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The summarization deal’s with giving the concepts precisely. The multi-document summarization gives the extract of the multiple documents into summarized single document. Here we summarize the document individually by extracting the key phrase using the RAKE algorithm, which perform well on the single document and does not depend on the corpus. This enables the reader to find out the documents, which are highly related to the document by using the TextRank algorithm that ranks the sentence based on the key phrase selected from the single document and they can read the entire document without going through all. The work finds the summary from the given documents and those are ranked and the high ranked documents selected are then used as input to the documents at the next level. The information gained from the previous level (i.e. Summary from documents) are used as the input for the next phase, which will give more information.

Multi Document Summarization, Extraction, Sentence Ranking

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[23] S.L. Patil, K.P.Adhiya, “Textual Similarity Detection from Sentence”, International Journal of Computer Sciences and Engineering, Sep, PP.835-839, 2018.
[24] B. Batra, S. Sethi, A.Dixit, “Improved Text Summarization Method for Summarizing Product Reviews”, International Journal of Computer Sciences and Engineering, Sep, PP.113-122, 2018.
[25] C.Y. Lin, E.Hovy, “Automatic evaluation of summaries using n-gram co-occurrence statistics”, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology – Volume 1, Association for Computational Linguistics, May, PP.71-78, 2003.

J.Tamilselvan, A.Senthilrajan, "Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.250-253, 2018.

In modern era of technology every one relies on technology. From start of day to end of day humans depends on machines and machines need input signal for performing tasks. Many systems have been developed which works on native language input speech. Punjabi is also one of them, there are many speeches and dialect recognition systems are available but all have some common problems like problem with different dialects words of Punjabi is main one. In Punjabi language Majha, Malwa, Doaba are main dialect in eastern Punjab, most of time words from Majha dialect is similar to Taksali Punjabi but when we talk in Doaba and most populated dialect Malwa it is difficult for speech recognition system to understand that word and perform tasks so that was whey dialect identification system is need of hour. The aim of this paper is discuss about new proposed algorithm by authors which works on Punjabi dialects and to compare with previous algorithms with respect to accuracy.

Dialect, Natural Language Processing, Taksali Punjabi, Automatic Speech Recognition, Artificial Neural Networks, Random forest

[1] Anterpreet Kaur, Parminder Singh and Kamaldeep Kaur,” Punjabi Dialects Conversion System for Majhi, Malwai and Doabi Dialects”, International Conference on Computing Modeling and Simulation, 2017.
[2]Arvinder Singh and Parminder Singh,” A Rule Based Punjabi Dialect Conversion System”, International Journal for Research in Applied Science & Engineering Technology, pp.398-404, 2015.
[3]Arvinder Singh and Parminder Singh,” Punjabi Dialect Conversion System for Malwai and Doabi Dialect”, International Journal of Science and Technology, vol. 8(27), 2015.
[4] Harjeet Singh,”Comparative Study of Standard Punjabi and Malwai Dialect with regard to Machine Translation,” An International Journal of Engineering Sciences, June 2013.
[5] Parneet Kaur and Simrat Kaur,” Machine Translation of languages and dialects “, International Research Journal of Engineering and Technology, 3, 2016.
[6] Parneet Kaur and Simrat Kaur,”Standard Punjabi Text to Lahndi Dialect Text Conversion System”, International Journal of Science and Research, 2015.
[7] Arshdeep Singh and Jagroop Kaur,” Identification of Dialects in Punjabi Language”, International Journal of Innovations & Advancement in Computer Science, vol.5, 2016.
[8] Wiqas Ghai and Navdeep Singh, “Analysis of Automatic Speech Recognition Systems for Indo-Aryan Languages: Punjabi a Case Study”, International Journal of Soft Computing and Engineering, vol.2, pp.379-385, 2012.
[9] Wiqas Ghai and Navdeep Singh, “Phone Based Acoustic Modeling for Automatic Speech Recognition for Punjabi Language”, International Journal of Computer Application, vol.1 (3), pp.69-83, 2013.
[10] Yogesh Kumar and Navdeep Singh, “An Automatic Spontaneous Live Speech Recognition System for Punjabi Language Corpus”, International Science Press, vol.9 (20), pp. 259-266, 2016.
[11] Harpreet Kaur and Rekha Bhatia, “Speech Recognition system for Punjabi language”, International Journal of Advanced Research in Computer Science and Software Engineering, vol.5, 2015.
[12]Mohit Dua and R.K Aggarwal,”Punjabi Speech to Text System for Connected Words”, Fourth International Conference on Advances in Recent Technologies, 2015.
[13]Alan W Black, Prasanna kumar Muthukumar,”Random Forest for statistical Speech Synthesis”, International Science Community Association, 2015.
[14]Urmila Shrawankar and Dr.VilasThakare,”Techniques for Feature Extraction in Speech Recognition System: A Comparative Study”, International Journal of Computer Applications in Engineering, Technology and Sciences, 2011.
[15] Manjutha M and Gracy J,”Automated Speech Recognition System-A Literature Review”, International Journal of Engineering trends and applications, vol.4, 2017.
[16] Leo Breiman,”Random Forest”, springer, vol.45, 2001.
[17]Fatemeh Noroozi, Tomasz Sapiński, Dorota Kamińska, Gholamreza Anbarjafar, ”Vocal based emotion recognition using random forests and decision tree”, Springer Science,2017.
[18] Kamaldeep Kaur and Vishal Gupta,” Name Entity Recognition for Punjabi Language”, International Journal of Computer Application, vol.2, 2012.
[19]. Bhojaraj Barhate, Dipashri Sisodiya, Rakesh Deore, “ Application of Speech Recognition: For Programming Languages ”, International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, 2018.
[20].Rohit Katyal,”Analysis of SMO and BPNN Model for Speech Recognition System”, International Journal of Computer Science and Engineering, Vol.4, 2016.

