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Senthil, S.
- Data Mining on Classifiers Prophecy of Breast Cancer Tissues
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1 School of CSA, REVA University, IN
1 School of CSA, REVA University, IN
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International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 8-12Abstract
The expression "breast cancer" includes to a harmful tumour that has created from cells in the breast. Disease happens because of transformations, or anomalous changes, in the qualities in charge of controlling the development of cells and keeping them solid. The qualities are in every cell's core, which goes about as the "control room" of every cell inside the body.The utilization of machine learning and data mining techniques strategies have transformed the entire procedure of breast cancer growth.
There are a few order calculations like-Naive Bayes, K-Star, Multiclass, Decision Table, Hoeffding Tree. Highlight Selection is the path towards picking a subset of noteworthy highlights (factors, markers) for use in presentation advancement and the part assurance computation. The results show that part decision can improve the precision of classifiers.
Keywords
Breast Cancer, Classifiers, Naïve Bayes, K-Star, Hoeffding Tree, Hybrid classifier.Full Text
References
- https://training.seer.cancer.gov/breast/intro/
- https://www.hindustantimes.com/health-and-fitness/world-cancer-day-india-begins-free-screening-for-oral-breast-and-cervical-cancers/story-1HhWe2qPCpRftX2kgjL5vK.html
- https://www.indiablooms.com/health-details/H/4261/india-urged-to-bridge-gap-between-evidence-and-policy-in-tackling-women-rsquo-s-cancers.html
- Ahmed Hamza Osman, “AN ANHANCED BREAST CANCERDIAGNOSIS SCHEME BASED ON TWO-STEPSVN TECHNIQUE”,IJACSA, International Journal of Advanced Computer Science andApplications, Vol. 8, in 2017.
- Prof Tejal Upadhyay and Arpita Sha, “SURVEY ON THE BREASTCANCER ANALYSIS USING MACHINE LEARNINGTECHNIQES”, IJARSE) Volume No.06, Issue No.06, June 2017 ISSN, 2319-8354.
- Breast Cancer Prediction Using Data Mining Method by Haifeng Wang and Sang Won Yoon, Department of Systems Science and Industrial Engineering State University of New York at Binghamton , Binghamton, NY 13902
- Amith Bhola and Arvindh Kumar Tiwari, “MACHINE LEARNINGBASED APPROACHES FOR CANCER CLASSIFICATION USING GENE EXPRESSION DATA”, Machine learning and applications: AnInternational Journal, 2(4/4), 01-12. MLAIJ in 2015.
- Kharya, D. Dubey and Soni,”PRIDICTIVE MACHINE LEARNINGTECHNIQES FOR DETECTING BREAST CANCER”, InternationalJournal of Computer science andInformationTechnologies,Vol,4(6), 2013, 1023-1028.
- Salama G I, Abdelhalim M and Zeid M A E 2012 Breast Cancer (WDBC)
- Han J. and Kamber M., Data Mining: Concepts and Techniques, 2nd ed., San Francisco, Morgan Kauffmann Publishers, 2001
- WBChttps://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+ (Original)
- WEKA Explorer software tool
- Somasundaram, Gayathri Devi. "Breast Cancer Prediction System using Feature Selection and Data Mining Methods." International Journal of Advanced Research in Computer Science 2, no. 1 (2011).
