Dr Ali Asghar Heidari's Projects
Implemented Adam optimizer in python
This is a collection of AI ecosystem, which gathers and organizes various interesting and useful AI-related projects
Jupyter notebooks and Python code for analyzing air quality (fine particle, PM2.5)
Ali Asghar Heidari has been an Exceptionally Talented Researcher with the School of Computing, National University of Singapore (NUS), University of Tehran, and an elite researcher of Iran’s National Elites Foundation (INEF). He was born in 1989 and has studied information systems as an outstanding ranked one student with several awards from the College of Engineering, University of Tehran. He has been ranked among the top scientists for Computer science prepared by Guide2Research (https://www.guide2research.com/u/ali-asghar-heidari), the best portal for computer science research, as an outstanding researcher with an impressive record of cooperation on many international research projects with different top researchers from the optimization and artificial intelligence community. He has been ranked in the world’s top 2% scientists list of Stanford University, and Publons has recognized him as the top 1% peer reviewer in computer science and cross-field because he has reviewed more than 350 ISI papers for top journals he published on them. He has authored more than 110 research articles with over 6300 citations (i10-index of 74 and H-index of 44) in prestigious international journals, such as IEEE internet of thing, IEEE Transactions on Industrial Informatics, Information Fusion, Information Sciences, Future Generation Computer Systems, Renewable, and Sustainable Energy Reviews, Energy, Cleaner Production, Energy Reports, Energy Conversion and Management, Applied Soft Computing, Knowledge-Based Systems, IEEE Access, and Expert Systems with Applications. He has several highly cited and hot cited articles. His research interests include performance optimization, advanced machine learning, evolutionary computation, optimization, prediction, solar energy, information systems, and mathematical modeling. He was the second top reviewer and “outstanding reviewer” of applied soft computing journal in 2018. For more information, researchers can refer to his website https://aliasgharheidari.com.
Config files for my GitHub profile.
An experimental open-source attempt to make GPT-4 fully autonomous.
A curated list of awesome things related to artificial intelligence tools around the world wide web
This repo includes ChatGPT prompt curation to use ChatGPT better.
An awesome repository of community-curated applications of ChatGPT and other LLMs im computational biology
A curated list of prompts, tools, and resources regarding the GPT-4 language model.
Collection of Open Source Projects Related to GPT/GPT相关开源项目合集🚀、精选🛠
Probably the best curated list of data science software in Python.
This is a PM2.5 Prediction Model using LSTM. The data is taken from UCI repository. Beijing PM2.5 data 2010-2014.
ChatGPT clone using openAI API
this app is a ChatGPT Clone with DALL.E using OpenAIs text-davinci-003 and image generation Model
A ChatGPT clone along with an image generator Machine Learing model developed by me.
A repository of 60 useful data science prompts for ChatGPT
AI image generator, using openai DALL-E API 🎉
Double Mutational Salp Swarm Algorithm: From Optimal Performance Design to Analysis, 04 October 2022 in Journal of Bionic Engineering
Easy to use model parallel large language models in JAX/Flax with pjit support on cloud TPU pods.
exploratory data analysis best arranged notebooks (beginner to advance)
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Source codes for HHO paper: Harris hawks optimization: Algorithm and applications: https://www.sciencedirect.com/science/article/pii/S0167739X18313530. In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).
A project of using machine learning model (tree-based) to predict short-term instrument price up or down in high frequency trading.
Visit: https://aliasgharheidari.com/HGS.html. HGS optimizer is a population-based method with stochastic switching elements that enrich its main exploratory and exploitative behaviors and flexibility of HGS in dealing with challenging problem landscapes. The algorithm has been compared to LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods.
The source codes of this algorithm are also publicly available at https://aliasgharheidari.com/INFO.html. This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems.
Collection of scripts and tools related to machine learning
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent