基于Deep Forest算法的对虾急性肝胰腺坏死病(AHPND)预警数学模型构建

为预报池塘养殖凡纳对虾(Penaeus vannamei)急性肝胰腺坏死病(AHPND)的发生,自2020年开始,笔者对凡纳对虾养殖区开展了连续监测工作,包括与疾病发生相关的环境理化因子、微生物因子、虾体自身健康状况等18个候选预警因子指标,通过数据标准化处理后分析病原、宿主与环境之间的相关性,对候选预警因子进行筛选,基于Python语言编程结合Deep Forest、Light GBM、XGBoost算法进行数据建模和预测性能评判,仿真环境为Python2.7,以预警因子指标作为输入样本(即警兆),以对虾是否发病指标作为输出结果(即警情),根据输入样本和输出结果各自建立输入数据矩阵和目标数据矩阵,利用原始数据矩阵对输入样本进行初始化,结合函数方程进行拟合,拟合的源代码能利用已知环境、病原及对虾免疫指标数据对目标警情进行预测。最终建立了基于Deep Forest算法的虾体(肝胰腺内)细菌总数、虾体弧菌(Vibrio)占比、水体细菌总数和盐度的4维向量预警预报模型,准确率达89.00%。本研究将人工智能算法应用到对虾AHPND发生的预测预报,相关研究结果为对虾AHPND疾病预警预报建立了预警数学模型,并为对虾健康养殖和疾病防控提供了技术支撑和有力保障。

User Purchase Intention Prediction Based on Improved Deep Forest

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

一种改进Deep Forest算法在保险购买预测场景中的应用研究

为了实现保险场景的精准营销,同时充分利用千万级客户和保单历史成交记录的数据特点,本文经热门算法研究和统计理论分析,提出一种基于XGBoost改造的Deep Forest级联算法。该算法采用XGBoost浅层机器学习算法作为Deep Forest级联构建块,同时用AUC-PR标准作为级联构建深度学习不平衡样本评价的自适应过程,并将此算法分别与原有XGBoost算法和原始Deep Forest算法进行性能比较。经实践,上述算法应用投产于保险购买预测场景中,分别比原有XGBoost算法和原Deep Forest算法提高5.5%和2.8%,效果显著;同时提出的浅层学习向基于Deep Forest深度优化操作流程,也为其他类似应用场景提供了实践参考方向。

WDBM: Weighted Deep Forest Model Based Bearing Fault Diagnosis Method

In the research field of bearing fault diagnosis,classical deep learning models have the problems of too many parameters and high computing cost.In addition,the classical deep learning models are not effective in the scenario of small data.In recent years,deep forest is proposed,which has less hyper parameters and adaptive depth of deep model.In addition,weighted deep forest(WDF)is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree.In this paper,weighted deep forest model-based bearing fault diagnosis method(WDBM)is proposed.The WDBM is regard as a novel bearing fault diagnosis method,which not only inherits the WDF’s advantages-strong robustness,good generalization,less parameters,faster convergence speed and so on,but also realizes effective diagnosis with high precision and low cost under the condition of small samples.To verify the performance of the WDBM,experiments are carried out on Case Western Reserve University bearing data set(CWRU).Experiments results demonstrate that WDBM can achieve comparative recognition accuracy,with less computational overhead and faster convergence speed.

Research on trend prediction of component stock in fuzzy time series based on deep forest

With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry.To improve the effectiveness of stock trend prediction and solve the problems in time series data processing,this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index.Meanwhile,in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge,this paper combines the deep forest model to train the stock data separately.The experimental results show that(1)the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit(GRU)model when the un-fuzzy index-adjusted dataset is used as features for input,(2)the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input,(3)the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and(4)the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.