- Block chain Technology for IoT Security and Privacy:“The discourse Analysis of a Sensible Home”
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1 School of CSA, REVA University, Bangalore, IN
2 School of Computer Science & Applications, REVA University, Bangalore, IN
1 School of CSA, REVA University, Bangalore, IN
2 School of Computer Science & Applications, REVA University, Bangalore, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 13-17Abstract
Internet of Things (IoT) security and protection re-principle a remarkable check, primarily as a result of the big scale and disseminated nature of IoT systems. Blockchain-based methodologies provide decentralised security and protection, nevertheless they embrace crucial vitality, delay, and procedure overhead that won't affordable for many quality duty-bound IoT gadgets. In our past work, we have a tendency to exhibited a light-weight mental representation of a blockchain particularly designed to be used in IoT by confiscating the Proof of labor (POW) and therefore the plan of coins. Our methodology was exemplified in an exceedingly sensible home setting and contains of 3 elementary levels to be specific: distributed storage, overlay, and keen home. during this paper we have a tendency to dig more and layout the various center components and components of the savvy home level. every savvy house is equipped a perpetually on the net, high quality contrivance, called "mineworker" that's answerable of taking care of all correspondence within and out of doors to the house. The excavator to boot protects a non-public and secure blockchain, used for dominant and reviewing correspondences. we have a tendency to exhibit that our planned BC-based splendid home structure is secure by through and thru gazing its security with relation to the basic security goals of mystery, trait, and availableness. Finally, we have a tendency to gift reenactment results to focus on that the overheads (with relation to traffic, handling time and essentiality usage) displayed by our framework square measure moot in relevancy its security and assurance gains.Full Text
References
- Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, “Security, privacy and trust in web of things: The road ahead,” pc Networks, vol. 76, pp. 146–164, 2015.
- Roman, J. Zhou, and J. Lopez, “On the options and challenges of security and privacy in distributed web of things,” pc Networks, vol. 57, no. 10, pp. 2266–2279, 2013.
- Chakravorty, T. Wlodarczyk, and C. Rong, “Privacy conserving information analytics for good homes,” in Security and Privacy Workshops (SPW), 2013 IEEE. IEEE, 2013, pp. 23–27.
- Nakamoto, “Bitcoin: A peer-to-peer electronic money system,” 2008.
- King, “Primecoin: Cryptocurrency with prime proof-of-work,” July 7th, 2013.
- Dorri, S. S. Kanhere, and R. Jurdak, “Blockchain in web of things: Challenges and solutions,” arXiv preprint arXiv:1608.05187, 2016.
- Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder, Bitcoin and cryptocurrency technologies. university Pres, 2016.
- Bogdanov, M. Knezˇevic´, G. Leander, D. Toz, K. Varıcı, and I. Ver- bauwhede, spongent: a light-weight Hash operate. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 312–325. F.-S. sense, https://sense.f-secure.com/, [Online; accessed 19-November- 2016].
- Delfs, H. Knebl, and H. Knebl, Introduction to cryptography. Springer, 2002, vol. 2.
- Komninos, E. Philippou, and A. Pitsillides, “Survey in good grid and good home security: problems, challenges and countermeasures,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1933–1954, 2014.
- wired,https://www.wired.com/2016/10/internet-outage-ddos-dns-dyn/, [Online; accessed 10-December-2016].
- Cooja,http://anrg.usc.edu/contiki/index.php/CoojaSimulator/, [Online; accessed 19-November-2016].
- Notra, M. Siddiqi, H. H. Gharakheili, V. Sivaraman, and R. Boreli,
- Classification Algorithm in Data Mining
Abstract Views :217 |
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Authors
Affiliations
1 School of Computer Science and Applications, REVA University,Bangalore, IN
2 School of Computer Science and Applications, REVA University, Bangalore, IN
1 School of Computer Science and Applications, REVA University,Bangalore, IN
2 School of Computer Science and Applications, REVA University, Bangalore, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 18-21Abstract
Every day, Data generated from business, society, science and engineering, medicine and almost any other aspect of life. By using a large amount of data, we can identify the hidden knowledge by using the data mining process. Data mining consists of anomaly detection, association rule learning, clustering, classification, regression, and summarization. Classification is the main method in data mining and generally used in different field. Classification is a machine learning method used to predict group of data [1]. The main aim of this paper is to study the different classification algorithms in data mining. Classification algorithms are C4.5, ID3, k-nearest neighbor, Naïve Bayes, Support Vector Machine, and Artificial Neural Network. Normally a classification techniques, consists of three approaches Statistical procedure-based learning, Machine Learning and Neural Network.Keywords
Data Mining, C4.5, ID3, ANN, SVM,, K-Nearest Neighbor, Limitation and Features of the Classification Algorithm.Full Text
References
- . Kesavaraj.G and Sukumaran.S,” A study on classification techniques in data mining”, IEEE-31661.
- S. Nikam,” A Comparative Study of Classification Techniques in Data Mining Algorithms”, ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY, Techno Research Publishers, ISSN: 0974-6471, April 2015, Vol. 8, No. (1)
- D. Michie, D.J. Spiegelhalter, C.C. Taylor “Machine Learning, Neural and Statistical Classification”, February 17, (1994).
