refactor(docs): update papers links [cd build]

Author: simonprovost

README.md

@@ -50,13 +50,13 @@
 
 `Auto-Scikit-Longitudinal` (Auto-Sklong) is an Automated Machine Learning (AutoML) library, developed upon the
 [`General Machine Learning Assistant (GAMA)`](https://openml-labs.github.io/gama/master/index.html#) framework, 
-introduces a brand-new [`search space`](https://auto-sklong.readthedocs.io/en/latest/tutorials/search_space/) leveraging both
+introducing a brand-new [`search space`](https://auto-sklong.readthedocs.io/en/latest/tutorials/search_space/) leveraging both
 [`Scikit-Longitudinal`](https://scikit-longitudinal.readthedocs.io/latest/) and [`Scikit-learn`](https://scikit-learn.org/stable/) 
 models to tackle the Longitudinal machine learning classification tasks.
 
-`Auto-Sklong` comes with various search methods to explore the [`search space`](https://auto-sklong.readthedocs.io/en/latest/tutorials/search_space/) introduced, such as `Bayesian Optimisation`.
+For more scientific details, you can refer to our [paper](https://doi.org/10.1109/BIBM62325.2024.10821737) published by `IEEE` in the [IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024 Edition](https://ieeexplore.ieee.org/xpl/conhome/10821710/proceeding).
 
-For more details, visit the [official documentation](https://auto-sklong.readthedocs.io/en/latest/).
+`Auto-Sklong` comes with various search methods to explore the [`search space`](https://auto-sklong.readthedocs.io/en/latest/tutorials/search_space/) introduced, such as `Bayesian Optimisation`.  For more details, visit the [official documentation](https://auto-sklong.readthedocs.io/en/latest/).
 
 ---
 

docs/publications.md

@@ -9,18 +9,16 @@ hide:
 
 Within each paper's tab, we will point to which Git branch to see the experiments of a given paper.
 
-=== "Scikit-Longitudinal official paper"
+=== "Auto-Sklong official paper"
 
     **Paper Name**: Auto-Sklong: A New AutoML System for Longitudinal Classification
 
     **Dataset Source**: https://www.elsa-project.ac.uk/
 
+    **Paper Link**: [10.1109/BIBM62325.2024.10821737](https://doi.org/10.1109/BIBM62325.2024.10821737)
+    
     **Git branch**: Incoming.
 
-    **DOI**: [10.1109/BIBM62325.2024.10821737](https://doi.org/10.1109/BIBM62325.2024.10821737)
-
-    !!! success "Status"
-        Paper has been accapted to 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
 
 !!! tip "Before You Begin"
     Ensure you have your dataset ready in a compatible format (e.g., CSV) and are familiar with the basic usage. 
@@ -34,9 +32,7 @@ Within each paper's tab, we will point to which Git branch to see the experiment
 
     **Publisher**: Journal of Open Source Software (JOSS)
 
-    **Paper Status**: Pre-Approved, current public peer review, available at [JOSS-Review](https://github.com/openjournals/joss-reviews/issues/8481)
-
-    **Paper Link**: Will be available at [JOSS](https://joss.theoj.org/)
+    **Paper Link**: [https://joss.theoj.org/papers/10.21105/joss.08481](https://joss.theoj.org/papers/10.21105/joss.08481)
 
     **Used For**: This library is used throughout the search space of the Auto-Sklong system.
 

pyproject.toml

@@ -2,7 +2,7 @@
 name = "Auto-Sklong"
 description = "A package for automated machine learning based on scikit-learn and sklong to tackle the longitudinal machine learning classification tasks."
 readme = "README.md"
-version = "0.0.8"
+version = "0.0.9"
 authors = [
     { name = "Provost Simon", email = "[email protected]" }, # First author & maintainer
     { name = "Pieter Gijsbers", email = "[email protected]" }, # Author of GAMA, credited for the original work
@@ -66,6 +66,7 @@ Documentation = "https://auto-sklong.readthedocs.io/en/latest/"
 Releases = "https://github.com/simonprovost/auto-sklong/releases"
 Repository = "https://github.com/simonprovost/Auto-Sklong"
 Issues = "https://github.com/simonprovost/Auto-Sklong/issues"
+"Scientific Paper" = "https://doi.org/10.1109/BIBM62325.2024.10821737"
 
 [project.scripts]
 gama = "gama.utilities.cli:main"