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Clinical features, treatment strategies and outcomes of craniocervical junction arteriovenous fistulas: a cohort study of 193 patients

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Stroke & Vascular Neurology 2024年第1期9卷 18-29页

作者： Yongjie Ma Zihao Song Yinqing Wang Jiachen Wang Chuan He Guilin Li Peng Zhang Tao Hong Liyong Sun Peng Hu Ming Ye Hongqi Zhang department of Neurosurgery Xuanwu Hospital Capital Medical University

Background Craniocervical junction(CCJ)arteriovenous fistulas(AVFs)are *** current treatment strategies for AVFs with different angioarchitecture need to be *** present study aimed to analyse the correlation between angioarchitecture and clinical characteristics,share our experience in treating this disease and identify risk factors associated with subarachnoid haemorrhage(SAH)and poor *** A total of 198 consecutive patients with CCJ AVFs from our neurosurgical centre were retrospectively *** patients were grouped according to their clinical manifestations,and their baseline clinical characteristics,angioarchitecture,treatment strategies and outcomes were *** The patients’median age was 56 years(IQR 47–62 years).The majority of patients were men with 166(83.8%)*** most common clinical manifestation was SAH(52.0%),followed by venous hypertensive myelopathy(VHM)(45.5%).The most common CCJ AVFs type was dural AVF,with 132(63.5%)*** most frequent fistula location was C-1(68.7%)and dural branch of vertebral artery(70.2%)was the most involved arterial feeders for *** most common direction of venous drainage was descending intradural drainage(40.9%),followed by ascending intradural drainage(36.5%).Microsurgery was the most common treatment strategy applied for 151(76.3%)patients,15(7.6%)patients were treated with interventional embolisation only,and 27(13.6%)received both interventional embolisation and microsurgical *** learning curve for microsurgery only was analysed by cumulative summation method,and the turning point was the 70th case,and blood loss in post-group was lower than that in pre-group(p=0.034).At the last follow-up,there were 155(78.3%)patients with favourable outcomes(modified Rankin Scale(mRS)<3).Age≥56(OR 2.038,95%CI 1.039 to 3.998,p=0.038),VHM as the clinical manifestation(OR 4.102,95%CI 2.108 to 7.982,p<0.001)and pretreatment mRS≥3(OR 3.127,95%CI 1.617 to 6.047,p<0

关键词： patients treatment hypertensive

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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

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Computers, Materials & Continua 2024年第2期78卷 1897-1914页

作者： Sang-min Lee Namgi Kim department of Computer Science Kyonggi UniversitySuwonKorea

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the *** Convolution Network(GCN)algorithms have been employed to implement the RIS ***,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning *** address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)*** proposed method involves multiplying the embedding results with different weights for each hop layer during graph *** applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding *** approach facilitates the learning of more hop layers within the GCN *** efficacy of the WF-GCN was demonstrated through its application to various *** the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,***,in the *** dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,***,the application of WF-GCN to Self-supervised Graph learning(SGL)and Simple Graph Contrastive learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

关键词： Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems

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Human Gait Recognition for Biometrics Application Based on Deep learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha department of Electrical Engineering HITEC UniversityTaxila47080Pakistan department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon department of Computer Science HITEC UniversityTaxila47080Pakistan department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 850-865页

作者： Maksym Manko Anton Popov Juan Manuel Gorriz Javier Ramirez Electronic Engineering department Igor Sikorsky Kyiv Polytechnic InstituteKyivUkraine department of Signal Theory Telematics and CommunicationsUniversity of GranadaGranadaSpain

Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment *** planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is *** proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this *** new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency *** proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the *** current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

关键词： 3‐D computer vision deep learning deep neural networks image segmentation medical image processing object segmentation

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Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach

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Big Data Mining and Analytics 2024年第1期7卷 107-130页

作者： Nadezhda Yu.Biziukova Sergey M.Ivanov Oga A.Tarasova department of Bioinformatics Institute of Biomedical ChemistryMoscow 119121Russia department of Bioinformatics Pirogov Russian National Research Medical UniversityMoscow 117997Russia

Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine,because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment *** of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic *** accumulation of various types of biological data,including interactions between proteins,regulation of genes transcription,proteomics,and metabolomics experiments results,allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic *** aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components,as well as for identifying their roles in the development of various types of *** automatically collect sets of abstract texts from the NCBI PubMed bibliographic *** recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach,while for all other proteins and genes machine learning method is *** association extraction,we develop a set of semantic *** complete the results of the text analysis with the gene set enrichment *** identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.

关键词： text-mining data mining Hedgehog pathway neoplastic processes enrichment analysis pathology molecularmechanisms

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Vibration-based hypervelocity impact identification and localization

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Frontiers of Information Technology & Electronic Engineering 2022年第4期23卷 515-529页

作者： Jiao BAO Lifu LIU Jiuwen CAO department of Computer Engineering Chengdu Technological UniversityChengdu611730China machine learning and I-health International Cooperation Base of Zhejiang Province Hangzhou Dianzi UniversityHangzhou310018China Artificial Intelligence Institute Hangzhou Dianzi UniversityHangzhou310018China

Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and *** this paper,we study the synchrosqueezed transform(SST)algorithm and the texture color distribution(TCD)based HVI source identification and localization using impact *** extracted SST and TCD image features are fused for HVI image *** achieve more accurate detection and localization,the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the *** conventional classification and regression models are merged by voting and stacking to achieve the final detection and *** demonstrate the effectiveness of the proposed algorithm,the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for *** experimental results show that the proposed HVI identification and localization algorithm is more accurate than other ***,based on sensor distribution,an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.

关键词： Ensemble learning Synchrosqueezied transform Gray-level co-occurrence matrix Image entropy Distance estimation

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato RMaaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany SHussein Hammam Alshazly department of Data Science University of WashingtonSeattleWashingtonUSA department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia department of Computer Science Jamia HamdardDelhiIndia department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia department of Computer Science HITEC UniversityTaxilaPakistan department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images

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Big Earth Data 2023年第4期7卷 961-985页

作者： Omid Ghorbanzadeh Khalil Gholamnia Pedram Ghamisi alnstitute of Advanced Research in Artificial Intelligence(IARAl) ViennaAustria department of Remote Sensing and GIS University of TabrizTabrizIran machine learning Group Helmholtz-Zentrum Dresden-RossendorfHelmholtz Institute Freiberg for Resource TechnologyFreibergGermany

Landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer *** learning algorithms,especially fully convolutional networks(FCNs),and variations like the ResU-Net have been used recently as rapid and automatic landslide detection *** FCNs have shown cutting-edge results in automatic landslide detection,accuracy can be improved by adding prior knowledge through possible *** study evaluates a rulebased object-based image analysis(OBIA)approach built on probabilities resulting from the ResU-Net model for landslide *** train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions,including our study area and test the testing area not used for *** the OBIA stage,we frst calculate land cover and image difference indices for pre-and post-landslide multi-temporal ***,we use the generated indices and the resulting ResU-Net probabilities for image segmentation;the extracted landslide object candidates are then optimized using rule-based *** the result validation section,the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net *** proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.

关键词： Deep learning(DL) Eastern Iburi Japan European Space Agency(ESA) Fully Convolutional Networks(FCNs) object-based image analysis(OBIA) rapid landslide mapping ResUnet Sentinel-2

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah department of Civil Engineering National Institute of Technology PatnaPatnaIndia department of Computer Engineering and Applications GLA UniversityMathuraIndia department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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Computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali department of Computer Applications Karpagam College of EngineeringCoimbatore641032India department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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