- Sudhir M. Garade, Ankit Deo, and Preetesh Purohit, "A Study of Some Data Mining Classification Techniques”, International Research Journal of Engineering and Technology (IRJET) e- ISSN: 2395-0056 p-ISSN: 2395-0072 Volume: 04 Issue: 04 | Apr- 2017.
- Srinivasan.B and Pavya.K,” A Comparative Study on Classification Algorithms in Data Mining”, IJISET International Journal of Innovative Science, Engineering & Technology, Vol. 3 Issue 3, March 2016, ISSN 2348 – 7968.
- Suguna.N, and Thanushkodi.K,” An Improved kNearest Neighbor Classification Using Genetic Algorithm”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 2, July 2010.
- Kshitiz Sirohi,” K-nearest Neighbors Algorithm, "Knearest Neighbors Algorithm”, https://towardsdatascience.com.
- LourduCaroline.A, Manikandan.S and Kanniamma.D, “Comparative study of Classification algorithms for Data Mining”, International Journal of Engineering Science Invention (IJESI) ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726.
- Prevalence of Mammography Images for Primal Prediction of Breast Cancer
Abstract Views :209 |
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Affiliations
1 School of CSA, REVA University, IN
1 School of CSA, REVA University, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 31-33Abstract
Breast cancer is the second most common tumour in the world and more common in the women population, not only a women disease its affecting men also, and since the main ischolar_main cause remain unsure, early observation and diagnosis is the best solution to prevent tumour development and allow a successful medical involvement, it’s a lifesavingand the cost reduction. In the absence of symptomsMammography is an x-ray of the breasts performed.Very tiny tumours are identified even before they are real or they are apparent to other symptoms. Mammography is presently the suggested procedure for early identification of Breast Cancer in women in the current scenario it’s an instant require for better pre-screening tool to detect the irregularity of the mammogram resemblance in the early-stage only. The main purpose of this paper is to give a summary current approach in the evolution of breast cancer diagnosis.Keywords
Breast Cancer, Mammography, Medical Image Processing, Tumor.Full Text
References
- M. Vasantha, S. Bharathi V and R. Dhamodharan, “Medical image feature, extraction, selection and classification”. Intl. J. Engg.Sci. & Technol. 2(6), 2071-2076, 2010.
- A. Sahar “Predicting the Serverity of Breast Masses with Data Mining Methods” International Journal of Computer Science Issues, Vol. 10, Issues 2, No 2, March 2013 ISSN (Print):1694-0814| ISSN (Online):1694-0784 www.IJCSI.org.
- S. Sondele and I. Saini, "Classification of Mammograms Using Bidimensional Empirical Mode Decomposition Based Features and Artificial Neural Network", International Journal of Bio-Science and BioTechnology, Vol.5, No.6 (2013), pp.171-180.
- Z. K. Senturk and R. “Breast Cancer Diagnosis Via Data Mining: Performance Analysis of Seven Different Algorithms”, Computer Science & Engineering: An International Journal (CSEIJ), Vol. 4, No. 1, February 2014.
- Ismaili, Florije, LuzanaShabani, BujarRaufi, JauminAjdari, and XhemalZenuni. "Enhancing breast cancer detection using data mining classification techniques." PressAcademiaProcedia 5, no. 1 (2017): 310-316.
