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Ahmed Fathalla预答辩公告
浏览次数:日期:2021-04-08编辑:研究生秘书

答辩公告

论文题目

On Improving the Product Price Prediction Methods Using Deep Learning Approaches

答辩人

Ahmed Fathalla

指导教师

Professor KENLI LI

答辩委员会

主席

学科专业

Computer Science and Technology

学院

College of Computer Science and Electronic Engineering

答辩地点

Online pre-defense

答辩时间

2020年04月12日

学位论文简介

Due to today’s transition to online shopping in the last few decades, predicting product prices of e-commerce is gaining importance. Likewise, commodity prices are a significant factor for mining projects, as price volatility is an important parameter for mining company evaluation and investment decision making. In this work, we propose four contributions (i.e., prediction models) to address four price prediction problems using statistical, machine learning and deep learning methods.  We utilized several algorithms from each domain.

The first two contributions address the price prediction problem of e-commerce products to fill in the gap of the current price prediction models for addressing second-hand items of e-commerce products.

The third and fourth contributions are introduced for addressing price forecasting of two vital energy markets, i.e., coal and oil markets using hybrid deep learning and ensemble learning methods, respectively.

In summary, the results demonstrate the superiority of the proposed methods over the baseline models. All the proposed methods lead to performance improvement.

主要学术成果

  1. Ahmed Fathalla, Ahmad Salah, Kenli Li, Keqin Li and Piccialli Francesco, Deep end-to-end learning for price prediction of second-hand items, Knowledge and Information Systems, 2020. SCI IF:2.936 (Q1).

  2. Zakaria Alameer, Ahmed Fathalla, Kenli Li, Haiwang Ye and Zhang Jianhua, Multistep-ahead forecasting of coal prices using a hybrid deep learning model, Resources Policy, 2020.  SSCI IF:3.986 (Q1).

  3. Ahmed Fathalla, Ken Li, Ahmad Salah and Marwa F Mohamed, An LSTM-based Distributed Scheme for Data Transmission Reduction of IoT Systems, Neurocomputing, 2021. SCI IF:4.438 (Q1).