Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—StackedContractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimizethe advanced neural network—Extreme learning machine (ELM) based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE. We mainly consider two objectives. One objective is to maximize the performance of ELM, which refers to the benefit of the SMONGE model. Another objective is to minimize the output weight normof ELM, which is related to the cost of the SMONGE model. We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE modelwithout SCAE across 20 open source software projects. The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.     

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features. In addition, software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques. To address these two issues, we propose the following two solutions in this paper: (1) We leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the representative features by selecting automatically the reasonable number and position of landmarks, which can reveal the complex intrinsic structure hidden behind the defect data. (2) We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques, which leverages denoising autoencoder to learn true input features that are not contaminated by noise, and utilizes deep neural network to learn the abstract deep semantic features. We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter. We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects. The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.     

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

With the continuous expansion of software scale, software update and maintenance have become more and more important. However, frequent software code updates will make the software more likely to introduce new defects. So how to predict the defects quickly and accurately on the software change has become an important problem for software developers. Current defect prediction methods often cannot reflect the feature information of the defect comprehensively, and the detection effect is not ideal enough. Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer Naive Bayes) based on improved transfer Naive Bayesian algorithm in this paper, which mainly considers the following two aspects: (1) Considering that the edge data of the test set may affect the similarity calculation and final prediction result, we remove the edge data of the test set when calculating the data similarity between the training set and the test set; (2) Considering that each feature dimension has different effects on defect prediction, we construct the calculation formula of training data weight based on feature dimension weight and data gravity, and then calculate the prior probability and the conditional probability of training data from the weight information, so as to construct the weighted bayesian classifier for software defect prediction. To evaluate the performance of the ITNB model, we use six datasets from large open source projects, namely Bugzilla, Columba, Mozilla, JDT, Platform and PostgreSQL. We compare the ITNB model with the transfer Naive Bayesian (TNB) model. The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary, precision and pd for within-project and cross-project defect prediction.     

A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.     

基于GSA-PELM的锅炉NOx预测模型

为了更加精确地实现对电厂循环流化床锅炉NOx排放量进行预测,提出了一类基于并行极端学习机的GSA-PELM模型。由于PELM的泛化能力及精度依赖于其权值的选择,因而利用万有引力算法优化PELM的权值,采用从某火电厂300MW的循环流化床锅炉在不同工况下实时采集的数据来检验模型的预测性能,并将GSA-PELM模型分别与PELM模型、ELM模型、万有引力算法优化的最小二乘支持向量机模型（GSA-LSSVM）、GSA-ELM模型进行比较,仿真结果表明GSA-PELM模型的精度相比其它所有模型提高了9个数量级以上,可以更加有效、准确地用于预测火电厂锅炉的NOx排放浓度。     

Unsupervised Domain Adaptation Based on Discriminative Subspace Learning for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) aims to predict the defects on target project by using a prediction model built on source projects. The main problem in CPDP is the huge distribution gap between the source project and the target project, which prevents the prediction model from performing well. Most existing methods overlook the class discrimination of the learned features. Seeking an effective transferable model from the source project to the target project for CPDP is challenging. In this paper, we propose an unsupervised domain adaptation based on the discriminative subspace learning (DSL) approach for CPDP. DSL treats the data from two projects as being from two domains and maps the data into a common feature space. It employs cross-domain alignment with discriminative information from different projects to reduce the distribution difference of the data between different projects and incorporates the class discriminative information. Specifically, DSL first utilizes subspace learning based domain adaptation to reduce the distribution gap of data between different projects. Then, it makes full use of the class label information of the source project and transfers the discrimination ability of the source project to the target project in the common space. Comprehensive experiments on five projects verify that DSL can build an effective prediction model and improve the performance over the related competing methods by at least 7.10% and 11.08% in terms of G-measure and AUC.     

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.     

基于GA-ELM的锂离子电池RUL间接预测方法

针对锂离子电池在线剩余寿命预测时容量难以直接测量及预测精度不高等问题,提出一种间接预测方法。首先,分析电池寿命状态特征参数,选取等压降放电时间作为锂电池间接健康因子;其次,引入遗传算法优化极限学习机模型参数,建立锂电池剩余使用寿命间接预测模型;最后,基于NASA锂电池实验数据和自主实验数据验证该预测方法的正确性和有效性。实验结果表明,相较高斯过程回归方法和极限学习机方法,该方法准确有效、测试速度快,并且预测结果输出稳定,精度较高。     

Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection, the detection of crop diseasesdirectly impacts the income of farmers. To address the problems of low crop-diseaseidentification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces thehill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and thresholds of the machine, and acquires the approximately optimal solution. And finally, a data set of crop diseases is used for verification, demonstrating that, compared with several other common machine learning methods, this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.     

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.     

Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research. This can be done by having less complex applications. While developers and other experts have made significant efforts in this context, the level of reliability is not the same as it should be. Therefore, further research into the most detailed mechanisms for evaluating and increasing software reliability is essential. A significant aspect of growing the degree of reliable applications is the quantitative assessment of reliability. There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software. However, none of these mechanisms are useful for all kinds of failure datasets and applications. Hence finding the most optimal model for reliability prediction is an important concern. This paper suggests a novel method to substantially pick the best model of reliability prediction. This method is the combination of analytic hierarchy method (AHP), hesitant fuzzy (HF) sets and technique for order of preference by similarity to ideal solution (TOPSIS). In addition, using the different iterations of the process, procedural sensitivity was also performed to validate the findings. The findings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.     

