Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions
MM Taye - Computation, 2023 - mdpi.com
Convolutional neural networks (CNNs) are one of the main types of neural networks used for
image recognition and classification. CNNs have several uses, some of which are object …
image recognition and classification. CNNs have several uses, some of which are object …
Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions
Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and
natural language processing (NLP). There is growing demand to automate analysis of user …
natural language processing (NLP). There is growing demand to automate analysis of user …
Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends
As the global population grows, and urbanization becomes more prevalent, cities often
struggle to provide convenient, secure, and sustainable lifestyles due to the lack of …
struggle to provide convenient, secure, and sustainable lifestyles due to the lack of …
A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
A survey of the vision transformers and their CNN-transformer based variants
Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
Large language models (LLMs): survey, technical frameworks, and future challenges
P Kumar - Artificial Intelligence Review, 2024 - Springer
Artificial intelligence (AI) has significantly impacted various fields. Large language models
(LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have …
(LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have …
Unleashing the power of artificial intelligence in materials design
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …
the field of materials engineering thanks to their power to predict material properties, design …
Deep Convolution Neural Network sharing for the multi-label images classification
S Coulibaly, B Kamsu-Foguem, D Kamissoko… - Machine learning with …, 2022 - Elsevier
Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …
applications. In this work. We present a multi-label classification architecture based on Multi …
Machine learning-assisted approaches in modernized plant breeding programs
In the face of a growing global population, plant breeding is being used as a sustainable tool
for increasing food security. A wide range of high-throughput omics technologies have been …
for increasing food security. A wide range of high-throughput omics technologies have been …
A comprehensive review of convolutional neural networks for defect detection in industrial applications
Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …