A fuzzy derivative and dynamical systems
- Authors: Mammadov, Musa
- Date: 2002
- Type: Text , Thesis , PhD
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- Description: "The purpose of this thesis is to develop and study new techniques for the mathematical modeling of dynamical systems and to apply these techniques to data classification problems. This approach is based on the notion of a fuzzy derivative. The main aim of the thesis is to examine this notion in data classification."
- Description: Doctor of Philosophy
Pattern based object segmentation using split and merge
- Authors: Karim, Ziaul , Paiker, Nafize , Ali, M. Ameer , Sorwar, Golam , Islam, M. M.
- Date: 2009
- Type: Text , Conference proceedings
- Relation: 2009 IEEE International Conference on Fuzzy Systems; Jeju Island; South Korea; 20th- 24th August 2009 p. 2166-2169
- Full Text: false
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- Description: Split and Merge (SM) algorithm is a well recognized algorithm for segmenting homogeneous regions in an image. Though SM algorithm is simple and easy, this algorithm is unable to segment all type objects in an image successfully due to huge variations among the objects in size, shape, color and intensity. Moreover, the SM algorithm is also highly dependent on threshold values used for split and merge stages. Addressing these issues, a new algorithm namely pattern based object segmentation using split and merge (PSM) considering the basic SM algorithm, the region stability, and the patterns for object extraction. The experimental results prove the superior segmentation performance of the PSM algorithm in comparison with the basic SM algorithm, suppressed fuzzy c-means (SFCM), and object based image segmentation using fuzzy clustering (FISG). ©2009 IEEE.
- Description: IEEE International Conference on Fuzzy Systems
Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
- Authors: Armaghani, Danial , Momeni, Ehsan , Abad, Seyed , Khandelwal, Manoj
- Date: 2015
- Type: Text , Journal article
- Relation: Environmental Earth Sciences Vol. 74, no. 4 (2015), p. 2845-2860
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- Reviewed:
- Description: One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R2) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R2, RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy. © 2015, Springer-Verlag Berlin Heidelberg.
An agriprecision decision support system for weed management in pastures
- Authors: Chegini, Hossein , Naha, Ranesh , Mahanti, Aniket , Gong, Mingwei , Passi, Kalpdrum
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Access Vol. 11, no. (2023), p. 92660-92675
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- Description: Pastures are a vital source of dairy products and cattle nutrition, and as such, play a significant role in New Zealand's agricultural economy. However, weeds can be a major problem for pastures, making it a challenge for dairy farmers to monitor and control them. Currently, most of the tasks for weed management are done manually, and farmers lack persistent technology for weed control. This motivated us to design, implement, and evaluate a Decision Support System (DSS) to detect weeds in pastures and provide decisions for the cleanup of weeds. Our proposed system uses two primary inputs: weeds and bare patches. We created a synthetic dataset to train a weed detection model and designed a fuzzy inference system to assess a pasture. We also used a neuro-fuzzy system in our DSS to evaluate our fuzzy model and tune its parameters for better functioning and accuracy. Our work aims to assist dairy farmers in better weed monitoring, as well as to provide 2D maps of weed density and yield score, which can be of significant value when no digital and meaningful images of pastures exist. The system can also support farmers in scheduling, recommending prohibitive tasks, and storing historical data for pasture analysis, collaborated by stakeholders. © 2013 IEEE.