Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Task classification"”.
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A Novel Approach to Movement Profiling: Multi-Task Classification for Enhanced Orthopedics Assessment
… from participants during a series of functional tasks, chosen for their clinical relevance in assessing lower-limb biomechanics. Preprocessing steps, including noise filtering, temporal resampling, and gap-filling, were applied to the raw data to ensure consistency and comparability across …
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Simultaneous Behavior Onset Detection and Task Classification for Patients with Parkinson Disease Using Subthalamic Nucleus Local Field Potentials
… (PD), as well as to investigate the models for classification of different behavioral tasks performed by PD patient. The detection is based on recorded Local Field Potentials (LFP) of the Subthalamic nucleus (STN), captured through Deep Brain Stimulation (DBS) process.</p> <p>One main part of …
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Tutoring systems based on user-interface dialogue specification
… specification which is utilised, the task classification structure, must be transformed from an operational to a pedagogic ordering. Heuristics are proposed to achieve this, although human expertise is required to apply them. The report approach is best suited to domains with …
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Efficient Continuous Pareto Exploration in Multi-Task Learning
Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced the concept of Pareto optimality to this field and directly cast multi-task learning as multiobjective …
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Machine Learning and Biomechanical Sensing Toward Real-Time In-The-Loop Gait and Joint Health Optimization
… low latency across a wide range of locomotion tasks; and (Aim 3) applied our acoustics sensing approach in a pediatric arthritis cohort to quantify how inflammation-related physiological alterations affect machine learning task classification performance, highlighting its potential as a …
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BantuBERTa : using language family grouping in multilingual language modeling for Bantu languages
… multiple Bantu languages on a higher-order NLP task (NER) and in a simpler NLP task (classification). This proves that this dataset can be used for Bantu multilingual pretraining and transfer to multiple Bantu languages. Additionally, it was researched whether using this Bantu dataset could …
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Development and Evaluation of Methods to Assess Physical Exposures in the Workplace
… the actual work environment to complete their tasks. However, in practice, relatively crude and/or time-consuming methods are often used, including self-reports, observational methods, and simple instrumentation, since directly assessing physical exposures is challenging in the workplace, and …
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Evaluation of Markerless Motion Capture to Assess Physical Exposures During Material Handling Tasks
Manual material handling (MMH) tasks are associated with the development of work-related musculoskeletal disorders (WMSDs). Minimizing the frequency and intensity of handling objects is an ideal solution, yet MMH remains an integral part of many industry sectors, including manufacturing, …
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Objective Assessment Methods for List Mode Imaging System
… of a medical imaging system, both the task and observer have to be well defined. The objective quality of the image depends on how well the specified observer can extract the desired information to perform the task. Classification or detection tasks are often considered in practice. Two …
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Workforce fatigue prediction via wearable sensor data analytics
… ergonomic risk factors, such as repetitive tasks, heavy lifting, and awkward postures, contribute to localised muscle fatigue, which over time can cause chronic injury according to fatigue failure theory. Although observational risk assessments are widely used and influential in setting …
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Data and Computation Efficient Meta-Learning
… present systems that are trained using multiple tasks such that it "learns how to learn" to solve new tasks from only a few examples. These systems can efficiently solve new, unseen tasks drawn from a broad range of data distributions, in both the low and high data regimes, without the need for …