Results 71 to 80 of about 211,846 (266)

Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse

IEEE Access, 2021
The sap flow of plants directly indicates their water requirements and provides farmers with a good understanding of a plant’s water consumption. Water management can be improved based on this information.
Amora Amir, Marya Butt, Olaf Van Kooten
doaj   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

Advanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu, Rui Zheng, Quan Liu, Jianwen Jiang   +3 more
wiley   +1 more source

Optimization of Electromyography (EMG) Signal Parameters for Assistive Device Control Using a Convolutional Neural Network (CNN)

Communications in Science and Technology
Facial Electromyography (EMG) signals offer a promising modality for intuitive human-machine interfaces (HMIs). The development of robust control systems, however, remains challenging in view of the inherent complexity, noise susceptibility, and ...
Irfan Wahyu Ramadhan, Sisdarmanto Adinandra   +1 more
doaj   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Comparative analysis of classical and Bayesian optimisation techniques: Impact on model performance and interpretability in credit risk modelling using SHAP and PDPs

Data Science in Finance and Economics
Financial institutions often hesitate to use complex models such as random forests and extreme gradient boosting (XGBoost) for credit risk assessments due to challenges in selecting the optimal hyperparameters and the interpretability of these 'black box'
Tatenda Shoko, Tanja Verster, Lindani Dube   +2 more
doaj   +1 more source

Confronting the concordance model of cosmology with Planck data

, 2014
We confront the concordance (standard) model of cosmology, the spatially flat $\Lambda$CDM Universe with power-law form of the primordial spectrum with Planck CMB angular power spectrum data searching for possible smooth deviations beyond the flexibility
Hazra, Dhiraj Kumar, Shafieloo, Arman
core   +1 more source

In Materia Shaping of Randomness with a Standard Complementary Metal‐Oxide‐Semiconductor Transistor for Task‐Adaptive Entropy Generation

Advanced Functional Materials, EarlyView.
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak, Ryun‐Han Koo, Youngchan Cho, Changhyeon Han, Dongbin Kim, Soi Jeong, Yunho Shin, Joonhyeok Choi, Jiseong Im, Jonghyun Ko, Jong‐Ho Lee, Jangsaeng Kim, Youngho Kang, Wonjun Shin, Daewoong Kwon   +14 more
wiley   +1 more source

Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach

Multimodal Technologies and Interaction
Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights.
S. J. Pawan, Matthew Muellner, Xiaomeng Lei, Mihir Desai, Bino Varghese, Vinay Duddalwar, Steven Y. Cen   +6 more
doaj   +1 more source

Recommending Learning Algorithms and Their Associated Hyperparameters [PDF]

, 2014
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can ...
Giraud-Carrier, Christophe, Martinez, Tony, Mitchell, Logan, Smith, Michael R.   +3 more
core