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All Journal International Journal of Electrical and Computer Engineering
Amghar, Mustapha
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Computer Science & IT Electrical & Electronics Engineering

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Automated machine learning: the new data science challenge Slimani, Ilham; Slimani, Nadia; Achchab, Said; Saber, Mohammed; El Farissi, Ilhame; Sbiti, Nawal; Amghar, Mustapha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4243-4252

Abstract

The world is changing quite rapidly while increasingly tuning into digitalization. However, it is important to note that data science is what most technology is evolving around and data is definitely the future of everything. For industries, adopting a “data science approach” is no longer an option, it becomes an obligation in order to enhance their business rather than survive. This paper offers a roadmap for anyone interested in this research field or getting started with “machine learning” learning while enabling the reader to easily comprehend the key concepts behind. Indeed, it examines the benefits of automated machine learning systems, starting with defining machine learning vocabulary and basic concepts. Then, explaining how to, concretely, build up a machine learning model by highlighting the challenges related to data and algorithms. Finally, exposing a summary of two studies applying machine learning in two different fields, namely transportation for road traffic forecasting and supply chain management for demand prediction where the predictive performance of various models iscompared based on different metrics.
Homogenous and interoperable signaling computer interlocking through IEC 61499 standard Abourahim, Ikram; Eleuldj, Mohsine; Amghar, Mustapha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6228-6239

Abstract

The technological evolution of signaling systems has created a dependency from infrastructure managers to suppliers and industrials dominating the market. Indeed, for each deployed computer interlocking, the modification of field equipment is required to allow an adaptation with the new interlocking in terms of communication protocols and logical interface. In addition, to ensure safe traffic of trains, the communication of railway signaling data is necessary between interlockings. However, delayed deployments from one station to another make the establishment of communication channels costly and difficult, or even impossible, since each supplier keeps confidential its communication protocols and usually opts for interfacing based on wired logic. This paper presents our approach to a homogeneous architecture of interlocking meeting modularity requirements, interoperability, and logical interfacing between interlockings. This approach relies on a classification of internal functions of the computer interlocking, a distribution of the execution of those functions and making useful information available for interfaces between adjacent interlockings through the IEC 61499 standard coupled with service-oriented architecture (SOA).
Machine learning-driven stock price prediction for enhanced investment strategy Guennioui, Omaima; Chiadmi, Dalila; Amghar, Mustapha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5884-5893

Abstract

Forecasting stock prices, a task complicated by the inherent volatility of the stock market, poses a significant challenge. The ability to accurately forecast stock prices is crucial, as it provides investors with crucial insights, enabling them to make informed strategic decisions. In this paper, we propose a novel investment strategy that relies on predicting stock prices. Our approach utilizes a hybrid predictive model that combines light gradient-boosting machine (LightGBM) and extreme gradient boosting (XGBoost). This model is designed to generate short to medium-term forecasts for a wide range of stocks. The strategy has shown promising results, surpassing the local market indices used as benchmarks in terms of both risk and return. Our findings demonstrate the strategy's effectiveness in both upward and downward market trends, underscoring its potential as a robust tool for portfolio management in diverse market conditions.
Co-Authors Abourahim, Ikram Achchab, Said Chiadmi, Dalila El Farissi, Ilhame Eleuldj, Mohsine Guennioui, Omaima Saber, Mohammed Sbiti, Nawal Slimani, Ilham Slimani, Nadia