| Paper Type | :: | Research Paper |
| Title | :: | Heart Disease Prediction System Using Classification |
| Country | :: | India |
| Authors | :: | Aditya Rai || Anvay Pakhale || Shashank Salian || Prachi Janrao |
| Page No. | :: | 01-08 |
Keyword :Cardiovascular Disease, Cost function , Decision Trees, Logistic regression, Random Forest Classifier, Sigmoid function.
| Paper Type | :: | Research Paper |
| Title | :: | Price Trend Prediction Using Data Mining Algorithm |
| Country | :: | India |
| Authors | :: | Mrs. PranjaliKasture |
| Page No. | :: | 09-16 |
Data mining can be applied on past and present financial data to generate patterns and decision making algorithm. The system will analysis the historical stock data's of some companies. Based on different factors which affect the stock values of company like demand and supply of shares, popularity of company, profit earned. The proposed system will predict future price, buy and sell possibility of the share by representing it in graphical manner. Here user can select the company in which user is interested or who is interested to know about stock behavior of that company. Going through many surveys, the conclusion we come across is that it is not possible to consistently predict moments with better than average result as prices are affected by companies growth,revenue.
Keywords: Data mining; Data preprocessing; Stock value; Stock exchange.
| Paper Type | :: | Research Paper |
| Title | :: | Implementation of Stock Market Prediction Using Regression |
| Country | :: | India |
| Authors | :: | Ms. Ishita Gupta || Mr. Anay Awasthi || Mr. Sagar Gour || Dr. Sheetal Rathi |
| Page No. | :: | 17-21 |
The stock market is a lucrative and interesting way to earn money. It revolves around buying and selling of stocks in order to generate profits. Many investment banks, hedge funds, employ a lot of developers, quants, and data scientists to make models to predict market movement, compute risk to reward ratios, etc. for their positions to decide the correct amount of exposure for each of their trade. These strategies and mathematical models are not publicly shared so that it doesn't lose its uniqueness. The project uses real life and live data and applies mathematical models and machine learning algorithms on it to gain tangible results (Profits or Losses). The project works by fetching data from the Upstox API for a given stock and then applying it to linear regression model............
Keywords: Regression, Risk-to-reward ratio, Selenium, Sentiment Analysis, Stock Market
[1]. GIRIJA V ATTIGERI, MANOHARAPAI M M, RADHIKA M PAI, APARNA NAYAK,, STOCK MARKET PREDICTION: A BIG DATA
APPROACH,, TENCON 2015- 2015 IEEE REGION 10 CONFERENCE, MACAO, CHINA, NOVEMBER 2015.
[2]. SHARIFA RAJAB AND VINOD SHARMA, PERFORMANCE EVALUATION OF ANN AND NEURO-FUZZY SYSTEM IN BUSINESS
FORECASTING, 2015 2ND INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT(INDIACOM),
NEW DELHI, INDIA, MARCH 2015.
| Paper Type | :: | Research Paper |
| Title | :: | Intelligent Attendance Automation System by Face Recognition using PCA |
| Country | :: | India |
| Authors | :: | Deven Mistry || Tejas Gupta |
| Page No. | :: | 22-27 |
Face being a multidimensional structure is a complex identity to a human. There is requisite of a highly efficient algorithm and technique to detect and recognize the faces. Our attempt on face recognition is done by Principal Component Analysis (PCA). Face will be categorized as familiar or unfamiliar by checking against the data set. If the face is unfamiliar then his/her face-based data will be stored in the database and on approval of the administrator, the data set for the student is created. The proposed system is an attendance system that enables the faculty to mark the attendance of the students in the lecture efficiently.