- Tingting Mu, Asoke K, Nandi, Rangaraj M. Rangayyan, Classification of breast Masses using Selected shapes, edge sharpness, teture features with linear and kernalnased classifies, Journal of Digital Imaging 2008, 21(2)153-169
- Breast Cancer Recurrence Prediction Due to Bosom Malignant Growth of Tumor
Abstract Views :216 |
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Authors
Affiliations
1 School of CSA, REVA University, IN
1 School of CSA, REVA University, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 34-37Abstract
Bosom Malignant growth is among the main sources of disease passing in ladies. As of date, the event of bosom malignant growth has expanded altogether and a great deal of associations are taking up the reason for spreading mindfulness about bosom disease. With early discovery and treatment it is conceivable that this sort of malignant growth will go into reduction Bosom disease is a noteworthy risk for moderately aged ladies all through the world and as of now this is the second most compromising reason for malignancy passing in ladies. Be that as it may, early location and counteractive action can altogether decrease the odds of death. A critical truth with respect to bosom malignant growth guess is to upgrade the likelihood of disease repeat. This paper goes for discovering bosom malignant growth repeat likelihood utilizing distinctive information mining systems. We additionally give an honorable methodology so as to enhance the precision of those models. Malignant growth patient's information were gathered from Wisconsin dataset of UCI AI Archive. This dataset contained complete 35 characteristics in which we connected Credulous Bayes, C4.5 Choice Tree and Bolster Vector Machine (SVM) order calculations and determined their expectation exactness. A productive component determination calculation helped us to enhance the exactness of every model by lessening some lower positioned properties. Not just the commitments of these characteristics are extremely less, yet their expansion likewise misleads the order calculations. After a watchful determination of upper positioned qualities we discovered a much enhanced exactness rate for each of the three calculations.Keywords
WEKA, Clustering, Association Rule Mining, Breast Cancer Dataset ,Technique, Breast Cancer, Method, SEER.Full Text
References
- http://www.cancer.org/cancer/breastcancer/detailedguide /breast-cancer-key-statistics
- http://www.wcrf.org/int/cancer-facts-figures/data-specificcancers/breast-cancer-statistics
- https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisc onsin+(Original)
- Imkampe AK, Bates T. Impact of a raised body mass index on breast cancer survival in relation to age and disease extent at diagnosis. Breast J 2010;16:156-161.
- Chlebowski RT, Aiello E, McTiernan A. Weight loss in breast cancer patient management. J Clin Oncol 2002;20:1128-1143.
- Benjamin F. Hankey, et. al. The Surveillance, Epidemiology, and End Results Program: A National Resource. Cancer Epidemiology Biomarkers & Prevention 1999; 8:1117-1121.
- Houston, Andrea L. and Chen, et. al.. Medical Data Mining on the Internet: Research on a Cancer Information System. Artificial Intelligence Review 1999; 13:437-466.
- Cios KJ, Moore GW. Uniqueness of medical data mining. Artificial Intelligence in Medicine 2002; 26:1-24. ,Zhou ZH, Jiang Y. Medical diagnosis with C4.5 Rule preceded by artificial neural network ensemble. IEEE Trans Inf Technol Biomed. 2003 Mar; 7(1):37-42
- Yap, B. W., Ong, S. H, & Nor Huselina, M. H. (2011). Using Data Mining to Improve Assessment of Credit Worthiness via Credit Scoring Models, Expert Systems with Applications, 38, 13274-13283
- Society for Women Health Research. (2010). Life After Early Breast Cancer (ABC) Disease Awareness Initiative.
- Risk of recurrence in early breast cancer. Available at: http://www.lifeabc.org/risk_recurrence_more.html
- An Approach of Image Processing for the Detection of Cercospora Fruit Spot and Bacterial Blight Disease on Pomegranate
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1 School of CSA, REVA University, IN
1 School of CSA, REVA University, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 93-98Abstract
India is one of the well-known countries in world, in the area of pharmacy specially food horticulture. India produces nearly 5.00 lakh tones/annum of pomegranate. Fruit gradation is one of the most vital parts in fruit horticulture. The project design presented by this paper is from same problematic area. In our project design we developed systems which classify diseases affecting pomegranates using K-means clustering and SVM techniques and routing algorithm. Now a days the disease Bacterial Blight which is caused by "Xanthomonas Axonopodis PV. Punicae" is growing rapidly day by day in pomegranate cultivation. This is fungal bacteria and caused by many parameters like environment, air, humidity, temperature. It causes heavy losses in production quality and quantity each year, especially in climates with rainfall and high temperature. Cercospora fruit spot is caused by the fungus and the full name of this fungal disease is “Pseudocercospora Angolensis”. Leaves of affected plants will produce circular spots with light brown to grayish centers. We are classifying the different pomegranate variety in accordance with their diseases. This paper deals with pomegranate grading and identification of disease system with judging parameters. The specialty of design is it creates a model which helps to decide appropriate criteria for healthy fruit. This project design is acts as advance system model in Indian horticulture for deciding ranges of mean, variance, entropy values by which the quality of fruit is decided. These parameters are judging parameters of our project design.Keywords
K-Means, SVM (Support Vector Machine), Mean, Variance, Imageprocessing, Bacterial Blight.Full Text
References
- Tejal Deshpande, Sharmila Sengupta, K. S. Raghuvanshi, “Grading & Identification of Disease in Pomegranate Leaf and Fruit,” Vol.5(3), 2014, 4638-4645.