Ravinder Singh, Anand Sharma, "Identification System for Different Punjabi Dialects Using Random Forest Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.254-259, 2018.

In high voltage transformers, the liquid insulations are used as the insulating medium as well as cooling medium. For the past several decades, the mineral based transformer oil is used traditionally for the purpose of liquid insulations. In the environmental aspect, there are several disadvantages in the mineral oil even though it has better insulating properties. By considering the environmental aspect and insulating properties, the researchers tend to find the alternate insulating fluids for the high voltage applications. Increasing power demand forces the development of the high-rated power transformers. In a transformer, petroleum-based mineral oil is used as insulation, currently Transformer oil produces environmental and health issues because it is non-biodegradable. Thus it has been thought that why not to use vegetable oils if found suitable. The present work investigates breakdown voltage, flash point & fire point of three different vegetable oils and result is tabulated. Results obtained from experiments are validated with benchmark results and are found to be in good agreement. The results are reported in dimensional form and presented graphically. The results provide a substantial insight in understanding the behavior of vegetable oil for high voltage applications.

Breakdown voltage(BDV); Breakdown Trials(BDT); Flash point; Fire point

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Anil Brahmin, D.D.Neema, Arpan Dwivedi, Devanand Bhonsel, "Breakdown Voltage & Flash point Comparison of Mustered Oils of Three Different Brands for High Voltage Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.260-263, 2018.

This paper proposes a new soft computing approach to artificial neural network (ANN) model identification. The new approach is based upon big bang big crunch (BB-BC) optimization algorithm .To test our approach we have identified two models one from control field namely rapid battery charger and second a rating system for institutes of higher learning. With about 20% of the total data being used for training the proposed approach was able to identify models successfully. In order to validate our proposed approach, we implemented the approach in the MATLAB and compared its training performance with 6 other well known classical training approaches namely Levenberg-Marquardt algorithm (LM), error back propagation(EBP), Resilent prop(RPROP), particle swarm optimization (PSO), ant colony optimization(ACO) and artificial bee colony(ABC). It was observed that BB-BC has faster convergence speed and produced better results than the other approaches.

