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Intrumer:A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen Banu A S.K.B.Sangeetha Department of computer science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection system(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

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Gradient maturation experiment on hairs implies taphonomic changes in fossil hairs

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science China Earth sciences 2025年第1期68卷 62-73页

作者： Xin LI Yibo ZHOU Tao ZHAO Xingjian HE Yanhong PAN State Key Laboratory for Mineral Deposits Research Center for Critical Earth Material Cycling Centre for Research and Education on Biological Evolution and Environment and Frontiers Science School of Earth Sciences and Engineering Nanjing University institute of Palaeontology Yunnan Key Laboratory of Earth System Science Yunnan Key Laboratory for PalaeobiologyYunnan University

Mammalian hair, as a key feature related to endothermy, physical protection, and camouflage, has a long geohistorical record that dates back even to the Mesozoic. However, the limited information on the taphonomy of hair hinders the understanding of its evolutionary significance in deep time. To address this, we conducted taphonomic experiments to simulate the transformation process of black hair. The changes in the morphological structure, microscopic characteristics, and chemical composition of hair during the simulated fossilization process were documented by scanning electron microscopy, Fourier transform infrared(FTIR) spectroscopy, and Raman spectroscopy. Our results revealed that damage to the morphology of hair began at a mild temperature(150–175°C), and hair transformed into a fluid-like mass at a relatively high temperature(200°C). In addition, the morphological structure of melanosomes had relatively greater preservation potential than keratins did, and the microscopic features of melanosomes were persistently stable. The Raman and FTIR spectra indicated that the characteristic melanin and keratin peaks changed as the temperature increased. Although the chemical signals were not lost completely, the variations in the characteristic amide bands in the FTIR spectra indicate that the keratins severely degraded at high temperatures(>200°C), whereas the characteristic melanin bands in the Raman spectra suggested that melanin signals were still recognizable, even at high temperatures(>200°C). These results were confirmed by principal component analysis(PCA) and principal component analysis linear discriminant analysis(PCA–LDA). In addition, the Raman signals of all the samples in the simulated experiments were distinguished from the signals of common geological carbonaceous materials(humic acid), which suggested that Raman spectroscopy can be used to detect melanin in fossils.

关键词： Mammalian hair Melanin Keratin Fossilization Experimental taphonomy

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in Engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of computer science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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NASTCNN: a spatio-temporal convolutional neural network with non-local attention mechanism for image-induced brain activity pattern recognition

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Optoelectronics Letters 2025年

作者： TAN Xiangxin SHAO Mingxiong ZENG Ke GU Yue School of computer science and Engineering， Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent system (Ministry of Education)， Tianjin University of Technology Krembil Research institute， University Health Network

Deciphering the brain’s visual processing mechanisms has always been a hot topic. Using neural signal patterns to predict the corresponding external visual stimuli is crucial in understanding the interaction of real visual scenes. There are some challenges in the study of image-induced brain activity pattern recognition based on electroencephalogram (EEG)， e.g. how to fully utilize the effective features of EEG signals to improve the classification， and how the frequency and the length of EEG effect the model performance. We design an experimental paradigm that minimizes the interaction between visual stimuli， and propose a spatio-temporal convolutional neural network with non-local attention mechanism (NASTCNN) for image-induced brain activity pattern recognition. The framework utilizes non-local feature extraction operations to realize global information interaction between EEG signals over long distances and enhance the feature expression of some important information. Our model achieves the best performance on both MNIST-EEG and ImageNet-EEG datasets. The classification results are 24.00% and 50.10% respectively， higher than the random probability. We also analyze the effects of different EEG frequency bands and time subsequences of different lengths on the classification performance and which brain regions are activated over time. The results show that when full frequency band and entire length sequence are selected， the classification result is optimal. The occipital region is essential for processing visual tasks， and the temporal region and some frontal regions also play important role in the later stage of visual decoding. This research can help us better understand the visual processing mechanism of the brain.

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Pulsed dynamic electrolysis enhanced PEMWE hydrogen production:Revealing the effects of pulsed electric fields on protons mass transport and hydrogen bubble escape

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Journal of Energy Chemistry 2025年第1期100卷 201-214页

作者： Xuewei Zhang Wei Zhou Yuming Huang Liang Xie Tonghui Li Huimin Kang Lijie Wang Yang Yu Yani Ding Junfeng Li Jiaxiang Chen Miaoting Sun Shuo Cheng Xiaoxiao Meng Jihui Gao Guangbo Zhao School of Energy science and Engineering Harbin Institute of Technology China Datang Technology Innovation Co. Ltd

The transition of hydrogen sourcing from carbon-intensive to water-based methodologies is underway,with renewable energy-powered proton exchange membrane water electrolysis （PEMWE） emerging as the preeminent pathway for hydrogen *** remarkable advancements in this field,confronting the sluggish electrochemical kinetics and inherent high-energy consumption arising from deteriorated mass transport within PEMWE systems remains a formidable *** impediment stems primarily from the hindered protons mass transfer and the untimely hydrogen bubbles *** address these challenges,we harness the inherent variability of electrical energy and introduce an innovative pulsed dynamic water electrolysis *** to constant voltage electrolysis (hydrogen production rate:51.6 m L h-1,energy consumption:5.37 kWh Nm-3H2),this strategy (hydrogen production rate:66 m L h-1,energy consumption:3.83 kWh Nm-3H2) increases the hydrogen production rate by approximately 27%and reduces the energy consumption by about 28%.Furthermore,we demonstrate the practicality of this system by integrating it with an off-grid photovoltaic （PV） system designed for outdoor operation,successfully driving a hydrogen production current of up to 500 mA under an average voltage of approximately 2 *** combined results of in-situ characterization and finite element analysis reveal the performance enhancement mechanism:pulsed dynamic electrolysis （PDE） dramatically accelerates the enrichment of protons at the electrode/solution interface and facilitates the release of bubbles on the electrode *** such,PDE-enhanced PEMWE represents a synergistic advancement,concurrently enhancing both the hydrogen generation reaction and associated transport *** promising technology not only redefines the landscape of electrolysis-based hydrogen production but also holds immense potential for broadening its application across a diverse spectrum of electrocatalytic endeavors.

关键词： Electrolysis

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A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach

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computer Modeling in Engineering & sciences 2025年第1期142卷 555-577页

作者： Linlin Sun Zihui Wang Shukun Cui Ziquan Yan Weiping Hu Qingchun Meng State Key Laboratory of High-Speed Railway Track system China Academy of Railway Sciences Corporation LimitedBeijing100081China Railway Engineering Research institute China Academy of Railway Sciences Corporation LimitedBeijing100081China School of Aeronautic science and Engineering Beihang UniversityBeijing100191China

Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed *** can cause significant wheel-rail dynamic interactions,leading to wheel-rail noise,component damage,and *** researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry ***,the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric *** this study,novel theoretical models were developed for three categories of rail weld irregularities,based on measurements of the high-speed railway from Beijing to *** vertical dynamic forces in the time and frequency domains were compared under different running *** forces generated by the rail weld irregularities that were measured and modeled,respectively,were compared to validate the accuracy of the proposed ***,based on the numerical study,the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network(ANN),and the optimum combination of parameters for this model is *** results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the *** ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.

关键词： Rail weld irregularity high-speed railway vehicle-track coupled dynamics wheel/rail dynamic vertical force artificial neural networks

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Security Strategy of Digital Medical Contents Based on Blockchain in Generative AI Model

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computers, Materials & Continua 2025年第1期82卷 259-278页

作者： Hoon Ko Marek R.Ogiela Division of computer science and Engineering Sunmoon UniversityAsan31460Republic of Korea AGH University of Krakow Cryptography and Cognitive Informatics LaboratoryKrakow30-059Poland

This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain *** combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical *** participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security *** multi faceted exploration led to an indepth evaluation of the model’s performance and ***,the correlation between accuracy,detection rate,and error rate was *** analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance *** study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical *** blockchain to generative AI-created medical content addresses key personal information protection *** utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical *** approach not only enhances security but also enables transparent and tamperproof ***,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient *** conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient *** proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content *** research and advancements

关键词： Digitalmedical content medical diagnostic visualization security analysis generativeAI blockchain vulnerability pattern recognition

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5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT systems

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computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of Data science Engineering University of HoustonHoustonTX 77204USA The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609USA School of systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of computer science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch School of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning

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Approximating Geographical Queries

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Journal of computer science & Technology 2009年第6期24卷 1109-1124页

作者： Arianna D’Ulizia Fernando Ferri Anna Formica Patrizia Grifoni institute of Research on Population and Social Policies National Research Council institute for system analysis and computer science National Research CouncilRome Italy

This article proposes a graph-theoretic methodology for query approximation in Geographic Information systems, enabling the relaxation of three kinds of query constraints： topological, semantic and structural. An approximate query is associated with a value corresponding to the degree of similarity with the original query. Such a value is computed for topological constraints on the basis of the topological distance between configurations, for semantic constraints using the information content approach, and for structural constraints revisiting the maximum weighted matching problem in bipartite graphs. Finally, the high correlation of our proposal with human judgment is demonstrated by an experiment.

关键词： query language spatial database GIS (Geographic Information system) query formulation

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Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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computer Modeling in Engineering & sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea Department of Information Convergence system Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

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