199 skills found · Page 2 of 7
mahdi-usask / Wind Speed Forecasting For Wind Power Generation Plant. Neural Network ML Based Prediction Algo. For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the recent literature in order to tackle this problem. This paper will use the artificial neural network (ANN) approach to get a prediction of wind speed using historical wind speed data. The historical data used here were gathered from NREL website ,as hourly basis from 80 meter hub height. The measurement location is NREL Flatirons Campus (M2). The readings displayed are derived from instruments mounted on or near a 82 meter (270 foot) meteorological tower located at the western edge of the Flatirons Campus (formerly NWTC) and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado. The tower is located at 39o 54' 38.34" N and 105o 14' 5.28" W (datum WGS84) with its base at an elevation of 1855 meters (6085 feet) above mean sea level. Data from year 2014 to 2018, in total 5 years of data has been used here as dataframe. Here the neural network has been implemented by Tensorflow’s Keras API. The used model is “sequential”. Four dense layer has been used in the optimized model. LSTM(Long- short-term memory) architecture has been used here as neural network architecture. Activation function being used in the dense layers are dropout function. The optimizer being used here is Adam. Here various range of Dropout function has been examined and chosen the best fit for this model. Also this paper examined various kinds of optimization method and used the best fitted one. The model performances were evaluated using the mean squared error using adam optimizer. Various kinds of data analytic techniques has been used here for better visualization and in depth understanding of the dataset and its variables. Since it is mostly a time series data so in the analytic part how the data is being changed with time has been shown. From the result of the predicted dataset it can be state that, this wind speed prediction model works best for all kinds of winds speed besides overfitted/ abnormal wind speeds which is a very rare case scenario.
AnswerXuan / Galformer Improved Transformer For Time Series PredictionGalformer: A Transformer with Generative Decoding and a Hybrid Loss Function for Multi-Step Stock Market Index Prediction
melihbodur / Bitcoin Analysis Python Bitcoin is widely used cryptocurrency for digital market. It is decentralised that means it is not own by government or any other company.Transactions are simple and easy as it doesn’t belong to any country.Records data are stored in Blockchain.Bitcoin price is variable and it is widely used so it is important to predict the price of it for making any investment.This project focuses on the accurate prediction of cryptocurrencies price using neural networks. We’re implementing a Long Short Term Memory (LSTM) model using keras; it’s a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.).We have used different activation function for analysing the efficiency of the system.Instead of historical data we are using live streaming data for better accuracy.
deadskull7 / Agricultural Price Prediction And Visualization On Android AppIn Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .
radi-cho / Tfjs FirebaseTrain a model with data from Firestore, save it to Cloud Storage and make predictions in Cloud Functions - entirely using NodeJS
ColeLab / ActflowToolboxThe Brain Activity Flow ("Actflow") Toolbox. Tools to quantify the relationship between connectivity and task activity through network simulations and machine learning prediction. Helps determine how connections contribute to specific brain functions.
biomed-AI / SPROF GOFast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion
drop-out / NonparametricKNNA KNN regressor that gives predictions based on customized loss function.
PeterRochford / SkillMetricsToolboxThis toolbox contains a collection of Matlab functions for calculating the skill of model predictions against observations.
ColinFX / Prot2Text V2Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment
stamakro / GCN For Structure And FunctionCode for reproducing results of "Unsupervised embeddings is all you need for protein function prediction"
JDE65 / CustomLossCustom Loss functions for asset return prediction with deep learning regression
yourh / DeepGraphGODeepGraphGO: graph neural network for large-scale, multispecies protein function prediction
gohsyi / PeerLossLearning with Noisy Labels by adopting a peer prediction loss function.
NasimAbdollahi / NodeCoderNodeCoder is a general framework based on graph convolutional neural network for protein function prediction.
jianlin-cheng / TransFunTransformer for protein function prediction
btbpanda / CAFA5 Protein Function Prediction 2nd PlaceNo description available
lyjps / Struct2GOStruct2GO:protein function prediction based on Graph pooling algorithm and AlphaFold2 structure information
pimlphm / Physics Informed Machine Learning Based On TCNA hybrid approach using physical information (PI) lightweight temporal convolutional neural networks (PI-TCN) for remaining useful life (RUL) prediction of bearings under stiffness degradation. It consists of three PI hybrid models: a) PI feature model (PIFM) - constructs physical information health indicators (PIHI) to increase the feature space; b) PI layer model (PILM) - encodes the physics governing equations in a hidden layer; c) PI layer-based loss model (PILLM) - designs PI conflicting losses, taking into account the integration of the physics input-output relationship module into the differences before and after the loss function. I have provided the original model and basic methodology here and welcome further optimisation of the structure and associated training methods. Interestingly, it is not the number of layers of physics knowledge that is more useful; the right structure for the right physics knowledge is the key to success. Similar to pure DL tuning, to design neural networks based on full physical knowledge is a direction that I am very interested in and would like to discuss with you.
Azure-Samples / Functions Python Tensorflow TutorialSource code for "Make machine learning predictions with TensorFlow and Azure Functions" tutorial on Microsoft Docs
