Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing me...
At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight cit...
BMC medical informatics and decision making
Nov 27, 2021
BACKGROUND: Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data ...
The objective of this study was to define coping style of sheep by using unsupervised machine learning approaches. A total of 105 Norduz sheep (age 3-5 years) were subjected to a 5-minute arena test. Agglomerative Hierarchical Clustering (HCA) was pe...
International journal of molecular sciences
Nov 25, 2021
BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell.
Computational intelligence and neuroscience
Nov 25, 2021
The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearab...
Computational intelligence and neuroscience
Nov 24, 2021
Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses...
Computational intelligence and neuroscience
Nov 18, 2021
Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of i...
BACKGROUND: Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to t...
Clustering Algorithms have just fascinated significant devotion in machine learning applications owing to their great competence. Nevertheless, the existing algorithms quite have approximately disputes that need to be further deciphered. For example,...