Building reliable vehicle detection models for intelligent transportation systems calls for large, well-annotated datasets—yet gathering and labelling real traffic data remains both costly and labour-intensive. This paper introduces Traffic Synth, an automated pipeline that generates synthetic training datasets by altering real traffic camera images rather than constructing entirely artificial scenes. The system begins by detecting vehicles through instance segmentation and removing them from the frame. It then places new vehicles directly into the cleared regions using diffusion-based inpainting, all while retaining the original road layout, lighting, and camera perspective. Doing so preserves the realistic scene context while broadening the visual variety of vehicles in the dataset. To ensure that the resulting traffic looks physically plausible, we incorporate a lane-aware prompting mechanism that matches each vehicle’s orientation to the direction of travel as seen from the camera. The system further draws on a weighted vehicle brand database that mirrors the makes and colours commonly found on European roads to better match actual deployment conditions. Class-specific mask processing—involving anisotropic scaling and relative dilation—rounds out the pipeline by improving generation quality across different vehicle size categories. The final output is a set of images with automatically generated annotations in a standard object detection format.

This paper presents a comparative analysis of advanced AI-based techniques for human face inpainting using semantic masks that fully occlude targeted facial components. The primary objective is to evaluate the ability of image inpainting methods to accurately restore semantically meaningful facial features. Our results show that existing inpainting models face significant challenges when semantic masks completely obscure the underlying facial structures. In contrast to random masks, which leave partial visual cues, semantic masks remove all structural information, making reconstruction substantially more difficult. We assess the performance of generative adversarial networks (GANs), transformer-based models, and diffusion models in restoring fully occluded facial components. To address these challenges, we explore three retraining strategies: using semantic masks, using random masks, and a hybrid approach combining both. While the hybrid strategy leverages the complementary strengths of each mask type and improves contextual understanding, fully accurate reconstruction remains challenging. These findings demonstrate that inpainting under fully occluding semantic masks is a critical yet underexplored area, offering opportunities for developing new AI architectures and strategies for advanced facial reconstruction.

Generative Artificial Intelligence (GenAI) has been a viable technology for decades, yet widespread adoption in healthcare and academic settings has remained limited to research. One possible explanation for this is limited understanding about the beliefs around GenAI use amongst faculty and students training in biomedical disciplines that frequently lead to non-physician healthcare careers, including physical therapy (PT), occupational therapy (OT), allied health (AH), and biomedical engineering (BME). Furthermore, no known studies exist assessing differences that may exist across those disciplines. Given the significant number of professionals in those disciplines and the outsized impact they have on the healthcare system, investigating their beliefs around GenAI use is vital before widespread adoption. Accordingly, we investigated the perceptions of GenAI among students and faculty in the aforementioned fields that frequently lead to careers in healthcare. We found that knowledge of GenAI significantly influences comfort with its use completing college coursework including whether respondents believed it contributed to the process of completing that coursework and whether use of GenAI enhances learning. Interestingly, however, there were no statistically significant differences in perceptions of GenAI across disciplines, roles, or institution sizes. Qualitative findings revealed concerns about plagiarism, decline of critical thinking skills, and ethical challenges, while also recognizing GenAI’s potential to enhance learning efficiency and idea generation. Critically, the study results emphasize the need for proper training and guidelines to ensure GenAI is integrated responsibly into healthcare-related education.

Digital Twins (DTs) have emerged within the last decade due to the adequate maturity of several key technologies contributing to the realization of real-time virtual–physical world synchronization. Advancements in sensing, connectivity, computing processing power, and artificial intelligence have contributed to the deployment of DTs in several application sectors, such as in agriculture. This work aims to provide a scoping review of recent advancements in digital twin technologies and agricultural applications. Results indicate a special focus on plant-level models, soil moisture, and machinery, while most works are based on drone imagery combined with machine learning routines. Several works use the term DTs rather loosely, often describing systems that resemble decision support tools rather than a fully synchronized virtual–physical setup. Data integration emerges as the most important bottleneck, especially when the system mixes satellite data, local sensory data, and simulation outputs. Yet it is suggested that DTs could eventually support more adaptive and resource-efficient farm management. However, the field is still missing common frameworks and long-term evaluations. Based on this review, progress depends on better data-handling pipelines, clearer definitions of operational DTs, and more attention to economic and practical constraints faced by farmers rather than just technical proofs of concept.

Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This work proposes an efficient, effective, end-to-end framework for Artificial Intelligence (AI)-based pathway analysis that predicts both cancer severity and mutation progression in order to recommend possible treatments. The proposed technique involves a novel combination of time-series machine learning models and pathway analysis. First, mutation sequences were isolated from The Cancer Genome Atlas (TCGA) Database. Then, a novel preprocessing algorithm was used to filter key mutations by mutation frequency. This data was fed into a Recurrent Neural Network (RNN) that predicted cancer severity. The model probabilistically used the RNN predictions, information from the preprocessing algorithm, and multiple drug-target databases to predict future mutations and recommend possible treatments. This framework achieved robust results and Receiver Operating Characteristic (ROC) curves (a key statistical metric) with accuracies greater than 60%, similar to existing cancer diagnostics. In addition, preprocessing played a key role in isolating a few hundred key driver mutations per cancer stage, consistent with current research. Heatmaps based on predicted gene frequency were also generated, highlighting key mutations in each cancer. Overall, this work is the first to propose an efficient, cost-effective end-to-end framework for projecting cancer prognosis and providing possible treatments without relying on expensive, time-consuming wet lab work.

