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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 4th International Conference on Computing Sciences (ICCS) - Jalandhar, India (2018.8.30-2018.8.31)] 2018 4th International Conference on Computing Sciences (ICCS) - A Survey on Image Processing Techniques for Seeds Classification
摘要： Agriculture is the department which has shown a rapid growth. Due to this growth today we have a lot of variety of seeds which belongs to the same breed, which may be a result of breed crossover. Now, this has become a challenge to classify the seeds from each other. In another contrast, we have some healthy seeds and some of the seeds becomes defected. One way of separating them from the healthy seeds was, do manually by a team of experts and some manual systems, which is a time consuming and laborious task. So there was need to build a automatic / intelligent system which classify them on the basis of some fixed parameters like shape, length, height, perimeter etc. This paper shows various techniques available for doing the same.
关键词： Fuzzy,Neural Networks,Seed Classification,Computational Intelligence,Feature Extraction,Image Processing
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) - Gammarth-Tunis, Tunisia (2019.4.28-2019.5.1)] 2019 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) - Fast and Accurate Simulation of Ultrascaled Carbon Nanotube Field-Effect Transistor Using ANN Sub-Modeling Technique
摘要： In this paper, we have proposed a new modeling methodology based on the artificial neural networks (ANN) to simulate the ultra-scaled carbon nanotube field-effect transistor (CNTFET). The sub-modeling concept has been employed to efficiently simplify the overall modeling process. The developed sub-models have been compared with the mode space non- equilibrium Green’s function ( MS-NEGF) simulations in terms of the resulted drain current, where a good agreement has been recorded. In addition, simulation tests have shown that the proposed smart models are faster of about two order of magnitude over the standard MS-NEGF simulation. The obtained results indicate that the proposed ANN-based sub- modeling is an accurate and computationally efficient approach, which can be successfully used to simulate, analyze, and optimize the ultra-scaled CNTFETs and the futuristic CNT-based nanoscale integrated circuits.
关键词： computational intelligence,Artificial neural networks (ANN),non-equilibrium Green’s function (NEGF),numerical modeling,carbon nanotube (CNTFET)
更新于2025-09-12 10:27:22
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Forecasting Solar Activity with Computational Intelligence Models
摘要： It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on performance evaluation of BELFIS as a predictor by forecasting solar cycles 16 to 24. The performance of BELFIS is compared with other computational models used for this purpose, and in particular with adaptive neuro-fuzzy inference system (ANFIS).
关键词： Adaptive Neuro-Fuzzy Inference System,Solar Activity Forecasting,Computational Intelligence Models,Brain Emotional Learning-based Fuzzy Inference System,Solar cycles
更新于2025-09-04 15:30:14