Volume-6 , Issue-7 , Jul - 2018 Go Back

In this technology driven world, we get an opportunity to share our views regarding different products by providing our valuable reviews. Through these reviews we a get a chance to extend our help in developing a better society by promoting genuine products into the market there by eliminating many false predictions. Through different review websites we get a chance to implore our ideas on different products. But we get information overloading problem. How to mine valuable information and provide the users with accurate data is a hectic task. Traditional Recommender system uses several factors such as user’s purchase record, product reputation and so on. But the main problem in this system is the rating is generated on whole. There is a chance of considering wrong reviews also. In order to avoid these problems and provide the users with desired information, a new system was developed which is user friendly to the users where the rating is generated from the textual reviews provided by the users individually. When the rating is generated the user can share that whole data to his/her Facebook timeline so that the genuine products can be brought into limelight. By this project an attempt is made to build a better society by promoting genuine products into the market

Recommender System, rating, Facebook, Genuine Products

[1]. JAVA Technologies
[2]. Java Script Programming by Yehuda Shiran
[3]. HTML and CSS by John Buckett
[4]. J2EE Professional by Shadab siddiqui
[5]. JAVA server pages by Larne Pekowsley
[6]. Php,mysql by O’ Reilly
[7]. HTML
[8]. HTML Black Book by Holzner
[9]. P5.js
[10]. AFINN-111 sentiment dictionary

T.Bhargavi, J.Niranjani, "Promoting Genuine Products Through Textual Review Rating in Collaboration With Social Networking," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1526-1530, 2018.

Breast cancer has a reason for the leading cause of death in women in various countries. The popular effective way to decrease breast cancer deaths is to detect it as earlier as possible. The classification of breast cancer data can be useful to predict the outcome of some disease or discover the genetic behavior of tumors. An early diagnosis method requires a more accurate and user reliable diagnosis techniques those are allow physicians to distinguish benign breast tumors from malignant ones without going for surgical biopsy. The objective of this paper is to find the classification of breast cancer as either benign or malignant .Then relative study on different cancer classification approaches viz, KNN, Decision tree and Neural Network classifiers are conducted where the accuracy of each of the classifier is also measured.

Classification techniques; Data mining techniques; breast cancer; Diagnosis; Prognosis

