Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Abstract

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work.

Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing

“…While these are not specifically focused on drug repurposing, the insights generated can inform the identification of existing drugs with potential repurposing opportunities for cancer treatment. ML, AI, and DL can be applied to literature searches, electronic health record (EHR)-based methods, and computational methods for drug-target interactions [110][111][112][113][114][115][116]. ML, AI, and DL are applied in various stages of drug discovery and development, including drug repurposing for cancer treatment (Figure 5).…”

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

“…The ANN includes grouped neurons called layers. The classification of types of neural networks is given in Table 1 [24,25]. A convolutional neural network (ConvNet / CNN) is a deep learning algorithm that is able to receive an image as an input, set digestible weights and biases to different areas in the image, and distinguish between these areas.…”

Machine Learning Training Optimization using the Barycentric Correction Procedure

“…Neuronal networks, now named deep learning, re-emerged after 2010 due to massive improvements in computer resources, some innovations, and successful applications [7]. Support vector machines and gradient boosting stand as pillars in the field of machine learning [8][9][10][11].…”