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 19 of 19 for “"CNN+LSTM"”.
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A CNN–LSTM–Attention Hybrid Architecture for Real-Time Intrusion Detection at the Data Link Layer
… that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) units, and an Attention mechanism for real-time detection of Layer 2 intrusions. A novel dataset, BCCC-DLLayer-IDS-2025, was developed as part of this research, comprising over 4.6 million labeled flow records …
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Development of an Emotion Recognition Classifier from Body Language Using Deep Learning for the Children with Autism to Help Identifying Human Emotions
… from body language: Convolution Neural Network (CNN), the combination of CNN and Recurrent Neural Network (CNN+RNN), and the combination of CNN and Long Short-Term Memory (CNN+LSTM). For this research, though the CNN model provides better accuracy than CNN+RNN and CNN+LSTM, CNN model ignores the …
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Development of a Bagging-based Ensemble Model for ECG Classification
… and recognition, is rapidly growing. CNN, LSTM, and Transformer models have demonstrated in various studies that, when implemented with robust architectures and supported by ample datasets, they can achieve highly accurate results. This study explores the application of bagging …
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Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model
… learning methods are used for prediction: CNN-LSTM and BiLSTM-BO-LightGBM. After training the models, the algorithm creates an optimal portfolio of assets over a simulated year of trading. The symmetric mean absolute percentage error of the algorithms on unseen data evaluates the prediction …
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Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks
… term memory networks, and hybrid models such as CNN- LSTM and ConvLSTM. Computer simulation results show that all artificial neural networks are able to provide satisfactory prediction. Among them, the multi-layer feedforward neural networks require less training time and often give reasonable …
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The Influence of AR Head-Mounted Displays on Spatial Perception and Worker Response in Construction Training
… environment, deep learning models, including 2D CNN-LSTM sequence modeling and 3D CNN-LSTM architectures that are applied to predict temporal and cognitive state changes from 4D EEG input (frequency, amplitude, time, channels), extending the framework from measurement to prediction. Together, …
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Speech enhancement using multisensory cooperative computing
… subtraction with visual speech detection. A CNN–LSTM module classifies short lip sequences into speech/no-speech labels to guide noise estimation and subtraction, overcoming the unreliability of audio-only voice activity detection (VAD) at low SNR. By isolating noise-only segments using …
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Machine Learning for Predicting Prosthetic Limb Movements
… these challenges, a Long Short-Term Memory (LSTM) based deep learning model was implemented to learn sequential patterns in time-series sEMG data from three selected exercises in the Ninapro DB5 dataset, and to predict intended limb movement. The Ninapro DB5 dataset provides captured sEMG …
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Physics Guided Machine Learning algorithm for MAX-DOAS retrieval
… that a ML model with appropriate architecture (CNN+LSTM) is capable of extracting aerosol extinction coefficient profile, single scattering albedo and asymmetry factor from one MAX-DOAS scan. Then more realistic atmosphere states were used for generating the training set. Due to the high time …
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Evaluating Mental Workload for AR Head-Mounted Display Use in Construction Assembly Tasks
… proposed by using (1) Long Short-Term Memory (LSTM) and (2) one-dimensional Convolutional Neural Network (1D CNN)-LSTM for forecasting EEG signal and, classifying task conditions and mental workload levels respectively. The approaches are tested to be effective and reliable for predicting and …
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Data driven modeling and MPC Based control for Pathological Tremors
… composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. This is then further extended by adding …
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Surface electromyography signal classification using SFDN+DNN for hand gesture recognition
… classifier used in this thesis is a Time Wrapped CNN+LSTM. The contribution of this research is to predict the hand movements (hand gestures) with a very high accuracy in order to enhance the efficiency of mechanical prosthetic hands and mimic the natural hand movement. To ensure the highest …
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Automatic feature extraction for time series analysis using deep and machine learning
… signals using deep learning approaches like CNNs, LSTMs and transformers etc. The analysis begins with a use case study of industrial collaboration with the lens manu- facturing industry where the process models called CRISP-DM and DMME for industrial data science are implemented to an …
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Modeling Hourly Storm Surges using Deep Learning Techniques
… Three algorithms, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid ConvLSTM are employed to model surges using atmospheric variables (e.g., sea level pressure and winds) as predictors. ConvLSTM outperforms the others in predicting the overall variability of surges, …
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ICT in Public Transport: Exploring the Potential of IoT and Machine Learning in the context of Automatic Passenger Counting Systems.
L'abstract è presente nell'allegato / the abstract is in the attachment
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Deep Multimodal Physiological Learning of Cerebral Vasoregulation Dynamics on Stroke Patients Towards Precision Brain Medicine
… techniques like Convolution Neural Networks (CNN), Mo bileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or …