The rapid expansion of email usage has been paralleled by a significant rise in spam, which not only clutters inboxes but also spreads malware and introduces serious security threats. While machine learning (ML) has proven to be a powerful approach for spam detection, single-classifier models often face issues such as poor generalization, overfitting, and elevated false positive rates. Key challenges in email spam detection include: (1) Improving accuracy while keeping false positives low to maintain usability; (2) Responding to threats in real-time; (3) Handling the ever-changing characteristics of email content; and (4) Automating model adaptation to these changes. To address these challenges, this study presents SHRED (Sifting Ham for Real-time Email Spam Detection), an ensemble-based ML model specifically built for real-time spam filtering. SHRED combines Naive Bayes, Decision Trees, Adaptive Boosting, Random Forests, and Artificial Neural Networks using a hybrid approach of voting and stacking to enhance detection performance. It employs preprocessing methods such as text normalization and TF-IDF-driven feature selection to produce resilient input representations that automatically adapt to evolving email patterns. When tested on a diverse dataset of 83,448 emails, SHRED achieved a detection rate of 98.4%, a false positive rate of 1.6%, and an overall accuracy of 98.45%, with an average processing time of just 0.0522 seconds per email. This includes all stages: preprocessing, feature extraction and selection, training, and detection, underscoring the system’s capability to accurately detect spam in real-time.

Background: The clinical imperative to reduce patient ionizing radiation exposure during diagnosis and treatment planning necessitates robust, high-fidelity synthetic imaging solutions. Current cross-modal synthesis techniques, primarily based on GANs and deterministic CNNs, exhibit instability and critical errors in modeling high-contrast tissues, thereby hindering their reliability for safety-critical applications such as radiotherapy. Objectives: Our primary objective was to develop a stable, high accuracy framework for 3D Magnetic Resonance Imaging (MRI) to Computed Tomography (CT) synthesis capable of generating clinically equivalent synthetic CTs (sCTs) across multiple anatomical sites. Methods: We introduce a novel 3D Latent Diffusion Model (3DLDM) that operates in a compressed latent space, mitigating the computational burden of 3D diffusion while leveraging the stability of the denoising objective. Results: Across the Head & Neck, Thorax, and Abdomen, the 3DLDM achieved a Mean Absolute Error (MAE) of 56.44 Hounsfield Units (HU). This result demonstrates a significant 3.63% reduction in overall error compared to the strongest adversarial baseline, CycleGAN (MAE = 60.07 HU, p < 0.05), a 10.76% reduction compared to NNUNet (MAE = 67.20 HU, p < 0.01), and a 20.79% reduction compared to the transformer-based SwinUNeTr (MAE = 77.23 HU, p < 0.0001). The model also achieved the highest structural similarity (SSIM = 0.885 ± 0.031), significantly exceeding SwinUNeTr (p < 0.0001), NNUNet (p < 0.01), and Pix2Pix (p < 0.0001). Likewise, the 3D-LDM achieved the highest peak signal-to-noise ratio (PSNR = 29.73 ± 1.60 dB), with statistically significant gains over CycleGAN (p < 0.01), NNUNet (p < 0.001), and SwinUNeTr (p < 0.0001). Conclusions: This work validates a scalable, accurate approach for volumetric synthesis, positioning the 3DLDM to enable MR-only radiotherapy planning and accelerate radiation-free multi-modal imaging in the clinic.

The increasing complexity and scale of cyber threats demand intelligent and adaptive defense mechanisms that extend beyond traditional approaches. Artificial Intelligence (AI) has emerged as a key enabler for enhancing cyber security through automated detection, analysis, and response. This paper presents a comprehensive survey of AI applications in cyber security across five major domains: malware detection, intrusion detection, phishing and spam detection, botnet detection, and cyber forensics. A systematic methodology based on data and methodological triangulation is employed to analyze 75 studies published between 2021 and 2025. The paper introduces a multi-layer taxonomy that maps cyber threats to application domains, analysis methods, AI approaches, and their associated capabilities and limitations. In addition, a cross-domain meta-analysis is conducted to identify recurring trends and assess the adoption of AI across different cyber security scenarios. The analysis reveals that deep learning and transformer-based models dominate data-intensive domains such as intrusion detection and malware analysis, whereas traditional machine learning techniques remain effective in structured and resource-constrained settings, particularly for phishing detection. Key challenges include dataset limitations, limited explainability, adversarial vulnerabilities, and computational constraints. Unlike existing surveys that focus on specific techniques or individual cyber security domains, this work provides a unified, application-oriented perspective on AI-driven cyber security. It further highlights emerging trends, open challenges, and future research directions toward more robust, scalable, and trustworthy cyber security systems.

