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A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network
Published 2019“…The reported accuracy dramatically outperforms the previous algorithms, including gradient tree boosting (GTB), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and linear discriminant analysis (LDA). …”
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Data Embedding in HEVC Video by Modifying the Partitioning of Coding Units
Published 2019Subjects: Get full text
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A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition
Published 2025“…A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. …”
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …”
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Development of an Optimization Algorithm for Internet Data Traffic
Published 2020“…The algorithm monitors data repetitions in IP datagram and prepares a compression code in response of this repetition. …”
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”
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Practical single node failure recovery using fractional repetition codes in data centers
Published 2016“…FR codes consist of a concatenation of an outer maximum distance separable (MDS) code and an inner fractional repetition code that splits the data into several blocks and stores multiple replicas of each on different nodes in the system. …”
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FPGA-Based Network Traffic Classification Using Machine Learning
Published 2020“…The results of the conducted experiments indicate that random forest outperforms other algorithms achieving a maximum accuracy of 98.5% and an F-score of 0.932. …”
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Anomaly Detection Based Framework for Profile Monitoring
Published 2023Get full text
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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FPGA-Based Network Traffic Classification Using Machine Learning
Published 2019Get full text
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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
Published 2024“…UniBFS exploits the inherent characteristic of binary algorithms-binary coding-to search the entire problem space for identifying relevant features while avoiding irrelevant ones. …”
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A systematic review of recent advances in the application of machine learning in membrane-based gas separation technologies
Published 2024“…The fingerprinting and descriptors are two commonly approach for polymer featurization. In terms of algorithms, <u>neural networks</u> (NNs), random forest (RF), and gaussian process regression (GPR) are among the most extensively applied methods. …”
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Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tr...
Published 2020“…The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. …”
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Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
Published 2021“…We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. …”