A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Jayakameswaraiah, M.
- Development of IoT Application in Agriculture Sector using Smart Sensors
Authors
1 School of Computer Science and Applications, Reva University, Bangalore, Karnataka, IN
Source
Digital Signal Processing, Vol 10, No 9 (2018), Pagination: 141-143Abstract
The growing world technology is increasing in a faster way. The main technology today we speak is IoT (Internet of Things). The IoT which helps us in our daily life and a promising technology which every sector and industrial were using. The main sector which IoT can be very helpful is agriculture sector, in which we can take the agriculture in our country to a different level. In past years our country has faced a lot of critical situations in agriculture where due to low rainfall or excess rainfall which made the life of farmers more critical. With the help of IoT we can make a lot of development in agriculture sector and also we can monitor corps and find out the status of the corps with low cost. This paper provides the detailed information on how the IoT technology will help the agriculture sector with smart sensors.
Keywords
Internet of Things, Applications, Smart Sensors, Agriculture Sector.References
- . Demirkol, C. Ersoy, F. Alagoz, “MAC protocols for wireless sensor networks: A survey”, IEEE Communication Magazine 44, 115–121, 2006.
- . H. Navarro-Hellín, J. Martínez-del-Rincon, R. Domingo-Miguel, F. Soto-Valles, R. Torres-Sánchez, “A decision support system for managing irrigation in agriculture”, Computers and Electronics in Agriculture, vol. 124, pp. 121-131, 2016.
- . J. Zhou, Z. Cao, X. Dong, and A. V. Vasilakos, “Security and privacy for cloud-based IoT: Challenges”, IEEE Commun. Magazine, vol. 55, no. 1, pp. 26–33, Jan. 2017.
- . Joaquín Gutiérrez, Juan Francisco Villa-Medina, Alejandra Nieto-Garibay, and Miguel Ángel Porta- Gándara, “Automated Irrigation System Using a Wireless Sensor Network and GPRS Module”, IEEE Transactions on Instrumentation and Measurements, 0018-9456,2013.
- . M. Zhang, T. Yu, G.F. Zhai, “Smart Transport System Based on The Internet of Things”, Amm. 48-49, 1073–1076, 2011.
- . M. Zorzi, A. Gluhak, S. Lange, A. Bassi, “From Today's Intranet of Things to a Future Internet of Things: A Wireless- and Mobility-Related View”, IEEE Wireless Communication 17, 43–51, 2010.
- . Nagoorijulfathna, Dr.G. Anjanbabu, M.Jayakameswaraiah, “Evaluating Cloud Technology Solutions for Business Development and Business Strategies”, International Journal of Scientific and Research Publications, Volume 4, Issue 2, ISSN: 2250-3153, 2014.
- . Orazio Mirabella. “A Hybrid Wired/Wireless Networking Infrastructure for Greenhouse Management.”, IEEE Transactions on Instrumentation and Measurement, VOL. 60, NO. 2, 2011.
- . Q. Wang, A. Terzis and A. Szalay, “A Novel Soil Measuring Wireless Sensor Network”, IEEE Transactions on Instrumentation and Measurement, pp. 412–415, 2010
- . Yunseop (James) Kim, Member, IEEE, Robert G. Evans, and William M. Iversen.“Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Networks”, IEEE Transactions on Instrumentation and Measurement, VOL. 57, NO. 7, 2008.
- A Novel Approach for the Assessment of Decision Stump & Upgraded Rf Classification Algorithms
Authors
1 School of Computer Science and Applications, REVA University, Bangalore, Karnataka, IN
2 Department of Computer Science, Sri Venkateswara University, Tirupati, Andhra Pradesh,, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No SP 5 (2019), Pagination: 105-108Abstract
The classification models in data mining consists of decision tree, neural network, genetic algorithm, rough set, statistical model, etc. In this research, we have proposed and deliberated a new algorithm called Upgraded Random Forest, which is applied for the classification of sensor discrimination dataset. Here we considered for classification of multisource Sensor Discrimination data. The Upgraded RF approach becomes extreme attention for multi-source classification. The methodology which we are developed is not only a nonparametric but it also applies for the assessment and significance of the specific variables in the classification.Keywords
Data Mining, Classification, Decision Stump, Random Forest and Upgraded RF.References
- . Dr.K.Suresh Kumar Reddy, Dr.M.Jayakameswaraiah, Prof.S.Ramakrishna, Prof.M.Padmavathamma,” Development Of Data Mining System To Compute The Performance Of Improved Random Tree And J48 Classification Tree Learning Algorithms”, International Journal of Advanced Scientific Technologies, Engineering and Management Sciences (IJASTEMS), Volume.3, Special Issue.1, March.2017, Page 128-132, ISSN: 2454-356X
- . Dr.M.Jayakameswariah,Dr.K.Saritha,Prof.S.Ramak rishna,Prof.S.Jyothi,“Development of Data Mining System to Evaluate Performance Accuracy of J48 and Enhanced Naïve Bayes Classifiers using Car Dataset”, International Journal Computational Science, Mathematics and Engineering,SCSMB-16-March-2016,PP- 167-170,E-ISSN: 2349-8439.
- . G. Subbalakshami et al., “Decision Support in Heart Disease System using Naïve Bayes”, IJCSE, Vol. 2 No. 2, pp. 170-176, 2011, ISSN : 0976-5166
- . L. Breiman, “Random Forests,” Machine Learning, Vol. 40. No. 1. 2001.
- . R. Duda, P. Hart and D. Stork. Pattern Classification, 2nd edition. John Wiley, New York, 2001.
- . S. Ramya , Dr. N. Radha, “Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithms”, International Journal of Innovative Research in Computer and Communication Engineering, pp. 812-820 Vol 4, issue 1, ISSN: 2320-9798, 2016.
- . S. Liu, R. Gao, D. John, J. Staudenmayer, and P. Freedson, “Multi-sensor data fusion for physical activity assessment,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 3, pp. 687-696, March 2012.
- . Sunil Joshi and R. C. Jain., “A Dynamic Approach for Frequent Pattern Mining Using Transposition of Database”, In proc of Second International Conference on Communication Software and Networks IEEE., p498-501. ISBN: 978-1-4244-5727-4, 2010.
- . T. Garg and S.S Khurana, “Comparison of classification techniques for intrusion detection dataset using WEKA,” In IEEE Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-5, 2014.
- . V.Karthikeyani,”Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction” International Journal of Computer Applications (0975 – 8887) Volume 60– No.12, December 2012.