Model Identification, ANN (Artificial Neural Network), Optimization

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[18] Chen, C.C. 2006. Design of PSO-based fuzzy classification systems.,Tamkang journal of science and engineering, vol. 9, no.1, 63-70.Khosla, A., Kumar, S. and Aggarwal, K. K. 2005. A framework for identification of fuzzy models through particle swarm optimization algorithm. In Proceedings of IEEE Indicon 2005 Conference, Chennai, India, 11-13 (Dec. 2005), 388-391.
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[20] Khosla, A., Kumar, S. and Aggarwal,K. K. 2005. A framework for identification of fuzzy models through particle swarm optimization algorithm. IEEE Indicon 2005 Conference, Chennai, India, Dec. 11-13. pp. 388-391.
[21] Shakti Kumar, Parvinder Bhalla, AP Singh, January 2011 “Fuzzy Rulebase Generation from Numerical Data using Big Bang-Big Crunch Optimization”, IE(I)Journal -ET, Volume 91, pp 1-8.
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[24] Shakti Kumar , S S Walia, S S Bhatti (2013) “Performance Evaluation of Institutes of Higher Learning: A hierarchical Fuzzy System Approach” IRACST – Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250-3498,Vol.3, No.4.
[25] Ashima Kalra, Shakti Kumar, Sukhbir Singh Walia, “ANN Model identification: Two Soft Computing Based Approaches”, International Journal of Research and Analytical Reviews, Vol. 4 , issue 2, June 2017, pp 79-86.
[26] Shakti Kumar, Nitika Ohri, Savita Wadhvan (2004) a,“ANN based design of rapid battery charger”, Trends Of Computational Techniques In Engineering Oct 15-16, , SLIET, Longowal Punjab pp 129-132.
[27] Erol O. K., Eksin I., 2006, A new optimization method: Big Bang-Big Crunch, Advances in Engineering Software, vol 37, 106-111.

Ashima Kalra, Shakti Kumar, Sukhbir Singh Walia, "ANN Model Identification: A BB-BC Optimization Algorithm Based Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.264-271, 2018.

This paper presents the Content Based Image Retrieval System .The Content Based Image retrieval (CBIR) is up-and-coming exploring area that deals with image retrieval using visual feature extraction, multidimensional indexing, and retrieval system design. Color, Texture and Shape information have been the primitive image descriptors in content based image retrieval systems. The goal is to retrieve the images from the database. Database contains lot of images which belongs to different categories. There are several methods to retrieve the images from large dataset, but they have some drawbacks. In this paper, techniques like clustering, association rules mining are used to mine the data. This paper also uses the fusion of multimodal features like visual and textual features. The proposed approach is simple and shows good results in term of efficiency.

Content based image retrieval, k-clustering, Association rule mining

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[10] Kanakam Siva Ram Prasad, “New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.16-21, 2017.

Priyanka Rawat, Shilpa Sethi, "Retrieval of Images Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.272-276, 2018.

Design of the commercial websites is a crucial and important factor that should be considered for improving its effectiveness, efficiency and satisfaction w.r.t services to citizens [1]. In this study the effectiveness of various design parameters (such as page size, composition, download time etc.) on commercial website will be analyzed by taking into considerations different website development standards recommended for them. The aim of this study is to analyze different commercial Website by testing their existing design with the help of design evaluation tool developed for the purpose to understand their deviations from the standards and to evaluate their performance with respect to the parameters considered by the tool. The results indicated that there is an urgent need to improve the design features of commercial websites in order to be more effective and user-centric. The authors took 20 commercial websites of India, analyze their different parameters and on the basis of analysis show their overall compliance with the standards and guidelines. With the help of the results obtained a graphical analysis of the websites is made by the authors that determine the effect of these parameters on the efficiency and accessibility of the commercial websites.

Website design, Webpage size, HTML, CSS, Website Evaluation, Website Standards, Website Guidelines, Page Loading Speed