[1]. A Study on Prediction of Breast Cancer Recurrence Using Data Mining Techniques. Uma Ojha Computer Science Department ARSD College, Delhi University Delhi-India and Dr. Savita Goel Sr.System Programmer IIT Delhi. 7th International Conference on Cloud Computing, Data Science & Engineering– Confluence. IEEE 2017.
[2]. Breast cancer prediction using data mining techniques. Padma Priya1, P.Sowmiya2. Assistant Professor & Head Department of Information Technology Sri Adi Chunchanagiri Women’s College, Cumbum,(India Research Scholar, Department of Computer Science, Sri Adi Chunchanagiri Women’s College, Cumbum,(India) .5th-6th janurary-2018
[3]. Performance Analysis Of Data Mining Algorithms For Breast Cancer Cell Detection Using Naïve Bayes, Logistic Regression and Decision Tree Subrata Kumar Mandal Information Technology Department, Jalpaiguri Government Engineering College Jalpaiguri, West Bengal, India, International Journal Of Engineering And Computer Science . Volume 6 Issue 2 Feb., 2017
[4]. Intelligent Breast Cancer Prediction Model Using Data Mining Techniques Runjie Shen, Yuanyuan Yang, Fengfeng Shao, Department of Control Science & Engineering Tongji University Shanghai, China. 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.2014 IEEE
[5]. A comparative survey on data mining techniques for breast cancer diagnosis and prediction Hamid Karim Khani Zand Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. Indian Journal of Fundamental and Applied Life Sciences , Volume.5 2015
[6]. A Survey on Breast Cancer Analysis Using Data Mining Techniques B.Padmapriya, T.Velmurugan 2014 IEEE International Conference on Computational Intelligence and Computing Research.
[7]. A Study on Prediction of Breast Cancer Recurrence Using Data Mining Techniques. Uma Ojha Computer Science Department ARSD College, Delhi University Delhi-India and Dr. Savita Goel Sr.System Programmer IIT Delhi. 7th International Conference on Cloud Computing, Data Science & Engineering– Confluence. IEEE 2017
[8]. Data Mining Techniques in Multiple Cancer Prediction Dr. A. R. PonPeriasamy Associate Professor of Computer Science Nehru Memorial College Puthanampatti, Trichy (DT) Tamilnadu, India K. Arutchelvan Assistant Professor / Programmer Department of Pharmacy AnnamalaiUniversity,ChidamparamTamilnadu, India ,International Journal of Advanced Research in Computer Science and Software Engineering. Volume 7, Issue 5 May 2017
[9]. Breast Cancer Prediction using Data Mining Techniques Jyotsna Nakte Student, Dept. of Information Technology MCT Rajiv Gandhi Institute of Technology Mumbai, India, VarunHimmatramka Student, Dept. of Computer Engineering MCT Rajiv Gandhi Institute of Technology Mumbai, India, International Journal on Recent and Innovation Trends in Computing and Communication. Volume: 4 Issue: 11 Nov-2016.
[10]. C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning Rutvija Pandya Diploma Computer Engineering Department, Gujarat Technological University Atmiya Institute of Tech & Sci Rajkot Jayati Pandya Bachelor in Computer science and Application,Saurashtra University K.P.Dholakiya InfoTech Amreli International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 16, May 2015
[11]. Performance Analysis of Data Mining Classification Techniques on Public Health Care Data Tanvi Sharma1, Anand Sharma2, Prof. Vibhakar Mansotra M. Tech Research Student, Dept. of Computer Science & I.T., University of Jammu, Jammu, J&K, India Research Scholar, Dept. of Computer Science & I.T., University of Jammu, Jammu, J&K, India Professor, Dept. of Computer Sci of Computer Science & I.T., University of Jammu, Jammu, J&K, India IJIRCE Volume: 3 Issue: 10 June 2016.
[12]. C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning. Rutvija Pandya Diploma Computer Engineering Department, Gujarat Technological University Atmiya Institute of Tech & Sci Rajkot and Jayati Pandya Bachelor in Computer science and Application,Saurashtra University K.P.Dholakiya InfoTech Amreli .IJAC Volume 117 – No. 16 May 2015 .
[13]. An Overview on Data Mining Approach on Breast Cancer data. Shiv Shakti Shrivastava1, Anjali Sant, Ramesh Prasad Aharwal, International Journal of Advanced Computer Research, Volume-3 Number-4 Issue-13 Dec-2013
[14]. American Cancer Society. Breast Cancer Facts & Figures 2005-2006. Atlanta: American Cancer Society, Inc. ( http://www.cancer.org/).

Disha Patel, Bhavesh Tanwala, Pranay Patel, "Breast Cancer Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1531-1536, 2018.

The data mining techniques is a major significant position in the field of healthcare and medical industry to analyze the medical data and finding the patterns from those data. The primary goal of the research analysis work is to predict the patient diseases from the medical data sets. Medical practitioners is getting difficult to predict the disease, actually it is one of the complex task which require their experience and knowledge. The main objective of data mining techniques to predict the possible disease from patient dataset and based on patient serious condition priority wise to reduce the congestion in the network. In this paper proposed the genetic approach is efficient for associative classification algorithm to predict the disease. The motivation is by using genetic algorithm in the discovery of high level prediction rules which can be highly comprehensible having high predictive accuracy and high interestingness values.

