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.

基于注意力机制的高光谱图像降维在纸质文物霉斑识别的研究

纸质文物作为文物传承的重要工具,用于记录不同时期人类历史及人文风貌,其在保存过程中极易受到霉菌等微生物的侵害。霉菌会加速纤维素的降解,在纸张表面生成霉斑,并且散落的孢子会随空气流动大范围传播,增加其他纸质文物发生霉变的风险。因此,定期对纸质文物进行霉斑检测对了解纸质文物现状和纸质文物修复至关重要。高光谱成像技术是一种非接触性、非破坏性的检测技术,能同时获得空间数据和光谱数据,与计算机技术结合可以实现纸质文物的大批次实时无损检测。针对黑曲霉这一广泛出现的霉菌,提出一种基于注意力机制的高光谱数据降维方法,通过采集其高光谱数据,实现了高光谱冗余数据的自适应预处理。采集了来自重庆中国三峡博物馆提供的20份纸质文物黑曲霉霉斑样本,使用ENVI软件分析得出在413~855 nm波段范围内,黑曲霉霉斑感染区域和健康区域的平均光谱曲线,平均反射率差异明显;在855~1021 nm波段范围内,黑曲霉霉斑感染区域和墨迹区域的平均光谱曲线,平均反射率差异明显。文中将所提出方法与传统主成分分析和独立成分分析预处理方法分别处理原始高光谱数据,并将结果在经典U-Net、SegNet、DeepLabV3+和PSPNet四个语义分割网络上进行了对比。结果表明,该算法预处理的数据在U-Net和SegNet经典网络中有明显优势,相较于主成分分析法和独立成分分析法,霉斑识别精度取得了较大提升达到89.49%和88.46%,验证了本文所提出算法的有效性,为文物保护领域提供有效的支撑和新的思路。

基于Transformer模型的时序数据预测方法综述

时序数据预测(TSF)是指通过分析历史数据的趋势性、季节性等潜在信息,预测未来时间点或时间段的数值和趋势。时序数据由传感器生成,在金融、医疗、能源、交通、气象等众多领域都发挥着重要作用。随着物联网传感器的发展,海量的时序数据难以使用传统的机器学习解决,而Transformer在自然语言处理和计算机视觉等领域的诸多任务表现优秀,学者们利用Transformer模型有效捕获长期依赖关系,使得时序数据预测任务取得了飞速发展。综述了基于Transformer模型的时序数据预测方法,按时间梳理了时序数据预测的发展进程,系统介绍了时序数据预处理过程和方法,介绍了常用的时序预测评价指标和数据集。以算法框架为研究内容系统阐述了基于Transformer的各类模型在TSF任务中的应用方法和工作原理。通过实验对比了各个模型的性能、优点和局限性,并对实验结果展开了分析与讨论。结合Transformer模型在时序数据预测任务中现有工作存在的挑战提出了该方向未来发展趋势。

Data Matrix二维条形码解码器图像预处理研究

DM码是一种常见的二维条形码,图像预处理是DM码解码器自动识别过程中的重要步骤。提出一种实用的DM码识别图像预处理方法。它没有使用传统的边缘检测和直线检测手段,因此受背景噪声、几何失真的影响较小。此外,使用了校正铁路线坐标,并按区域取样生成码流,显著提高了DM码的识别速度和识别率。实验结果表明,该算法可以克服DM码识别过程中易受噪声干扰、光照不均和几何失真等影响的问题。

基于优化预处理方法的时变重力场反演精度分析

针对GRACE Level1B观测数据中存在缺失数据及含有粗差的问题,提出补全SCA1B缺失数据的优化方法,同时对KBR1B和运动学轨道数据采用优化策略剔除粗差,用于时变重力场模型反演。此外,还分别基于合成数据和实测数据分析ACC1B数据在Y轴方向的误差对反演结果的影响,并提出优化校正策略。利用优化方法能够有效恢复SCA1B的缺失数据,且充分考虑了观测数据在整个弧段的变化特征。采用优化策略校正后的ACC1B数据计算结果显示,其精度比未校正的数据计算结果提高3.7 mm。从结果可知,由优化方法处理后的数据解算的重力场模型与三大官方机构解算结果相比,总体精度相当,但不同机构的解算结果在局部区域的细节信号表现有差异,表明优化的数据预处理策略有效可行。

Approach based on wavelet analysis for detecting and amending anomalies in dataset

It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.

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.

Time-varying Reliability Analysis of Long-span Continuous Rigid Frame bridge under Cantilever Construction Stage based on the Monitored Strain Data

In general,the material properties,loads,resistance of the prestressed concrete continuous rigid frame bridge in different construction stages are time-varying.So,it is essential to monitor the internal force state when the bridge is in construction.Among them,how to assess the safety is one of the challenges.As the continuous monitoring over a long-term period can increase the reliability of the assessment,so,based on a large number of monitored strain data collected from the structural health monitoring system(SHMS)during construction,a calculation method of the punctiform time-varying reliability is proposed in this paper to evaluate the stress state of this type bridge in cantilever construction stage by using the basic reliability theory.At the same time,the optimal stress distribution function in the bridge mid-span base plate is determined when the bridge is closed.This method can provide basis and direction for the internal force control of this type bridge in construction process.So,it can reduce the bridge safety and quality accidents in construction stages.

Systematic review of data-centric approaches in artificial intelligence and machine learning

Artificial intelligence(AI)relies on data and algorithms.State-of-the-art(SOTA)AI smart algorithms have been developed to improve the performance of AI-oriented structures.However,model-centric approaches are limited by the absence of high-quality data.Data-centric AI is an emerging approach for solving machine learning(ML)problems.It is a collection of various data manipulation techniques that allow ML practitioners to systematically improve the quality of the data used in an ML pipeline.However,data-centric AI approaches are not well documented.Researchers have conducted various experiments without a clear set of guidelines.This survey highlights six major data-centric AI aspects that researchers are already using to intentionally or unintentionally improve the quality of AI systems.These include big data quality assessment,data preprocessing,transfer learning,semi-supervised learning,machine learning operations(MLOps),and the effect of adding more data.In addition,it highlights recent data-centric techniques adopted by ML practitioners.We addressed how adding data might harm datasets and how HoloClean can be used to restore and clean them.Finally,we discuss the causes of technical debt in AI.Technical debt builds up when software design and implementation decisions run into“or outright collide with”business goals and timelines.This survey lays the groundwork for future data-centric AI discussions by summarizing various data-centric approaches.

Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

Application of data fusion on multi-function earth drill

taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control dependence, the detecting method of the earth drill’s working state is introduced. Multi sensor data fusion is done with the aid of BP neural network in Matlab. The data to be interfused are pre processed and the program of simulation and “point checking” is given.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

Challenges Analyzing RNA-Seq Gene Expression Data

The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.

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。

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。