DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.

Research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer

Dual-layer spectral detector CT is a new spectrum CT imaging technology based on detector being able to obtain both images similar to true plain and spectral images in one time scanning.The reconstructed multi-parameter spectral images can not only improve image quality,enhance tissue contrast,increase the visualization and detection ability of occult lesions,but also provide qualitative and quantitative analysis of the lesions,so as to provide more imaging information and multi-dimensional diagnostic basis.The research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer were reviewed in this article.

Resting-state electroencephalography theta predicts neurofeedback treatment 4-month follow-up response in nicotine addiction

Background The high rate of long-term relapse is a major cause of smoking cessation failure.Recently,neurofeedback training has been widely used in the treatment of nicotine addiction;however,approximately 30%of subjects fail to benefit from this intervention.Our previous randomised clinical trial(RCT)examined cognition-guided neurofeedback and demonstrated a significant decrease in daily cigarette consumption at the 4-month follow-up.However,significant individual differences were observed in the 4-month follow-up effects of decreased cigarette consumption.Therefore,it is critical to identify who will benefit from pre-neurofeedback.Aims We examined whether the resting-state electroencephalography(EEG)characteristics from pre-neurofeedback predicted the 4-month follow-up effects and explored the possible mechanisms.Methods This was a double-blind RCT.A total of 60 participants with nicotine dependence were randomly assigned to either the real-feedback or yoked-feedback group.They underwent 6 min closed-eye resting EEG recordings both before and after two neurofeedback sessions.A follow-up assessment was conducted after 4 months.Results The frontal resting-state theta power spectral density(PSD)was significantly altered in the real-feedback group after two neurofeedback visits.Higher theta PSD in the real-feedback group before neurofeedback was the only predictor of decreased cigarette consumption at the 4-month follow-up.Further reliability analysis revealed a significant positive correlation between theta PSD pre-neurofeedback and post-neurofeedback.A leave-one-out cross-validated linear regression of the theta PSD pre-neurofeedback demonstrated a significant correlation between the predicted and observed reductions in cigarette consumption at the 4-month follow-up.Finally,source analysis revealed that the brain mechanisms of the theta PSD predictor were located in the orbital frontal cortex.Conclusions Our study demonstrated changes in the resting-state theta PSD following neurofeedback training.Moreover,the resting-state theta PSD may serve as a prognostic marker of neurofeedback effects.A higher resting-state theta PSD predicts a better long-term response to neurofeedback treatment,which may facilitate the selection of individualised interventions.

Performance of novel 3D printing tools in removing coronary-artery calcification tissue

Rotational atherectomy is an effective treatment for severe vascular calcification obstruction,and relies on high-speed grinding(typically 130,000–210,000 r/min)with miniature grinding tools to remove calcified tissue and restore blood flow.However,reports of intraoperative complications are common because of the grinding force,temperature,and debris directly acting on the body during the grinding process,which can easily cause damage to patients.In this study,three novel grinding tools were designed and fabricated and a series of experiments have been conducted to analyze the effects of tool geometry and parameters on grinding performance,that is,force,temperature,and specimen surface morphology.The results show that these tools can effectively remove simulated calcified tissue and that they have two motions,rotation and revolution,in the tube.At higher rotational speeds,grinding force and temperature increase noticeably,while the amount of debris decreases significantly.In addition,by observing the surface morphology of the specimens,we concluded that the material removal rate per unit time is influenced by both rotational speed and tool geometry,and that high rotational speed and a rough tool surface can improve the material removal rate efficiently.

Lightweight Classification Network for Pulmonary Tuberculosis Based on CT Images

With the continuous development of medical informatics and digital diagnosis,the classification of tuberculosis(TB)cases from computed tomography(CT)images of the lung based on deep learning is an important guiding aid in clinical diagnosis and treatment.Due to its potential application in medical image classification,this task has received extensive research attention.Existing related neural network techniques are still challenging in terms of feature extraction of global contextual information of images and network complexity in achieving image classification.To address these issues,this paper proposes a lightweight medical image classification network based on a combination of Transformer and convolutional neural network(CNN)for the classification of TB cases from lung CT.The method mainly consists of a fusion of the CNN module and the Transformer module,exploiting the advantages of both in order to accomplish a more accurate classification task.On the one hand,the CNN branch supplements the Transformer branch with basic local feature information in the low level;on the other hand,in the middle and high levels of the model,the CNN branch can also provide the Transformer architecture with different local and global feature information to the Transformer architecture to enhance the ability of the model to obtain feature information and improve the accuracy of image classification.A shortcut is used in each module of the network to solve the problem of poor model results due to gradient divergence and to optimize the effectiveness of TB classification.The proposed lightweight model can well solve the problem of long training time in the process of TB classification of lung CT and improve the speed of classification.The proposed method was validated on a CT image data set provided by the First Hospital of Lanzhou University.The experimental results show that the proposed lightweight classification network for TB based on CT medical images of lungs can fully extract the feature information of the input images and obtain high-accuracy classification results.

Distributed Cooperative Learning for Discrete-Time Strict-Feedback Multi Agent Systems Over Directed Graphs

This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs.Compared with the previous DCL works based on undirected graphs,two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric,and the derived weight error systems exist n-step delays.Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying(LTV)systems with different phenomena including the nonsymmetric Laplacian matrix and time delays.Subsequently,an adaptive neural network(NN)control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law.Then,by using two novel lemmas on the extended exponential convergence of LTV systems,estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced.The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the“mod”function and proper time series.A simulation comparison is shown to demonstrate the validity of the proposed DCL method.

Dynamical pattern recognition for univariate time series and its application to an axial compressor

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

Early detection of rotating stallin axial flow compressors via deterministic learning:detectability analysis

Rotating stall and surge are two violent unstable phenomena of an aero-engine compressor.The early detection of rotating stall is a critical and difficult issue in the operation of a compressor.Recently,a deterministic learning based stall inception detection approach(SIDA)has been developed for modeling and detecting stall inception in aero-engine compressors.This paper considers the derivation of analytical results on the detection capabilities for the SIDA based on deterministic learning.First,by utilizing the input/output stability of the residual system,a detectability condition of the SIDA is presented,and how to choose the parameters of the diagnostic system is also analyzed.Second,based on the relationship between NN approximation capabilities and radial basis function(RBF)network structures,the influence of RBF network structures on the performance properties of the SIDA is analyzed.Finally,a simulation study is presented,in which the Mansoux-C2 compressor model is utilized to verify the effectiveness of the proposed SIDA.