Keywords:Principle Component Analysis, Data Flow Diagram, Facial Recognition Technology, Haarcascade Classifier
[1]. Prof. V.P. Kshirsagar, M.R.Baviskar, M.E.Gaikwad, "Face Recognition Using Eigenfaces" 2011 3rd International Conference on
Computer Research and Development
[2]. Sang-Jean Lee, Sang-Bong Jung, Jang-Woo Kwon*, Seung-Hong Hong, "Face Detection and Recognition Using PCA",
Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information
Infrastructure' (Cat. No.99CH3703)
[3]. Shruti Sehgal, Harpreet Singh, Mohit Agarwal, V. Bhasker, Shantanu, "Data Analysis Using Principal Component Analysis" 2014
International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)
[4]. M. Sharkas, M. Abou Elenien, "Eigenfaces vs. Fisherfaces vs. ICA for Face Recognition; A Comparative Study," 9th International
Conference on Signal Processing, 2008, ICSP 2008., 2008, pp. 914–919
[5]. D. Huang , C. Shan , M. Ardabilian , Y. Wang and L. Chen "Local binary patterns and its application to facial image analysis: A
survey", IEEE Trans. Syst., Man, Cybern., C, vol. 41, pp.765 -781 2011
| Paper Type | :: | Research Paper |
| Title | :: | Android-Based Heart Monitoring Systems |
| Country | :: | India |
| Authors | :: | Shraddha S. More || Dr. Rajesh Bansode || Anita Chaudhari || Aruna Pavate || Brinzel Rodrigues |
| Page No. | :: | 28-33 |
Steady advances in wireless technology, medical sensors and interoperability of software creates exciting ways in improving the ways in which we provide emergency care. Nowadays Healthcare Atmosphere has developed technology oriented. People are facing a problem of unpredicted death due to the cause of a heart attack, which is because of absence of medical care to patient at the right time. So proposed system designed to avoid such sudden death rates by using Heartbeat sensor (HBS) technology. In this proposed system a patient will be carrying hardware having sensors and android phone will be contained an application, the sensors will sense the heart rate of the patient and these data is transformed to android smart phone via GSM modem. The device even allows the patient to move freely and can be monitored continuously. The android phone will be contained an application which will receive heart rate, according to the received data respectively, and if any abnormalities are found, then the message contains patient's heart rate will be sent to the patient's doctor.
Keywords: Heart rate, patient, Heartbeat sensor, GSM modem, Body temperature, Remote Monitoring.
[1]. http://www.healthline.com/galecontent/physical-examination. (August 31, 2013).
[2]. D. Mukherjee, K. Gupta, M. Pandey, and A. Agrawal, "Microcontroller Based Cardiac Counter System", IJEAM, Vol. 02, Issue 01,
April 2013 ISSN (Online):2320-6608.
[3]. C. K. Das, M. W. Alam and M. I. Hoque "Wireless Heartbeat and Temperature Monitoring System for Remote Patients," Proceedings of the International Conference on Mechanical Engineering and Renewable Energy (ICMERE2013) 1-3 May 2014,
Chittagong, Bangladesh.
[4]. Sharanabasappa Sali, Pooja Durge, Monika Pokar, Namrata Kasge, "Microcontroller Based Heart Rate Monitor", in International
Journal of Science and Research (IJSR), May 2016.
[5]. R. Trivedi, G. Mathur, A. Mathur, "A Survey on Platinum Temperature Sensor", IJSCE, ISSN: 2231-2307, Volume-1, Issue-
NCAI2011, June 2011
| Paper Type | :: | Research Paper |
| Title | :: | Study on Plant Leaf Disease Detection using Segmentation and Clustering |
| Country | :: | India |
| Authors | :: | Suhasini Parvatikar || Dr. Deepa Parasar |
| Page No. | :: | 34-37 |
Plants play an important role in our environment. Plants maintain the balance of O2 and CO2 of earth's atmosphere. Without plants, there will be no existence of the earth's ecology. In addition to the conservation aspect, identification of the plant diseases is also necessary to utilize their medicinal properties and using them as sources of alternative energy sources like bio-fuel. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and require the excessive processing time. Hence, in this paper we are studying about image processing concept for the detection of plant diseases. It involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification.
Keywords- Clustering, Feature Extraction, Image Processing, Preprocessing, Segmentation.
[1]. H.Al-Hiary,S.Bani-Ahmad, M.Reyalat,M.Braik &Z.AlRahamneh, "Fast & accurate detection & classification of plant diseases",
International journal of computer applications(0975-8887), volume 17- no.1,2011, pp-31-38.
[2]. Al-Bashish, D., M. Braik and S. Bani-Ahmad, " Detection and classification of leaf diseases using Kmeans- based segmentation and
neural-networks-based classification" Information Technology Journal 10: 267-275. DOI: 10.3923/itj.2011.267.275
[3]. Ali, S. A., Sulaiman, N., Mustapha, A. and Mustapha, N, " K-means clustering to improve the accuracy of decision tree response
classification" Information Technology Journal, 8:1256-1262.DOI:10.3923/itj.2009.1256.1262
[4]. J. K. Sainis, R. Rastogi, V. K. Chadda "Applications of image processing in biology and agriculture", BARC newsletter,2009.
[5]. S. Ananthi and S. Vishnu Varthini, " Detection And Classification Of Plant Leaf Diseases",IJREAS Volume 2, Issue 2 (February
2012) ISSN: 2249-3905.
| Paper Type | :: | Research Paper |
| Title | :: | E-Garage Management System |
| Country | :: | India |
| Authors | :: | Er. Swati Ganar || Gulhasan Siddiquee || Attaullah Khan || Soyab Anwar |
| Page No. | :: | 38-41 |
There are three panels which are enrolled in this service, first is user who want to take service, second is the garage who provide the requested services and the Admin who control and monitor all activity taking place between user and service provider (i.e. garage).With the help of this platform user first give the vehicle registration number ,after that they can select his/her willing garage notify problems and get the service(e.g. Tyre puncher, Break and clutch related, engine related etc) and estimated charges will also be generated at the time of placing order. The admin notifies the garage and send OTP to both user as well as service provider. Garage will provide the service and take the OTP in order to notify the admin, after completion of work. Toll-Free service can be used in case of emergency. User is able to give feedback to the service provider based on the performance which helps the admin to give rating to the garage.
Keywords: Active garage, Android app, E-garage, Nearest garage list, Toll-free emergency service.
[1]. "Hanamant B. Sale , Dharmendra Bari , TanayDalvi , Yash Pandey", "Online Management System for Automobile Services",
International Journal of Engineering Science and Computing (IJESC), Volume 8 Issue No.02, March-2018.
[2]. "Prof. Shilpa Chavan Saket Adhav, Rushikesh Gujar, Mayur Jadhav, Tushar Limbore","Automobile Service Center Management
System", International Journal of Scientific and Research Publications, Volume 4, Issue 3, March 2014.
[3]. "An improvement of the shortest path algorithm based on Dijkstra algorithm Computer and Automation Engineering (ICCAE),
2010 The 2nd International Conference on (Volume:2 ). Ji-xian Xiao Coll. of Sci., Hebei Polytech. Univ., Tangshan, China
FangLing Lu.
[4]. "N.Shivshankaran,P.Senthilkumar","Scheduling of Mechanics in Automobile Repair shop", N. Shivasankaran et.al / Indian Journal
of Computer Science and Engineering (IJCSE), Vol. 5 No.2 Apr-May 2014.
