Towards accurate prediction of drivers’ mental workload during continuous driving tasks with multimodal measures and machine learning regression models

Although several machine learning driven solutions are deemed to be effective at detecting data breaches, the recent proliferation in data breach incidents resulting from cyber attacks on computer networks demands an updated, thorough analysis of Machine Learning (ML) based data breach countermeasures to identify research gaps and guide future studies. In view of this, this study employs a systematic approach and draws insight from 89 research articles to classify machine learning based data breach countermeasures using eight criteria namely learning tasks, learning classifiers, datasets, feature engineering methods, multimodal approaches, pre-training approaches and performance. In classifying the studies, we: (a) propose a taxonomy of feature extraction and representation to classify studies using ten sub-criteria, (b) classify multimodal machine learning approaches used in the studies into three fusion sub-criteria: namely early fusion, intermediate fusion and late fusion, (c) show a comparison of studies based on pre-training techniques employed such as pre-text objective, learning model and data used in pre-training, (d) classify the datasets used in the study evaluation into two categories: real dataset and simulated dataset and (e) evaluate studies by detection performance and effectiveness against data breaches on unknown and obfuscated network traffic. To aid the literature identification, we analyse forty recent incidents and obtain prevalent cyber attack vectors of data breaches, which we present as the general workflow for data breaches due to cyber attacks. Finally, we highlight the research issues associated with existing ML-based data breach countermeasures and recommend future research directions.

A multi-modal fusion-based deep learning with finetuned LLaMA 3 for lung disease diagnosis using PACS radiology reports

Multi-modal collaborative learning with vision foundation model prompt boosts 3D semi-supervised semantic segmentation

Entity alignment based on multi-modal representation learning and constrained weakly-supervised clustering

Scenario-based multimodal deep learning framework for simultaneous detection of construction accident causal factors and risk evaluation

Preoperative identification of deep myometrial invasion in endometrial cancer: a multicenter MRI study with a vision foundation model-enhanced multimodal deep learning framework.

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

Transformer self-attention encoder–decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring

Correlative analysis between ocular surface features and carotid plaque : A multimodal machine learning framework.

A multi-modal multi-task learning network for intelligent parameter measurement in gas–liquid two-phase flow

The study focuses on the inclusive school’s approach to literacy development for special needs students (SNS). The research aims to delve into the inclusive school’s methodologies concerning literacy development, particularly tailored to cater to the diverse needs of special education students. The study was conducted in 17 inclusive schools across 3 provinces in Indonesia, all of which have demonstrated remarkable progress in literacy development. This comprehensive investigation involved meticulously surveying these schools to gather insights into their practices and achievements. The findings of the study highlight several patterns in the approach to literacy development in inclusive schools. A comprehensive inclusive literacy development model is proposed, which emphasizes personalized planning, multimodal and experiential learning, differentiated instruction, recognition of achievements, and ongoing professional development. The study’s insights are relevant for educators, policymakers, and researchers interested in enhancing inclusive literacy practices for SNS in inclusive school settings schools. The practicality of the curriculum is seen from the aspect of good communication, cooperation, sufficient time, and the ease of understanding the material.

MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.

Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage.

CILF-CIAE: CLIP-driven image-language fusion for correcting inverse age estimation.

A longitudinal study of digital multimodal learning activities and achievement motivation: A superposition perspective from Chinese EFL learners

MGML: A plug-and-play meta-guided multi-modal learning framework for incomplete multimodal brain tumor segmentation.

A two-stage multimodal learning framework based on text-driven vision pretraining and cross-modal feature fusion for thyroid ultrasound diagnosis

• Provides a unified taxonomy that organizes more than 100 VLA architectures. • Maps 26 major VLA datasets using a framework based on task difficulty and modality richness. • Presents a large-scale quantitative analysis linking model design choices to normalized performance. • Demonstrates that diffusion-based decoders and hierarchical fusion significantly improve manipulation success. • Introduces the VLA-FEB benchmark with new metrics for measuring multimodal fusion quality and alignment. • Proposes an agentic VLA framework where LLM planners verify and re-plan actions using uncertainty-driven feedback for self-improving robotic autonomy. Vision Language Action (VLA) models represent a new frontier in robotics by unifying perception, reasoning, and control within a single multimodal learning framework. By integrating visual, linguistic, and action modalities, they enable multimodal fusion systems designed for instruction-driven manipulation and generalist autonomy. This systematic review synthesizes the state of the art in VLA research with an emphasis on architectures, algorithms, and applications relevant to robotic manipulation. We examine 102 models, 26 foundational datasets, and 12 simulation platforms, categorizing them according to their fusion strategies and integration mechanisms. Foundational datasets are evaluated using a novel criterion based on task complexity, modality richness, and dataset scale, allowing a comparative analysis of their suitability for generalist policy learning. We further introduce a structured taxonomy of fusion hierarchies and encoder-decoder families, together with a two-dimensional dataset characterization framework and a meta-analytic benchmarking protocol that quantitatively links design variables to empirical performance across benchmarks. Our analysis shows that hierarchical and late fusion architectures achieve the highest manipulation success and generalization, confirming the benefit of multi-level cross-modal integration. Diffusion-based decoders demonstrate superior cross-domain transfer and robustness compared to autoregressive heads. Dataset analysis highlights a persistent lack of benchmarks that combine high-complexity, multimodal, and long-horizon tasks, while existing simulators offer limited multimodal synchronization and real-to-sim consistency. To address these gaps, we propose the VLA Fusion Evaluation Benchmark to quantify fusion efficiency and alignment. Drawing on both academic and industrial advances, the review outlines future research directions in adaptive and modular fusion architectures, computational resource optimization, and the deployment of interpretable, resource-efficient robotic systems. We further propose a forward-looking agentic VLA paradigm where LLM planners integrate VLA skills as verifiable tools within a closed feedback loop for adaptive and self-improving robotic control. This work provides both a conceptual foundation and a quantitative roadmap for advancing embodied intelligence through multimodal information fusion across robotic domains. A public repository summarizing models, datasets, and simulators is available at: https://muhayyuddin.github.io/VLAs/ .

A novel multimodal learning method for predicting treatment resistance in MPO-AAV with lung involvement