matched Filtering an Top-down Grouping for Handwritten Documents”, IEEE, 2014 11th IAPR International Workshop on Document Analysis System, PP 365 – 369, 2014.
[7] Mullick, Banerjee, Bhatacharya, “An Efficient Segmentation Approach for Handwritten Bangla Document Image, IEEE, 2015.
[8] Guillaume Renton, Clement Chatelian, “Handwritten text line segmentation using Fully Convolutional Network”, IEEE, 2017 14th International Conference on Document Analysis and Recognition, PP 5 – 9, 2017..
[9] David Aldavert, Marcal Rusinol, “Manuscript Text Line Detection and Segmentation using Second Order Derivatives”, IEEE, 2018 13th IAPR International Workshop ON Document Analysis Systems, PP 293 – 298, 2018.
[10] https://towardsdatascience.com/model evaluation-i-precision-and-recall-166ddb257c7b
[11] Vishal Chavan, Kapil Mehrotra, “Text Line Segmentation of Multilingual Handwritten Documents Using Fourier Approximation”, IEEE, 2017 Fourth International Conference on Image Information Processing(ICIIP), PP 250 – 255, 2017.
[12] Ayush Padhan, Sidharth Behra, Paushpalata Pujari, “Comparative Study on Recent Text Line Segmentation Methods of Unconstrained Handwritten Scripts”, IEEE, International Conference on Energy, Communication, Data Analysis and Soft Computing(ICECDS-2017), PP 3853 – 3858, 2017.
[13]Themos Stafylakis, Vasilis Papavassiliou, “Robust Test-Line and word segmentation for Handwritten Documents Images”, IEEE, PP 3393 – 3396, 2008.
[14]Sanchez, Suarez, Mello, Oliveria, Alves,”Text Line Segmentation Images of Handwritten Historical Documents”, IEEE, Image Processing Theory, Tools and Applications, 2008.
[15] Rodolfo P dos Santos, Gabriela S Clemente, Tsang Ing Ren, “Text Line Segmentation Based on Morphology and Histogram Projection”, IEEE, 2009 10th International Conference on Document Analysis and Recognition PP 651 – 655, 2009..
[16] Rajiv Kumar, Amardeep Singh, “Detection and Segmentation of Lines and Words in Gurmukhi Handwritten Text”, IEEE, 2010 IEEE 2nd International Advance Computing Conference, PP 353 – 356, 2010.
[17] Naresh Kumar Garg, Lakswinder Kaur, Jindal, “A new method for Line Segmentation of Handwritten Hindi Text”, IEEE, 2010 Seventh International Conference on Information Technology, PP 392 – 397, 2010.
[18] Jija Das Gupt, Bhabatosh Chanda, “A model based Text Line Segmentation method for Off-Line Handwritten Documents”, IEEE, 2010 12th International Conference on Frontiers in Handwritting Recognition, pp 125-129, 2010.
[19] Vijaya Kumar Koppula, Atul Negi, “ Fringe Map based Text Line Segmentation of Printed Telugu Document Images”, IEEE, 2011 International Conference on Document Analysis and Recognition, PP 1294 – 1298, 2011.
[20] Rajib Ghosh, Debnath Bhattacharyya, Tai hoon kim, Gang soo Lee, “ New Algorithm for Skewing Detection of Handwritten Bangla Words”, Springer – Verlag Berlin Heidelberg, PP 153 – 159, 2011.
[21] Ines ben Messaoud, Hamid Amiri, “A Multilevel Text Line Segmentation Framework for Handwritten Historical Documents”, IEEE, 2012 International Conference on Frontiers in Handwriting Recognition, PP 515-520, 2012.
[22] Hande Adiguzel, Emre Sahin, Pinar Duygulu, “A Hybrid Approach for Line Segmentation in Handwritten Documents”, IEEE, 2012 International Conference on Frontiers in Handwriting Recognition, PP 503-508, 2012.
[23] Xi Zhang, Chew Lim Tan, “ Text Line Segmentation for Handwritten Documents Using Constrained Seam Carving”, 2014 14th International Conference on Frontiers in Handwriting Recognition, IEEE, PP 98-103, 2014.
[24]Rapeeporrn Chamchong, Chum Che Fung, “A Combined Method of Segmentation for Connected Handwritten on Palm Leaf Manuscripts”,IEEE, 2014 IEEE International Conference on Systems, Man and Cybernetics, PP 4158 – 4161, 2014.
[25] Dona Valy, Michel Verleysen, Kimheng Sek, “Line Segmentation Approach for Ancient Palm Leaf Manuscripts using Competitive Algorithm”, IEEE, 2016 15th International Conference on Frontiers in Handwriting Recognition, PP 108-113, 2016.
[26]Banumathi, Jagadeesh Chandra, “ Line and Word Segmentation of Kannada Handwritten Text documents using Projection Profile Technique”, IEEE, 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) PP 196 – 201, 2016.
[27]Quang Nhat Vo, GueeSang Lee, “ Dense Prediction for Text Line segmentation in Handwritten Document Images”, IEEE, ICIP 2016, PP 3264 – 3268, 2016.
[28] Kathirvalavakumar Thangairulappan, Karthigai selvi Mohan, “ efficient Segmentation of Printed Tamil Script into Characters Using Projection and Structure”, IEEE, 2017 Fourth International Conference on Image Information Processing(ICIIP), PP 484 – 489, 2017.

