Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
International Journal of Approximate Reasoning
Applied fuzzy systems
Database modeling and design (3rd ed.)
Database modeling and design (3rd ed.)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms for Machine Learning
Genetic Algorithms for Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Industrial Applications of Fuzzy Technology
Industrial Applications of Fuzzy Technology
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Genetic Algorithms and Machine Learning
Machine Learning
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
A genetic algorithm for resource-constrained scheduling
A genetic algorithm for resource-constrained scheduling
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Production planning in manufacturing/remanufacturing environment using genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Neural Computing and Applications
D-SCIDS: distributed soft computing intrusion detection system
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Application of distributed SVM architectures in classifying forest data cover types
Computers and Electronics in Agriculture
Editorial: Application reviews
Applied Soft Computing
Artificial neural networks and multicriterion analysis for sustainable irrigation planning
Computers and Operations Research
Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data
Computers and Electronics in Agriculture
Application of support vector machine technology for weed and nitrogen stress detection in corn
Computers and Electronics in Agriculture
Fusion of soft computing and hard computing in industrial applications: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fusion of soft computing and hard computing: computational structures and characteristic features
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
Computers and Electronics in Agriculture
Decision support system for nitrogen fertilization using fuzzy theory
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Predicting the impact of hospital health information technology adoption on patient satisfaction
Artificial Intelligence in Medicine
Yield prediction in apples using Fuzzy Cognitive Map learning approach
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
| Hi-index | 0.00 |
Soft computing is a set of ''inexact'' computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed.