Survey of deep learning paradigms for speech processing
KB Bhangale, M Kothandaraman - Wireless Personal Communications, 2022 - Springer
Over the past decades, a particular focus is given to research on machine learning
techniques for speech processing applications. However, in the past few years, research …
techniques for speech processing applications. However, in the past few years, research …
[HTML][HTML] Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review
Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat
to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to …
to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to …
[HTML][HTML] Video summarization using deep learning techniques: a detailed analysis and investigation
One of the critical multimedia analysis problems in today's digital world is video
summarization (VS). Many VS methods have been suggested based on deep learning …
summarization (VS). Many VS methods have been suggested based on deep learning …
[HTML][HTML] How do machines learn? artificial intelligence as a new era in medicine
O Koteluk, A Wartecki, S Mazurek… - Journal of Personalized …, 2021 - mdpi.com
With an increased number of medical data generated every day, there is a strong need for
reliable, automated evaluation tools. With high hopes and expectations, machine learning …
reliable, automated evaluation tools. With high hopes and expectations, machine learning …
Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientation
J Li, Q Wang - Information Fusion, 2022 - Elsevier
Multi-modal fusion combines multiple modal information to overcome the limitation of
incomplete information expressed by a single modality, so as to realize the complementarity …
incomplete information expressed by a single modality, so as to realize the complementarity …
Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model
Conventional automation approaches for postharvest operations are plagued by time and
data inefficiency seldom leading to suboptimal solutions. Automatic machines often require …
data inefficiency seldom leading to suboptimal solutions. Automatic machines often require …
Astronomical big data processing using machine learning: A comprehensive review
Astronomy, being one of the oldest observational sciences, has collected a lot of data over
the ages. In recent times, it is experiencing a huge data surge due to advancements in …
the ages. In recent times, it is experiencing a huge data surge due to advancements in …
Deep learning for multiphase segmentation of X-ray images of gas diffusion layers
High-resolution X-ray computed tomography (micro-CT) has been widely used to
characterise fluid flow in porous media for different applications, including in gas diffusion …
characterise fluid flow in porous media for different applications, including in gas diffusion …
[HTML][HTML] Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory
A Mohammadifar, H Gholami, S Golzari - Scientific Reports, 2022 - nature.com
This research introduces a new combined modelling approach for mapping soil salinity in
the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence …
the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence …
[HTML][HTML] An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works
Abstract The World Health Organization (WHO) declared a pandemic in response to the
coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the …
coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the …