Understanding of machine learning with deep learning: architectures, workflow, applications and future directions

MM Taye - Computers, 2023 - mdpi.com
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 …

Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges

M Polese, L Bonati, S D'oro, S Basagni… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
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 …

[HTML][HTML] Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges

SO Abioye, LO Oyedele, L Akanbi, A Ajayi… - Journal of Building …, 2021 - Elsevier
The growth of the construction industry is severely limited by the myriad complex challenges
it faces such as cost and time overruns, health and safety, productivity and labour shortages …

CNN variants for computer vision: History, architecture, application, challenges and future scope

D Bhatt, C Patel, H Talsania, J Patel, R Vaghela… - Electronics, 2021 - mdpi.com
Computer vision is becoming an increasingly trendy word in the area of image processing.
With the emergence of computer vision applications, there is a significant demand to …

Study on artificial intelligence: The state of the art and future prospects

C Zhang, Y Lu - Journal of Industrial Information Integration, 2021 - Elsevier
In the world, the technological and industrial revolution is accelerating by the widespread
application of new generation information and communication technologies, such as AI, IoT …

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Z Ahmad, A Shahid Khan, C Wai Shiang… - Transactions on …, 2021 - Wiley Online Library
The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

A comparative study of deep learning and Internet of Things for precision agriculture

T Saranya, C Deisy, S Sridevi… - … Applications of Artificial …, 2023 - Elsevier
Precision farming is made possible by rapid advances in deep learning (DL) and the internet
of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to …

Part of speech tagging: a systematic review of deep learning and machine learning approaches

A Chiche, B Yitagesu - Journal of Big Data, 2022 - Springer
Natural language processing (NLP) tools have sparked a great deal of interest due to rapid
improvements in information and communications technologies. As a result, many different …

A survey of deep learning techniques for weed detection from images

ASMM Hasan, F Sohel, D Diepeveen, H Laga… - … and electronics in …, 2021 - Elsevier
The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

A silicon photonic–electronic neural network for fibre nonlinearity compensation

C Huang, S Fujisawa, TF de Lima, AN Tait, EC Blow… - Nature …, 2021 - nature.com
In optical communication systems, fibre nonlinearity is the major obstacle in increasing the
transmission capacity. Typically, digital signal processing techniques and hardware are …