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Mishra, Subhadra
- Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper
Abstract Views :240 |
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Authors
Affiliations
1 Siksha 'O' Anusandhan University, Bhubaneswar - 751030, Odisha, IN
2 Orissa University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, IN
1 Siksha 'O' Anusandhan University, Bhubaneswar - 751030, Odisha, IN
2 Orissa University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, IN
Source
Indian Journal of Science and Technology, Vol 9, No 38 (2016), Pagination:Abstract
Objective: This paper has been prepared as an effort to reassess the research studies on the relevance of machine learning techniques in the domain of agricultural crop production. Methods/Statistical Analysis: This method is a new approach for production of agricultural crop management. Accurate and timely forecasts of crop production are necessary for important policy decisions like import-export, pricing marketing distribution etc. which are issued by the directorate of economics and statistics. However one has understand that these prior estimates are not the objective estimates as these estimate requires lots of descriptive assessment based on many different qualitative factors. Hence there is a requirement to develop statistically sound objective prediction of crop production. That development in computing and information storage has provided large amount of data. Findings: The problem has been to intricate knowledge from this raw data , this has lead to the development of new approach and techniques such as machine learning that can be used to unite the knowledge of the data with crop yield evaluation. This research has been intended to evaluate these innovative techniques such that significant relationship can be found by their applications to the various variables present in the data base. Application / Improvement: The few techniques like artificial neural networks, Information Fuzzy Network, Decision Tree, Regression Analysis, Bayesian belief network. Time series analysis, Markov chain model, k-means clustering, k nearest neighbor, and support vector machine are applied in the domain of agriculture were presented.Keywords
Artificial Neural Network, Decision Tree, Machine Learning, Regression Analysis, Time Series Analysis.Full Text
- Unsupervised Transudative TL Feature Learning for Image Feature Extraction and Representation
Abstract Views :25 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, St. Joseph College of Engineering, IN
2 Department of Electronics and Instrumentation Engineering, Kamaraj College of Engineering and Technology, IN
3 Department of Computer Science and Application, Odisha University of Agriculture and Technology, IN
4 Department of Artificial Intelligence and Machine Learning, Shri Ramdeobaba College of Engineering and Management, IN
1 Department of Information Technology, St. Joseph College of Engineering, IN
2 Department of Electronics and Instrumentation Engineering, Kamaraj College of Engineering and Technology, IN
3 Department of Computer Science and Application, Odisha University of Agriculture and Technology, IN
4 Department of Artificial Intelligence and Machine Learning, Shri Ramdeobaba College of Engineering and Management, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3079-3086Abstract
In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in an unsupervised transductive setting remains relatively unexplored. The research gap lies in the scarcity of methods that can effectively learn meaningful image representations without access to labeled data in the target domain, hindering the broader applicability of transfer learning in computer vision. Our research seeks to bridge this gap by proposing a novel framework that leverages unsupervised feature learning to enhance the adaptability of models across different image domains, thus contributing to the advancement of transfer learning in the field of computer vision. Experimental results demonstrate the effectiveness of our method in addressing this critical research gap and its potential for real-world applications.Keywords
Unsupervised, Transductive Transfer Learning, Image Feature Extraction, Representation, Deep Neural Networks
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