Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …
video, etc., are showing better performance than individual modalities (ie, unimodal) …
Rice-fusion: A multimodality data fusion framework for rice disease diagnosis
Rice leaf infections are a common hazard to rice production, affecting many farmers all over
the world. Early detection and treatment of rice leaf infection are critical for promoting healthy …
the world. Early detection and treatment of rice leaf infection are critical for promoting healthy …
[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments
Fire detection and management is very important to prevent social, ecological, and
economic damages. However, achieving real‐time fire detection with higher accuracy in an …
economic damages. However, achieving real‐time fire detection with higher accuracy in an …
[Retracted] Employing Multimodal Machine Learning for Stress Detection
In the current information age, the human lifestyle has become more knowledge‐oriented,
leading to sedentary employment. This has given rise to a number of health and mental …
leading to sedentary employment. This has given rise to a number of health and mental …
[HTML][HTML] A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization
CC Corrigan, SA Ikonnikova - The Extractive Industries and Society, 2024 - Elsevier
In the effort to rapidly transform the way we use energy, valuable minerals are coming
increasingly into high demand. Various metals, such as copper and cobalt, are required to …
increasingly into high demand. Various metals, such as copper and cobalt, are required to …
Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature
J Oh, SH Kim, MJ Lee, H Hwang, W Ku, J Lim… - Sensors and Actuators B …, 2022 - Elsevier
Abstract Machine learning (ML) methodologies were applied to detect and discriminate five
indoor volatile organic compounds (VOCs) such as benzene, xylene, toluene, formaldehyde …
indoor volatile organic compounds (VOCs) such as benzene, xylene, toluene, formaldehyde …
Flexible vertical federated learning with heterogeneous parties
We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm
that trains a smooth, nonconvex function in a distributed system with vertically partitioned …
that trains a smooth, nonconvex function in a distributed system with vertically partitioned …
Multitask deep learning-based pipeline for gas leakage detection via E-nose and thermal imaging multimodal fusion
O Attallah - Chemosensors, 2023 - mdpi.com
Innovative engineering solutions that are efficient, quick, and simple to use are crucial given
the rapid industrialization and technology breakthroughs in Industry 5.0. One of the areas …
the rapid industrialization and technology breakthroughs in Industry 5.0. One of the areas …
Single and multiple drones detection and identification using RF based deep learning algorithm
B Sazdić-Jotić, I Pokrajac, J Bajčetić… - Expert Systems with …, 2022 - Elsevier
Unmanned aerial systems, especially drones have gone through remarkable improvement
and expansion in recent years. Drones have been widely utilized in many applications and …
and expansion in recent years. Drones have been widely utilized in many applications and …
Multimedia analysis of robustly optimized multimodal transformer based on vision and language co-learning
Recently, research on multimodal learning using all modality information has been
conducted to detect disinformation on multimedia. Existing multimodal learning methods …
conducted to detect disinformation on multimedia. Existing multimodal learning methods …