Hello, I'm Alireza

Jahandoost

Glosseta is an open, decentralized Web3(i.e. the new internet backed by blockchains) glossary for protocols, terms, people, applications, and more. Glosseta aims to be the long term companion of anyone looking to demystifying the Web3 space and ecosystem with the north star vision of being the best open, localized and decentralized educational platform for anybody of any skill level.

A portfolio project in deployment with React and Sanity

After some projects, finally, I decided to start my portfolio. I tried to make it as customizable as I can, so that others can use it as well. The features that are currently supported: * customizable `About me` and `Contact me` sections * unlimited number of projects to include * categorizing projects in project sections * add unlimited number of skills * categorizing skills into `Fluented` and `Familiar` categories * filtering projects by skills or project sections * search in projects

This is a practical project, written with Laravel, to strengthen my understanding of laravel. Features: * Create posts with inline editor * Categorize posts in categories * Create new categories * Create new roles with desired permissions * Change users roles * View post statistics

This is a practical project, to get involved with redux, and how it can be tested by jest js The features that are currently supported: * Get weather information of a specific place in a specific time

[Paper's IEEE webpage](https://ieeexplore.ieee.org/abstract/document/10326291) Crude oil, as a prerequisite for many industries, is vital in today’s world. In this regard, predicting its future price is crucial for many purposes. Even though plenty of research has been done in this field with many methods, such as evolutionary algorithms, neural networks (NN), and other machine learning techniques, because of the extremely unpredictable nature of crude oil prices, the outcomes are not satisfactory. This study employs 39 features for oil price prediction and proposes a hybrid architecture for deep NNs (DNN) to take advantage of features in different periods. Attention-based DNNs are utilized in the proposed architecture, and the comparisons are based on the mean absolute error. The results show that (1) attention-based DNNs are useful for forecasting the crude oil price with many features, and (2) the proposed architecture can enhance the accuracy of previous models.