Results 71 to 80 of about 20,583 (298)

Using statistical and machine learning approaches to describe estuarine tidal dynamics

open access: yesJournal of Hydroinformatics
Estuaries are ecologically valuable regions where tidal forces move large volumes of water. To understand the ongoing physical processes in such dynamic systems, a series of estuarine monitoring stations is required.
Franziska Lauer, Frank Kösters
doaj   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Digital circuits dataset for anomaly detection + ground truth explanations

open access: yes
This dataset is an anomaly benchmark dataset with local ground-truth explanations. It was created based on 4 digital circuits from ISCAS '85 and 74x series benchmarks: C17, 74182, 74283 and 74181. The dataset includes: Truth_Tables Each circuits has 4
Lab, XAI
core   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Strategies to Exploit XAI to Improve Classification Systems

open access: yes, 2023
Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions.
Apicella A.   +4 more
core   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Conceptual Misalignment in XAI

open access: yes
This paper argues that the prevailing XAI paradigm suffers not from technical limitations but from a profound philosophical misconception: the assumption that explanation is primarily about transparency. We argue that the development of useful explanation is not fundamentally a question of information transfer, but of epistemic parity.
Rolf Hvidtfeldt   +7 more
openaire   +1 more source

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

open access: yesAdvanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik   +6 more
wiley   +1 more source

The Machine as an Autonomous Explanatory Agent

open access: yesFelsefe Dünyası
The holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties.
Dilek Yargan
doaj   +1 more source

XAI in Healthcare. [PDF]

open access: yes
The evolution of Explainable Artificial Intelligence (XAI) within healthcare represents a crucial turn towards more transparent, understandable, and patient-centric AI applications. The main objective is not only to increase the accuracy of AI models but also, and more importantly, to establish user trust in decision support systems through improving ...
Gezici, Gizem   +6 more
openaire   +2 more sources

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