Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Report...
Multimodal remote sensing data classification can enhance a model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks ha...
Multimodal Learning Analytics (MMLA) is an extension of Learning Analytics that combines multiple data streams such as audio, video, physiological signals, logs, and spatial trails to analyze learning processes that cannot be easily captured through...
Autopilot technology in the field of aviation has developed over many years. However, it is difficult for an autopilot system to autonomously operate a civil aircraft under bad weather conditions. In this paper, we present a reinforcement learning (R...
The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order...
Lung disease is one of the leading causes of death worldwide. This emphasizes the need for early diagnosis in order to provide appropriate treatment and save lives. Physicians typically require information about patients’ clinical symptoms, var...
In social interactions, people who are perceived as competent win more chances, tend to have more opportunities, and perform better in both personal and professional aspects of their lives. However, the process of evaluating competence is still poorl...
Fruit quality is an important aspect in determining the consumer preference in the supply chain. Thermal imaging was used to determine different pineapple varieties according to the physicochemical changes of the fruit by means of the deep learning m...
The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framewo...
25 June 2023
Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs),...
Background: Heterogeneous data integration remains a major challenge in intelligent information systems, particularly under missing-modality and cross-domain conditions. Existing multimodal fusion approaches often rely on complete datasets and weak a...
Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while und...
ADHD classification has traditionally relied on accelerometer-derived tabular features, which summarise static activity but fail to capture spatial-temporal patterns, potentially limiting model performance. We developed a multimodal deep learning fra...
In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and in...
Building height is a key indicator of vertical urbanization and urban morphological complexity, yet accurately mapping building height at fine spatial resolution and large spatial scales remains challenging. This study proposes an attention-based dee...
Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited...
Food quality and safety inspection increasingly requires advanced solutions because conventional methods are challenged by the complexity of modern food systems. Effective inspection must address product quality, authenticity, and safety, together wi...
Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived,...
This study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. The study analyzes the impact of different neural network architectures...
High-precision monitoring of arid wetlands is vital for ecological conservation, yet traditional methods incur prohibitive labeling costs due to complex features. In this study, the wetland of Bosten Lake in Xinjiang is selected as a case area, where...
With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Trad...
A deep learning-based tree pruning evaluation system is proposed in this study, which integrates hyperspectral images, sensor data, and expert system rules. The system aims to enhance the accuracy and robustness of tree pruning tasks through multimod...
Simulating animal movement has long been a central focus of study in the area of wildlife behaviour studies. Conventional modelling methods have difficulties in accurately representing changes over time and space in the data, and they generally do no...
Grasping objects, from simple tasks to complex fine motor skills, is a key component of our daily activities. Our approach to facilitate the development of advanced prosthetics, robotic hands and human–machine interaction systems consists of co...
The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source re...
Cyberbullying involves the use of social media platforms to harm or humiliate people online. Victims may resort to self-harm due to the abuse they experience on these platforms, where users can remain anonymous and spread malicious content. This high...
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the pr...
Fire chemical sensing for indoor detection of fire plays an essential role because it can detect chemical volatiles before smoke particles, providing a faster and more reliable method for early fire detection. A thermal imaging camera and seven disti...
Historic cultural districts must balance heritage conservation with contemporary urban vitality, because excessive commercialization and overly static preservation can weaken both everyday use and cultural authenticity. This study examines 350 street...
The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proacti...
The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as wel...
This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion classification is important in diverse fields, inc...
Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and mor...
The Internet of Things (IoT) uses networked devices, dispersed sensors, and cameras to create huge, diverse data streams. Concept drift, in which the underlying data distribution shifts over time, is frequently caused by the non-stationary and multim...
Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality...
Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients&rsquo...
The clinical diagnosis of Parkinson’s disease (PD) primarily relies on clinician-administered observational assessment tools, such as the Unified Parkinson’s Disease Rating Scale (UPDRS). However, these approaches are significantly influe...
Images and text have become essential parts of the multimodal machine learning (MMML) framework in today’s world because data are always available, and technological breakthroughs bring disparate forms together, and while text adds semantic ric...
Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (U...
Accurate alfalfa biomass prediction is crucial for pasture management and sustainable livestock production. However, traditional methods often perform poorly under complex field conditions. To address the limited prediction accuracy of traditional me...
This study presents a spatially aware machine learning model for probabilistic groundwater potential assessment using multimodal geospatial data derived from topography, hydrotopography, climate, soil, land use, Sentinel-1 SAR, and water balance vari...
A critical task in infrastructure security is to model user traffic in transportation systems to alert whenever anomalous behavior is observed. Discerning those abnormal samples is possible by auditing the available data, which then enables proper po...
Brain cancer detection is dependent on multiple diagnostic techniques. Histopathological diagnosis, although the most effective, requires the extraction of cancer cells, which is very risky and painful for a patient. Another popular noninvasive image...
Accurate early prediction and multi-stage diagnostic classification compliant with AJCC TNM staging criteria are essential for effective treatment planning in pancreatic cancer. Although deep learning (DL) approaches show promising results, interpret...
Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic...
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocar...
Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soyb...
Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life....
Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the...
Breast cancer ranks as the second most prevalent cancer globally and is the most frequently diagnosed cancer among women; therefore, early, automated, and precise detection is essential. Most AI-based techniques for breast cancer detection are comple...
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