From b66c02bd31b0b1b9e30961899990bf946c8d4f7d Mon Sep 17 00:00:00 2001 From: Thieu Nguyen Date: Mon, 20 Jan 2025 10:18:03 +0700 Subject: [PATCH] Update citation --- README.md | 19 +-- docs/source/pages/support.rst | 27 ++-- tutorials/example_california_housing.ipynb | 176 +++++++++++++++++---- 3 files changed, 171 insertions(+), 51 deletions(-) diff --git a/README.md b/README.md index f0df354..0b272d0 100644 --- a/README.md +++ b/README.md @@ -48,15 +48,16 @@ The paper can be accessed at the following [this link](https://doi.org/10.1016/j Please include these citations if you plan to use this library: -```code -@software{nguyen_van_thieu_2023_8249046, - author = {Nguyen Van Thieu}, - title = {IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine}, - month = aug, - year = 2023, - publisher = {Zenodo}, - doi = {10.5281/zenodo.8249045}, - url = {https://github.com/thieu1995/IntelELM} +```bibtex +@article{van2025intelelm, + title={IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine}, + author={Van Thieu, Nguyen and Houssein, Essam H and Oliva, Diego and Hung, Nguyen Duy}, + journal={Neurocomputing}, + volume={618}, + pages={129062}, + year={2025}, + publisher={Elsevier}, + doi={10.1016/j.neucom.2024.129062} } @article{nguyen2020new, diff --git a/docs/source/pages/support.rst b/docs/source/pages/support.rst index e38cbb9..08520b7 100644 --- a/docs/source/pages/support.rst +++ b/docs/source/pages/support.rst @@ -8,17 +8,20 @@ Citation Request The paper can be accessed at the following `this link `_ -Please include these citations if you plan to use this library:: - - @software{nguyen_van_thieu_2023_8249046, - author = {Nguyen Van Thieu}, - title = {IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine}, - month = aug, - year = 2023, - publisher = {Zenodo}, - doi = {10.5281/zenodo.8249045}, - url = {https://github.com/thieu1995/IntelELM} - } +Please include these citations if you plan to use this library + +.. bibtex:: + + @article{van2025intelelm, + title={IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine}, + author={Van Thieu, Nguyen and Houssein, Essam H and Oliva, Diego and Hung, Nguyen Duy}, + journal={Neurocomputing}, + volume={618}, + pages={129062}, + year={2025}, + publisher={Elsevier}, + doi={10.1016/j.neucom.2024.129062} + } @article{nguyen2020new, title={A new workload prediction model using extreme learning machine and enhanced tug of war optimization}, @@ -39,7 +42,7 @@ Please include these citations if you plan to use this library:: publisher={Elsevier}, doi={10.1016/j.sysarc.2023.102871} } -``` + If you have an open-ended or a research question, you can contact me via nguyenthieu2102@gmail.com diff --git a/tutorials/example_california_housing.ipynb b/tutorials/example_california_housing.ipynb index 79e3829..d852d29 100644 --- a/tutorials/example_california_housing.ipynb +++ b/tutorials/example_california_housing.ipynb @@ -2,14 +2,22 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# Example: Using `IntelELM` for Building a House Price Predictor" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Install `IntelELM` library" ] @@ -17,7 +25,11 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -59,7 +71,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Import libraries" ] @@ -67,7 +83,11 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "from sklearn import datasets, metrics\n", @@ -80,7 +100,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Loading California Housing Dataset" ] @@ -88,7 +112,11 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "california_housing = datasets.fetch_california_housing(as_frame=True)" @@ -96,14 +124,22 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Dataset: Train/Test Split & Preprocessing" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* We choose some features for training our model." ] @@ -111,7 +147,11 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "features = [\"AveRooms\", \"AveBedrms\", \"AveOccup\", \"Population\"]\n", @@ -121,7 +161,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Train/test split" ] @@ -129,7 +173,11 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(data, california_housing.target.values, test_size=0.3, shuffle=True, random_state=1)" @@ -137,7 +185,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Feature scaling with `MinMaxScaler`" ] @@ -145,7 +197,11 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "min_max_scaler = MinMaxScaler()\n", @@ -156,14 +212,22 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Regression with Standard ELM Model" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Tran an `ElmRegressor`" ] @@ -171,7 +235,11 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -195,7 +263,11 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "elm_predicted = elm_regressor.predict(X_test_scaled)" @@ -203,7 +275,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Report regression results" ] @@ -211,7 +287,11 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -229,14 +309,22 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Regression with `MhaElmRegressor`" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Train a model" ] @@ -244,7 +332,11 @@ { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "opt_params = {\n", @@ -259,7 +351,11 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stderr", @@ -314,7 +410,11 @@ { "cell_type": "code", "execution_count": 12, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "mha_elm_predicted = mha_elm_regressor.predict(X_test_scaled)" @@ -322,7 +422,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* Report regression results" ] @@ -330,7 +434,11 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -348,7 +456,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "* It can be seen that using meta-heuristics based ELM reduces both RSME and MAE errors." ] @@ -356,7 +468,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [] } @@ -382,4 +498,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file