240 skills found · Page 1 of 8
pySTEPS / PystepsPython framework for short-term ensemble prediction systems.
wei-mao-2019 / LearnTrajDepcode for learning trajectory dependencies for human motion prediction
hungchun-lin / Stock Price Prediction Using GANIn this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.
gionanide / Speech Signal Processing And ClassificationFront-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
vedic-partap / Event Driven Stock Prediction Using Deep LearningA deep learning method for event driven stock market prediction. Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price movements
Abhijit-Bhumireddy99 / RUL Predictionremaining Useful Life (RUL) Prediction of Mechanical Bearings using Continuous Wavelet Transform (CWT), Convolution Neural Network (CNN), and Long Short Term Memory (LSTM) unit
jiaxiang-cheng / PyTorch LSTM For RUL PredictionPyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.
jinglescode / Time Series Forecasting TensorflowjsPull stock prices from online API and perform predictions using Long Short Term Memory (LSTM) with TensorFlow.js framework
ritikdhame / Electricity Demand And Price ForecastingBuilding Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction
psu1 / DeepRNNlong-term blood pressure prediction with deep recurrent neural networks
suprobe / AT Conv LSTMA Hybrid Deep Learning Model with Attention based ConvLSTM Networks for Short-Term Traffic Flow Prediction
huzaifi18 / RUL PredictionThe project focused on "Battery Remaining Useful Life (RUL) Prediction using a Data-Driven Approach with a Hybrid Deep Model combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM)." This repository aims to revolutionize battery health estimation by leveraging the power of deep learning to predict the remaining useful life
ethz-asl / 3d Vsg3D Variable Scene Graphs for long-term semantic scene change prediction.
luisvalesilva / MultisurvMultimodal deep learning model for long-term cancer survival prediction
sangmin-git / LMC MemoryOfficial PyTorch implementation of "Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning" (CVPR 2021 Oral)
brain-research / Long Term Video Prediction Without SupervisionImplementation of Hierarchical Long-term Video Prediction without Supervision
girishp92 / Human Activity Recognition Using Recurrent Neural Nets RNN LSTM And Tensorflow On SmartphonesThis was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.
GestaltCogTeam / DSformerCode for our CIKM'23 paper DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
rubenvillegas / Icml2017hierchvidTensorflow implementation of the ICML 2017 paper: Learning to Generate Long-term Future via Hierarchical Prediction
johnmartinsson / Blood Glucose PredictionBlood glucose prediction using long short-term memory recurrent neural networks.