[1] Guidelines for Indian Government Websites, National Informatics Centre (NIC), Department of Information Technology, Ministry of Communications and Information Technology, Government of India.
[2] Shanshan Qi, Crystal Ip, Rosanna Leung, Rob Law,A New Framework on Website Evaluation, International Conference on E-Business and E-Government, 2010.
[3] Policy Guidelines on Web-site Development, Hosting and Maintenance, Department of Administrative Reforms and Public Grievances, Ministry of Personnel, Public Grievances and Pension.
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[6] Grant Warren Sherson, Website Design Principles: Researching and Building a Website Evaluation tool, Victoria University of Wellington 2002.
[7] Jennifer Niederst Robbins, “Learning Web Design”, Oreilly Publishers, June 2018.
[8] Sanjay Dahiya, Ved Parkash, T.R. Mudgal, “Comprehensive Approach for Cross Compatibility Testing of Website”, International Journal of Computer Applications (IJCA), 2012.
[9] Hye-Yeon Lim, The Effect of Color in Web Page Design, University of Texas – Austin.
[10] Dave Gehrke, Efraim Turban, Determinants of Successful Website Design: Relative Importance andRecommendations for Effectiveness, 32nd Hawaii International Conference on System Sciences – 1999.
[11] Handaru Jati, Dhanapal Durai Dominic, Quality Evaluation of E-Government Website Using Web Diagnostic Tools: Asian Case, International Conference on Information Management and Engineering 2009.
[12] Seyyed Mohammad Reza Farshchi, Fariba Gharib, Reza Ziyaee, Study of Security Issues on Traditional and New Generation ofE-commerce Model, International Conference on Software and Computer Applications, IPCSIT vol.9 (2011) © (2011) IACSIT Press, Singapore, 2011.
[13] Rebecca Wilkins, Abel Nyamapfene, Usability Driven Website Design – An Equine Sports Case Study, School of Engineering, Computing and Mathematics, University of Exeter, UK
[14] Yu-Lung Wu, Yu-Hui Tao , Pei-Chi Yang, The Discussion on Influence of Website Usability towards User Acceptability, I-Shou University, Kaohsiung County, Taiwan.
[15] Thiam Kian Chiew and Siti Salwa Salim, Webuse: Website Usability Evaluation Tool, Malaysian Journal of Computer Science, Vol. 16 No. 1, June 2003
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[20] Michael O. Leavitt, Ben Shneiderman, Research-Based Web Design & Usability Guidelines,Secretary of Health and Human Services, Professor of Computer Science, University of Maryland.
[21] PurushottamPanta, Web Design, Development and Security, YOUNGSTOWN STATE UNIVERSITY, May 2009.
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Mubashir Hussain, Jatinder Manhas, "Design Evaluation of Commercial Websites of India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.277-291, 2018.

Recently, more attention is being given to detect breast cancer in women. But, Due to the lack the diagnostic to suggest whether breast cancer is presented in a person is still a research issue. The proposed work gives a hybrid methodology based on global thresholding and region merging for segmentation of breast cancer in Mammogram Images. In the proposed algorithm we use wiener filtering to remove Gaussian noise then apply image normalization based on histogram shrink to enhance the quality of image. Next, Global thresholding using Otsu’s method is used in order to segment the masses resulting Region of Interest(ROI) and then Region merging is used to extract segmented masses from image. Accuracy rate of the proposed method is 82% and Error rate is only 18%.

Breast Cancer, Gaussian Noise, Mammogram Mass, Otsu’s Method, Region Merging

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Nidhi Singh, S. Veenadhari, "Breast Cancer Segmentation Using Global Thresholding and Region Merging," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.292-297, 2018.

Automated number plate recognition is an image processing technique that uses number plate for identification and authorization of products. The paper aims to design a vehicle identification system by using template matching. The proposed system detects an authorized vehicle followed by capturing its image. The system is highly efficient and can be installed in high security zone like government offices including parliament and Supreme Court. The system is implemented in MAT lab. The experimental result of proposed system is also compared with existing pattern matching technique.

Automated Number Plate Recognition (ANPR), Template Matching

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Nancy Aggarwal, Shilpa Sethi, "Automated Number Plate Recognition Using Template Matching," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.298-304, 2018.

In the past few decades, heart diseases had been found as leading cause of death across the world. But at the same time, it is also discovered as most preventable and controllable disease. Further, it is identified that its early and timely diagnosis helps in controlling its growth and reducing treatment cost remarkably. So it become essential to discover accurate and reasonable tools capable of extracting high risk data for timely diagnosis of such a critical disease The proposed work aims to identify vital parameters leading to heart diseases and develop a model based on data mining techniques.

Data mining, Heart disease Prediction, Naive Bayes, Technique

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Ranjana Joshi, Shilpa Sethi, "Prediction of Heart Disease Using Data Mining Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.305-309, 2018.

The main objective of this research paper is to present an analysis of different types of data mining techniques for the detection of glaucoma. It is one of the serious eye diseases. The Glaucoma affects the optic nerve in retina. In which the retinal ganglion cells are in dead condition and this leads to permanent loss of vision. So the early detection of glaucoma is needed to prevent the patients from diseases. The Manual analysis of retinal images is fairly time-consuming and accuracy depends on the expertise of the professionals. By the proposed Medical diagnosis system mass screening is possible to help the doctor for take proper treatment.

SVM classifier, glaucoma, K-means, PCA, Fundus images

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G.Pary, M. Aramudhan, N.Thirumoorthy, "Medical Diagnosis System for Glaucoma Diseases Detection Based On Retinal Images Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.310-314, 2018.