An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm

Due to the anonymity of blockchain,frequent security incidents and attacks occur through it,among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses.Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes.However,there are still some flaws in current research,e.g.,insufficient feature extraction of Ponzi scheme smart contracts,without considering class imbalance.In addition,there is room for improvement in detection precision.Aiming at the above problems,this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting(AdaBoost)algorithm.Firstly,this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features,which helps to improve the feature extraction effect.Meanwhile,adaptive synthetic sampling(ADASYN)is introduced to deal with class imbalanced data,and integrated with the Adaboost classifier.Finally,this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts.Experimentally,this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision.Moreover,this article compares and discusses the state of art methods with our method in four aspects:data acquisition,data preprocessing,feature extraction,and classifier design.Both experiment and discussion validate the effectiveness of our model.

Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous

HIV and AIDS has continued to be a major public health concern, and hence one of the epidemics that the world resolved to end by 2030 as highlighted in sustainable development goals (SDGs). A colossal amount of effort has been taken to reduce new HIV infections, but there are still a significant number of new infections reported. HIV prevalence is more skewed towards the key population who include female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID). The study design was retrospective and focused on key population enrolled in a comprehensive HIV and AIDS programme by the Kenya Red Cross Society from July 2019 to June 2021. Individuals who were either lost to follow up, defaulted (dropped out, transferred out, or relocated) or died were classified as attrition;while those who were active and alive by the end of the study were classified as retention. The study used density analysis to determine the spatial differences of key population attrition in the 19 targeted counties, and used Kilifi county as an example to map attrition cases in smaller administrative areas (sub-county level). The study used synthetic minority oversampling technique-nominal continuous (SMOTE-NC) to balance the datasets since the cases of attrition were much less than retention. The random survival forests model was then fitted to the balanced dataset. The model correctly identified attrition cases using the predicted ensemble mortality and their survival time using the estimated Kaplan-Meier survival function. The predictive performance of the model was strong and way better than random chance with concordance indices greater than 0.75.

Using deep learning to detect small targets in infrared oversampling images

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3–4 orders of magnitude, and has more powerful target detection performance.

An oversampling system for ECG acquisition

Traditional ECG acquisition system lacks for flexibility. To improve the flexibility of ECG acquisition system and the signal-to-noise ratio of ECG, a new ECG acquisition system was designed based on DAQ card and Labview and oversampling was implemented in Labview. And analog signal conditioning circuit was improved on. The result indicated that the system could detect ECG signal accurately with high signal-to-noise ratio and the signal processing methods could be adjusted easily. So the new system can satisfy many kinds of ECG acquisition. It is a flexible experiment platform for exploring new ECG acquisition methods.

Necessity of Oversampling Theorem for Affine Frames

In this paper we prove that n is relatively prime to a which is also necessary.

Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Learning from imbalanced data is one of the greatest challenging problems in binary classification,and this problem has gained more importance in recent years.When the class distribution is imbalanced,classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority.Therefore,the accuracy may be high,but the model cannot recognize data instances in the minority class to classify them,leading to many misclassifications.Different methods have been proposed in the literature to handle the imbalance problem,but most are complicated and tend to simulate unnecessary noise.In this paper,we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering,called GK-Means.The new method aims to avoid generating noise and control imbalances between and within classes.Various experiments have been carried out with six classifiers and four oversampling methods.Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.

Symbol Synchronization of Single-Carrier Signal with Ultra-Low Oversampling Rate Based on Polyphase Filter

An efficient single-carrier symbol synchronization method is proposed in this paper,which can work under a very low oversampling rate.This method is based on the frequency aliasing squared timing recovery assisted by pilot symbols and time domain filter.With frequency aliasing squared timing recovery with pilots,it is accessible to estimate timing error under oversampling rate less than 2.The time domain filter simultaneously performs matched-filtering and arbitrary interpolation.Because of pilot assisting,timing error estimation can be free from alias and self noise,so our method has good performance.Compared with traditional time-domain methods requiring oversampling rate above 2,this method can be adapted to any rational oversampling rate including less than 2.Moreover,compared with symbol synchronization in frequency domain which can operate under low oversampling rate,our method saves the complicated operation of conversion between time domain and frequency domain.By low oversampling rate and resource saving filter,this method is suitable for ultra-high-speed communication systems under resource-restricted hardware.The paper carries on the simulation and realization under 64QAM system.The simulation result shows that the loss is very low(less than 0.5 dB),and the real-time implementation on field programmable gate array(FPGA)also works fine.

Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1⁃Bit ADCs and Oversampling

A reconfigurable intelligent surface(RIS)aided massive multiple-input multiple-output(MIMO)system is considered,where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters(ADCs).To compensate for the per-formance loss caused by the coarse quantization,oversampling is applied at the receiver.The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling.In this work,Bussgang decomposition is applied to deal with the coarse quantization,and a Markov chain is developed to char-acterize the banded structure of the oversampling filter.An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels.Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

基于高倍过采样与加窗插值FFT的电力谐波分析

为提高谐波分析精度,分析了信号加窗引起的信噪比损失以及AD转换产生的量化误差,阐述了过采样技术提高信噪比的原理。在此基础上,提出了基于高倍过采样和加窗插值快速傅里叶变换(fast Fourier transform, FFT)的谐波分析方法。该方法充分利用AD转换器的潜力,以尽量高的采样速率进行AD采样,同时通过均值滤波避免高倍过采样引起的采样数据量激增问题。详细研究了所提谐波分析方法对信号中谐波分量幅值和相位的影响,并给出了简洁实用的谐波幅值和相位校正方法。仿真表明,所提方法可在不增加系统成本的前提下改善加窗插值FFT的抗噪声能力,提高谐波分析精度。

基于权重距离的优势边界小类样本合成算法

提出基于权重距离的优势边界小类样本合成算法(ABWD)来克服数据类别不平衡的问题.ABWD算法具有如下特点:1)定义权重距离,并基于该距离选取样本近邻;2)根据样本近邻确定该样本是否为小类的边界样本;3)对每个小类的边界样本确定其合成位置与合成数量,使该小类样本合成后近邻中小类个数不少于大类的个数,确保该小类样本具有优势边界.实验结果表明,与其他典型过抽样算法相比,算法较大提高了小类的分类性能,在G-mean、F-measure及查全率三种度量上均取得很好的实验结果.

基于批数据过采样的中医临床记录四诊描述抽取方法

中医临床记录四诊描述抽取对中医临床辨证论治的提质增效具有重要的应用价值,然而该任务尚有待探索,类别分布不均衡是该任务面临的关键挑战之一。该文围绕中医临床记录四诊描述抽取任务展开研究,首先构建了中医临床四诊描述抽取语料库;然后基于无标注的中医临床记录微调通用预训练语言模型实现该模型的领域适应;最后利用小规模标注数据,采用批数据过采样算法,完成中医临床记录四诊描述抽取模型的训练。实验结果表明,该文提出的抽取方法的总体性能均优于对比方法,并且与对比方法的最优结果相比,该文方法将少见类别的抽取性能F1值平均提升了2.13%。

多尺度卷积与双注意力机制融合的入侵检测方法

为提高互联网入侵检测方法的准确率,提出一种卷积神经网络与注意力机制结合的入侵检测方法。利用Borderline-SMOTE过采样算法和MinMax归一化对数据进行预处理,有效缓解入侵数据量差异较大问题,提升非平衡数据检测性能;使用卷积神经网络Inception结构多尺度对数据进行特征提取,并配合注意力机制进行维度更新,提高模型处理海量数据时特征表达的准确性。研究结果表明:入侵检测方法的平均准确率为99.57%;相较于SVM方法、CNN方法、RNN方法、BLS-GMM方法,准确率分别提升了4.48%、1.35%、1.62%和0.04%,召回率分别提高了4.48%、1.36%、1.62%和0.14%。

一种基于随机森林的OFDM系统自适应算法

针对动态变化的信道环境,自适应正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统可以对子载波间隔和循环前缀长度进行调整,以最大化系统的吞吐量。为了能够快速准确地找到OFDM系统在不同信道环境中的最优子载波间隔和循环前缀长度取值,本文提出了基于随机森林的OFDM系统自适应算法。随机森林算法基于集成的思想,能够有效处理高维度数据,并且具有高效率、高准确率和强泛化能力等优势,可以在复杂的数据场景下进行有效的分类。通过提取通信过程中信噪比、用户移动速度、最大多普勒频率和均方根时延扩展等信道特征与OFDM系统的子载波间隔和循环前缀长度组成训练样本,利用随机森林算法创建了OFDM系统参数多分类模型。所提模型可以根据输入的信道特征,实现OFDM系统子载波间隔和循环前缀长度的自适应分配。同时,针对训练样本主要集中在少数几个系统参数类别的情况,利用合成少数类过采样技术对较少样本数的类别进行扩充,满足了随机森林算法对训练样本类别平衡化的需求,进一步提高了算法的分类准确率。相比传统的自适应算法,所提算法具有更高的分类准确率和模型泛化能力。分析和仿真结果表明,与子载波间隔和循环前缀长度固定的OFDM系统相比,本文所提出的自适应算法能够准确选择出最优的系统参数,可以有效地减轻信道中符号间干扰和子载波间干扰的影响,从而在整个信噪比范围上提供最大的平均频谱效率。基于随机森林的OFDM系统自适应算法能够动态地分配子载波间隔和循环前缀长度,增强OFDM系统的通信质量和抗干扰能力,实现在不同信道环境下的可靠传输。

