122 skills found · Page 1 of 5
nekhtiari / Image Similarity Measures:chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
cshum / Imagorvideoimagor video thumbnail server in Go and ffmpeg C bindings
thieu1995 / PermetricsArtificial intelligence (AI, ML, DL) performance metrics implemented in Python
fuhailin / Recommender System利用MovieLens数据，Pearson相似度，分别基于User和Item构建一个简单的kNN推荐系统，并给出RMSE评测
akshitvjain / Product Sales ForecastingForecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models
PetWorm / Sim3 Evaluate ToolA matlab tool to evaluate trajectory RMSE using sim3 alignment.
iameminmammadov / Dash Predictive MaintenanceDashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.
ShreyaShinde25 / Data Driven Modelling Of Lithium Ion BatteriesDeveloped a data-driven prognostic model using the Long short-term memory (LSTM) algorithm to predict the state of charge (SoC) and state of health (SoH) of the lithium-ion battery where the dataset was taken from the NASA Repository. The proposed LSTM algorithm was compared against other deep learning algorithms based on RMSE value.
nathan-b / RmseRPGMaker Save Editor
storieswithsiva / Movie Recommendation Netflix🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧
AmirhosseinHonardoust / Stock LSTM ForecastingPredict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.
Tirgit / MissComparemissCompare R package - intuitive missing data imputation framework
salmansust / Machine Learning TSF Petroleum ProductionTime series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy
purus15987 / CSI PredictionA comparative study of deep learning models for predicting Channel State Information (CSI) in massive MIMO systems. Integrates COST2100 dataset with STNet compression and evaluates models based on NMSE, RMSE, and spectral efficiency.
ruofeidu / ImageQualityCompareCompare the quality between two images using RMSE, SSIM, and PSNR. (part of I3D 2018 Montage4D.com)
AmirhosseinHonardoust / Demand ForecastingEnd-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day forecasts with confidence intervals. Reproducible scripts and visualizations for portfolio showcase.
kushwahavishal646 / Load Forecasting Using Different Deep Learning Architecturesthis project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.
AmirhosseinHonardoust / LSTM Time Series ForecastingA hands-on project for forecasting time-series with PyTorch LSTMs. It creates realistic daily data (trend, seasonality, events, noise), prepares it with sliding windows, and trains an LSTM to make multi-step predictions. The project tracks errors with RMSE, MAE, MAPE and shows clear plots of training progress and forecast results.
asuri2 / CarND Extended Kalman Filter P6Self Driving Car Project 6 - Sensor Fusion(Extended Kalman Filter)
yuan508 / CNN LSTM Battery SOC Prediction本项目实现了一个 CNN + BiLSTM 混合深度学习模型，用于 时间序列预测与回归任务（如电池充电曲线建模、健康状态估计等）。 数据处理：滑动窗口切片 + 归一化，自动生成训练/验证/测试集。 模型结构： CNN 提取局部特征 BiLSTM 捕捉双向时序依赖 全连接层输出预测结果 训练特性：早停机制、梯度裁剪、最佳模型权重保存 结果输出：支持 RMSE/MAE 评估，保存预测结果对比表格与可视化图表 该框架可作为 时间序列建模的基础模板，便于快速复现与扩展到不同场景。