R. Spurgen Ratheash, M. Mohamed Sathik, "A Detailed Survey of Text Line Segmentation Methods in Handwritten Historical Documents and Palm Leaf Manuscripts", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.99-103, 2019.

Big data is continuously creating new challenges and opportunities, all of which have been forged by the information revolution. The ecommerce industry are already using data sets to introduce a new level of strategic marketing and provide better customer service experiences .Data growth has undergone a renaissance, by ever lower computing power in the internet. It will be top method shift in the E-commerce sector; as data is no longer seen as by the product of their business activities, but as their biggest trademark providing: the needs of their customers, predicting trends in customer’s behavior, democratizing of advertisement to suits consumers varied taste, as well as providing a performance metric to assess the effectiveness in meeting customers’ needs. This paper provides the unique features that differentiate big data from traditional datasets. In this way the application of big data analytics in the E-commerce and its technologies that make analytics of consumer data possible is discussed. Improvement of technology usage, the methods to measure and collect data also increase. One of the methodsto understand our world is to study trends in behavior.

Big Data Analytics, Computing

[1]. http://www.harapartners.com/blog/big-data-affect-our-daily-e-commerce-experience
[2]. Yaser Ahangari Nanehkaran, An Introduction To Electronic Commerce, International Journal Of Scientific & Technology Research Volume 2, Issue 4, April 2013,Pg190-193
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[6]. Chalmers, S., Bothorel, C. & CLEMENTE, R., 2013. Big Data-State of the Art. Thesis. Brest: Telecom Bretagne, Institute Mines-Telecom

M. Gandhimathi, "Big Data Computing and Analytics Business Value in - E-Commerce", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.104-107, 2019.

Educator says that the learning process should be all about creativity and interaction. While teachers do not necessarily need to recruit all students into subject, their goal is to get them interested in a subject. Augmented Reality technology has the potential to blend both real and virtual environment to create an enhanced learning experience. Besides compared to paper-based solutions and conventional mobile applications, AR book system with scaffolding models they developed could help students to learn, particularly those with low reading ability. Objective of this paper is to highlight the applications and opportunities of research on AR applications in education.

Augmented Reality, Education, Learning, Marker based

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K. Ganesh Kumar, "Future Generation Education on Augmented Reality", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.108-113, 2019.

