Results 141 to 150 of about 9,169,616 (322)
Advanced Science, EarlyView.Coexisting DCIS and IDC samples are profiled using spatial transcriptomics, single‐cell RNA sequencing, and single‐cell DNA sequencing. Integrative multi‐omics analysis reveals distinct malignant epithelial and microenvironmental features between DCIS and IDC, which are further validated using Xenium and multiplex immunohistochemistry.
Ning Zhang +18 more
wiley +1 more source
Privacy-Preserving Classification of Vertically Partitioned Data via Random Kernels [PDF]
, 2007 We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns are divided into groups belonging to different entities.
Mangasarian, Olvi +2 more
core
Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization
Advanced Electronic Materials, EarlyView.Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali +3 more
wiley +1 more source
Advanced Energy Materials, EarlyView.In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu +6 more
wiley +1 more source
Jixie chuandong, 2018 The support vector machine（SVM） can avoid the overlearning phenomenon in the case of small training samples,so that the generalization ability can be maximized. The problem that the SVM parameters cannot be selected adaptively is studied.
Yu Yang, Bai Rui, Yang Ping
doaj
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Memory-efficient large-scale linear support vector machine
, 2014 Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines as very effective classifiers.
Takeda, Akiko +2 more
core +1 more source
Signature Verification Using Support Vector Machine (SVM)
International Journal of Scientific Research and Management, 2017 Automated signature verification has many applications in our daily life like Bank-cheque processing,document authentication, ATM access etc. Handwritten signatures have proved to be important inauthenticating a person's identity, who is signing the document.
openaire +1 more source
Machine learning driven many‐objective moving horizon scheduling optimization
AIChE Journal, EarlyView.Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley +1 more source
Techno.ComIndustri 4.0 menandai transformasi besar dalam sektor manufaktur, termasuk industri otomotif, dengan integrasi teknologi cerdas seperti machine learning untuk meningkatkan efisiensi dan kualitas produksi.
Mailia Putri Utami +4 more
doaj +1 more source