Blockchain Papers

Soufiane El Amine El Alami, Abderazzak Mouiha, Abdelatif Hafid, Ahmed El Hilali Alaoui

This systematic review examines how machine learning (ML) and deep learning (DL) have transformed forecasting, decision-making, and financial modelling, promoting innovation and efficiency in financial systems. Following PRISMA 2020 guidelines, we analyze 22 peer-reviewed and open-access articles (2024 to 2026) indexed in Scopus, applying ML and DL models across credit risk prediction, cryptocurrency, asset pricing, and macroeconomic policy modeling. The most used models include Random Forest, XG-Boost, Support Vector Machine, Long Short-Term Memory (LSTM), Bidirectional LSTM, Convolutional Neural Network (CNN), and hybrid or ensemble approaches combining statistical and AI methods. ML and DL techniques outperform traditional models by capturing nonlinear dependencies and enhancing predictive accuracy, while explainable AI methods (e.g., SHAP and feature importance analysis) improve transparency and interpretability. Emerging trends include cross-domain applications and the integration of responsible AI in finance. Despite notable progress, challenges remain in interpretability, generalizability, and data quality. Overall, this review provides a comprehensive overview of AI-driven computational finance and outlines future research directions.

Ahmed Asimi

Prime numbers, whose properties are important subjects in mathematics, are also fundamental in computer science notably in IT security, Cryptocurrencies as Bitcoin and Blockchain, cryptography, Code theory notably Error detection codes, integer factorization, and random number generation. Finding prime numbers is too an active area of research in mathematics. There are many methods for identifying and generating them and many primality tests which are often complex and expensive in terms of calculation time, and many conjectures and theorems related to prime numbers, such as the prime number theorem, Goldbach's conjecture, and the Riemann hypothesis. My objective in this work is to propose two conjectures : $1)$ the integer $1+3.2^{20n}$ is not a prime number for all $n=0$ or $1$ mod $3$, and $2)$ the integer $1+3.2^{4+20n}$ is not a prime number for all $n=2$ mod $3$. Keywords : Prime numbers; IT security; Cryptography.

Alexander Rahn, Eldar Sultanow, Idriss J. Aberkane

The Collatz dynamic is known to generate a complex quiver of sequences over natural numbers which inflation propensity remains so unpredictable it could be used to generate reliable proof of work algorithms for the cryptocurrency industry. Here we establish an ad hoc equivalent of modular arithmetic for Collatz sequences to automatically demonstrate the convergence of infinite quivers of numbers, based on five arithmetic rules we prove apply on the entire Collatz dynamic and which we further simulate to gain insight on their graph geometry and computational properties. We then formally demonstrate these rules define an automaton that is playing a Hydra game on the graph of undecided numbers we also prove is embedded in 24N-7, proving that in ZFC the Collatz conjecture is true, before giving a promising direction to also prove it in Peano arithmetic.

Jing Min Zhu

By transforming the Zeta function into a real function through Laplace inverse transformation, an algebraic research paradigm for prime number distribution was established, and important results were obtained (page 10). This method has received positive feedback from Sir Atiyah (see page 14 for details) and demonstrates significant potential for applications in the field of cryptography and blockchain. Core breakthrough: Revealing the essence of Zeta function, replacing complex analysis with convolutional algebra, making the proof path of Riemann hypothesis clear, concise, and computable.