In this paper, use of engineering in building materials on behalf of a spread of applied science mechanism is mentioned. Strength, sturdiness and different properties of materials are significantly affected underneath a scale of nano meter(10-9m).This paper in addition tells however the employment of nano technology makes concrete more stronger, sturdy and additional simply placed. differing kinds of nano materials used are mentioned with its wide applications. The properties like self-sensing, self- treatment, self-structural health watching are studied. Following this the analysis were applied in versatile structural composites together with its improved properties, low repairs coatings, higher properties of building material materials, reduction of the thermal transfer rate of fireside retardation and insulation, varied nanosensors, sensible materials, intellectual construction technology.

Civil Infrastructure Application, Nanotechnology properties

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P. Krishnaveni, "Application of Nanotechnology in Civil Infrastructure", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.114-119, 2019.

The mobile computing is a computing organization in which a computer and all necessary accessories like files and software are taken outto the field. It is a system of computing through which it is being able to use a computing device even when someone being mobile and therefore changing location. The portability is one of the important aspects of mobile computing. The mobile phones are being used to gather scientific data from remote and isolated places that could not be possible to retrieve by other means. The expertsaremakingtoproceduremobiledevicesand web-based applications to scientifically explore interesting scientific aspects of their surroundings, ranging from climate change, environmental pollution to earthquake monitoring.Thismobilerevolutionempowersnew ideas and innovations to spread out more speedily andefficiently.Herewewilldiscussinbriefabout the mobile computing technology, its guessing, challenges and theapplications.

Mobile Computing, Mobile Sensing, Applications

[1]R. Les Cottrell, “The internet, mobile computing and mobile phones in developing countries” published in “m-Science sensing, computing and dissemination” Editors: E. Canessa and M. Zennaro, ICTP (2010), ICTP Science Dissemination Unit
[2]“3GPP Long Term Evolution”
:http://en.wikipedia.org/wiki/3GPP_Long_Term_Evolution
[3]“First cell phone a true brick”, Associated Press, see:http://www.msnbc.msn.com/id/7432915
[4]http://en.wikipedia.org/wiki/MVEDR
[5]Dashboard Center piece aka Dashtop Mobile:http://driveonpay.com/Dash Cente piece.ht ml.

A. Rajeswari, "An In-Depth Study of Mobile Computing Devices Applications and Challenges", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.120-124, 2019.

Diabetic Retinopathy is the damage caused to the retina of the eye due to the diabetes. There are a large number of people, who are suffering from Diabetic Retinopathy that leads to blurring of vision or even blindness, if not treated at an early stage. Hence it is important to detect Diabetic Retinopathy at an earlier stage and provide treatment otherwise it may lead to vision demages. This paper presents to identify or earlier detection of the High Exudates and Cotton Wool Spots in Diabetic Retinopathy through Color transformation and Image Enhancement using FFT.

High Exudates ,Cotton Wool Spots,FFT etc

[1]. http://www.isi.uu.nl/Research/Databases/DRIVE
[2]. Zahira Asifa Tarannum, B.Srilatha “Detection of Diabetic Retinopathy with Feature Extraction using Image Processing” in IJEECS.
[3]. 3. R.ManjulaSri 1 , M.Raghupathy Reddy and K.M.M.Rao “ Image Processing for Identifying Different Stages of Diabetic Retinopathy” ACEEE in Int. J. of Recent Trends in Engineering & Technology, Vol. 11, June 2014.
[4]. Dr. Prasannakumar. S.C, Mrs. Deepashree Devaraj,’Automatic exudatete detection for the diagnosis of diabetic rethinopathy ’,issn 2319- 9725, Vol 2 issue.
[5]. Nikhil Amrutkar , Yogesh Bandgar , Sharad Chitalkar, S.L.Tade “ RETINAL BLOOD VESSEL SEGMENTATION ALGORITHM FOR DIABETIC RETINOPATHY AND ABNORMALITY DETECTION USING IMAGE SUBSTRACTION” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 4, April 2013.

S. Ponnammal, "Automated Detection of High Exudates and Cotton Wool Spots in Diabetic Retinopathy", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.125-128, 2019.

