Results 121 to 130 of about 601,753 (327)

Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization

International Journal of Distributed Sensor Networks, 2017
The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy ...
Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu
doaj   +1 more source

Biomolecular Interaction Prediction: The Era of AI

Advanced Science, EarlyView.
This review offers a thorough examination of recent progress in deep learning for predicting biomolecular interactions, including those involving proteins, nucleic acids, and small molecules. It covers data processing strategies, representative model architectures, and evaluation metrics, while highlighting current methodological limitations.
Haoping Wang, Xiangjie Meng, Yang Zhang
wiley   +1 more source

Perovskite‐CIGSe Tandem Solar Cell: Over One Year of Outdoor Monitoring

Advanced Energy and Sustainability Research, EarlyView.
This work presents the first data analysis of year‐round measurements under outdoor conditions of a perovskite‐CIGS tandem solar cell. In addition, the data acquired is used to verify the possibility of predicting the immediate performance of an all‐thin‐film tandem using a multiple linear regression model with accuracies generally exceeding 90 ...
Guillermo Farias‐Basulto, Ivona Kafedjiska, Tobias Bertram, Maximilian Riedel, Quiterie Emery, Marko Remec, Paolo Graniero, Mark Khenkin, Christian A. Kaufmann, Iver Lauermann, Reiner Klenk, Rutger Schlatmann, Carolin Ulbrich   +12 more
wiley   +1 more source

GED‐CRN Breaks the Data Barrier: High‐Fidelity Electron Density Prediction Using Only 19 Training Molecules

Aggregate, EarlyView.
GED‐CRN: A Machine Learning Framework for Predicting Electron Density Distributions from Molecular Geometries via a Cube‐Sampling Approach. ABSTRACT We present GED‐CRN, a 3D convolutional residual network that achieves quantum‐chemical accuracy (MAE =7.6×10−4$= 7.6 \times 10^{-4}$ bohr−3${\rm bohr}^{-3}$) in predicting electron densities for AIE‐active
Junyi Gong, Haoran Wang, Zijie Qiu, Zheng Zhao, Ben Zhong Tang   +4 more
wiley   +1 more source

Hybrid rule‐based and optimization‐driven framework for the synthesis of end‐to‐end optimal pharmaceutical processes

AIChE Journal, EarlyView.
Abstract The modernization of pharmaceutical manufacturing is driving a shift from traditional batch processing to continuous alternatives. Synthesizing end‐to‐end optimal (E2EO) manufacturing routes is crucial for the pharmaceutical industry, especially when considering multiple operating modes—such as batch, continuous, or hybrid (containing both ...
Yash Barhate, Daniel J. Laky, Daniel Casas‐Orozco, Gintaras V. Reklaitis, Zoltan K. Nagy   +4 more
wiley   +1 more source

Digital design and optimization of the integrated synthesis and crystallization process using data‐driven approaches

AIChE Journal, EarlyView.
Abstract This study presents a data‐driven modeling and multi‐objective optimization framework for an integrated section of continuous pharmaceutical manufacturing, focusing on flow synthesis and continuous crystallization. To address data scarcity and trade‐offs among product quality, efficiency, and environmental impact, the framework combines ...
Yiming Ma, Wei Li, Jiaxu Liu, Gao Shang, Huaiyu Yang, Junbo Gong, Zoltan K. Nagy, Brahim Benyahia   +7 more
wiley   +1 more source

Semi-supervised learning of geographical gazetteers from the internet [PDF]

, 2003
Olga Uryupina
openalex   +1 more source

Mapping uncertainty using differentiable programming

AIChE Journal, EarlyView.
Abstract Uncertainty quantification (UQ) and propagation is a ubiquitous challenge in science, permeating our field in a general fashion, and its importance cannot be overstated. Recently, the commoditization of differentiable programming, motivated by the development of machine learning, has allowed easier access to tools for evaluating derivatives of
Victor Alves, Carl D. Laird, Fernando V. Lima, John R. Kitchin   +3 more
wiley   +1 more source

Semi-Supervised Learning on Riemannian Manifolds [PDF]

, 2004
Mikhail Belkin, Partha Niyogi
openalex   +1 more source

Muffled Semi-Supervised Learning

, 2016
We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than ...
Balsubramani, Akshay, Freund, Yoav
openaire   +2 more sources