Search Results - (( using remote learning algorithm ) OR ( using optimization based algorithm ))

Development of deep reinforcement learning based resource allocation techniques in cloud radio access network by Amjad, Iqbal

“…A step towards long network performance optimization is theterm use of deep reinforcement learning (DRL), which can learn the best policy via interaction with the environment. …”
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Enhancing land cover classification in remote sensing imagery using an optimal deep learning model by Motwake, Abdelwahed, Hassan Abdalla Hashim, Aisha, Obayya, Marwa, Eltahir, Majdy M.

“…The current study presents an Improved Sand Cat Swarm Optimization with Deep Learning-based Land Cover Classification (ISCSODL-LCC) approach on the RSIs. …”
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Earthquake prediction model based on geomagnetic field data using automated machine learning by Yusof, Khairul Adib, Mashohor, Syamsiah, Abdullah, Mardina, Amiruddin, Mohd, Rahman, Abd, Abdul Hamid, Nurul Shazana, Qaedi, Kasyful, Matori, Khamirul Amin, Hayakawa, Masashi

“…Several features were extracted from them through wavelet scattering transform (WST). The features were used as the input to model optimization, of which the strategy for automatic algorithm selection and hyperparameter tuning was performed based on the asynchronous successive halving algorithm (ASHA). …”
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A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks by Azeez, Omer Saud, M. Shafri, Helmi Z., Alias, Aidi Hizami, Haron, Nuzul Azam

“…A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data by Fanos, Ali Mutar, Pradhan, Biswajeet, Mansor, Shattri, Md Yusoff, Zainuddin, Abdullah, Ahmad Fikri, Jung, Hyung Sup

“…In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.…”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD by Bhandari, A.K., Soni, V., Kumar, A., Singh, G.K.

“…The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). …”
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Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm by Tilwari, Valmik, Dimyati, Kaharudin, Hindia, Mhd Nour, Fattouh, Anas, Amiri, Iraj

“…The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
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Design and implementation of a deep learning based hand gesture recognition system for Rehabilitation Internet-Of-Things (RIOT) environments using MediaPipe by Mohd Dhuzuki, Nurul Hanis, Zainuddin, Ahmad Anwar, Kamarul Zaman, Nur Anis Sofea, Ahmad Razmi, Alin Nur Maisarah, Kaitane, Wonderful Shammah, Ahmad Puzi, Asmarani, Johar, Mohd Naqiuddin, Yazid, Maslina, Mohd Nordin, Nor Azlin, Sidek, Shahrul Naim, Mohd Zaki, Hasan Firdaus

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Determining malaria risk factors in Abuja, Nigeria using various statistical approaches by Segun, Oguntade Emmanuel

“…Therefore, this was not incorporated in BBN models. Based on cross-validation analysis, the score-based algorithm outperformed the constraint-based algorithms in the structural learning. …”
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EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT by Ren Jie, Chin, Sai Hin, Lai

“…Advancements in computational power and data availability enable machine learning to complement traditional models. Algorithms such as ANNs, SVMs, and RF enhance hydrological forecasting, while deep learning methods (LSTMs, CNNs) improve spatio-temporal predictions. …”
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Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar by Jaafar, Jurina

“…The process from the satellite information allows an optimal judgment to decide the most appropriate Manning roughness to be used in the simulation of surface runoff. …”
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Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS by Zhang, Guanjin, Roslan, Siti Nur Aliaa, Mohd Shafri, Helmi Zulhaidi, Zhao, Yanxi, Wang, Ci, Quan, Ling

“…The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). …”
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Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation by Pande C.B., Srivastava A., Moharir K.N., Radwan N., Mohd Sidek L., Alshehri F., Pal S.C., Tolche A.D., Zhran M.

“…Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. …”

Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches by Latif S.D., Alyaa Binti Hazrin N., Hoon Koo C., Lin Ng J., Chaplot B., Feng Huang Y., El-Shafie A., Najah Ahmed A.

“…Using remote monitoring instrument input features, the machine-learning model can predict precipitation. …”

The operational role of remote sensing in assessing and predicting land use/land cover and seasonal land surface temperature using machine learning algorithms in Rajshahi, Bangladesh by Zullyadini A. Rahaman

“…The operational role of remote sensing in assessing and predicting land use/land cover and seasonal land surface temperature using machine learning algorithms in Rajshahi, Bangladesh by Zullyadini A. …”
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Calibrating hand gesture recognition for stroke Rehabilitation Internet-of-Things (RIOT) using MediaPipe in smart healthcare systems by Zainuddin, Ahmad Anwar, Mohd Dhuzuki, Nurul Hanis, Ahmad Puzi, Asmarani, Johar, Mohd Naqiuddin, Yazid, Maslina

“…This work aims to address these issues by calibrating hand gesture recognition systems using the Rehabilitation Internet-of-Things (RIOT) framework and examining the effectiveness of machine learning algorithms in conjunction with the MediaPipe framework for gesture recognition calibration. …”
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Building detection using object-based Image analysis (OBIA) and machine learning (ML) algorithms / Hanani Mohd Shahar by Mohd Shahar, Hanani

“…Thus, by enhancing the classification techniques in OBIA, building extraction accuracy using ML algorithms for medium resolution images can be improved and the expenses also can be reduced indirectly.…”
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Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data by Kalantar, Bahareh, Ueda, Naonori, Saeidi, Vahideh, Ahmadi, Kourosh, Abdul Halin, Alfian, Shabani, Farzin

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