The goal of this paper is to survey about various image compression techniques. Image compression is a very important to implement in digital image processing. It is the case of converting an image file in such a way that it consumes smaller space than the original file. Compression technique that reduces the size of an image file without disturbing or degrading its quality to a greater extent. The main intention of the compression is to minimize the amount or unnecessary data.The goal behind is to save the amount of memory required to save the images or to make practical network bandwidth in efficient manner.

Image Compression, Run length encoding, Entropy encoding, LZW

[1]. Yogesh Chandra, Shikha Mishra “Review Paper of Image Compression”, International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-2, Issue-2, February 2015.
[2]. Dalvir Kaur, Kamaljit Kaur, “Huffman Based LZW Lossless Image Compression Using Retinex Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 8, August 2013.
[3]. Mrs.Bhumika Gupta, “Study Of Various Lossless Image Compression Technique”, IJETTCS, volume 2, issue 4, July- August 2013.
[4]. David Jeff Jackson & Sidney Joel Hannah, “Comparative Analysis of image CompressionTechniques,” System Theory 1993, Proceedings SSST ’93, 25th Southeastern Symposium,pp 513-517, 7 –9March 1993.
[5]. Malwinder Kaur, Navdeep Kaur, “A Litreature Survey on Lossless Image Compression”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2015.
[6]. Mridul Kumar Mathur, Seema Loonker, Dr. Dheeraj Saxena, “Lossless Huffman Coding Technique For Image Compression And Reconstruction Using Binary Trees”, IJCTA, Vol 3 , Jan-Feb 2012.
[7]. Jagadeesh B, Ankitha Rao, “An approach for Image Compression Using Adaptive Huffman Coding”, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 12, December – 2013.
[8]. Xiaoqiang Ma ,Chengdong Xu ,Pengfei Zhang ,Chunsheng Hu, “The Application of the Improved LZW Algorithm in the Data Processing of GNSS Simulation”, Fourth International Conference on Computational and Information Sciences 2012.
[9]. JayavrindaVrindavanam ,SaravananChandran, Gautam K. Mahanti, “A Survey of Image Compression Methods” International Journal of Computer Applications® (IJCA) 2012.

C. Jenifer Kamalin, G. Muthu Lakshmi, "Survey on Image Compression Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.129-132, 2019.

Natural Language Understanding (NLU) is really challenging sub domain of language processing as far as any highly agglutinative and morphologically rich south Indian Languages are concerned. It requires highly complex procedures and techniques to extract its inflections and grammatical information, thereby make a computer to understand the real sense of the language as human beings does. This paper aims not only providing insights into natural language understanding but also gathers information about the various existing techniques in Malayalam Language. Natural language Understanding refers to the understanding of any language by a machine with the help of an intermediate representation. Here we have briefed the various techniques and algorithms used with Morphological Analyzer, POS tagger, chunking, Parsing, Named Entity Recognition, and Word Sense Disambiguation which are the inevitable components for understanding any Natural Language.

Natural Language Understanding, Lemmatization, Suffix stripping, Sequence labelling, Stemming, POS tagger, Hidden Markov Model, Support Vector Machine, Morphology, Parsing, Chunking, Sandhi Splitter, Named Entity Recognition, Word Sense Disambiguation

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Usha K, S Lakshmana Pandian, "Natural Language Understanding of Malayalam Language", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.133-138, 2019.

In Recent Years, suspicious activity detection is used to detect traffic in different surveillance videos with high accuracy and high speed in daytime. This surveillance video detection method includes Adaptive Background, Object Modeling, Object Tracking, Activity Recognition, and Segmentation. The semantic segmentation using suspicious activity detection techniques plays a major role in the segmentation of the surveillance video. U-Net is one of the popular Fully Convolutional Networks (FCN) which is applicable for image segmentation. This method could found the different anomalies activity from the videos.

Segmentation, FCN, Object Modeling, Suspicious Activity Detection, Surveillance Video

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S. Santhiya, T. Ratha Jeyalakshmi, "Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.139-142, 2019.