Data Mining, Association Rule, Keyword Based Clustering, Genetic algorithm, Classification

[1] S. Vijayarani* and S. Sudha “An Efficient Clustering Algorithm for Predicting Diseases from Hemogram Blood Test Samples” Indian Journal of Science and Technology, Vol 8(17), August 2015.
[2] Aswathy Wilson, Gloria Wilson, Likhiya Joy K “Heart disease prediction using data mining techniques”
[3] G. Purusothaman* and P. Krishnakumari “A Survey of Data Mining Techniques on Risk Prediction: Heart Disease” Indian Journal of Science and Technology, Vol 8(12), DOI: 10.17485/ijst/2015/v8i12/58385, June 2015.
[4] Jyoti Soni Ujma Ansari Dipesh Sharma “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction” international Journal of Computer Applications, Volume 17– No.8, March 2011
[5] Hnin Wint Khaing, “Data Mining based Fragmentation and Prediction of Medical Data”, International Conference on Computer Research and Development, ISBN: 978-1-61284-840-2,2011
[6]Himigiri. Danapana, M. Sumender Roy,‖ Effective Data Mining Association Rules for Heart Disease Prediction System‖ IJCST Vol. 2, Issue 4, Oct . - Dec. 2011.
[7]Fariba Shadabi and Dharmendra Sharma,‖ Artificial Intelligence and Data Mining Techniques in Medicine – Success Stories‖ International Conference on BioMedical Engineering and Informatics- 2008.
[8] J. Liu, Y.-T. HSU, and C.-L. Hung, “Development of Evolutionary Data Mining Algorithms and their Applications to Cardiac Disease Diagnosis,” in WCCI 2012 IEEE World Congress on Computational Intelligence, 2012, pp. 10–15.
[9] P. Chandra, M. . Jabbar, and B. . Deekshatulu, “Prediction of Risk Score for Heart Disease using Associative Classification and Hybrid Feature Subset Selection,” in 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012, pp. 628– 634.
[10] S. U. Amin, K. Agarwal, and R. Beg, “Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors,” in Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), 2013, no. Ict, pp. 1227– 1231.
[11]Zhao, Q., Rezaei, M., Chen, H., Franti, and P.: Keyword clustering for automatic categorization. Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, (2012).
[12]Michael Pucher, F. T. W.: Performance Evaluation of WordNet-based Semantic Relatedness Measures for Word Prediction in Conversational Speech. (2004).
[13] K. Sudhakar, “Study of Heart Disease Prediction using Data Mining,” vol. 4, no. 1, pp. 1157–1160, 2014.
[14] R. Chitra and V. Seenivasagam, “REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES,” Journal on Soft Computing (ICTACT), vol. 3, no. 4, pp. 605–609, 2013.
[15]. Shanta kumar, B.Patil,Y.S.Kumaraswamy, “Predictive data mining for medical diagnosis of heart disease prediction” IJCSE Vol .17, 2011
[16]. M. Anbarasi et. al. “Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm”, International Journal of Engineering Science and Technology Vol. 2(10), 5370-5376 ,2010.
[17]. Hnin Wint Khaing, “Data Mining based Fragmentation and Prediction of Medical Data”, IEEE, 2011.
[18] MA.Jabbar, Priti Chandra, B.L.Deekshatulu..:Cluster based association rule mining for heart attack prediction,JATIT,vol 32,no2,(Oct 2011)
[19] Ping Ning tan, Steinbach, vipin Kumar. : Introduction to Data Mining, Pearson Education, (2006).
[20] Picek, S., Golub, M.: On the Efficiency of Crossover Operators in Genetic Algorithms with Binary Representation. In: Proceedings of the 11th WSEAS International Conference on Neural Networks (2010)
[21] P.S.Mishra “Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction”, International Journal of Computer Science and Engineering Vol. 6(6), 2347-2693, 2018.
[22] K. Sivaranjani, A. Nisha Jebaseeli “Survey on Disease Diagnostic using Data Mining Techniques”, International Journal of Computer Science and Engineering Vol. 6(2), 2347-2693 ,2018.

J. Adamkani, M. Wasim Raja, "Genetic Algorithm Based Approach For Predict Disease and Avoid Congestion in Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1537-1543, 2018.

