Results 181 to 190 of about 32,304 (305)
Applying explainable artificial intelligence to interpret supervised ensemble learning models for robust credit card fraud detection. [PDF]
Awad SS +3 more
europepmc +1 more source
Transactions in Corporate Control
openaire +2 more sources
This experimental study investigates the thermodynamic limits of the Al–Mn τ ferromagnetic phase within complex‐composition alloys (CCAs). Using nine rare‐earth‐free compositions, it explores a broad region of the pseudobinary AlMnCoFeNi system. The results reveal intricate links between composition, phase stability, and magnetic behavior, highlighting
Sacha Plagnol‐Chauzu +4 more
wiley +1 more source
Ensuring the integrity of AI models: a blockchain-based approach for protecting medical imaging training data. [PDF]
Shinde R, Patil S, Kotecha K, Mishra S.
europepmc +1 more source
Precipitation Simulations of the O‐Phase in Ti2AlNb Alloys Processed by Laser Powder Bed Fusion
Simulated and experimental evolution of the O‐phase volume fraction during postprocessing of a Ti‐21Al‐25Nb (at.%) alloy processed by laser powder bed fusion. With results of sensitivity to input parameters from a thorough and quantified analysis, the interfacial energy matrix/precipitate is the most relevant input parameter for the simulation of the O‐
Silvana Tumminello +7 more
wiley +1 more source
AT-BSS: A Broker Selection Strategy for Efficient Cross-Shard Processing in Sharded IoT-Blockchain Systems. [PDF]
Su Y, Xiang Y, Nguyen K, Sekiya H.
europepmc +1 more source
A Dislocation Perspective on Strength and Toughness in Ceramics
Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley +1 more source
Securing e-governance against shadow attacks with blockchain technology. [PDF]
Mishra P, R G.
europepmc +1 more source
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source

