Accurate glioma grading using noninvasive multiparametric MRI is essential to minimize biopsy risks and guide treatment planning. Current AI-based approaches struggle to prioritize 3D pathological features due to the lack of shape-sensitive multi-dimensional spatial features and exhibit channel redundancy, while multi-scale fusion lacks semantic patch (MRI subregion) relationships. To address these limitations, we propose a Wavelet Multi-Dimensional and Mamba-Guided Semantic Graph Feature Fusion Network (WM-MSGFF-Net) for glioma grading that integrates two innovative modules. First, the WaveletAdaptive Feature Refinement (WAFR) module processes features through the Adaptive Wavelet Multi-Dimensional Collaboration (AWMDC) sub-module, utilizing wavelet-based convolutions to extract joint shape-sensitive spatial features across depth, height, and width dimensions, coupled with adaptive channel attention to capture sparse pathological features. Subsequently, WAFR applies Self-attention to model patch-level dependencies. Second, the Semantic-Aware Multi-Scale Feature Fusion (SAMSFF) module constructs glioma-specific graphs from WAFR and encoderderived patches, using cosine similarity to define edges and leveraging Mamba-enhanced Graph Convolutional Networks (GCNs) to establish semantic relationships. Experiments on publicly available datasets demonstrate that WM-MSGFF-Net achieves an accuracy of 95.7 % on BraTS2020 and 95.6 % on UCSF-PDGM, outperforming 8 baseline methods. By integrating effective volumetric feature extraction with semantic patch relationships, WM-MSGFF-Net offers a promising approach to noninvasive glioma grading that could support diagnostic decision-making.

Current 3D object detection methods face limitations in both single-modal and multimodal approaches. Single-modal detectors struggle with inadequate depth perception or difficulty distinguishing semantically similar objects, while multimodal systems, which combine LiDAR and camera data, encounter challenges like integration complexity and inefficient feature fusion. This study presents ROI-based ViT Fusion (RVF3D), a multimodal 3D object detector combining sparse 3D and dense 2D data, outperforming current methods. The RVF3D enhances the understanding of rich LiDAR and camera representations through improved query generation, feature sampling, and multimodality cross-ViT fusion. We propose an ROI-based Query Sampling (RQS) multimodal 3D object identification pipeline, eliminating laborious non-maximum suppression (NMS) postprocessing and complex prior box configurations. The RQS module uses learnable filters to aggregate image and point representations, retaining foreground characteristics for object localization and recognition while removing background noise. Our RVF3D leverages a hierarchical vision transformer-based approach, ViT-Fusion, which includes CameraViT and LidarViT components for embedding representations of input data in each modality. These representations are fused to enable hierarchical learning and deeper feature extraction. Comprehensive tests on the KITTI and nuScenes datasets demonstrate that RVF3D achieves 89.88% mAP in BEV and 75.2% mAP in 3D, respectively.

In computer vision, accurately detecting objects in three-dimensional (3D) scenes is indispensable for intelligent transportation, robotics, vision applications, and augmented reality. While self-attention mechanisms have demonstrated remarkable success in enhancing feature representations and capturing long-range dependencies, their application to 3D object detection remains challenging due to inherent limitations to capturing intricate contextual dependencies among points to refining 3D proposals. This paper proposes a high-quality Chunk-wise Reweighting Transformer for 3D Object Detection (CRT3D) architecture explicitly tailored for 3D object detection tasks. CRT3D comprises three modules: self-attention-based encoder-decoder for effective modelling of spatial context; channel-based Reweighting captures temporal dependencies; and Chunk-wise Reweighting improves computational efficiency to efficiently handle sparse point cloud data, advancing real-time 3D object detection in autonomous vehicles. Our CRT3D model can effectively balance the trade-off between capturing local and global context, leading to more informative and contextually rich representations. Through extensive experiments on the benchmark dataset KITTI [1], we demonstrate the efficacy of CRT3D in achieving better performance by 3.49% and 4.25% in 3D AP with PointPillar and PointRCNN backbone, respectively. CRT3D enhances 3D object detection framework accuracy, particularly at medium range and occlusion cases, while minimally impacting real-time performance and model parameters.

The growing vulnerabilities in computer networks highlight the critical need for robust intrusion detection techniques, particularly host-based systems. This paper evaluates five host-based intrusion detection systems (HIDS) to identify the most effective solution. Among these, the distributed intrusion detection system (DIDS) stands out due to its dual functionality, addressing threats at both local hosts and network levels for enhanced security. Computer applications, comprising interconnected hardware and tools, enable efficient send data and applications and data sharing but are face to various threats. Network threats, such as machine tampering, electrical sparks, and environmental factors, can compromise network infrastructure. Likewise, software threats, including malware and prohibitted access, often exploit hidden vulnerabilities, posing severe risks to data integrity and system performance. Therefore, this study emphasizes proactive measures, including secure setups, regular monitoring, good services, redundant power lines, and controlled environmental conditions. High-level threat detection and vulnerability management are essential for software threats. By implementing these approaches, target-based systems can reduce both hardware and software threats, ensuring network security and scalability. The results underscore the importance of preemptive tasks to identify and address vulnerabilities and improve the resilience of host-based intrusion detection systems in safeguarding modern computer networks against evolving threats.