Essays about: "Mobile learning effectiveness"
Showing result 1 - 5 of 11 essays containing the words Mobile learning effectiveness.
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1. Explainable AI for Multi-Agent Control Problem
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. READ MORE
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2. Integration of Continual Learning and Semantic Segmentation in a vision system for mobile robotics
University essay from Luleå tekniska universitet/RymdteknikAbstract : Over the last decade, the integration of robots into various applications has seen significant advancements fueled by Machine Learning (ML) algorithms, particularly in autonomous and independent operations. While robots have become increasingly proficient in various tasks, object instance recognition, a fundamental component of real-world robotic interactions, has witnessed remarkable improvements in accuracy and robustness. READ MORE
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3. Road Damage Segmentation for Mobile Hardware
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The detection and early repair of road damage are paramount for the quality and safety of roads. Current detection efforts typically rely on Deep Learning methods for object detection with bounding boxes, with calculations performed on high-performance hardware. READ MORE
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4. Time-series Generative Adversarial Networks for Telecommunications Data Augmentation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Time- series Generative Adversarial Networks (TimeGAN) is proposed to overcome the GAN model’s insufficiency in producing synthetic samples that inherit the predictive ability of the original timeseries data. TimeGAN combines the unsupervised adversarial loss in the GAN framework with a supervised loss adopted from an autoregressive model. READ MORE
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5. Sensor numerical prediction based on long-term and short-term memory neural network
University essay from Mittuniversitetet/Institutionen för informationssystem och –teknologiAbstract : Many sensor nodes are scattered in the sensor network,which are used in all aspects of life due to their small size, low power consumption, and multiple functions. With the advent of the Internet of Things, more small sensor devices will appear in our lives. READ MORE