Results 181 to 190 of about 59,702 (288)

Benford's Law in Basic RNN and Long Short‐Term Memory and Their Associations

Applied AI Letters, Volume 6, Issue 3, October 2025.
This work examines neural networks from a novel perspective, demonstrating two key findings as proof of concept: (1) Benford's Law is present in neural network weights, and (2) higher‐accuracy models exhibit stronger conformity to Benford's Law. ABSTRACT Benford's Law describes the distribution of numerical patterns, specifically focusing on the ...
Farshad Ghassemi Toosi
wiley   +1 more source

Information Theory Quantifiers in Cryptocurrency Time Series Analysis. [PDF]

Entropy (Basel)
Suriano M, Caram LF, Caiafa C, Merlino HD, Rosso OA.   +4 more
europepmc   +1 more source

Cryptocurrency [PDF]

, 2017
Ivanchenko, I. A., Meshkov, V. I., Solonovich, Anton, Zavorin, Ivan   +3 more
core  

Taxation in the Age of Smart Contracts: The CryptoKitty Conundrum [PDF]

, 2020
Christians, Allison
core  

Harmonic Growth—The Harmonic Number Applied to ROI, Business Scaling, Nth Plant Economics, Micro Combined Heat and Power (mCHP), Biorefineries, and Biochar Production

Mathematical Methods in the Applied Sciences, Volume 48, Issue 15, Page 13999-14013, October 2025.
ABSTRACT Scaling is a topic of utmost importance in business. When to scale and how has been the subject of countless books and seminars. This paper looks at modular businesses that have income and costs directly proportional to the number of units.
Micah N. Jasper
wiley   +1 more source

Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum. [PDF]

PeerJ Comput Sci
Kaur R, Uppal M, Gupta D, Juneja S, Arafat SY, Rashid J, Kim J, Alroobaea R.   +7 more
europepmc   +1 more source

COMPARATIVE ANALYSIS OF VOLATILITY OF CRYPTOCURRENCIES AND FIAT MONEY

, 2018
Grigory O. Krylov, A. Yu. Lisitsyn, Leonid I. Polyakov   +2 more
openalex   +2 more sources

Intricacy of cryptocurrency returns

Economics Letters
This paper quantifies the intricacy, i.e., non-linearity and interactions of predictor variables, in explaining cryptocurrency returns. Using data from several thousand cryptocurrencies spanning 2014 to 2022, we observe a notably high level of intricacy.
openaire   +3 more sources

Sludge, dark patterns and dark nudges: A taxonomy of online gambling platforms' deceptive design features

Addiction, Volume 120, Issue 10, Page 1916-1923, October 2025.
Abstract Background Gambling research has highlighted various aspects of deceptive design. For example, land‐based casinos are physically designed to encourage people to gamble there for longer. Similarly, various electronic gambling products, such as electronic gaming machines, exhibit “structural characteristics” that likely encourage people to ...
Philip Newall
wiley   +1 more source

An empirical evaluation of fuzzy bidirectional long short-term memory with soft computing based decision-making model for predicting volatility of cryptocurrencies. [PDF]

Sci Rep
Ragab M.
europepmc   +1 more source