Background: Positron emission tomography (PET) provides physiologic information central to oncologic staging and treatment assessment, but its availability is limited by cost, radiation exposure, and scanner access. Synthesizing PET from computed tomography (CT) is attractive but challenging, as tracer uptake is only partially constrained by anatomy, making the mapping inherently one-to-many. Methods: We propose a conditional 3D latent diffusion framework (3D-LDM) for CT-to-PET synthesis in the head–neck and thoracic region. The pipeline localizes anatomy by segmenting lungs in CT and restricting the volume to reduce irrelevant variability. PET volumes are encoded into a compact latent space using a KL-regularized 3D autoencoder, and a conditional 3D diffusion U-Net learns to generate PET latents conditioned on CT via a denoising diffusion process. The model was trained and evaluated on 900 paired PET/CT studies. Performance was assessed in SUV space using MAE, PSNR, and SSIM, and compared against transformer-, CNN-, and GAN-based baselines. Results: On the held-out test cohort, 3D-LDM achieved the best overall quantitative fidelity (MAE = 303.05 ± 22.16 SUV units, PSNR = 32.64 ± 1.79, SSIM = 0.86 ± 0.03), outperforming all baselines with statistically significant differences (p < 0.001). At the lesion level, the model achieved a precision of 0.76 (95% CI: 0.71, 0.81) and recall of 0.76 (95% CI: 0.72, 0.80), detecting an average of 3.19 lesions per scan with a false-positive rate of 0.72/scan. Lesion-wise NMSE was 11.37%, significantly outperforming GAN and transformer baselines. Conclusions: 3D-LDM enables efficient, high-fidelity PET synthesis in the head–neck and thoracic regions, substantially improving lesion-level accuracy over state-of-the-art baselines. While it is not a replacement for diagnostic PET, these results support the model’s potential as a clinical decision support tool.

Accurate cone-beam CT (CBCT)-to-synthetic CT (sCT) translation is essential for image-guided adaptive radiotherapy (IGART), where Hounsfield unit (HU) fidelity and structural accuracy directly affect dose calculation. We propose a conditional 3D Latent Diffusion Model (3DLDFM) for volumetric CBCT-to-sCT synthesis. The framework comprises two stages: (i) a 3D variational autoencoder with KL regularization that compresses CBCT volumes into a three-channel latent representation, trained with a composite loss combining L1 reconstruction, perceptual, KL, and adversarial terms; and (ii) a conditional 3D U-Net diffusion model that performs iterative denoising in latent space using a DDPM-style noise schedule, conditioned on the input CBCT. We evaluated 3DLDFM on the multi-center SynthRAD2023 dataset comprising 955 paired CBCT/CT volumes spanning head-and-neck, thorax, and abdominal sites. Performance is benchmarked against SwinUNETR, nnUNet, CycleGAN, and Pix2Pix using Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) within body masks. Across all regions, 3DLDFM achieves the lowest overall MAE (51.40 HU) and the highest overall SSIM (0.9124), while maintaining competitive PSNR (30.60 dB), surpassing all baselines in HU accuracy and structural fidelity. These results demonstrate that the proposed latent diffusion framework provides a robust and generalizable solution for CBCT-to-CT synthesis and strengthens the feasibility of simulation-free adaptive radiotherapy workflows.

Android malware continues to evolve in sophistication, creating a need for accurate, efficient, and interpretable detection mechanisms. This paper proposes a novel Android malware detection framework that integrates graph-based software engineering analysis, explainable artificial intelligence (XAI), and heterogeneous ensemble learning. The framework decompiles Android applications, constructs call graphs and control flow graphs, and extracts structural, functional, and behavioral software engineering metrics that are transformed into statistical feature representations. To improve feature quality and reduce dimensionality, LIME and SHAP are engineered into a threshold-based feature relevance mechanism, reducing the original 89 features to 65 and 61 features, respectively. A new heterogeneous ensemble classifier, RF–LNN–GRU, is then introduced to combine the complementary strengths of random forest, lightweight neural networks, and gated recurrent units through asymmetric probabilistic voting. Experiments conducted on 10,365 Android applications demonstrate that the proposed framework consistently outperforms conventional machine learning and ensemble baselines, achieving a detection rate of 98.98%, an F1-score of 96.2%, and an AUC of 98.1% while maintaining an inference latency below 0.4 ms per sample. These results demonstrate that the proposed combination of graph-derived software engineering metrics, XAI-guided feature engineering, and heterogeneous ensemble learning provides an effective and computationally efficient solution for Android malware detection.