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
Selvi, S.
- Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network
Authors
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, ET
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2438-2443Abstract
Accurate and effective mapping of soil properties is regarded as a critical task in environmental and agricultural management. The evaluation of properties of soil is a daunting task while monitoring and sensing the environment. Existing sampling methods is a time-consuming and laborious job and they are limited based on the regions. However, the need of soil analysis and its properties is essential at landscape level. In this paper, we use Recurrent Convolution Neural Network (RCNN) to assess the soil properties via its classification task. The model in turn is compared with conventional geostatistical spatial interpolation methods. The utilization of Recurrent Neural Network (RNN) aims at studying the spatial and temporal variability of the properties of soil that adopts Kriging interpolation technique. The simulation is conducted to study the efficacy of the model under different soil conditions and the efficacy of RCNN is reported. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than other models.Keywords
Regional Convolutional Neural Network, Deep Learning, Soil Properties, Prediction.References
- V. Chang, B. Gobinathan, A. Pinagapani, S. Kannan and G. Dhiman, “Automatic Detection of Cyberbullying using Multi-Feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 107186-107198, 2021.
- S.B.V. Sara, M. Anand and S.S. Priscila, “Design of Autonomous Production using Deep Neural Network for Complex Job”, Materials Today: Proceedings, Vol. 58, No. 3, pp. 1-12, 2021.
- A. Shukla, G. Kalnoor and A. Kumar, “Improved Recognition Rate of Different Material Category using Convolutional Neural Networks”, Materials Today: Proceedings, Vol. 56, No. 2, pp. 1-12, 2021.
- N.V. Kousik, M. Sivaram and R. Mahaveerakannan, “Improved Density-Based Learning to Cluster for User Web Log in Data Mining”, Proceedings of International Conference on Inventive Computation and Information Technologies, pp. 813-830, 2021.
- H. Azath, M. Mohanapriya and S. Rajalakshmi, “Software Effort Estimation using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network”, Journal of Intelligent Systems, Vol. 29, No. 1, pp. 251-263, 2018.
- S. Khaki and L. Wang, “Crop Yield Prediction using Deep Neural Networks”, Frontiers in Plant Science, Vol. 10, pp. 621-632, 2019.
- H. Azath and R.S.D. Wahidabanu, “Function Point: A Quality Loom for the Effort Assessment of Software Systems”, International Journal of Computer Science and Network Security, Vol. 8, No. 12, pp. 321-328, 2008.
- J.H. Jeong, J.P. Resop and N.D. Mueller, “Random Forests for Global and Regional Crop Yield Predictions”, PLoS One, Vol. 11, No. 6, pp. 1-18, 2016.
- S. Fukuda, W. Spreer, E. Yasunaga and K. Yuge, “Random Forests Modelling for The Estimation of Mango Fruit Yields under Different Irrigation Regimes”, Agricultural Water Management, Vol. 116, pp. 142-150, 2013.
- J. Liu and C.E. Goering, “A Neural Network for Setting Target Corn Yields”, Transactions of the ASAE, Vol. 44, No. 3, pp. 705-713, 2001.
- C.J. Ransom, N.R. Kitchen and J.J. Camberato, “Statistical and Machine Learning Methods Evaluated for Incorporating Soil and Weather into Corn Nitrogen Recommendations”, Computers and Electronics in Agriculture, Vol. 164, pp 1-17, 2019.
- S.T. Drummond, K.A. Sudduth, A. Joshi and S.J. Birrell, “Statistical and Neural Methods for Site-Specific Yield Prediction”, Transactions of the ASAE, Vol. 46, No. 1, pp. 1-5, 2003.
- M. Shahhosseini, R.A. Martinez-Feria and G. Hu, “Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms”, Environmental Research Letters, Vol. 14, No. 12, pp. 1-13, 2019.
- M.M. Awad, “Toward Precision in Crop Yield Estimation using Remote Sensing and Optimization Techniques”, Agriculture, Vol. 9, No. 3, pp. 54-65, 2019.
- D. Jiang, X. Yang and N. Wang, “An Artificial Neural Network Model for Estimating Crop Yields using Remotely Sensed Information”, International Journal of Remote Sensing, Vol. 25, No. 9, pp. 1723-1732, 2004.
- A.K. Prasad, L. Chai and M. Kafatos, “Crop Yield Estimation Model for Iowa using Remote Sensing and Surface Parameters”, International Journal of Applied Earth Observation and Geoinformation, Vol. 8, No. 1, pp. 26-33, 2006.
- J.R. Romero and P.F. Roncallo, “Using Classification Algorithms for Predicting Durum Wheat Yield in the Province of Buenos Aires”, Computers and Electronics in Agriculture, Vol. 96, pp. 173-179, 2013.
- S. Khaki, Z. Khalilzadeh and L. Wang, “Classification of Crop Tolerance to Heat and drought-A Deep Convolutional Neural Networks Approach”, Agronomy, Vol. 9, No. 12, pp. 833-845, 2019.
- J. You, X. Li, M. Low and D. Lobell, “Deep Gaussian Process for Crop Yield Prediction based on Remote Sensing Data”, Proceedings of International Conference on Artificial Intelligence, pp. 1-6, 2017.
- N. Kim, K. Ha, K. J., Park and J. Cho, “A Comparison between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States”, International Journal of Geo-Information, Vol. 8, No. 5, pp. 240-254, 2019.
- A.X. Wang, C. Tran and N. Desai, “Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data”, Proceedings of International Conference on Computing and Sustainable Societies, pp. 1-5, 2018.
- Q. Yang, L. Shi and J. Han, “Deep Convolutional Neural Networks for Rice Grain Yield Estimation at the Ripening Stage using UAV-based Remotely Sensed Images”, Field Crops Research, Vol. 235, pp. 142-153, 2019.
- R. Tibshirani, “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, No. 1, pp. 267-288, 1996.
- I. Goodfellow, Y. Bengio and A. Courville, “Machine Learning Basics”, Deep Learning, Vol. 1, No. 7, pp. 98-164, 2016.
- N.V. Kousik, “Privacy Preservation between Privacy and Utility using ECC-based PSO Algorithm”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 567-573, 2021.
- S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- G. Kiruthiga, G.U. Devi and N.V. Kousik, “Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks”, Proceedings of International Conference on Distributed Artificial Intelligence, pp. 277-290, 2020.
- K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.
- K.M. Baalamurugan and S.V. Bhanu, “Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, pp. 303-307, 2018.
- Evaluation of Selenium and Chromium Content in Selected Foods - II
Authors
1 Sri Avinashilingam Home Science College for Women, Coimbatore-641020, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 25, No 5 (1988), Pagination: 140-143Abstract
Selenium was found to be an essential trace element in 1957, when it was discovered that animals deficient in selenium had increased susceptibility to liver necrosis. Foods may contain excess or deficiency of selenium, thus leading to signs of toxicity or of deficiency disease. 'Alkali disease' in cattle occurs when grains and forages containing high levels of selenium are ingested. Geological and meteorological factors determine the relative abundance of selenium in the soil in different regions of the world, giving rise to practical problems of selenium toxicity or selenium deficiency that are of considerable economic importance.- An Efficient Interactive media Encryption using Hybrid Cryptographic Approaches
Authors
1 PSG College of Arts and Science, Coimbatore, TamilNadu, IN
2 Chikkanna Govt.Arts College, Tripur, TamilNadu, IN
Source
Networking and Communication Engineering, Vol 9, No 6 (2017), Pagination: 137-142Abstract
Data Security these days is turning into an essential worry for any correspondence procedure particularly when it is having classified data and needs to go through an uncertain medium of correspondence. There are numerous methods to shield the information from unapproved get to. Techniques like Symmetric Encryption makes utilization of a mystery key which is utilized by the sender and the beneficiary for scrambling and unscrambling the substance separately. Strategies, for example, lopsided encryption makes utilization of two distinctive keys to do a similar undertaking. The previous technique is quicker when contrasted with the last mentioned however needs as far as security. The last technique makes utilization of an open key framework to make the encryption procedure open however decoding private henceforth expanding the general intricacy. The best known symmetric figure AES [11] makes utilization of 256 piece keys to do the encryption. Exceptionally notable awry strategy RSA [12] makes utilization of 1024 piece keys to do a similar occupation, in this manner expanding the time and space intricacy at the cost of expanded security. Another awry partner ECC [13] makes utilization of a totally unique approach by changing over the characters into relative purposes of an elliptic bend. It makes utilization of 160 piece keys and delivers a similar outcome at a greatly improved pace when contrasted with RSA. In this paper a novel proficient model of Hybrid Encryption including AES and ECC is advanced which scrambles any interactive media information i.e. content, picture, sound, video, and so on. The benefits of both AES and ECC are used to make an all the more intense half and half figure to cross over any barrier of speed and security. With the assistance of lesser estimated keys the time figure required to do the encryption is diminished. The outcomes gotten after execution mirrored a 100 % precision and a colossal speed increase over the current symmetric and hilter kilter innovations. The execution is completed in the Java condition by making separate keys and using them to do the encryption.
Keywords
ECC, Hybrid Encryption, Steganography Multimedia Encryption, Cryptography, Symmetric Encryption, Asymmetric Encryption.References
- Hafid Mammass and Fattehallah Ghadi, “Implementation of Smartcard Personalization Software,” International Journal of Future Generation Communication and Networking 2012; vol 5(4), p.39-54
- F. Amounas and E.H. El Kinani, “A Novel Encryption Scheme of Amazigh Alphabet Based Elliptic Curve using Pauli Spin ½ Matrice,”International Journal of Information & Network Security (IJINS) 2013; vol 2(3),p. 190-196
- Md.Zaheer Abbas, Dr.JVR Murthy, Authenticated And Policy - Compliant Source Routing. International Journal of Engineering Research and Applications (IJERA) 2012; vol 2(3), p.1347-1352.
- Sridhar C. Iyer, R.R. Sedamkar, Shiwani Gupta, “A Novel Idea on Multimedia Encryption Using Hybrid Crypto Approach,” 7th International Conference on Communication, Computing and Virtualization-2016 (ICCCV-2016), Procedia Computer Science; Vol 79, p.293-298, ISSN: 1877-0509.
- The Base16, Base32, and Base64 Data Encodings. IETF. October 2006. RFC 4648. Retrieved March 18, 2010
- www.csrc.nist.gov/groups/ST/toolkit/documents/dss/NISTReCur.pdf; (1999).
- NIST Special Publication 800-57, Recommendation for Key Management – Part 1: General, original version 2005, Table 4.
- www.ecc-brainpool.org/download/Domain-parameters.pdf; (2005)
- B. Padmavathi, S. Ranjitha Kumari, “A Survey on Performance Analysis of DES, AES and RSA Algorithm along with LSB Substitution Technique,” International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064; Volume 2 Issue 4, April 2013;p.170-174
- L.D.Singh and K.M.Singh, “Image Encryption using Elliptic Curve Cryptography,” Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015),Procedia Computer Science 54 ( 2015 );p.472 – 481.
- Daemen, Joan, Rijmen, Vincent, “AES Proposal: Rijndael,” National Institute of Standards and Technology 2003; p. 1. Retrieved 21 February 2013.
- R.L. Rivest, A. Shamir, and L. Adleman, “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems” Communications of the ACM 1977; p. 120-126
- Christof Paar, Jan Pelzl, "Elliptic Curve Cryptosystems", Chapter 9 of "Understanding Cryptography, A Textbook for Students and Practitioners". Springer, 2009.
- Luciano, Dennis, Gordon Prichett,"Cryptology: From Caesar Ciphers to Public-Key Cryptosystems". The College Mathematics Journal 18; p.2–17. doi:10.2307/2686311
- Jawahar Thakur, Nagesh Kumar, “DES, AES and Blowfish: Symmetric Key Cryptography Algorithms Simulation Based Performance Analysis,” International Journal of Emerging Technology and Advanced Engineering Dec 2011; vol 1(2); p.6-12
- S.M.Celestin, V.K.Muneeswaran, “Implementation of Text based Cryptosystem using Elliptic Curve Cryptography,” IEEE International Conference on Advanced Computing Dec 2009; p. 82-85.