印刷品缺陷在线检测算法的研究

介绍了基于数字图像处理的印刷品缺陷在线检测系统的原理和工作流程.提出了一种改进的图像配准算法对标准模板图像和待测图像进行匹配.实验结果表明,该算法计算速度快、检测精度高,满足了系统设计的要求.     

基于KPCA-SVM模型的电力负荷最大值短期预测方法

电力负荷最大值预测是电网企业调度工作的重要组成部分,其预测结果的准确度将对电能的配送、有效利用率、供电服务的质量以及电力系统的发展产生重要影响。以安徽某市81天的电力负荷最大值数据为基础,选取影响当天电力负荷最大值的10个因素,并采用核主成分分析（kernel principal component analysis,KPCA）算法将10维的影响因素降为5维,其累计贡献率可达93.70%。以降维后的5维数据为输入,以径向基函数为核函数,并采用交叉验证选择支持向量机（support vector machine,SVM）回归的最佳参数,随机选取54组数据训练SVM预测模型,最后进行27组数据的拟合预测,拟合预测的均方误差为0.004 1,相关系数为0.963 1。研究结果表明,应用KPCA结合的SVM预测模型对电力负荷最大值具有很好的预测能力。     

基于支持向量机的变压器碳排放预测模型

目的 解决变压器中主要设计参数影响下的碳排放量预测问题。方法 本文利用随机森林（Random Forest,RF）算法和支持向量机（Support Vector Machine,SVM）算法进行对比,构建一个变压器碳排放预测模型。结果 通过对变压器的全生命周期进行评价,确定铁芯的长宽比为影响碳排放量的主要因素,对给定参数下的碳排放量进行预测,并与实际值进行对比分析得出,3类预测模型中,SVM高斯核模型的平均绝对误差值约为5.37,与碳排放实际值最为接近,故采用高斯核函数的非线性支持向量机预测模型最优。结论 证明支持向量机高斯核函数预测模型更具有预测准确性和有效性,以期能为生产企业进行低碳设计提供参考依据,为电力行业生产设备的可持续设计研究提供一定的借鉴意义。     

基于机器学习算法的夹层玻璃冲击破坏预测模型研究

在诸如风致飞射物撞击等刚体冲击作用下,建筑夹层玻璃因自身脆性特征极易破坏。针对这个问题提出了在刚体冲击下夹层玻璃破坏状态的预测方法,综合考虑了玻璃构型、中间胶层、支撑条件及尺寸等多种设计参数。首先针对多类夹层玻璃进行往复刚体冲击试验,建立567组PVB及210组SGP的两种不同中间胶层的夹层玻璃试验数据库;随后基于鲸鱼优化下的核极限学习机(WOA-KELM)机器学习算法,建立夹层玻璃破坏状态的预测模型,并与支持向量机(Support Vector Machine, SVM)及最小二乘支持向量机(Least Squares Support Vector Machine, LSSVM)建立的相应预测模型进行对比分析。结果表明, WOA-KELM模型破坏状态预测精度达88.45%,能较好地预测夹层玻璃的破坏,满足工程应用的需求,且预测模型精度及实时性均优于其他模型。     

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution, and Random Forest Model. The analysis upon dataset has been performed considering the time duration from January 1^st 2020 to16^th July 2021. The model has been developed to obtain the forecast values till September 2021. This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country. In India, the cases are rapidly increasing day-by-day since mid of Feb 2021. The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave. To empower the prediction for future validation, the proposed model works effectively.     

Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network. A genetic algorithm is used to obtain the optimal features to be used for the model. To prove the proposed model’s effectiveness, we have used a four-phase technique using Jeju island’s real energy consumption data. In the first phase, we have obtained the results by applying the CB-GB-MLP model. In the second phase, we have utilized a GA-ensembled model with optimal features. The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model. The fourth stage is the final stage, where we have applied the GA-ECLE model. We obtained a mean absolute error of 3.05, and a root mean square error of 5.05. Extensive experimental results are provided, demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensiveexperiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

基于机器学习的钛合金弹性模量预测方法研究

目的 探索一种高效可行的预测方法以提高钛合金弹性模量的预测精度,采用第一性原理计算方法与机器学习相结合的方式建立高精度的预测模型。方法 通过数据挖掘获取材料数据库中钛合金的力学性质微观结构参数,结合第一性原理计算方法构建初始数据集,并对其进行预处理,包括噪音消除、归一化及标准化,以得到高质量的数据集。同时,采用随机森林特征重要性分析法对输入参数进行筛选,去除弱相关变量以降低预测模型的复杂度。在此基础上,构建随机森林模型、支持向量机模型、BP神经网络模型及优化后的GA-BP神经网络模型,综合对比各模型的回归能力,分析误差后选出最优的算法模型。结果 最终建立了钛合金弹性模量预测模型,其中随机森林模型、支持向量机模型、BP神经网络模型、GA-BP神经网络模型的预测相关系数R分别为0.836、0.943、0.917、0.986。结论 GA-BP模型对弹性模量的预测误差基本保持在5%～7%。遗传算法可以优化BP神经网络的权值和阈值,使预测精度大幅提升。说明通过该方法可以实现钛合金弹性模量的预测,大大节省研发和实验成本,加快高性能材料的筛选。