The given paper introduces a multi-staged AI-driven intrusion prevention system (IPS) that utilizes mathematical foundations like statistical analysis, probabilistic models, and optimization techniques to design strat...
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The increasing reliance on medical image segmentation for disease diagnosis, treatment planning, and therapeutic assessment has highlighted the need for robust and generalized deep learning (DL)-based segmentation fra...
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The increasing reliance on medical image segmentation for disease diagnosis, treatment planning, and therapeutic assessment has highlighted the need for robust and generalized deep learning (DL)-based segmentation frameworks. However, existing models often suffer from task-specific limitations, catastrophic forgetting, and poor scalability due to their dependency on narrowly annotated datasets. This creates a significant gap in developing unified, multi-organ segmentation systems that leverage distributed and partially labeled datasets across diverse clinical institutions. To address these challenges, we propose the Federated 3D Knowledge Distillation Network (Fed3D-KDNet), a hybrid federated learning (FL) framework that integrates both global and local knowledge distillation mechanisms. Our model adapts the Segment Anything Model (SAM) for volumetric medical imaging by introducing architectural enhancements, including 3D spatial feature adapters and an Auto Prompt Generator (APG), to optimize spatial representation and reduce reliance on manually crafted prompts. Fed3D-KDNet employs a dual knowledge distillation strategy to mitigate catastrophic forgetting and improve cross-client knowledge transfer, ensuring robust generalization across heterogeneous datasets. The proposed methodology was evaluated on multi-organ CT datasets, including the BTCV benchmark, under centralized and federated settings. Experimental results demonstrate that Fed3D-KDNet achieves state-of-the-art performance with an average Dice score of 80.53% and an average Hausdorff Distance (HD) of 11.43 voxels in federated experiments involving seven clients, showing 5.04% improvement in Dice accuracy and a 4.35 voxel reduction in HD. Moreover, our model demonstrates superior efficiency with a computational cost of 371.3 GFLOPs, 26.53 million tuned parameters, and an inference time of 0.058 seconds per iteration. These results validate the efficacy, scalability, and computational efficiency of Fed3D-K
This paper presents a novel framework aimed at enhancing the experimental methodology within cognitive psychology, integrating both informal and formal components to accommodate varied reasoning approaches. The framew...
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Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip...
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Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated...
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The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immun...
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Educational institutions face inherent uncertainties in student performance, stakeholder priorities, and data analysis. This paper explores how cloud computing, with its data storage, analytics, and collaboration tool...
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5G technology represents a progressive leap in Wi-Fi conversation, providing unheard-of pace, connectivity, and capacity. Its deployment has profound implications for modern engineering, impacting industries such as t...
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Security and safety remain paramount concerns for both governments and individuals *** today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to ***,t...
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Security and safety remain paramount concerns for both governments and individuals *** today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to ***,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent *** advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying *** paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection *** SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction *** experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy *** these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
Recently, deep learning-based video salient object detection (VSOD) has achieved some breakthroughs, but these methods rely on expensive annotated videos with pixel-wise annotations or weak annotations. In this paper,...
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