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Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
Published 2024“…Modern agriculture recognizes weed detection systems as crucial tools to reduce the obstacles caused by weeds, enhancing crop growth and yield. This project aims to develop a weed detection prototype specifically for agricultural settings by utilizing Convolutional Neural Networks (CNN) algorithm. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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Development of sorting system for oil palm in vitro shoots using machine vision approach
Published 2014“…Close results between the performance of the developed sorting algorithm and SVM algorithm demonstrate that it is satisfactory and efficient. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Artificial intelligence in sustainability reporting / Prof. Dr Corina Joseph
Published 2023“…Today, AI is being employed to tackle socioeconomic and environmental sustainability challenges, contributing to the achievement of the Sustainable Development Goals (SDGs). For instance, in realizing SDG 6 (Clean Water and Sanitation), AI is being utilized to improve crop yields and reduce water consumption in agriculture. …”
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Moving camera automatic number plate recognition using neural network in android platform
Published 2019“…Successfully detected license plate image is segmented and each character is bounded with a rectangular bounding box and cropped out. Each cropped character is feed into CNN or BPFFNN model for character recognition process. …”
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Prediction of rice biomass using machine learning algorithms
Published 2022“…Further, the Q-TESI, C-TESI, and LTESI minimise the proportionality of interpolation error to the square of the distance between the data points compared to the LN-TESI. Consequently, the Q-TESI, C-TESI, and L-TESI may approximate the nonlinear changes of crop phenology in time-spaced sampling, thereby reducing the cost of sampling for scientists. …”
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