3,035 research outputs found
Support matrix machine: A review
Support vector machine (SVM) is one of the most studied paradigms in the
realm of machine learning for classification and regression problems. It relies
on vectorized input data. However, a significant portion of the real-world data
exists in matrix format, which is given as input to SVM by reshaping the
matrices into vectors. The process of reshaping disrupts the spatial
correlations inherent in the matrix data. Also, converting matrices into
vectors results in input data with a high dimensionality, which introduces
significant computational complexity. To overcome these issues in classifying
matrix input data, support matrix machine (SMM) is proposed. It represents one
of the emerging methodologies tailored for handling matrix input data. The SMM
method preserves the structural information of the matrix data by using the
spectral elastic net property which is a combination of the nuclear norm and
Frobenius norm. This article provides the first in-depth analysis of the
development of the SMM model, which can be used as a thorough summary by both
novices and experts. We discuss numerous SMM variants, such as robust, sparse,
class imbalance, and multi-class classification models. We also analyze the
applications of the SMM model and conclude the article by outlining potential
future research avenues and possibilities that may motivate academics to
advance the SMM algorithm
The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning has rapidly advanced the state of the art in
many fields of science and engineering, including experimental fluid dynamics,
which is one of the original big-data disciplines. This perspective will
highlight several aspects of experimental fluid mechanics that stand to benefit
from progress advances in machine learning, including: 1) augmenting the
fidelity and quality of measurement techniques, 2) improving experimental
design and surrogate digital-twin models and 3) enabling real-time estimation
and control. In each case, we discuss recent success stories and ongoing
challenges, along with caveats and limitations, and outline the potential for
new avenues of ML-augmented and ML-enabled experimental fluid mechanics
LSTSVR-PI: Least square twin support vector regression with privileged information
In an educational setting, a teacher plays a crucial role in various
classroom teaching patterns. Similarly, mirroring this aspect of human
learning, the learning using privileged information (LUPI) paradigm introduces
additional information to instruct learning models during the training stage. A
different approach to train the twin variant of the regression model is
provided by the new least square twin support vector regression using
privileged information (LSTSVR-PI), which integrates the LUPI paradigm to
utilize additional sources of information into the least square twin support
vector regression. The proposed LSTSVR-PI solves system of linear equations
which adds up to the efficiency of the model. Further, we also establish a
generalization error bound based on the Rademacher complexity of the proposed
model and incorporate the structural risk minimization principle. The proposed
LSTSVR-PI fills the gap between the contemporary paradigm of LUPI and classical
LSTSVR. Further, to assess the performance of the proposed model, we conduct
numerical experiments along with the baseline models across various
artificially generated and real-world datasets. The various experiments and
statistical analysis infer the superiority of the proposed model. Moreover, as
an application, we conduct experiments on time series datasets, which results
in the superiority of the proposed LSTSVR-PI
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin
Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a
significant risk of serious health complications and negative impacts on the
quality of life. Given the impact of individual characteristics and lifestyle
on the treatment plan and patient outcomes, it is crucial to develop precise
and personalized management strategies. Artificial intelligence (AI) provides
great promise in combining patterns from various data sources with nurses'
expertise to achieve optimal care. Methods: This is a 6-month ancillary study
among T2D patients (n = 20, age = 57 +- 10). Participants were randomly
assigned to an intervention (AI, n=10) group to receive daily AI-generated
individualized feedback or a control group without receiving the daily feedback
(non-AI, n=10) in the last three months. The study developed an online
nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive
digital twin (PDT). The PDT was developed using a transfer-learning-based
Artificial Neural Network. The PDT was trained on participants self-monitoring
data (weight, food logs, physical activity, glucose) from the first three
months, and the online control algorithm applied particle swarm optimization to
identify impactful behavioral changes for maintaining the patient's glucose and
weight levels for the next three months. The ONLC provided the intervention
group with individualized feedback and recommendations via text messages. The
PDT was re-trained weekly to improve its performance. Findings: The trained
ONLC model achieved >=80% prediction accuracy across all patients while the
model was tuned online. Participants in the intervention group exhibited a
trend of improved daily steps and stable or improved total caloric and total
carb intake as recommended.Comment: Submitted for revie
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