Scour depth at abutment is a major cause of bridge failure and significant issue towards maintenance cost of a bridge. Thus, early estimation of scour depth at abutment is essential for safe and cost-effective abutment structure design. Extensive research has been carried out to develop methods for predicting the depth of abutment scour. Despite various models presented by researchers to estimate the equilibrium local scour depth, an efficient technique with enhanced estimation capability will be more beneficial. The paper is aimed at investigating the applicability of soft computing (SC) models viz. artificial neural network, gene-expression programming (GEP) and hybrid techniques for estimation of scour depth around vertical, semi-circular and 45° wing-wall abutments using laboratory data compiled from published literature. The paper also emphasizes on further enhancement of the performances of the SC based models. On experimentations, the performance of multilayer perceptron (MLP) neural network for each type of abutment was found more effective than radial basis function network, GEP model and empirical equations. The generalization performance of optimal MLP network developed for each type of abutment was then improved with evolving connection weights of the MLP by Genetic Algorithm (GA-MLP). Finally, the hybrid model is validated with different types of validation techniques. The study demonstrates the suitability of the SC based hybrid methodology in improving the predictive accuracy of scour depth around different types of abutments.

Scour depth, artificial neural network, genetic algorithm, hybrid technique, GEP

[1] A. K. Barbhuya, S. Dey, “Local scour at abutments: a review”, Sadhana, Vol. 29, Issue 5, pp. 449–476, 2004.
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[10] T. Gamal, M. Hosny, T. Al_Samman, N. Aboul_Atta, “Artificial Neural Network Prediction Of Maximum Scour Around Brige Piers Due To Aquatic Weeds’ Racks”, International Journal of Engineering, Research & Technology, Vol. 2, Issue 6, pp. 2322-2336, 2013.
[11] S. A. Begum, A. K. Md. Fujail, A. K. Barbhuiya, “Radial Basis Function to predict scour depth around bridge abutment”, IEEE Proceedings of the 2011 2nd National Conference on Emerging Trends and Applications in Computer Science, (Shillong, Meghalaya, India), ISBN- 978-1-4244-9581-8, pp. 76-82, 2011.
[12] S. A. Begum, A. K. Md. Fujail, A. K. Barbhuiya, “Artificial Neural Network to Predict Equilibrium Local Scour Depth around Semicircular Bridge Abutments”, 6thSASTech, Malaysia, Kuala Lumpur, 2012.
[13] H. M. Azamathulla, A. Ab. Ghani, “ANFIS-Based Approach for Predicting the Scour Depth at Culvert Outlets”, Journal of Pipeline Systems Engineering and Practice, ASCE, Vol. 2 Issue 1, pp. 35-40, 2011.
[14] M. Muzzammil, J. Alam, “ANFIS-based approach to scour depth prediction at abutments in armored beds”, Journal of Hydroinformatics, IWA Publishing, Vol. 13, Issue 4, pp. 699-713, 2011.
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[16] R. Mohammadpour, A. Ab. Ghani, H. M. Azamathulla, “Estimating time to equilibrium scour at long abutment by using genetic programming”, 3rd International conference on managing rivers in the 21th century: Sustainable Solutions for Global Crisis of Flooding, Pollution and Water Scarcity, Rivers, Penang, Malaysia, pp. 369-374, 2011.
[17] R. Mohammadpour,, A. Ab. Ghani, H. M. Azamathulla, “Prediction of equilibrium scour time around long abutments”, Water Management, pp. 1-8, 2012.
[18] M. Khan, H. M. Azamathulla, M. Tufail, A. Ab. Ghani, “Bridge pier scour prediction by gene expression programming”, Water Management, Vol. 165, Issue 9, pp. 481-493, 2012.
[19] H. M. Azamathulla, M. Azlan, M. Yusoff, “Soft computing for prediction of river pipeline scour depth”, Neural Comput & Applic, Springer-Verlag London, pp. 1-5, 2012.
[20] R. Mohammadpour, A. Ab. Ghani, H. M. Azamathulla, “Estimation of dimension and time variation of local scour at short abutment”, Intl. J. River Basin Management, Vol. 11, Issue 1, pp. 121–135, 2013.
[21] S. E. Coleman, C. S. Lauchlan, B. W. Melville, “Clear-water scour development at bridge abutments”, Journal of Hydraulic Research, Vol. 41, Issue 5, pp. 521–531, 2003.
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[23] M. H. Mazumder, Live-bed scour at abutment, PhD thesis, NIT, Silchar, 2015,.
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A.K. Md. Fujail, S.A. Begum, A.K. Barbhuiya, "Genetic Algorithm-Based Neural Network for Estimation of Scour Depth Around Bridge Abutment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1544-1554, 2018.