- Monika Jhuria, Ashwani Kumar, Rushikesh Borse, “Image Processing for Smart Farming: Detection of Diseases and Fruit Grading,” IEEE ICIIP, pp.521-526, 2013.
- Sindhuja Sankarana, Ashish Mishra Reza Ehsania, Cristina Davisb, “A review of advanced techniques for detecting plant diseases,” Computers and Electronics in Agriculture, vol. 72, pp.1-13, 2010.
- Usmail Kavdir, Daniel E. Guyer, “Apple Grading Using Fuzzy Logic”, Turk J Agric (2003), 375-382
- C.C. Teoh and A.R Mohd Syaifudin,” Image processing and analysis techniques for estimating weights of Chikanan mangoes” J. Trop.Agric.and fd. Sc., vol35 (1), pp.183-190, 2007.
- Sanjeev S Sannakki1, Vijay S Rajpurohit, V B Nargund, ArunKumar R, Prema S Yallur, “Leaf Disease Grading by MachineVision and Fuzzy Logic”, Int. J. Comp. Tech. Appl., Vol 2 (5),1709-1716, 2011
- Home Automation with Smart Electric Power Supply using IOT
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Authors
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1 School of CSA, REVA University, IN
1 School of CSA, REVA University, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 109-115Abstract
In the 21st century, where the automation is acquiring every platform and every place with reducing lots of human efforts, all by replacing the human hands into the machinery, including the industry, the automation is also occupying the personal and economic life which in simple can call as the home automation, smart cities and much more.
The Smart Electric Power Supply is project using IOT technology for the smart monitoring, tracking, analysis and prediction of the flow of electricity in the city, this also useful for the easy detection of the electricity loss like electricity theft and illegal connections and this also gives the information of the status of the devices, their conditions and also the regular usage and smart electricity monitoring of individual building, area, city and others as we maintain the electricity.
This including the smart electricity supply system this also provide the built-in support to the smart automation of the devices with auto programmable solution as the open source, this makes the machine to dynamically update and operate the devices with all the facilities which are required for the users.
This technology is built on the IOT technology, specifically in the Raspberry Pi board, and also built on the REST API technology.
Keywords
Internet, Automation, Layer, Process, Rest API, Crontab, Storage, Devices, Home User, Centralized Authority.Full Text
References
- Rajeev Piyareand Seong Ro Lee: “Smart HomeControl and Monitoring System Using Smart Phone” ,July 2013, ResearchGate.
- Deepali Jawlie, Mohd. Mohsin, Shreerang Nandanwar, Mayur Shingate: “Home Automation and Security System Using Android ADK”, Volume – 3 Issue 2 (March 2013), IJECCT.
- Abhay Kumar, Neha Tiwari: “Energy Efficient Smart Home Automation System”, Volume 3 Issue 1, January 2018.
- Mukesh Kumar, Shimi S. L.: “Voice recognition Based home Automation Ssytem for paralyzed people”, Volume 4, Issue 10, October 2015.
- Condoluci, M., Dohler, M., Araniti, G., Molinaro, A., Zheng, K.: Toward 5G densenets: architectural advances for effective machine-type communications over femtocells. IEEE, Commun. Mag. 53(1), 134–141 (2015),doi: 10.1109/MCOM.2015.7010526
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- Hosek, J., Masek, P., Ries, M., Kovac, D., Bartl, M., Kropfl, F.: Use case study on embedded systems serving as smart home gateways. In: Recent Advances in Circuits, Systems, Automatic Control. Budapest: EUROPMENT, pp. 310–315 (2013). ISBN: 978-960-474-349-0.
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