199 skills found · Page 3 of 7
LuChang-CS / ChetCodes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
tendence / Deep Fashion AttributeA sub-function of deep fashion network for attribute prediction
yanzhanglab / Graph2GOGraph-based representation learning method for protein function prediction
YihePang / DisoFLAGAccurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model
psipred / FFPredGANImproving protein function prediction with synthetic feature samples created by generative adversarial networks
vkgnandhu177 / Bayesian Regression And Bitcoin# Bayesian-Regression-to-Predict-Bitcoin-Price-Variations Predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, we will have to familiarize ourself with a machine learning technique, Bayesian Regression, and implement this technique in Python. # Datasets We have the datasets in the data folder. The original raw data can be found here: http://api.bitcoincharts.com/v1/csv/. The datasets from this site have three attributes: (1) time in epoch, (2) price in USD per bitcoin, and (3) bitcoin amount in a transaction (buy/sell). However, only the first two attributes are relevant to this project. To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In _90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices. # Project Requirements We are expected to implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper. The main parts to focus on are Equation 6 and the Predicting Price Change section. # Logic in bitcoin.py 1. Compute the price variations (Δp1, Δp2, and Δp3) for train2 using train1 as input to the Bayesian Regression equation (Equations 6). Make sure to use the similarity metric (Equation 9) in place of the Euclidean distance in Bayesian Regression (Equation 6). 2. Compute the linear regression parameters (w0, w1, w2, w3) by finding the best linear fit (Equation 8). Here you will need to use the ols function of statsmodels.formula.api. Your model should be fit using Δp1, Δp2, and Δp3 as the covariates. Note: the bitcoin order book data was not available, so you do not have to worry about the rw4 term. 3. Use the linear regression model computed in Step 2 and Bayesian Regression estimates, to predict the price variations for the test dataset. Bayesian Regression estimates for test dataset are computed in the same way as they are computed for train2 dataset – using train1 as an input. 4. Once the price variations are predicted, compute the mean squared error (MSE) for the test dataset (the test dataset has 50 vectors => 50 predictions).
AlexanderKroll / KM Prediction FunctionNo description available
IBG4-CBCLab / TopECProtein function prediction using protein structures and deep graph neural networks.
SamusRam / ProFunLibrary of models for Protein Function prediction (part of the 18th top solution out of 1625 teams in CAFA5)
vita-epfl / Stay On TrackOfficial implementation of paper "Stay on Track: Novel Loss Functions for Vehicle Trajectory Prediction"
weijiaheng / Multi Class Peer Loss FunctionsLearning with Noisy Labels by adopting a peer prediction loss function (deep learning & multi-class version).
ltnetcase / BedAnnoAnnotate genomics variations of hg19 by using a BED format database, which construct from NCBI annotation release 104
imShub / DigifarmerDigiFarmer is an Artificial Intelligence and Machine Learning based project which can perform various operations/functions related to farming prediction such as Crop Quality, Yeild Prediction, Disease Detection and Weed Detection, etc. This Project is build using Flutter with dart and for backend we used the ML model's as TenserflowLite.
fchirono / Amiet ToolsCollection of Python functions for prediction of turbulence-flat plate interaction noise using Amiet's model
Charrick / SDN2GOA Deep Learning Model for Protein Function Prediction
HUBioDataLab / ProtHGTA Heterogeneous Graph Transformer (HGT)-based model for protein function prediction using biological knowledge graphs and protein language models
BIOSmode / PositionBasedonCommunicationSatelliteThis software has developed a positioning software based on the Iridium satellite signal based on the C/C++ language and windows + QT platform, which basically realizes the following functions: 1. Doppler information processing and extraction and image presentation after the hardware collects the input data; 2. TLE update tool mainly for Iridium; 3. Automatic identification, overhead satellite prediction and positioning based on the extracted Doppler information and the satellite number of the TLE file, and displayed on the graphical interface based on Baidu Map API;
jddeguia / Compare Forecast ModelsEnergy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.
cmu-snap / SLOMORelevant Material for SIGCOMM 2020 paper "Contention-Aware Performance Prediction for Virtualized Network Functions"
bioinfocqupt / SfcnnA scoring function model based on 3D convolutional neural network for protein-ligand binding affinity prediction.