In this paper, we propose a microstrip high pass filter using optimum distributed technique for GSM Applications. Here we designed 5th order High Pass Filter at 1.8 GHz and it is implement on FR4 substrate of relative permittivity 4.3, loss tangent 0.02 with a thickness of 1.6mm.The performance of HPF is improved by using Defected Ground Structure which is rectangular in shape. Two slots at Left hand side and two slots at right hand are of same size and symmetric in nature along with the middle slot of comparatively big size are chosen in parallel . All the dimensions of Microstrip HPF is calculated with the help of optimum distributed approach at 1.8 GHz resonant frequency. Emerging applications such as wireless communications continue to challenge RF/microwave filters with ever more stringent requirements like higher performance, smaller size, lighter weight, and lower cost. Results are simulated using computer simulation technology software (CST).

High Pass Filter, Microstrip Filter, Optimum Distributed Filter, Chebyshev Filter, Quasilumped Elements Filter

[1] R. Levy, R. V. Snyder, and G. Matthaei, “Design of microwave filters,” IEEE Trans. Microw. Theory Tech., vol. 50, PP. 783-793,March 2002.
[2] Pozar, David M. “Microwave Engineering” 2nd Edition, USA: John Wiley &Sons/ D. M. Pozar, “Microwave engineering newyork,” John Wile Yand Sons,Third Edition.
[3] Jia-Sheng Hong, M. J. Lancaste “Microstrip filters for RF / Microwave Application” A Wiley-IntersciencePublivation book.
[4] Weng, L. H., Y. C. Gue, X. W. Shi, and X. Q. Chen, “An overview on defected ground structure,” Progress In Electromagnetics Research B, Vol. 7, 173–189, 2008.
[5] A. Boutejdar, A. Omar, “Compensating For DGS Filter Loss”, Microwave & RF Journal [Design Features], pp. 68-76, febuary 2012.
[6] J. Helszajn, Synthesis of Lumped Element, Distributed and Planar Filters, McGraw- Hill, London, 1990.
[7] S. Darlington, “Synthesis of reactance-four-poles which produce prescribed insertion loss characteristics,” J. Math. Phys., 30, 257–353, Sept. 1939.
[8] R. Saal and E. Ulbrich, “On the design of filters by synthesis,” IRE Trans., CT-5,284–327, Dec. 1958.
[9] G. Mattaei, L. Young, and E. M. T. Jones, Microwave Filters, Impedance-Matching Networks, and Coupling Structures, Artech House, Norwood, MA, 1980.
[10] R. Levy, “A new class of distributed prototype filters with applications to mixed lumped/distributed component design,” IEEE Trans., MTT-18, 1064 – 1071, December 1970.
[11] CST (computer Simulation Technology) microwave software studio 2010.
[12] Atul Makrariya, P. K. Khare, “Five pole optimum distributed high pass microwave filter: Design, Analysis and Simulation on Microstrip at 2.4 GHZ” International Journal of Engineering Sciences & Research Technology, Vol. 5, Issue 8, pp. 934-938,2016

Neha Mittal, Mahendra Kumar Pandey, "Design of Microstrip High Pass Filter using Optimum Distributed Technique for GSM Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1555-1558, 2018.

In Indian agriculture, farming activities like sowing, harvest, and irrigation all these activities must be performed in appropriate manner and most significantly at appropriate time in order to get good yield. However, sadly most of the farmers unknown concerning the impact of these activities on yield of their crop. Thus, a mobile based application is developed to teach farmers concerning it. The developed mobile application is essentially for dissemination of data to farmers concerning the right approach of playing varied farming activities in regional language Hindi, of two main crops of Haryana- Wheat, and Paddy. The system is an android based application developed using android studio which is the official Integrated Development Environment (IDE) for developing an Android application.

Android, Scheduling, Farming Activities, Wheat, Paddy, Hindi

[1]https://www.researchgate.net/publication. ROLE_OF_INFORMATION_AND_COMMUNICATION_TECHNOLOGY_IN_AGRICULTURE_DEVELOPMENT_A_STUDY_OF_NABARANGPURDISTRICT
[2] TheDeSilva H and Ratnadiwakara D (2005) Using ICT to Reduce Transaction Costs in Agriculture through Better Communication: A Case-study from Sri Lanka. Colombo.
[3]https://statista.com/statistics/271496/global-market-share-held-by-smartphone-vendors-since-4th-quarter-2009/.pp[3].
[4] Singh S K (2008) The diffusion of mobile phones in India. Telecommunications Policy.
[5] Mittal S and Tripathi G (2009) Role of mobile phone technology in improving small farm productivity. Agr Econ Res Rev.
[6]Sahota C (2009) Use of Mobile Phones in Agricultural Extension: A Study in Uttarakhand, Unpublished M.Sc. Thesis,Department of Agricultural Communication, Govind
[7]"Masuki K F G, Kamugisha R, Mowo JG, Tanui J, TukahirvJ,(2010):- "Role of mobile phones in improving communication and information delivery for agricultural development chapter from South Western Uganda." Workshop at Makerere University Makerere University, Uganda.
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[9] Qiang C Z and Kuek S C and Dymond A, Esselaar S and Unit, ICT Sector (2011) Mobile applications for agriculture and rural development. World Bank, Washington, DC
[10] Ramaraju G.V., Anurag, T.S. Singh, H. K. and Kumar S. (2011) ICT in agriculture: Gaps and way forward. Info Tech in Dev Countries
[11] Gichamba A and Lukandu I A (2012) A model for designing M-agriculture applications for dairy Intl J Adv Res Computer Sci Software Eng
[12] "Lomotey R K, Jamal S, Chai Y and Deters R (2013) MobiCrop:-Supporting Crop Farmers with a Cloud-Enabled Mobile App". Proc 6th Service-Oriented Computing and Applications,1..
[13]Ghogare S A and Monga P M (2015) ―E-Agriculture‖ Introduction and Figuration of its Application. Afr J Info Sys.
[14] Mohan J (2015),Importance of mobile in dissemination of Agriculture Information among Indian Farmers . Intl J Emerging Techno in Computational App Sci.
[15] Ghanshyam K, Pooja K, Pooja N and Yogita G (2016)AGRONOMY-An Android Application Regarding Farmer Utility. J Emerging Tech Innov Res.

Mukhesh Bhyana, Sakshi Dhingra, "Krishi Suchna: Review Paper On Mobile Based Android Application On Agriculture Scheduling System For Farmers in Regional Language (Hindi) Using Weather Conditions," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1559-1564, 2018.

Ultrasound is the first priority in kidney image processing. Many medical experts use this for initial screening of kidneys’ condition as it does not require any inflammation of body parts or instruments to be inserted into the body. High frequency sound waves are applied to produce recognized images. Ultrasound can be used to calculate the size and appearance of the kidneys, stones present in them, detect congenital abnormalities, swelling and blockage of urine flow. The completely automated and systematic algorithm is given to calibrate the kidney stones by proper analysis. The main theme of the detection is to find renal stones, mark the renal regions and to measure the space occupied by kidney stones. Sometimes the user usually finds difficulty in knowing the boundary of the kidney in the US image even though done by an expertise sonographer. In addition to this human error might also occur during acquisition of ultrasound image by untrained sonographer. So to reduce this distortion and noise, image processing techniques can be used. These techniques also detect the area of the human kidney and stones. US imaging can also be used to scan soft tissues and classify them accordingly find to possible diseases. This paper focuses on the literature review of classification of kidney images using ultrasound.

Medical Ultrasound, Medical Sonography, Morphological-Image Processing, Image Texture Analysis

[1]. R.A Raja , J.J. Ranjani, “Segment Based Detection and Quantification of Kidney Stones and Its Properties Based on Logical Operator with Ultrasound Scanning”, International Journal of Computer Application, pp. 8-15, 2013.
[2]. PR.Tamilselvi, P.Thangaraj, “Segmentation of Calculi from Ultra Sound Kidney Images by Region Indicates Contour Segmentation Method”, Global Journal of Computer Science and Technology, volume 11(Issue 22) version 1.0, pp.43-51, Dec 2011.
[3]. Akkasaligar, P.T. and Biradar S., “Analysis of polycystic kidney disease in medical ultrasound images”, Int. J. Medical Engineering and Informatics, volume 10, No. 1, pp.49–64, Jan 2018.
[4]. A.H. Ali, E.H. Hasan, M.R. Naeemah, “Kidney Texture Classification Using Local Binary Pattern and Material Features”, IOSR Journal of Research and Method in Education, ISSN-2320-7388.P-ISSN: 2320-7372, volume 6(Issue 3), pp.57-60 May-June 2016.
[5]. M. Koklu, K.Tutunlu, R.Morcco, “Classification of Chronic Kidney Disease with Must Know Data Mining Methods”, 45th International Journal, ISBN-978-93-86291-8, pp.21-25, 8th -9th Dec 2016.
[6]. H. Kordylewski , D. Graupe, “A Novel Large-Memory Neural Network of an Aid in Medical Diagnosis Application”, IEEE Transaction On Information Technology in Bio Medical , volume 5, no. 3, pp. 202-209, Sep 2001.
[7]. G. Kalanthaives, and G. Ravindran, “Web Based Diagnostic Classifier System for Kidney Lesion using Image Texture Parameter”, IEEE Indian Conference, Chennai, India, pp.220-223, 13 Dec 2005.
[8]. K.viswanth, R.Gunasundari, “Design and Analysis Performance of Kidney Stone Detection from Ultrasound Image by Level Set Segmentation and ANN Classification”, International Conference on Advances in Computing Communication and Informalities ISBN-978-1-4799-3080-7, pp.407-414, 2014.
[9]. K.B. Raja, M.Madheswaran and K.Thyagarajah, “Analysis of kidney Images using Content Descriptive Multiple Features for Disorder Identification and ANN Based Classification”, International Conference Computing IEEE, ISBN-0-7695-2770-1, 2017.
[10]. V.Velmurgen and P.Gnanasivam, “A Segmentation Approach of the Ultrasound Image to Detect Renal Calculi”, International Journal of Latest Trends in Engineering and Technology, volume 9 (Issue1), e-ISSN-2278-621X, pp.001-005, Sep 2017.
[11]. E. S. Samundeeswari, P. K. Saranya, “A Study on Multi scale Approach for Despeckling Ultrasound Image”, International Journal of Computer Application, ISSN-(0975-8887) pp. 17-23, 2015.
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Prema T. Akkasaligar, Sunanda Biradar, Sharan Badiger, Rohini Pujari, "Review on Classifications of Medical Ultrasound Images of Kidney," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1565-1568, 2018.

This paper exhibits an audit of the different examination made dissimilar by researchers, representatives, scientists concerning of ICT Tools on country social orders of India; significance and part of ICT in Rural Advancement; Rural Community improvement and subsequent to knowing the perceptions made by different analyst, examiner and specialists finished up that ICTs assume a noteworthy part in naturally economical country advancement; country network improvement. ICTs have remarkable commitment towards change of financial and social improvement of social orders in rustic India. In creating nation like India, to make data rich social orders, to engage needy individuals, to diminish computerized isolate, practical advancement of rustic network`s dispersal of ICT in grassroots level of country towns is vital.

ICT, Gram Panchayats, Grassroot, IT, Networks, Broadband

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Suresh Kumar, Vijay Athavale, "A Literature Review on Influence of ICT Infrastructure, Use & Education on Rural Community of India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1569-1573, 2018.