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Sagar, B. M.
- A Study of Temperature Data Analytics and to Analyze the Future Value Forecasting
Authors
1 Department of ISE, R.V. College of Engineering, Bengaluru, Karnataka-560059, IN
Source
Programmable Device Circuits and Systems, Vol 10, No 5 (2018), Pagination: 101-103Abstract
Temperature analysis is the process of analyzing the temperature records to make a study on temperature behaviors, its variations etc.in different regions. These records are usually collected from the meteorological center. In this paper, the work carried out is on the data analytics of temperature. Temperature data analytics is performed on the data collected from meteorological center of India for a period of 20 years (1995-2014) of the Bengaluru station. The hidden pattern analysis is carried out on that data and also a forecasting model is built on it. A linear regression algorithm and ARIMA (Autoregressive Integrated Moving Average) algorithm is used to perform the forecasting. The qualifying results such as analysis on the factor that influences the temperature, the summarization of temperature of 20 years, prediction of next year data are performed. The paper gives a quick summarization of data of temperature which is easy to understand and helps the researcher to perform further analysis.
References
- Badhiye S. S, Dr. Chatur P. N, Wakode B. V., “Temperature and Humidity Data Analysis for Future Value Prediction: An Approach”, Government College of Engineering, Amravati, Maharashtra, India, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 1, January 2012.
- Ayham Omary, Ahmad Wedyan, Ahmed Zghoul, Ahmad Banihani, and Izzat Alsmadi, “An Interactive Predictive System for Weather Forecasting”, Computer science and IT Faculty Yarmouk University, Irbid, Jordan, Umm Al-Qura University , Salman bin Abdulaziz University, KSA, 978-1-4673-1550-0/12/$31.00 ©2012 IEEE
- Chris Fraley, Adrian Raftery, Tilmann Gneiting, McLean Sloughter and Veronica Berrocal, “Probabilistic Weather Forecasting in R”, the R Journal Vol. 3/1, June 2011 ISSN 2073-4859
- Iza Sazanita Isa, Saodah Omar, Zuraidi Saad, Norhayati Mohamad Noor, Muhammad Khusairi Osman, “Weather Forecasting Using Photovoltaic System and Neural Network”, Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Malaysia. 2010 Second International Conference on Computational Intelligence, Communication Systems and Networks. 978-0-7695-4158-7/10 $26.00 © 2010 IEEE DOI 10.1109/CICSyN.2010.63
- Sara khan, Mohd Muqeem, Nashra Javed, Assistant Professor, “A Critical Review of Data Mining Techniques in Weather Forecasting”, Department of Computer Application, Integral University, Lucknow, India. International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 4, April 2016, DOI 10.17148/IJARCCE.2016.54266
- Vaibhavi Mistry, Vibha Patel , “Weather Condition Prediction Using Semi-Supervised Data Mining Technique”, M.Tech Student, Dept. of Computer engineering, Uka Tarsadia University, Bardoli. Gujarat. INDIA. International Journal of Engineering Trends and Technology (IJETT) – Volume 20 Number 4, ISSN: 2231-5381 – Feb 2015.
- Meghali A. Kalyankar Prof. S. J. Alaspurkar, “Data Mining Technique to Analyse the Metrological Data”, University of Amravati, Maharashtra, India. International Journal of Advanced Research in Computer Science and Software Engineering. ISSN: 2277 128X Volume 3, Issue 2, February 2013.
- Javier Gutie ´ Rrez Illa ´ N, Chris D. Thomas, Julia A. Jones, Weng-Keen Wong, Susan M. Shirley And Matthew G. Betts, Global Change Biology (2014), “Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds”, doi: 10.1111/gcb.12642.
- S. Kotsiantis and et. al., “Using Data Mining Techniques for Estimating Minimum, Maximum and Average Daily Temperature Values”, World Academy of Science, Engineering and Technology 2007 pp. 450-454
- Tasha R. Inniss “Seasonal clustering technique for time series data”, European Journal of Operational Research (175) 2006 pp. 376–384
- J. Berrocal, Y. Gel, A. E. Raftery, and T. Gneiting. ProbForecastGOP: Probabilistic weather field forecast using the GOP method, 2010. URL http://CRAN. R- project.org/package=ProbForecastGOP. R package version 1.3.2.
- V. J. Berrocal, A. E. Raftery, and T. Gneiting. Combining spatial statistical and ensemble information in probabilistic weather forecasts. Monthly Weather Review, 135:1386–1402, 2007.
