Abdelatif Hafid

Abdelatif Hafid, Maad Ebrahim, Ali Alfatemi et al.

The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially true for the cryptocurrency market, which is known for its extreme volatility, making it challenging for traders and investors to make wise and profitable decisions. This study introduces a machine learning approach to predict cryptocurrency prices. Specifically, we make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We demonstrate our approach through an analysis focusing on the closing prices of Bitcoin cryptocurrency. We evaluate the model's performance through various simulations, showing promising results that suggest its usefulness in aiding/guiding cryptocurrency traders and investors in dynamic market conditions.

Zakaria Lamine, Abdelatif Hafid, Mohamed Rahouti

This study presents a novel mathematical model derived from cohomology, leveraging the KEEL-proven theorem that establishes cohomology as tautological, generated by boundary classes of curves with fixed dual graphs. Simplicial complexes are constructed using skew-commutative graded algebra, and the structure theorem is applied to connect distinct homologies, enabling precise interpretations of the resulting geometric forms. The proposed model is utilized for protein structure analysis and prediction, with a specific application to the Flagellar Motor structure. This approach offers new insights into the geometric and algebraic foundations of biological macromolecular modeling, highlighting its potential for advancement in structural biology.

Abdelatif Hafid, Abdelhakim Senhaji Hafid, Adil Senhaji

Blockchain technology has been gaining great interest from a variety of sectors, including healthcare, supply chain and cryptocurrencies. However, Blockchain suffers from its limited ability to scale (i.e. low throughput and high latency). Several solutions have been appeared to tackle this issue. In particular, sharding proved that it is one of the most promising solutions to Blockchain scalability. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve a good performances (i.e. good throughput with a reasonable latency), but raise security issues. This article attends that analyze the security of the second category. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. Finally, to show the effectiveness of the proposed model, we conduct a numerical analysis and evaluate the results obtained.

Hafid Abdelatif, Senhaji Hafid Abdelhakim, Samih Mustapha

Blockchain like Bitcoin and Ethereum suffer from scalability issues. Sharding is one of the most promising and leading solutions to scale blockchain. The basic idea behind sharding is to divide the blockchain network into multiple committees, where each processing a separate set of transactions, rather than the entire network processes all transactions. In this paper, we propose a probabilistic approach to analyze the security of sharding-based blockchain protocols. Based on this approach, we investigate the threat of Sybil attacks in these protocols. The key contribution of our paper is a tractable probabilistic approach to accurately compute the failure probability that at least one committee fails and ultimately compute the probability of a successful attack. To show the effectiveness of our approach, we conduct a numerical and comparative analysis of the proposed approach with existing approaches.