基于自适应GA-RF的用户流失预测研究

针对电信用户流失问题,文章提出一种自适应遗传算法优化随机森林的预测模型。首先对Kaggle平台提供的电信数据进行数据清洗、特征提取及无量纲化处理,然后运用SMOTE过采样以解决数据不平衡问题,对决策树、随机森林等模型预测的召回率、F1和AUC值进行对比。最后提出一种自适应遗传算法优化随机森林的电信用户流失预测模型。结果表明,自适应遗传算法优化的随机森林模型的预测性能优于单一分类模型。

利用可信反事实的不平衡数据过采样方法

针对传统过采样方法不能充分利用数据集信息的缺陷,提出一种基于反事实(counterfactual,CF)的不平衡数据过采样方法,并进一步对生成的少数类合成样本进行了“可信”清除。其核心思想是依据数据集原有实例特征值合成新样本,相比传统过采样的插值法,更能充分挖掘数据中的边界决策信息,从而为分类器提供更多的有用信息,提高分类性能。在9个来自KEEL与UCI的不平衡数据集、5种不同分类器(SVM、DT、Logistic、RF、AdaBoost)上与4种传统过采样方法(SMOTE、B1-SMOTE、B2-SMOTE、ADASYN)进行了大量对比实验,结果表明,所提方法具有更高的AUC值、F1值和G-mean值,可以更为有效地解决类不平衡问题。

基于FPGA的脉压信号采集卡设计

由于频带较宽的脉压信号在采集和传输过程中容易受到噪声的干扰而导致信号失真,论文针对这一问题,设计了一种高精度的脉压信号采集电路,从脉压信号的频率特性出发,分别从硬件电路和逻辑控制两方面对信号调理滤波及采样量化做了优化处理。采集信号在经过LTC1569的组合滤波电路后,在250kHz处衰减在3dB以内,且通带内信号波形稳定。同时通过优化采样电路,采用大于输入信号频率的8倍过采样和去极值算法滤波算法,经过多次实验,该采集卡的通道采集精度均优于0.1%,满足设计要求。

考虑不同天气类型样本的光伏功率日内预测模型

太阳能具有清洁、安全、可再生的优点,光伏发电可减轻资源消耗,助力可持续发展,然而光伏功率易受天气影响,针对不同天气类型下光伏功率的预测也是一个研究难点。该研究着手于在不同天气类型下应用人工少数类过采样法(synthetic minority over-sampling technique,SMOTE)和机器学习进行光伏功率预测。首先,通过皮尔逊相关系数法选择出对光伏功率影响最大的气象因子;然后,根据重要程度较大的气象因子计算日照时数,通过给日照时数设定阈值进行划分,将天气分类为晴天、多云或阴天、覆雪,再通过SMOTE技术对各种天气类型下的样本进行扩充;最后,通过多种机器学习算法分别针对不同天气场景以及数据扩充前后构建光伏功率预测模型。通过案例分析可知,所提算法能对不同天气类型进行划分,并为不同天气类型下光伏功率预测存在的样本不平衡问题提供了一种解决方案,提升了不同天气场景下光伏功率的预测精度。

用于不平衡类癌症数据的非标记检测和识别的SERS分析新策略

SERS技术与机器学习结合的策略在检测和识别不平衡类癌症中发挥着极其重要的作用.为了解决类不平衡问题给分类精度造成的影响,设计了一种SERS技术结合APS-SMOTE-PCA-KNN(synthetic minority oversampling method with adaptive priority selection(APS-SMOTE)-principal component analysis(PCA)-k-nearest neighbors(KNN))的癌症诊断新策略,实现快速、灵敏、非标记地检测和识别癌症.结果显示:新策略能够测得健康人和癌症患者血清的特征光谱,很好地解决了类不平衡问题给分类精度造成的影响,准确率可以达到85.0%.SERS技术结合APS-SMOTE-PCA-KNN对于未来癌症诊断具有积极的借鉴意义.