10th International Conference on Intelligent Systems and Communication Networks (IC-ISCN 2019)
(Volume-1)

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
Heart Disease Prediction System (HDPS) is an application that predicts occurrence of Heart Disease from medical data like pain location, smoking, diabetes, whether pain relieved after rest, years, family history using data mining technique. Early identification and prediction of Cardiovascular disease (CVD) is important for treatment which in turn can decrease mortality. Data mining plays an essential role in the field of heart disease prediction. Hence, there emerges a need to develop a computerized system to check the condition of a heart in a patient. The main aim of this project is to reduce the individual and government healthcare expenditure. The traditional medical tests like MRI scan..............

Keyword :Cardiovascular Disease, Cost function , Decision Trees, Logistic regression, Random Forest Classifier, Sigmoid function.

[1]. V.V. Ramalingam, Ayantan Dandapath, M Karthik Raja ,Heart disease prediction using machine learning techniques: a survey, International Journal of Engineering & Technology, 7 (2.8),(2018) 684-687.
[2]. Animesh Hazra,Subrata Kumar Mandal,Amit Gupta,Arkomita Mukherjee and Asmita Mukherjee, Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review, Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 7 (2017) pp. 2137-2159.
[3]. Asha Rajkumar and B. Sophia Reena, Diagnosis of Heart Disease Using Data mining Algorithm , Global Journal of Computer Science and Technology, Vol. 10, No. 10, pp.38 - 43, 2010
[4]. Latha Parthiban and R. Subramanian, Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm , International Journal of Biological and LifeScience, Vol. 15, pp. 157 - 160, 2007.5.1.
[5]. Chaitrali S. Dangare, Sulabha S. Apte, Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques,International Journal of Computer Applications (0975 – 888)Volume 47– No.10, June 2012
[6]. R. Chitra and V. Seenivasagam, Review ofHeart disease predictionsystem using data mining and hybrid intelligent techniques, ICTACT Journal on Soft Computing, JULY 2013, Volume: 03, Issue: 04.

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.

[1]. G. S. Navale,NishantDudhwala,KunalJadhav, "Prediction of Stock Market using Data Mining and Artificial Intelligence", International Journal of Computer Applications (0975 – 8887) Volume 134 – No.12, January 2016
[2]. Prashant S. Chavan, Shrishail. T. Patil, " Parameters for Stock Market Prediction", ISSN:2229-6093 , IJCTA | Mar-Apr 2013
[3]. Ruchi Desai, Snehal Gandhi, " Stock Market Prediction Using Data Mining", 2014 IJEDR | Volume 2, Issue 2 | ISSN: 2321-9939
[4]. Anuj Mahajan, LipikaDey, Sk. MirajulHaque," Mining Financial News for Major Events and Their Impacts on the Market", 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
[5]. Zhao, Lei, Wang, Lin," Price Trend Prediction of Stock Market Using Outlier Data Mining Algorithm", 2015 IEEE Fifth International Conference on Big Data and Cloud Computing

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