- Analysis of Machine Learning Algorithms in Prediction of Cardiovascular Diseases
Authors
1 RVCE, Bengaluru, IN
Source
Automation and Autonomous Systems, Vol 10, No 5 (2018), Pagination: 90-92Abstract
Heart failure is considered as one among the most fatal diseases in the contemporary world. Diabetes mellitus, hypertension, and dyslipidemia are considered as the observed predictors of cardiovascular disease. Few routine style risk factors include depression, physical inactivity, smoking, alcohol consumption, stress, food habits and obesity which are the major causes for cardiovascular disease. In India, heart failure among people is increasing at an alarming rate because there is lack of proper estimation for the ischolar_main cause of cardiovascular diseases and the absence of surveillance programme in order to track the occurrence, extensiveness and outcomes of heart failure. Data mining techniques prove to be an efficient approach in predicting the risk of cardiovascular diseases in the data deluge age. In this research study, data mining techniques are applied to get useful information from medical reports of patients. Using machine learning algorithms, the impact of each risk factor on heart disease is predicted. Firstly, the heart disease dataset is collected from the Cleveland Heart Disease database. With the help of the dataset, the attributes significant to the heart attack prediction are extracted. The dataset is split into training and test dataset. Different classification techniques are applied on preprocessed data to measure their accuracy in predicting the risk of heart disease. Two such algorithms are Logistic Regression and Gradient Boosting Algorithm. The objective is to attain high accuracy in the prediction of risk of cardiovascular diseases among patients. In order to prevent the occurrence of the cardiovascular diseases, the prevalence of risk factors should be minimized. Further, early conclusion and treatment can enhance quality and future of individuals who have heart disappointment.
Keywords
Data Mining, Cardiovascular Diseases, Machine Learning Algorithms, Logistic Regression, Gradient Boosting Algorithm.References
- Niti Guru, Anil Dahiya, Navin Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. I (January - June 2007.
- M. Hertzong, and B. Pozehl, “Cluster analysis of symptom occurrence to identify subgroups of heart failure patients: A pilot study,” Journal of Cardiovascular Nursing, vol. 25, pp. 273–283, July/August 2010.
- M. Panahiazar, V. Taslimitehrani, N. Pereira, and J. Pathak, “Using EHRs and machine learning for heart failure survival analysis,” Studies in health technology and informatics, MedInfo, vol. 216, pp. 40-44, 2015.
- K. Kwon, H. Hwang, H. Kang, K. G. Woo, amd K. Shim, “A remote cardiac monitoring system for preventive care in Consumer Electronics (ICCE),” Proc. IEEE, pp. 197-200, January 2013.
- Crop Yield Prediction Using IoT
Authors
1 Department of ISE, Rashtreeya Vidyalaya College of Engineering, Bengaluru-560059, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 5 (2018), Pagination: 126-128Abstract
Internet of Things (IoT) devices are used to communication between different things is effective. The application of IoT in agriculture industry plays a key role to make functionalities easy. Using the concept of IoT and Wireless Sensor Network (WSN), smart farming system has been developed in many areas of the world. Atmosphere, crop hereditary qualities, crop management (intensity as well as management skill level) and the substance and physical properties of soils have significant effects on crop yield soil conditions, especially change stunningly from farm to residence and field to field and conditions can contrast even inside an individual field.
Keywords
IoT, WSN, Precision Agriculture, Yield Prediction.References
- LIU Dan, Cao Xin, Huang Chongwei, JI Liang Liang, “Intelligent agent greenhouse environment monitoring system based on IOT technology”, 2015 International Conference on Intelligent Transportation, Big Data & Smart City.
- Joseph Haule, Kisangiri Michael, “Deployment of wireless sensor networks (WSN) in automated irrigation management and scheduling systems: a review”, Science, Computing and Telecommunications (PACT), 2014, Pan African Conference.
- Weimin Qiu, Linxi Dong, Haixia Yan, Fei Wang, “Design of Intelligent Greenhouse Environment Monitoring System Based on ZigBee and embedded technology”, 2014 IEEE International conference.
- Yuan Guo, “The Application of a ZigBee Based Wireless Sensor Network in the LED Street Lamp Control System”, 2013, College of Automation & Electronic Engineering, Qingdao University of Scientific & Technology, Qingdao, China embedded technology, Consumer Electronics - China, 2014 IEEE International Conference.
- D.K. Sreekantha, Kavya A.M.. "Agricultural crop monitoring using IOT - a study", 2017 11th International Conference on Intelligent Systems and Control (ISCO), 2017.
- Piara Singh, N. P. Singh, K. J. Boote, S. Nedumarian, K. Srinivas, “Management options to increase groundnut productivity under climate change”, Anantapur, Mahboobnagar and Junagadh, 2015.
- Rakesh Kumar, M.P. Singh, Prabhat Kumar and J.P. Singh, “ Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique” International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM),2015.
- Ramesh A Medar and Vijay S Rajpurohit “ A survey on Data Mining Techniques for Crop Yield Prediction” International Journal of Advance Research in Computer Science and Management Studies Volume(2) 2014.
- Ratchaphum Jaikla, Sansanee Auephanwiriyakul and Attachai Jintrawet, “Rice Yield Prediction using a Support Vector Regression method” , Proceedings of ECTI-CON 2008.