[5]. "NehaSelokar, Vijay Masne, Roshani Pimpalkar, SrushtiPuranik, NidhiBhoyar", "24*7 Vehicle Management Systems for
Automobile Industry" International Journal of Modern Engineering Research (IJMER)Vol.3, Issue.1, JanFeb. 2013 .
| Paper Type | :: | Research Paper |
| Title | :: | Data minng techniques and IoT used for building a futuristic water conservation system |
| Country | :: | India |
| Authors | :: | Mr Loukik Salvi || Mrs Harshali Patil |
| Page No. | :: | 42-48 |
Water scarcity is the major issue in the current scenario. Many places still face water issues. We aim at minimizing the wastage of water. This can be achieved at house level and later can be upgraded to industrial level as well. We plan on analyzing the water usage pattern of every family. Water scarcity is the major issue in the current scenario. Many places still face water issues. We aim at minimizing the wastage of water. This can be achieved at house level and later can be upgraded to industrial level as well. We plan on analyzing the water usage pattern of every family..........
Keywords : SOM,C4.5,CART,Naïve Bayes, SVM, KNN, ADA Boost,EM,FLowmeters ,IoT.
[1]. A.E. Ioannou, D. Kofinas, A. Spyropoulou and C. Laspidou* "Data mining for household water consumption analysis using
selforganizing maps.", E.W. Publications, Volos 38334, Greece, 2017.
[2]. http://www.flowmeters.com
[3]. ThinagaranPerumal,Md Nasir Sulaiman,Leong.C.Y,"Internet of Things (IoT) Enabled Water Monitoring System", 4th Global
Conference on Consumer Electronics (GCCE),IEEE 2015.
[4]. Perumal, T.; Sulaiman, M.N.; Mustapha, N.; Shahi, A.Thinaharan, R., "Proactive architecture for Internet of Things (IoTs)
management in smart homes," Consumer Electronics (GCCE), 2014 IEEE 3rd Global Conference on, pp.16,17, 7-10 Oct. 2014.
[5]. Perumal, T.,M.N.Sulaiman and Leong C.Y, "ECA-Based interoperability framework for intelligent building. Automation in
Construction. 31, 274–280 (2013).
| Paper Type | :: | Research Paper |
| Title | :: | Liveness Detection on Mobile Biometric |
| Country | :: | India |
| Authors | :: | Reshma P A || Divya K V || Subair T B || Sajanraj TD |
| Page No. | :: | 49-55 |
Liveness detection in mobile biometric is a challenging issue in iris recognition system security. RGB iris image is used for the image acquisition. In this work feature extracted between the genuine and fake by comparing the chromatic feature, blurred feature and pupil displacement. High quality printed iris images are considering the presentation attacks in this project. The printed images on glossy and matte paper, images shown in laptop, tablet screen with high resolution. Pupil localization technique using one dimensional processing of the eye region is evaluated. SVM classifier is used to classify the live or fake one.
[1]. K. H. Kevin W. Bowyer and P. J. Flynn. A survey of iris biometrics research: 2008-2010. Handbook of Iris Biometrics, 2012.
[2]. Justin Lee, Biometrics researcher developing new is liveness detection technologies. Jul 24, 2016.
[3]. J. Galbally, J. Fierrez, and J. Ortega-Garcia. Vulnerabilities in biometric systems: attacks and recent advances in liveness detection.
DATABASE, 1(3):4, 2007.
[4]. Niladri B. Puhan, N. Sudha, Suhas Hegde A. A new iris liveness detection method against contact lens spoofing, IEEE 15th
international symposium on consumer electronics, ISSN - 978-1-61284-6/11.
[5]. J. Galbally, J. Ortiz-Lopez, J. Fierrez, and J. Ortega-Garcia. Iris liveness detection based on quality related features. In 5th IAPR
International Conference on Biometrics (ICB), pages 271–276. IEEE, 2012.
| Paper Type | :: | Research Paper |
| Title | :: | Migration: From RDBMS to NoSQL Database |
| Country | :: | India |
| Authors | :: | Husain S. Amreliwala || Mohd. Shibli Qureshi || Taha Bikanerwala || Er. Zainab Mirza |
| Page No. | :: | 56-60 |
Data today is increasing on tremendous scale making it a challenge for existing systems to handle it properly. Statistics state that on an average about 2.5 exabytes that is, 2.5 billion gigabytes (GB) of data is generated every day as of now. Traditional databases such as SQL databases start to fail miserably once the size of data crosses the maximum data handling threshold. With expanding the size of the data, we also observe the variation in the structure of data. NoSQL database technology was developed to handle this expansion and variety with simplicity and ease. Although one can efficiently develop and test the applications using structured data,but when unstructured and semi-structured operational data comes into the picture, NoSQL technology is the optimal choice in case of efficiency. This paper provides such examples and proofs that argue how one can migrate from RDBMS (SQL) to NoSQL and also one can enhance existing applications by opting data migration to NoSQL
Keywords: NoSQL, SQL, MongoDB, Big Data, RDBMS
[1]. Jagdev Bhogal, Imran Choksi, Handling Big Data using NoSQL, IEEE, 2015.
[2]. Yishan Li, Sathiamoorthy Manoharan, A performance comparison of SQL and NoSQL databases, IEEE, 2013.
[3]. Rashid Zafar, Eiad Yafi, Megat F. Zuhairi, Hassan Dao, Big Data : The NoSQL and RDBMS review, IEEE, 2016.
[4]. Surya Narayana Swaminathan, Ramez Elmasri, Quantitative Analysis of Scalable NoSQL Databases, IEEE, 2016.
[5]. Venkat N Gudivada, Dhana Rao, Vijay V. Raghavan, NoSQL Systems for Big Data Management, IEEE, 2014.
[6]. Bogdan George Tudorica, Cristian Bucur, A comparison between several NoSQL databases with comments and notes, IEEE, 2011.
| Paper Type | :: | Research Paper |
| Title | :: | A study on types of Machine learning techniques in Mental Healthcare Domain |
| Country | :: | India |
| Authors | :: | Ela Gore |
| Page No. | :: | 61-64 |
Mental Healthcare is a growing problem plaguing our society. It is that source of trouble which
society conveniently tries to avoid it but later on faces severe brunt of its consequences affecting the person's
health as well as that of the whole family.Therefore, there is an urgent need to treat basic mental health
problems that prevail among people which may lead to complicated problems, if they are not treated at an early
stage. The existence of sophisticated (machine learning) algorithms that can process and learn from the data
helping to draw varied patterns from the patient's dataset. Psychiatrists now can have an unaccustomed
opportunity to benefit from diverse patterns in brain, behavior, and genes using methods from machine
learning..........
Keyword: Diagnosis, Machine Learning, Mental Healthcare, Predictive Analytics, Psychiatrist
[1]. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th ed.; DSM-5
Task Force; American Psychiatric Association: Washington, DC, USA, 2013; p. xliv. 947p.
[2]. Chien-Te Wu, Daniel G. Dillon, Hao-Chun Hsu, Shiuan Huang, Elyssa Barrick and Yi-Hung Liu, "Depression
Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support
Vector Machine",Appl. Sci. July 2018, 8, 1244; doi:10.3390/app8081244 www.mdpi.com/journal/applsci
[3]. Mallikarjun H M, Dr. H N Suresh, "Depression level prediction using eeg signal processing",2014 International
Conference on Contemporary Computing and Informatics (IC3I),978-1-4799-6629-5/14/$31.00 c2014 IEEE
[4]. Tuka Alhanai, Mohammad Ghassemi, and James Glass, "Detecting Depression with Audio/Text Sequence Modeling
of Interviews",Interspeech 2018 2-6 September 2018, Hyderabadhttp://www.iscaspeech.
org/archive/Interspeech_2018/abstracts/2522.html
[5]. Gregory Bramble, TejasDharamsi, Payel Das, TejaswiniPedapati, , Vinod Muthusamy, Horst Samulowitz, and Kush
R. Varshney,IBM Research AI &YuvarajRajamanickam, John Thomas, and Justin Dauwels,Nanyang Technological
University, "Neurology-as-a-Service for the Developing World",arXiv:1711.06195v2 [stat.ML] 22 Nov 2017
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