Search Results - (( based irrigation learning algorithm ) OR ( java implication based algorithm ))

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency by Ramli, Muhammad Shahrul Azwan, Zainal Abidin, Mohamad Shukri, Hasan, Nor Shahida, Md Reba, Mohd Nadzri, Kolawole, Keshinro Kazeem, Ardiansyah, Rizqi Andry, Mpuhus, Sikudhan Lucas

“…This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. …”
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IOT-based fertigation system / Mohamad Amir Furqan Darus by Darus, Mohamad Amir Furqan

“…These sensors provide real-time data about the crops’ environment, which is then sent to a central hub or cloud platform. Advanced algorithms and machine learning processes this data to determine the ideal irrigation and fertilization needs. …”
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Water optimization technique for precision irrigation system using IoT and machine learning by Maria Manuel Vianny, D., John, A., Kumar Mohan, S., Sarlan, A., Adimoolam, Ahmadian, A.

“…The cloud environment is used to store the observed parameters from the IoT components. The machine learning algorithm is useful in taking the process of data, from the cloud environments, and do forecasting on the irrigation system. …”
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Precision agriculture for corn using reinforcement learning by Tan, Carlton

“…Beyond data access, RACKY incorporates a sophisticated reinforcement learning agent based on the Proximal Policy Optimization (PPO) algorithm within the SWATGym. …”
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Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms by Essam Y., Huang Y.F., Birima A.H., Ahmed A.N., El-Shafie A.

Precision agriculture for corn using reinforcement learning by Tan, Carlton

“…Beyond data access, RACKY incorporates a sophisticated reinforcement learning agent based on the Proximal Policy Optimization (PPO) algorithm within the SWATGym. …”
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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms by Essam Y., Huang Y.F., Ng J.L., Birima A.H., Ahmed A.N., El-Shafie A.

Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…This study aims at comparing the application of deep learning algorithms and conventional machine learning algorithms for predicting reservoir inflow. …”

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning by Abdulrazak Yahya, Saleh, Roselind, Tei

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Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning by Roselind, Tei

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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor by Abdul Rashid, Raghdah Rasyidah, Shaharudin, Shazlyn Milleana, Sulaiman, Nurul Ainina Filza, Zainuddin, Nurul Hila, Mahdin, Hairulnizam, Mohd Najib, Summayah Aimi, Hidayat, Rahmat

“…To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. …”
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Automatic detection of oil palm tree from UAV images based on the deep learning method by Xinni, Liu, Kamarul Hawari, Ghazali, Fengrong, Han, Izzeldin, I. Mohd

“…This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. …”
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Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks by Rayner Alfred, Joe Henry Obit, Christie Chin Pei Yee, Haviluddin Haviluddin, Yuto Lim

“…We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. …”
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Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications by Irwan D., Ali M., Ahmed A.N., Jacky G., Nurhakim A., Ping Han M.C., AlDahoul N., El-Shafie A.

“…Additionally, we discuss a variety of modelling methods such as deep learning (DL) that have proven to boost the efficiency compared to traditional machine learning (ML) models. …”

Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

“…As for the NNE, a novel meta-learner based on the stochastic-enabled extreme learning machine integrated with whale optimisation algorithm (WOA-ELM) was developed and used in such an application for the first time. …”
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Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network by Dayang Suhaila, Awang Suhaili

“…The back propagation algorithm was adopted for this study. The optimal model for predicting missing data found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. …”
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Water level predictio for Limbang basin using multilayer perceptron (mlp) and radial basis function (rbf) neural network by Muhammad Noor Hisyam, Abg Hashim

“…MLP is trained with conjugate gradient algorithms, trainscg and RBF with newrb. The optimal model found in this study is the MLP which is using four days of antecedent data with combination of learning rate and number of neurons in the hidden layer of 0.6 and 60. …”
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Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq by Yaseen, Z.M., Jaafar, O., Deo, R.C., Kisi, O., Adamowski, J., Quilty, J., El-Shafie, A.

“…The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. …”
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