Machine learning (ML) enables a system to scrutinize data and deduce knowledge. It goes beyond simply learning or extracting knowledge, to utilizing and improving knowledge over time and with experience. In essence, the goal of ML is to identify and exploit hidden patterns in “training” data....
Vertical Federated Learning (VFL) has many applications in the field of smart healthcare with excellent performance. However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training d
Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to provide sustainable improvements in computing throughput and energy efficiency. Underlying the different CIM schemes is the implementation of two kinds of computing pri
Recently, machine learning (ML) has become attractive in genomic prediction, but its superiority in genomic prediction over conventional (ss) GBLUP methods and the choice of optimal ML methods need to be investigated. In this study, 2566 Chinese Yorkshir
Deep learning (DL) is a specific form of ML that uses artificial neural networks with hidden layers to make predictions directly from datasets Full size image It is important for clinicians and researchers working in CMR to understand the impact of ML on the field. Thus, the purpose of this...
The remaining useful life (RUL) of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators. Recently, different deep learning (DL) techniques have been used for RUL prediction and ac
purpose of online learning. The CNN model is distributed into two parts so that the training occurs at the cloud and prediction happens at the edge server. Moreover, the questionnaire was used to obtain the students’ learning status, understand their difficulties in dance learning, and record ...
Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilisi
However, manual interpretation is time-consuming and vulnerable to human error, particularly in the face of the intricate patterns presented by brain tumors. In response to these challenges, deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a transformative force in ...
34] that has demonstrated that various modern neural network architectures, such as Convolutional Neural Networks (CNNs) [35,36,37], weighted probabilistic neural networks [38] and ensembles of deep neural networks [36, 39] can achieve extremely high accuracy in the classification of MRI and ...