Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges
The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance
specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes …
specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes …
A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction
Hepatocellular carcinoma (HCC) is a form of liver cancer that is widespread in Europe,
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
A survey on deep learning for human activity recognition
Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …
home. In this study, we provide a comprehensive survey on recent advances and challenges …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
An overview of deep semi-supervised learning
Deep neural networks demonstrated their ability to provide remarkable performances on a
wide range of supervised learning tasks (eg, image classification) when trained on extensive …
wide range of supervised learning tasks (eg, image classification) when trained on extensive …
[HTML][HTML] Predicting academic success in higher education: literature review and best practices
E Alyahyan, D Düştegör - … Journal of Educational Technology in Higher …, 2020 - Springer
Student success plays a vital role in educational institutions, as it is often used as a metric for
the institution's performance. Early detection of students at risk, along with preventive …
the institution's performance. Early detection of students at risk, along with preventive …
Investigating the impact of data normalization on classification performance
Data normalization is one of the pre-processing approaches where the data is either scaled
or transformed to make an equal contribution of each feature. The success of machine …
or transformed to make an equal contribution of each feature. The success of machine …
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …
[图书][B] Learning from imbalanced data sets
Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …
represent the concepts in a given problem follow a different distribution. The main issue …