Search Results - (( risk optimization svm algorithm ) OR ( java application optimization algorithm ))

Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol by Md Fisol, Nur Atiqah Izzati

“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Optimizing sentiment analysis of Indonesian texts: Enhancing deep learning models with genetic algorithm-based feature selection by Siti, Mujilahwati, Noor Zuraidin, Mohd Safar, Ku Muhammad Naim, Ku Khalif, Nasyitah, Ghazalli

“…This study examines the optimization of Indonesian text sentiment analysis through the integration of feature selection using a genetic algorithm (GA) with deep learning models. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

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Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Polycystic Ovary Syndrome (PCOS) prediction system using PSO-SVM / Lukman Hakim Shaufee, Hamidah Jantan and Ummu Fatihah Mohd Bahrin by Shaufee, Lukman Hakim

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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia by Lay, Usman Salihu

“…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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Route Optimization System by Zulkifli, Abdul Hayy

“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

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SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration by Arshad, U., Taqvi, S.A.A., Buang, A., Awad, A.

“…All the trained models yielded comparable results with unseen test data set. However, the SVM model with Bayesian optimizer approach can effectively assess the risk of ignition based on dust MIT under the influence of dispersion pressure and dust cloud concentration among all the approaches adopted in this study. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…The classification accuracy of the RF classifier is observed to be higher than that of SVM using either all features or only the optimal features. …”
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Evaluation of K-fold value in breast cancer diagnosis technique using SVM and bio-inspired optimization algorithm (JA-ABC5) by Nadarajan, Ravindran, Sulaiman, Noorazliza

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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Dasril, Yosza, swanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi b, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning* by Much Aziz Muslim, Much Aziz Muslim, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning* by Muslim, Much Aziz, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi b, Dwika Ananda Agustina Pertiwi b, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning* by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Dwika Ananda Agustina Pertiwi b, Dwika Ananda Agustina Pertiwi b, Subhan b, Subhan b, Jumanto, Jumanto, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Iswanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning* by Muslim, Much Aziz, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. …”
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