Hybrid 1DCNN-Attention with Enhanced Data Preprocessing for Loan Approval Prediction

In order to reduce the risk of non-performing loans, losses, and improve the loan approval efficiency, it is necessary to establish an intelligent loan risk and approval prediction system. A hybrid deep learning model with 1DCNN-attention network and the enhanced preprocessing techniques is proposed for loan approval prediction. Our proposed model consists of the enhanced data preprocessing and stacking of multiple hybrid modules. Initially, the enhanced data preprocessing techniques using a combination of methods such as standardization, SMOTE oversampling, feature construction, recursive feature elimination (RFE), information value (IV) and principal component analysis (PCA), which not only eliminates the effects of data jitter and non-equilibrium, but also removes redundant features while improving the representation of features. Subsequently, a hybrid module that combines a 1DCNN with an attention mechanism is proposed to extract local and global spatio-temporal features. Finally, the comprehensive experiments conducted validate that the proposed model surpasses state-of-the-art baseline models across various performance metrics, including accuracy, precision, recall, F1 score, and AUC. Our proposed model helps to automate the loan approval process and provides scientific guidance to financial institutions for loan risk control.

Data preprocessing and preliminary results of the Moon-based Ultraviolet Telescope on the CE-3 lander

The Moon-based Ultraviolet Telescope （MUVT） is one of the payloads on the Chang＇e-3 （CE-3） lunar lander. Because of the advantages of having no at- mospheric disturbances and the slow rotation of the Moon, we can make long-term continuous observations of a series of important celestial objects in the near ultra- violet band （245-340 nm）, and perform a sky survey of selected areas, which can- not be completed on Earth. We can find characteristic changes in celestial brightness with time by analyzing image data from the MUVT, and deduce the radiation mech- anism and physical properties of these celestial objects after comparing with a phys- ical model. In order to explain the scientific purposes of MUVT, this article analyzes the preprocessing of MUVT image data and makes a preliminary evaluation of data quality. The results demonstrate that the methods used for data collection and prepro- cessing are effective, and the Level 2A and 2B image data satisfy the requirements of follow-up scientific researches.

Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning

Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.

DATA PREPROCESSING AND RE KERNEL CLUSTERING FOR LETTER

Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification(KNN), Radial Basis Function Neural Network(RBFNN), and Support Vector Machine(SVM). Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.

Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is low.This paper proposes a data preprocessing method which combines POT with DBSCAN(POT-DBSCAN)to improve the prediction efficiency of wind power prediction model.Firstly,according to the data of WT in the normal operation condition,the power prediction model ofWT is established based on the Particle Swarm Optimization(PSO)Arithmetic which is combined with the BP Neural Network(PSO-BP).Secondly,the wind-power data obtained from the supervisory control and data acquisition(SCADA)system is preprocessed by the POT-DBSCAN method.Then,the power prediction of the preprocessed data is carried out by PSO-BP model.Finally,the necessity of preprocessing is verified by the indexes.This case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile method.Therefore,the accuracy of data and prediction model can be improved by using this method.

D-IMPACT: A Data Preprocessing Algorithm to Improve the Performance of Clustering

In this study, we propose a data preprocessing algorithm called D-IMPACT inspired by the IMPACT clustering algorithm. D-IMPACT iteratively moves data points based on attraction and density to detect and remove noise and outliers, and separate clusters. Our experimental results on two-dimensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved.

Untargeted LC–MS Data Preprocessing in Metabolomics

Liquid chromatography–mass spectrometry(LC–MS)has enabled the detection of thousands of metabolite features from a single biological sample that produces large and complex datasets.One of the key issues in LC–MS-based metabolomics is comprehensive and accurate analysis of enormous amount of data.Many free data preprocessing tools,such as XCMS,MZmine,MAVEN,and MetaboAnalyst,as well as commercial software,have been developed to facilitate data processing.However,researchers are challenged by the inevitable and unconquerable yields of numerous false-positive peaks,and human errors while manually removing such false peaks.Even with continuous improvements of data processing tools,there can still be many mistakes generated during data preprocessing.In addition,many data preprocessing software exist,and every tool has its own advantages and disadvantages.Thereby,a researcher needs to judge what kind of software or tools to choose that most suit their vendor proprietary formats and goal of downstream analysis.Here,we provided a brief introduction of the general steps of raw MS data processing,and properties of automated data processing tools.Then,characteristics of mainly free data preprocessing software were summarized for researchers’consideration in conducting metabolomics study.

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively.

基于CWT-RES34的风电机组叶片裂纹状态评估

为有效进行风电机组叶片运行时的裂纹状态评估,提出一种基于连续小波变换(Continue Wavelet Transform,CWT)和残差神经网络(Residual Networks,ResNet)结合的叶片裂纹状态评估方法。首先对叶片加速度振动信号做CWT后生成二维彩色时频图像,然后将图像分别作为训练集和测试集,使用34层ResNet进行训练和诊断,最后选取天津某风电场提供的1.5 MW风力发电机作为研究对象,根据其样本数据将叶片故障程度按照裂纹长度和宽度分为健康、轻微、中等、严重、危险5种状态,评估平均准确率高达98.23%,方法的有效性和可行性得到验证。

基于GA-BP神经网络的大型客机气流角估计方法

为了解决硬件冗余难以克服的气流角传感器共因故障问题,进一步提高飞机气流角信号的可靠性,研究了基于GABP神经网络的气流角估计方法。通过BP神经网络融合姿态角、加速度、风速等参数来实现不依赖气流角传感器的气流角估计;引入遗传算法对神经网络权值和阈值进行全局优化,提高估计精度;对某大型客机的试飞数据预处理后用于模型的训练和测试。仿真结果表明,训练完成的GA-BP神经网络模型对气流角的估计值贴近实际值,稳定性和精度明显高于BP神经网络。上述方法给飞机增加一个余度的气流角信号,可用于传感器故障时为飞机提供可靠的气流角信号。

引入神经网络极限学习机的关键数据查询模型

网络空间数据的结构具有较高相似性,海量数据的不断增量更新,导致关键数据查询结果存在冗余和偏离问题。因此提出基于神经网络极限学习机的关键数据查询方法。建模描述关键数据查询问题。基于此引入神经网络极限学习机,建立关键数据查询模型。预处理数据库中无用数据和重复数据做,通过输出权值范数的最小二乘解,避免算法陷入局部最优。结合输出矩阵,训练查询模型,输出结果结果即为关键数据查询结果。为证明上述方法的性能优势,设计对比实验,结果表明提出的方法应用于关键数据查询的均方根误差不超过1.2,平均绝对百分比误差最高为4.1%,关系数F可达0.6,网络节点的使用率低于20%。以上实验数据验证了上述方法数据查询精度较高,可应用性更强。

基于人工神经网络的透射水体亚硝酸盐含量模拟估计

亚硝酸盐是水体的重要测试指标,对水体质量的评估有着重要意义。采用透射高光谱结合人工神经网络(ANN)建立水体亚硝酸盐含量估算模型。首先采用试剂配制10种浓度的亚硝酸氮标准溶液(0.02、0.04、0.06、0.08、0.10、0.12、0.14、0.16、0.18和0.20 mg/L),并使用OCEAN-HDX-XR微型光纤光谱仪扫描10次各浓度亚硝酸盐溶液在181.1~1023.1 nm的透射光谱,取平均值作为各浓度亚硝酸盐溶液原始透射光谱,分别使用最大最小均一化(MMN)、标准正态变化(SNV)、多元散射校正(MSC)、以及二阶差分(SOD)四种光谱预处理方法,并结合ANN方法建立水体亚硝酸盐含量估算模型,通过比较模型的精度来选择最优的模型进行水体亚硝酸盐含量的估计。结果显示,基于二阶差分预处理下的BP-ANN神经网络预测模型中的均方根误差RMSE为0.032367,平均绝对误差MAE为0.016895,决定系数R^(2)为0.987403,与二次有理高斯过程回归(QR-GPR)和二次支持向量机(Q-SVM)预测模型相比,该模型的拟合效果更好,精确度更高。提出了反向传播人工神经网络(BP-ANN)高光谱水质亚硝酸盐参数的反演方法,为水质亚硝酸盐参数动态检测提供了新方法。

进化计算在大规模高维特征选择中的应用综述

随着大数据时代的到来,数据的规模和特征维度呈现爆炸式增长,这给数据处理带来了前所未有的挑战。特征选择作为数据预处理的关键环节,在处理大规模高维数据时显得尤为重要。而进化计算方法因其出色的全局搜索能力和高效的优化性能,越来越多的研究者开始对其进行研究,其在大规模高维特征选择中得到了广泛的应用。本文首先介绍了大规模高维数据处理的重要性;然后简单介绍了部分经典和较新的进化计算方法,并详细介绍了其在大规模高维特征选择中的应用情况;最后对目前进化计算在大规模高维特征选择中存在的问题进行总结,并展望了其未来的发展方向。

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

Optimizing Network Security via Ensemble Learning: A Nexus with Intrusion Detection

