Results 111 to 120 of about 24,904 (254)
A Model-Driven Multi-UAV Spectrum Map Fast Fusion Method for Strongly Correlated Data Environments
Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance.
Shengwen Wu +7 more
doaj +1 more source
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source
The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
The incorporation of ℓ1 regularization in Lasso regression plays a crucial role by inducing convexity to the objective function, thereby facilitating its minimization; when compared to non-convex regularization, the utilization of ℓ1 regularization ...
Wei Chen +3 more
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SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li +6 more
wiley +1 more source
An Entropic Approach to Constrained Linear Regression
We introduce a novel entropy minimization approach for the solution of constrained linear regression problems. Rather than minimizing the quadratic error, our method minimizes the Fermi–Dirac entropy, with the problem data incorporated as constraints. In
Argimiro Arratia, Henryk Gzyl
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FluoAI is a two‐stage, label‐free method for quantifying nanomaterial‐induced cytotoxicity from conventional fluorescence microscopy images. Mask R‐CNN segmentation and DenseNet‐121 classification provide rapid live/dead classification matching human accuracy.
Ugur C. Topkiran +6 more
wiley +1 more source
Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica +38 more
wiley +1 more source
Review on Isotonic and Convex Regression
Shape-restricted regression provides a framework for estimating an unknown regression function $f_0: \Omega \subset \mathbb{R}^d \rightarrow \mathbb{R}$ from noisy observations \((\boldsymbol{X}_1, Y_1), \ldots, (\boldsymbol{X}_n, Y_n)\) when no explicit functional relationship between $\boldsymbol{X}$ and $Y$ is known, but $f_0$ is assumed to satisfy ...
openaire +1 more source
Non-convex optimization problems often challenge gradient-based algorithms, such as Gradient Descent. Neural network training, a prominent application of gradient-based methods, heavily relies on their computational efficiency. However, the cost function
Mohammad Sakka, Mohammad Reza Bahrami
doaj +1 more source

