Search
Search Results
-
A Version of Predicate Logic with Two Variables That has an Incompleteness Property
In this paper, we consider predicate logic with two individual variables and general assignment models (where the set of assignments of the variables...
-
Decidability of Inquisitive Modal Logic via Filtrations
Inquisitive logic is an extension of classical logic which can express questions. To enable this expressiveness, a possible world semantics is used....
-
Transferable preference learning in multi-objective decision analysis and its application to hydrocracking
Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low...
-
A hyperparameter study for quantum kernel methods
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for...
-
TRAA: a two-risk archive algorithm for expensive many-objective optimization
Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have...
-
Hybrid genetic optimization for quantum feature map design
Kernel methods are an import class of techniques in machine learning. To be effective, good feature maps are crucial for map** non-linearly...
-
Probabilistic Semantics and Calculi for Multi-valued and Paraconsistent Logics
We show how to obtain a probabilistic semantics and calculus for a logic presented by a valuation specification. By identifying general forms of...
-
A novel bayesian network-based ensemble classifier chains for multi-label classification
In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by...
-
Reading Between the Lines: Machine Learning Ensemble and Deep Learning for Implied Threat Detection in Textual Data
With the increase in the generation and spread of textual content on social media, natural language processing (NLP) has become an important area of...
-
CommentClass: A Robust Ensemble Machine Learning Model for Comment Classification
Enormous amounts of data are generated in the form of feedback or comments from online platforms such as social media, e-commerce, education, and...
-
More Limits of Abductivism About Logic
Logical abductivism is the method which purports to use Inference to the Best Explantion (IBE) to determine the best logical theory. The present...
-
Constructive Validity of a Generalized Kreisel–Putnam Rule
In this paper, we propose a computational interpretation of the generalized Kreisel–Putnam rule, also known as the generalized Harrop rule or simply...
-
Exploring superionic conduction in lithium oxyhalide solid electrolytes considering composition and structural factors
Lithium (Li) oxyhalides have emerged as promising solid electrolyte candidates for all-solid-state batteries (ASSBs) due to their superior ionic...
-
Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature
Accelerating the design of Ni-based single crystal (SX) superalloys with superior creep resistance at ultrahigh temperatures is a desirable goal but...
-
Failure analysis in smart grid solar integration using an extended decision-making-based FMEA model under uncertain environment
Failures in the integration of solar energy into smart grids can have significant implications for energy reliability and environmental...
-
Design of polar boundaries enhancing negative electrocaloric performance by antiferroelectric phase-field simulations
Electrocaloric refrigeration which is environmentally benign has attracted considerable attention. In distinction to ferroelectric materials, which...
-
Privacy-preserving matrix factorization for recommendation systems using Gaussian mechanism and functional mechanism
Building a recommendation system involves analyzing user data, which can potentially leak sensitive information about users. As shown in numerous...
-
MDGCL: Graph Contrastive Learning Framework with Multiple Graph Diffusion Methods
In recent years, some classical graph contrastive learning(GCL) frameworks have been proposed to address the problem of sparse labeling of graph data...
-
Parameters optimization and precision enhancement of Takagi–Sugeno fuzzy neural network
Takagi–Sugeno fuzzy neural network (TSFNN) has been widely used in intelligent prediction. The prediction accuracy of TSFNN is impacted by its model...
