Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods

A Linear Approximate Algorithm for Earth Mover's Distance with Thresholded Ground Distance

Effective and efficient image comparison plays a vital role in content-based image retrieval (CBIR). The earth mover’s distance (EMD) is an enticing measure for image comparison, offering intuitive geometric interpretation and modelling the human perceptions of similarity. Unfortunately, computing EMD, using the simplex method, has cubic complexity. FastEMD, based on min-cost flow, reduces the complexity to (O(N2log⁡N)). Although both methods can obtain the optimal result, the high complexity prevents the application of EMD on large-scale image datasets. Thresholding the ground distance can make EMD faster and more robust, since it can decrease the impact of noise and reduce the range of transportation. In this paper, we present a new image distance metric, EMD+, which applies a threshold to the ground distance. To compute EMD+, the FastEMD approach can be employed. We also propose a novel linear approximation algorithm. Our algorithm achieves ON complexity with the benefit of qualified bins. Experimental results show that (1) our method is 2 to 3 orders of magnitude faster than EMD (computed by FastEMD) and 2 orders of magnitude faster than FastEMD and (2) the precision of our approximation algorithm is no less than the precision of FastEMD

Vitamin E stabilizes iron and mitochondrial metabolism in pulmonary fibrosis

Introduction: Pulmonary fibrosis (PF) is a fatal chronic lung disease that causes structural damage and decreased lung function and has a poor prognosis. Currently, there is no medicine that can truly cure PF. Vitamin E (VE) is a group of natural antioxidants with anticancer and antimutagenic properties. There have been a few reports about the attenuation of PF by VE in experimental animals, but the molecular mechanisms are not fully understood.Methods: Bleomycin-induced PF (BLM-PF) mouse model, and cultured mouse primary lung fibroblasts and MLE 12 cells were utilized. Pathological examination of lung sections, immunoblotting, immunofluorescent staining, and real-time PCR were conducted in this study.Results: We confirmed that VE significantly delayed the progression of BLM-PF and increased the survival rates of experimental mice with PF. VE suppressed the pathological activation and fibrotic differentiation of lung fibroblasts and epithelial-mesenchymal transition and alleviated the inflammatory response in BLM-induced fibrotic lungs and pulmonary epithelial cells in vitro. Importantly, VE reduced BLM-induced ferritin expression in fibrotic lungs, whereas VE did not exhibit iron chelation properties in fibroblasts or epithelial cells in vitro. Furthermore, VE protected against mitochondrial dysmorphology and normalized mitochondrial protein expression in BLM-PF lungs. Consistently, VE suppressed apoptosis in BLM-PF lungs and pulmonary epithelial cells in vitro.Discussion: Collectively, VE markedly inhibited BLM-induced PF through a complex mechanism, including improving iron metabolism and mitochondrial structure and function, mitigating inflammation, and decreasing the fibrotic functions of fibroblasts and epithelial cells. Therefore, VE presents a highly potential therapeutic against PF due to its multiple protective effects with few side effects

A comprehensive AI model development framework for consistent Gleason grading

Background: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. Results: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. Conclusions: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows

Face mask integrated with flexible and wearable manganite oxide respiration sensor

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices