Machine Learning and Knowledge Extraction

Encoder–decoder models are widely used for pixel-level segmentation due to their ability to capture and combine multiscale features. However, skip connections between the encoder and decoder often require cropping to mitigate border pixel loss during convolutions, which can introduce inefficiencies and limit performance. This study explores the potential of modifying these connections by removing direct encoder-to-decoder links to enhance segmentation accuracy. We propose a novel architecture, termed XCC-Net, which features two context-capturing pathways and two symmetric pathways for enlargement. These pathways are interconnected via channels, enabling automated detection of structures with varied shapes. The XCC-Net’s X-shaped architecture links skip connections exclusively between encoder-to-encoder and decoder-to-decoder, omitting direct encoder-to-decoder feature transfers to potentially improve performance. The XCC-Net model was evaluated on multiple medical imaging datasets, including wireless capsule endoscopy (WCE), colonoscopy, and dermoscopy images. Experimental results showed that XCC-Net outperformed state-of-the-art segmentation models, achieving dice coefficients of 91.70%, 89.26%, 87.15%, and 79.07% on the MICCAI 2017 (Red Lesion), PH2, CVC-ClinicDB, and ISIC 2017 datasets, respectively. XCC-Net’s X-shaped architecture, with its unique skip connections, demonstrates improved segmentation performance across various medical imaging tasks.

The increasing sophistication of malware has challenged the effectiveness of conventional detection techniques, motivating the adoption of Graph Neural Networks (GNNs) for their ability to model the structural and semantic information embedded in control flow graphs. While GNNs offer high detection performance, their lack of transparency limits their applicability in security-critical domains. To address this, we present an explainable malware detection framework, which contains a dual explainer. This dual explainer integrates a GNN explainer with a neural subgraph matching approach and the VF2 algorithm. The proposed method identifies and verifies discriminative subgraphs during training, which are later used to explain new predictions through efficient matching. To enhance the generalization of the neural subgraph matcher, we train it using curriculum learning, gradually increasing subgraph complexity to improve matching quality. Experimental evaluations on benchmark datasets demonstrate that the proposed framework retains high classification accuracy while significantly improving interpretability. By unifying explainable graph learning techniques with subgraph matching, the proposed framework enables analysts to gain actionable insights, fostering greater trust in GNN-based malware detectors.

Artificial Intelligence (AI) is increasingly used to enhance project management practices, especially in risk analysis, where traditional tools often lack predictive capabilities. This study introduces an AI-based tool that supports project teams in identifying and interpreting risks through machine learning and integrated documentation features. A synthetic dataset of 5000 project instances was generated using deterministic rules across 27 input variables, enabling the training of multi-output Decision Tree and Random Forest models to predict risk type, impact, probability, and response strategy. Due to the rule-based structure of the dataset, both models achieved near-perfect classification performance, with Random Forest showing slightly better regression accuracy. These results validate the modelling pipeline but should not be interpreted as real-world predictive accuracy. The trained models were deployed within a web platform offering prediction visualization, automated PDF reporting, result storage, and access to a structured risk management plan template. Survey feedback highlights strong user interest in AI-assisted mitigation suggestions, dashboards, notifications, and mobile access. The findings demonstrate the potential of AI to improve proactive risk assessment and decision-making in project environments.