Network intrusion detection systems need to be updated due to the rise in cyber threats. In order to improve detection accuracy, this research presents a strong strategy that makes use of a stacked ensemble method, which combines the advantages of several machine learning models. The ensemble is made up of various base models, such as Decision Trees, K-Nearest Neighbors (KNN), Multi-Layer Perceptrons (MLP), and Naive Bayes, each of which offers a distinct perspective on the properties of the data. The research adheres to a methodical workflow that begins with thorough data preprocessing to guarantee the accuracy and applicability of the data. In order to extract useful attributes from network traffic data—which are essential for efficient model training—feature engineering is used. The ensemble approach combines these models by training a Logistic Regression model meta-learner on base model predictions. In addition to increasing prediction accuracy, this tiered approach helps get around the drawbacks that come with using individual models. High accuracy, precision, and recall are shown in the model’s evaluation of a network intrusion dataset, indicating the model’s efficacy in identifying malicious activity. Cross-validation is used to make sure the models are reliable and well-generalized to new, untested data. In addition to advancing cybersecurity, the research establishes a foundation for the implementation of flexible and scalable intrusion detection systems. This hybrid, stacked ensemble model has a lot of potential for improving cyberattack prevention, lowering the likelihood of cyberattacks, and offering a scalable solution that can be adjusted to meet new threats and technological advancements.

基于机器学习的不平衡数据下个人信用评分预测模型研究

文章介绍了个人信用评分的基本概念,以及不平衡数据及其处理方法和机器学习算法在信用评分中的应用。然后,通过数据预处理,包括数据来源与特性、数据清洗与整理、数据不平衡分析、数据增强方法和效果评估,为后续模型构建提供基础。最后,使用实际数据集进行模型训练和测试,并评估模型的性能。实验结果表明,基于机器学习的不平衡数据下个人信用评分预测模型能够有效地预测个人信用风险,对于金融机构的风险管理和信贷决策具有重要意义。

基于LBSN数据聚类分析的城市POI感知方法

城市POI的分布情况客观反映了一个城市各行各业的发展情况,传统获取POI的测绘手段成本高、更新周期长、时效性差,而基于位置的社交网络(Location-Based Social Network,LBSN)平台的发展为实现城市POI的感知提供了一种新思路。本文提出一种基于LBSN数据聚类分析的城市POI感知方法,首先,对LBSN数据进行预处理,包括清洗重复数据、删除无效数据、数据预分类等,以提高数据的有效性;其次,提出一种改进的DBSCAN算法,对处理后的数据进行聚类分析,从而得到准确度较高的城市各类POI分布情况。实验结果表明,与传统的DBSCAN算法以及K-means算法相比,本文提出的算法有更好的聚类效果,且在聚类指标上有更大的CH指数值和更小的DBI指数值。

基于数据预处理和计算机VMD-LSTM-GPR的储能系统离子电池剩余寿命预测

离子电池剩余寿命影响储能系统运行能力,准确预测电池寿命,有助于判断系统的实时运行状态,为获得较为可靠的预测结果,提出基于数据预处理和计算机VMD-LSTM-GPR的储能系统离子电池剩余寿命预测方法。针对储能系统离子电池剩余寿命预测的相关理论问题进行研究,并联合储能数据预处理标准与计算机VMDLSTM-GPR模型,计算锂离子电池的容量退化能力,从而评估剩余电池寿命,实现基于数据预处理和计算机VMD-LSTM-GPR的储能系统离子电池剩余寿命预测。

单细胞DNA甲基化测序数据处理流程与分析方法

单细胞DNA甲基化测序技术近年来取得了飞速发展,在揭示细胞间异质性及表观遗传学调控机制方面发挥着重要作用。随着测序技术的进步,单细胞甲基化数据的质量与数量也在不断提高,标准化的预处理流程与合适的分析方法对确保数据的可比性与结果的可靠性尤为关键。然而,目前尚未形成一套完整的数据分析流程来指导研究人员对现有数据进行挖掘。本文系统综述了单细胞甲基化数据预处理步骤和分析方法,简要介绍了相关算法和工具,并探讨了单细胞甲基化技术在脑科学、血细胞分化及癌症研究中的应用前景,旨在为研究人员分析数据时提供指导,推动单细胞甲基化测序技术的发展和应用。

SMOTE数据预处理算法在砂型铸造复杂铸件缺陷预测中的应用

针对实际生产过程采集的复杂转向桥铸件工艺数据中冷隔、气孔、砂眼、缩孔等缺陷类别的数据量严重不平衡、复杂铸件缺陷预测模型准确率不高的问题,结合砂型铸造实际工况,引入了SMOTE(Synthetic Minority Oversampling Technique)数据预处理算法,探究其在砂型铸造复杂铸件缺陷预测中的应用。根据采集到的复杂铸件不平衡数据集的特点,基于SMOTE数据预处理算法,科学扩充了不平衡数据集,创建了可用于训练复杂铸件缺陷预测模型的平衡数据集,数据预处理前后的模型预测准确率从86.50%提高至97.91%。