-- Air traffic controllers (ATCOs) issue high - intensity voice commands in dense airspace, where accurate workload modeling is critical for safety and efficiency. This paper proposes a multimodal deep learning framework that integrates structured data, trajectory sequences, and image features to estimate two key parameters in the ATCO command lifecycle: the time offset between a command and the resulting aircraft maneuver, and the command duration. A hi gh - quality dataset was constructed, with maneuver points detected using sliding window and histogram - based methods. A CNN - Transformer ensemble model was developed for accurate, generalizable, and interpretable predictions. By linking trajectories to voice commands, this work offers the first model of its kind to support intelligent command generation and provides practical value for workload assessment, staffing, and scheduling. A. Background As global air traffic demand increases, airspace operations have become more complex and congested, presenting major challenges for air traffic control (ATC) systems. Although surveillance and communication technologies have improved, ATC performance still largely depends on human operators, particularly air traffic controllers (ATCOs), who monitor flights, assess conditions, and issue maneuver instructions to ensure safe and efficient operations. This human bottleneck has become a key constraint on ATC efficiency and safety, emphasizing the importance of quantifying task intensity and evaluating workload to support fatigue management, staff scheduling, and the development of in telligent ATC solutions . Early studies on ATCO workload modeling primarily focused on statistical methods and subjective assessments such as NASA Task Load Index (NASA - TLX) [1] .

Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patients. Predicting progression-free survival (PFS) could support individualized treatment planning. This study evaluates laboratory, imaging, and multimodal deep learning models for PFS prediction in PRRT-treated patients. In this retrospective, single-center study 116 patients with metastatic NETs undergoing 177Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CT) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Explainability was evaluated by feature importance analysis and gradient maps. Forty-two patients (36%) had short PFS (< 1 year), 74 patients long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated gamma-GT (p = 0.002), and fewer PRRT cycles (p < 0.001) in short-PFS patients. The Random Forest model trained only on laboratory biomarkers reached an AUROC of 0.59 +- 0.02. Unimodal three-dimensional convolutional neural networks using SR-PET or CT performed worse (AUROC 0.42 +- 0.03 and 0.54 +- 0.01, respectively). A multimodal fusion model laboratory values, SR-PET, and CT -augmented with a pretrained CT branch - achieved the best results (AUROC 0.72 +- 0.01, AUPRC 0.80 +- 0.01). Multimodal deep learning combining SR-PET, CT, and laboratory biomarkers outperformed unimodal approaches for PFS prediction after PRRT. Upon external validation, such models may support risk-adapted follow-up strategies.

Air pollution monitoring in resource-constrained regions remains challenging due to sparse sensor deployment and limited infrastructure. This work introduces AQFusionNet, a multimodal deep learning framework for robust Air Quality Index (AQI) prediction. The framework integrates ground-level atmospheric imagery with pollutant concentration data using lightweight CNN backbones (MobileNetV2, ResNet18, EfficientNet-B0). Visual and sensor features are combined through semantically aligned embedding spaces, enabling accurate and efficient prediction. Experiments on more than 8,000 samples from India and Nepal demonstrate that AQFusionNet consistently outperforms unimodal baselines, achieving up to 92.02% classification accuracy and an RMSE of 7.70 with the EfficientNet-B0 backbone. The model delivers an 18.5% improvement over single-modality approaches while maintaining low computational overhead, making it suitable for deployment on edge devices. AQFusionNet provides a scalable and practical solution for AQI monitoring in infrastructure-limited environments, offering robust predictive capability even under partial sensor availability.

Multimodal deep learning harnesses diverse imaging modalities, such as MRI sequences, to enhance diagnostic accuracy in medical imaging. A key challenge is determining the optimal timing for integrating these modalities-specifically, identifying the network layers where fusion modules should be inserted. Current approaches often rely on manual tuning or exhaustive search, which are computationally expensive without any guarantee of converging to optimal results. We propose a sequential forward search algorithm that incrementally activates and evaluates candidate fusion modules at different layers of a multimodal network. At each step, the algorithm retrains from previously learned weights and compares validation loss to identify the best-performing configuration. This process systematically reduces the search space, enabling efficient identification of the optimal fusion timing without exhaustively testing all possible module placements. The approach is validated on two multimodal MRI datasets, each addressing different classification tasks. Our algorithm consistently identified configurations that outperformed unimodal baselines, late fusion, and a brute-force ensemble of all potential fusion placements. These architectures demonstrated superior accuracy, F-score, and specificity while maintaining competitive or improved AUC values. Furthermore, the sequential nature of the search significantly reduced computational overhead, making the optimization process more practical. By systematically determining the optimal timing to fuse imaging modalities, our method advances multimodal deep learning for medical imaging. It provides an efficient and robust framework for fusion optimization, paving the way for improved clinical decision-making and more adaptable, scalable architectures in medical AI applications.

