Pinned Repositories
A-stock-prediction-algorithm-based-on-machine-learning
(陆续更新)重新整理过的基于机器学习的股票价格预测算法,里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测,包含:LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM等
COA
A new metaheuristic for global optimization problems proposed in the IEEE Congress on Evolutionary Computation (CEC), 2018
GWOSVR
Sanitized Grey Wolf Optimizer(SGWO)-Support Vector Regressor (SVR)
IntelligentMachine
天池工业AI大赛-智能制造质量预测,排名89/2539
LFforecast
电力系统短期负荷预测
LSTM-demo
LSTM进行时间序列预测
lstm-power-load-forecast
LSTM单变量时间预测(电量负载预测)
machine_learning_python
不调库,纯python实现机器学习经典算法
ML_CHL_prediction
基于Keras框架,结合LSTM/GRU/Arima/WNN实现多方式的水质参数预测
STudy
时空预测模型PyTorch复现
Quan1995417's Repositories
Quan1995417/Transfer-Learning-for-COVID-19-cases-and-deaths-forecast-using-LSTM-network
The data and codes realted to published work:
Quan1995417/smart-ml
Machine Learning toolbox
Quan1995417/matlab-ddsp-plugin
Quan1995417/Denoising-neural-network
This filder contains most important works that i have made with deoinsing Autoencoders.
Quan1995417/Use-SageMaker_XGBoost-convert-Time-Series-into-Supervised-Learning-for-predictive-maintenance
使用SageMaker+XGBoost,将时间序列转换为监督学习,完成预测性维护的实践
Quan1995417/lssvmigwo
Our Load Forecasting using LSSVM tuned by IGWO. Paper coming soon
Quan1995417/Transformer-for-time-series-forecasting-
Time series forecasting by transformer
Quan1995417/COA
A new metaheuristic for global optimization problems proposed in the IEEE Congress on Evolutionary Computation (CEC), 2018
Quan1995417/Sparrow-Search-Algorithm-Matlab
Quan1995417/Lung-Cancer-Classification
The primary goal of developing a smart city is to enhance the quality of lives of its citizens by providing infrastructure and offering smart healthcare. Smart Healthcare plays a significant role in achieving this objective of developing smart cities. Here, in this proposal our objective is to develop a smart health care system which uses artificial intelligence for the development of a decision support system in the medical field for the detection and segmentation of lung cancer. The proposed system is consisting of two phases, First phase will be consisting of various stages like Pre-processing, feature extraction, feature selection, classification and finally segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of Crow Search Optimization Algorithm, later Artificial Neural Network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the Fuzzy K-Means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. This methodology delivers a 96% of accuracy, 100% specificity and sensitivity of 99%. The accuracy of the decision taken by the Smart Health Care System exceed when compared to the accuracy of the decision taken by the doctors.
Quan1995417/container-sim
基于时序数据的容器负载预测和容器节能调度实验
Quan1995417/VGAELDA
VGAELDA: a representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Quan1995417/social-LSTM
复现CVPR2016李飞飞团队提出的轨迹预测网络social-LSTM
Quan1995417/opfunu
A collection of Benchmark functions for numerical optimization problems. Framework of OPtimization FUnction in NUmpy (opfunu)
Quan1995417/pyridge
Supervised Ridge classification. Machine learning algorithms applied to classification machine learning problems
Quan1995417/keras-tcn
Keras Temporal Convolutional Network.
Quan1995417/Binary-Whale-Optimization-Algorithm
Tawhid, M.A., Ibrahim, A.M. Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm. Int. J. Mach. Learn. & Cyber. 11, 573–602 (2020). https://doi.org/10.1007/s13042-019-00996-5
Quan1995417/Feature_selection
Feature selection methods using optimization algroithms
Quan1995417/CEC2017-BoundContrained
Quan1995417/TPA-LSTM
Temporal Pattern Attention for Multivariate Time Series Forecasting
Quan1995417/Nomad-Algorithm
A testing platform for intelligent optimization algorithm based on Matlab with CEC2013 benchmark
Quan1995417/LSTM-for-Time-Series-Forecasting-Pytorch
使用LSTM、GRU、BPNN进行时间序列预测。Using LSTM\GRU\BPNN for time series forecasting. (Pytorch Edition)
Quan1995417/Salp-Swarm-Algorithm-for-Feature-Selection
Application of Salp Swarm Algorithm (SSA) in the feature selection tasks.
Quan1995417/GLCM-DEM
基于灰度共生矩阵(GLCM)提取数字高程模型(DEM)的纹理特征。
Quan1995417/Hybrid-Binary-GWO-FS
Al-Tashi, Q. et al(2019). Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access, 7, 39496-39508. Link for algorithm details: Paper https://ieeexplore.ieee.org/abstract/document/8672550
Quan1995417/Deep_Autoencoder_with_ELM
Deep Autoencoder with Extreme Learning Machines
Quan1995417/Self-Attention-GAN
Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)
Quan1995417/Improved-whale-optimization-in-matlab-and-stance-datasets
A new improved whale optimization algorithm and its use in stance detection
Quan1995417/LFforecast
电力系统短期负荷预测
Quan1995417/models
Models and examples built with TensorFlow