This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was collected and used in this research. The result is an integrated framework, combining a genetic algorithm (GA) with a CatBoost-based surrogate model for multi-objective optimization of the injection molding machine parameters. The aim of the optimization is to minimize the cycle time and cycle energy while maintaining the product quality. Ten process parameters were optimized, which are machine-specific. An evolutionary optimization using the NSGA-II algorithm is used to generate the recommended parameter set. The proposed GA-surrogate hybrid approach produces the optimal set of parameters that reduced the cycle time by 4.5%, for this specific product, while maintaining product quality. Cycle energy was evaluated on an hourly basis; its variation across candidate solutions was limited, but it was retained as an optimization objective to support energy-based process optimization. A total of 95% of the generated solutions satisfied industrial quality constraints, demonstrating the robustness of the proposed optimization framework. While classical Design of Experiment (DOE) approaches require sequential physical trials, the proposed GA-surrogate framework achieves convergence in computational iterations, which significantly reduces machine usage for optimization. This approach demonstrates a practical way to automate data-driven process optimization in an injection mold machine for an industrial application, and it can be extended to other manufacturing systems that require adaptive control parameters.

The study of muscle signal classification has been widely explored for the control of myoelectric prostheses. Traditional approaches rely on manually designed features extracted from time- or frequency-domain representations, which may limit the generalization and adaptability of EMG-based systems. In this work, an autoencoder-based framework is proposed for automatic feature extraction, enabling the learning of compact latent representations directly from raw EMG signals and reducing dependence on handcrafted features. A custom instrumentation system with three surface EMG sensors was developed and placed on selected forearm muscles to acquire signals associated with five hand movements from 20 healthy participants aged 18 to 40 years. The signals were segmented into 200 ms windows with 75% overlap. The proposed method employs a recurrent autoencoder with a symmetric encoder–decoder architecture, trained independently for each sensor to achieve accurate signal reconstruction, with a minimum reconstruction loss of 3.3×10−4V2. The encoder’s latent representations were then used to train a dense neural network for gesture classification. An overall efficiency of 93.84% was achieved, demonstrating that the proposed reconstruction-based approach provides high classification performance and represents a promising solution for future EMG-based assistive and control applications.

Effective and transparent medical diagnosis relies on accurate and interpretable classification of medical images across multiple modalities. This paper introduces an explainable multi-modal image analysis framework based on a dual-stream architecture that fuses handcrafted descriptors with deep features extracted from a custom MobileNet. Handcrafted descriptors include frequency-domain and texture features, while deep features are summarized using 26 statistical metrics to enhance interpretability. In the fusion stage, complementary features are combined at both the feature and decision levels. Decision-level integration combines calibrated soft voting, weighted voting, and stacking ensembles with optimized classifiers, including decision trees, random forests, gradient boosting, and logistic regression. To further refine performance, a hybrid class-specific feature selection strategy is proposed, combining mutual information, recursive elimination, and random forest importance to select the most discriminative features for each class. This hybrid selection approach eliminates redundancy, improves computational efficiency, and ensures robust classification. Explainability is provided through Local Interpretable Model-Agnostic Explanations, which offer transparent details about the ensemble model’s predictions and link influential handcrafted features to clinically meaningful image characteristics. The framework is validated on three benchmark datasets, i.e., BTTypes (brain MRI), Ultrasound Breast Images, and ACRIMA Retinal Fundus Images, demonstrating generalizability across modalities (MRI, ultrasound, retinal fundus) and disease categories (brain tumor, breast cancer, glaucoma).

Recommendation systems are popular information systems that help consumers manage information overload. Whilst personality has been recognised as an important factor influencing consumers’ choice, it has not yet been fully exploited in recommendation systems. This study proposes a restaurant recommendation approach that integrates customer personality traits, opinions and preferences, extracted either directly from online review platforms or derived from electronic word of mouth (eWOM) text using information extraction techniques. The proposed method leverages the concept of venue personality grounded in personality–brand congruence theory, which posits that customers are more satisfied with brands whose personalities align with their own. A novel model is introduced that combines fine-tuned BERT embeddings with linguistic features to infer users’ personality traits from the text of their reviews. Customers’ preferences are identified using a custom named-entity recogniser, while their opinions are extracted through structural topic modelling. The overall framework integrates neural collaborative filtering (NCF) features with both directly observed and derived information from eWOM to train an extreme gradient boosting (XGBoost) regression model. The proposed approach is compared to baseline collaborative filtering methods and state-of-the-art neural network techniques commonly used in industry. Results across multiple performance metrics demonstrate that incorporating personality, preferences and opinions significantly improves recommendation performance.

This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday environments. To overcome these limitations, we propose a comprehensive solution that utilizes only single-camera 2D images. Our pipeline comprises four distinct stages: (1) extraction of 2D human pose keypoints for multiple persons using a pretrained Human Pose Estimation model; (2) a novel ensemble learning model for person-view classification—distinguishing between front, back, and side perspectives—which is critical for accurate subsequent analysis; (3) a view-specific module that calculates body-segment angles, robustly handling movement pairs (e.g., flexion–extension) and mirrored joints; and (4) a pose assessment module that evaluates calculated angles against established biomechanical Range of Motion (ROM) standards to detect potentially injurious movements. Evaluated on a custom dataset of high-risk poses and diverse images, the end-to-end pipeline demonstrated an 87% success rate in identifying dangerous postures. The view classification stage, a key contribution of this work, achieved a 90% overall accuracy. The system delivers individualized, joint-specific feedback, offering a scalable and deployable solution for enhancing human health and safety in various settings, from home environments to workplaces, without the need for specialized equipment.