Molecular subtyping of breast cancer is crucial for personalized treatment and prognosis. Traditional classification approaches rely on either histopathological images or gene expression profiling, limiting their predictive power. In this study, we propose a deep multimodal learning framework that integrates histopathological images and gene expression data to classify breast cancer into BRCA.Luminal and BRCA.Basal / Her2 subtypes. Our approach employs a ResNet-50 model for image feature extraction and fully connected layers for gene expression processing, with a cross-attention fusion mechanism to enhance modality interaction. We conduct extensive experiments using five-fold cross-validation, demonstrating that our multimodal integration outperforms unimodal approaches in terms of classification accuracy, precision-recall AUC, and F1-score. Our findings highlight the potential of deep learning for robust and interpretable breast cancer subtype classification, paving the way for improved clinical decision-making.

This paper presents a generative approach to multimodal deep learning based on a product-of-experts (PoE) inference network. The main idea is to assume the joint distribution over all modalities factorises into a product of single-modality data-generating distributions when conditioned on the latent space, and use this to derive the structure and factorisation of the variational posterior. The proposed model shares parameters to efficiently handle any combination of missing modalities, and experiments indicate the model's efficacy on various benchmark datasets. The idea is intuitive, the exposition is well-written and easy to follow, and the results are thorough and compelling. I have a few questions / comments, mainly about the relationship of this work with respect to previous approaches ([15] and [21] in the text).

This project intends to study the image representation based on attention mechanism and multimodal data. By adding multiple pattern layers to the attribute model, the semantic and hidden layers of image content are integrated. The word vector is quantified by the Word2Vec method and then evaluated by a word embedding convolutional neural network. The published experimental results of the two groups were tested. The experimental results show that this method can convert discrete features into continuous characters, thus reducing the complexity of feature preprocessing. Word2Vec and natural language processing technology are integrated to achieve the goal of direct evaluation of missing image features. The robustness of the image feature evaluation model is improved by using the excellent feature analysis characteristics of a convolutional neural network. This project intends to improve the existing image feature identification methods and eliminate the subjective influence in the evaluation process. The findings from the simulation indicate that the novel approach has developed is viable, effectively augmenting the features within the produced representations.

Large-scale multi-modal deep learning models have revolutionized domains such as healthcare, highlighting the importance of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), limited access to GPUs and data poses significant challenges, often leaving CPUs as the sole resource. To address this, we advocate for leveraging vector embeddings to enable flexible and efficient computational methodologies, democratizing multimodal deep learning across diverse contexts. Our paper investigates the efficiency and effectiveness of using vector embeddings from single-modal foundation models and multi-modal Vision-Language Models (VLMs) for multimodal deep learning in low-resource environments, particularly in healthcare. Additionally, we propose a simple yet effective inference-time method to enhance performance by aligning image-text embeddings. Comparing these approaches with traditional methods, we assess their impact on computational efficiency and model performance using metrics like accuracy, F1-score, inference time, training time, and memory usage across three medical modalities: BRSET (ophthalmology), HAM10000 (dermatology), and SatelliteBench (public health). Our findings show that embeddings reduce computational demands without compromising model performance. Furthermore, our alignment method improves performance in medical tasks. This research promotes sustainable AI practices by optimizing resources in constrained environments, highlighting the potential of embedding-based approaches for efficient multimodal learning. Vector embeddings democratize multimodal deep learning in LMICs, particularly in healthcare, enhancing AI adaptability in varied use cases.

This project investigates the human multi-modal behavior identification algorithm utilizing deep neural networks. According to the characteristics of different modal information, different deep neural networks are used to adapt to different modal video information. Through the integration of various deep neural networks, the algorithm successfully identifies behaviors across multiple modalities. In this project, multiple cameras developed by Microsoft Kinect were used to collect corresponding bone point data based on acquiring conventional images. In this way, the motion features in the image can be extracted. Ultimately, the behavioral characteristics discerned through both approaches are synthesized to facilitate the precise identification and categorization of behaviors. The performance of the suggested algorithm was evaluated using the MSR3D data set. The findings from these experiments indicate that the accuracy in recognizing behaviors remains consistently high, suggesting that the algorithm is reliable in various scenarios. Additionally, the tests demonstrate that the algorithm substantially enhances the accuracy of detecting pedestrian behaviors in video footage.

The risk of hardware Trojans being inserted at various stages of chip production has increased in a zero-trust fabless era. To counter this, various machine learning solutions have been developed for the detection of hardware Trojans. While most of the focus has been on either a statistical or deep learning approach, the limited number of Trojan-infected benchmarks affects the detection accuracy and restricts the possibility of detecting zero-day Trojans. To close the gap, we first employ generative adversarial networks to amplify our data in two alternative representation modalities, a graph and a tabular, ensuring that the dataset is distributed in a representative manner. Further, we propose a multimodal deep learning approach to detect hardware Trojans and evaluate the results from both early fusion and late fusion strategies. We also estimate the uncertainty quantification metrics of each prediction for risk-aware decision-making. The outcomes not only confirms the efficacy of our proposed hardware Trojan detection method but also opens a new door for future studies employing multimodality and uncertainty quantification to address other hardware security challenges.