A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
A survey of 3D indoor localization systems and technologies
Indoor localization has recently and significantly attracted the interest of the research
community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically …
community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically …
RORNet: Partial-to-partial registration network with reliable overlap** representations
Three-dimensional point cloud registration is an important field in computer vision. Recently,
due to the increasingly complex scenes and incomplete observations, many partial-overlap …
due to the increasingly complex scenes and incomplete observations, many partial-overlap …
A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning
Offloading assists in overcoming the resource constraints of specific elements, making it one
of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery …
of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery …
A QoS-aware technique for computation offloading in IoT-edge platforms using a convolutional neural network and Markov decision process
Offloading is one of the critical enablers of the Internet of Things (IoT) as it helps overcome
the resource limitations of individual objects. Offering enough computational power for IoT …
the resource limitations of individual objects. Offering enough computational power for IoT …
Automatic early warning of rockbursts from microseismic events by learning the feature-augmented point cloud representation
S Tang, J Wang, L Tang, S Ding - Tunnelling and Underground Space …, 2024 - Elsevier
This study proposes an automatic early warning system for rockbursts in deeply buried
tunnels using microseismic data. A novel machine learning model is introduced, which is the …
tunnels using microseismic data. A novel machine learning model is introduced, which is the …
Novel cuckoo search-based metaheuristic approach for deep learning prediction of depression
Depression is a common illness worldwide with doubtless severe implications. Due to the
absence of early identification and treatment for depression, millions of individuals …
absence of early identification and treatment for depression, millions of individuals …
Municipal solid waste classification and real-time detection using deep learning methods
N Li, Y Chen - Urban Climate, 2023 - Elsevier
Waste management has become a significant issue in most develo** countries. Municipal
solid waste generation has been steadily increasing over the past decade. Recycling is …
solid waste generation has been steadily increasing over the past decade. Recycling is …
Pulse repetition interval modulation recognition using deep CNN evolved by extreme learning machines and IP-based BBO algorithm
Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic
intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar …
intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar …
Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios
High oil prices and concern about limited oil reserves lead to increase interest in enhanced
oil recovery (EOR). Selecting the most efficient development plan is of high interest to …
oil recovery (EOR). Selecting the most efficient development plan is of high interest to …