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REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION
Published 2006“…Apparently, latency is one of the most frequently cited shortcomings of current Virtual Reality (VR) applications. To compensate latency, previous prediction mechanisms insert a complex mathematical algorithm, which may not be appropriate for complex virtual training applications. …”
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Virtual reality in algorithm programming course: practicality and implications for college students
Published 2024“…The reliability of VR supports various variations in learning, including learning programming algorithms. …”
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Harmonizing Tradition and Technology: A Review of Tarannum and Its Prospects in Computing Research
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Robotics in Education
Published 2026“…Subsequent chapters examine new trends such as VR, IoT, and data-driven assessment alongside robotics's global influence in education and career readiness strategies. …”
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Design of experiments meets immersive environment: optimising eating atmosphere using artificial neural network
Published 2022“…While the results of our study are of obvious interest to those in the hospitality industry, this work further highlights the transferability of methods across different disciplines and the applicability of time-tested methods to new emerging areas.…”
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Effect of chaos noise on the learning ability of back propagation algorithm in feed forward neural network
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A modified generalized RBF model with EM-based learning algorithm for medical applications
Published 2006“…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA
Published 2012“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. …”
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A dataset for emotion recognition using virtual reality and EEG (DER-VREEG): Emotional state classification using low-cost wearable VR-EEG headsets
Published 2022“…Secondly, we use a low-cost wearable EEG headset that is both compact and small, and can be attached to the scalp without any hindrance, allowing freedom of movement for participants to view their surroundings inside the immersive VR stimulus. Finally, we evaluate the emotion recognition system by using popular machine learning algorithms and compare them for both intra-subject and inter-subject classification. …”
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Ensemble dual recursive learning algorithms for identifying flow with leakage
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Active force control with iterative learning control algorithm for a vehicle suspension
Published 2013“…The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. …”
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Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding
Published 2007“…The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.…”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm
Published 2018“…Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. …”
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