面向ICS的CGAN-DEEPFOREST入侵检测

随着工业化与信息化的深度融合,工业控制系统(ICS)的安全问题广受关注,ICS领域出现了许多入侵检测模型.但是,现存模型存在局限性,无法同时解决数据不平衡、分类时间长、小样本检测率低和准确率低的问题.因此,本文提出CGAN-DeepForest入侵检测模型解决上述问题.首先,采用改进的条件生成对抗网络(CGAN)定向扩充数据来改善数据的不平衡性.其次,采用随机森林对平衡后的数据集进行特征提取,降低分类模型训练时间和分类时间.再次,采用深度森林(DeepForest)进行分类,提高小样本检测率和整体准确率,输出分类结果.最后,使用数据集Gas验证模型效果.实验结果表明,本文模型与简单深度森林模型相比准确率整体提升3%,小样本数据NMRI、MFCI、Dos的查全率、查准率、F1分别提高至95%、84%、90%;与随机森林模型相比,准确率整体提高6%,小样本NMRI的查全率提升23%;与深度卷积神经网络相比,准确率接近94%时,模型训练时间和分类时间提高约50%.

Dark-Forest:Analysis on the Behavior of Dark Web Traffic via DeepForest and PSO Algorithm

The dark web is a shadow area hidden in the depths of the Internet,which is difficult to access through common search engines.Because of its anonymity,the dark web has gradually become a hotbed for a variety of cyber-crimes.Although some research based on machine learning or deep learning has been shown to be effective in the task of analyzing dark web traffic in recent years,there are still pain points such as low accuracy,insufficient real-time performance,and limited application scenarios.Aiming at the difficulties faced by the existing automated dark web traffic analysis methods,a novel method named Dark-Forest to analyze the behavior of dark web traffic is proposed.In this method,firstly,particle swarm optimization algorithm is used to filter the redundant features of dark web traffic data,which can effectively shorten the training and inference time of the model to meet the realtime requirements of dark web detection task.Then,the selected features of traffic are analyzed and classified using the DeepForest model as a backbone classifier.The comparison experiment with the current mainstream methods shows that Dark-Forest takes into account the advantages of statistical machine learning and deep learning,and achieves an accuracy rate of 87.84%.This method not only outperforms baseline methods such as Random Forest,MLP,CNN,and the original DeepForest in both large-scale and small-scale dataset based learning tasks,but also can detect normal network traffic,tunnel network traffic and anonymous network traffic,which may close the gap between different network traffic analysis tasks.Thus,it has a wider application scenario and higher practical value.

An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels

The paper proposes a new deep structure model,called Densely Connected Cascade Forest-Weighted K Nearest Neighbors(DCCF-WKNNs),to implement the corrosion data modelling and corrosion knowledgemining.Firstly,we collect 409 outdoor atmospheric corrosion samples of low-alloy steels as experiment datasets.Then,we give the proposed methods process,including random forests-K nearest neighbors(RF-WKNNs)and DCCF-WKNNs.Finally,we use the collected datasets to verify the performance of the proposed method.The results show that compared with commonly used and advanced machine-learning algorithms such as artificial neural network(ANN),support vector regression(SVR),random forests(RF),and cascade forests(cForest),the proposed method can obtain the best prediction results.In addition,the method can predict the corrosion rates with variations of any one single environmental variable,like pH,temperature,relative humidity,SO2,rainfall or Cl-.By this way,the threshold of each variable,upon which the corrosion rate may have a large change,can be further obtained.

DeepRanger:覆盖制导的深度森林测试方法

深度学习软件的结构特征与传统软件存在明显差异,因此即使展开了大量测试,依然无法有效衡量测试数据对深度学习软件的覆盖情况和测试充分性,并造成后续使用过程中依然可能存在大量未知错误.深度森林是一种新型深度学习模型,其克服了深度神经网络存在的一些缺点,例如:需要大量训练数据、需要高算力平台、需要大量超参数.但目前还没有相关工作对深度森林的测试方法进行研究.针对深度森林的结构特点,制定了一组由随机森林结点覆盖率RFNC、随机森林叶子覆盖率RFLC、级联森林类型覆盖率CFCC和级联森林输出覆盖率CFOC组成的测试覆盖率评价指标.在此基础上,基于遗传算法设计了覆盖制导的测试数据自动生成方法DeepRanger,可自动生成能有效提高模型覆盖率的测试数据集.为对所提出覆盖指标的有效性进行验证,在深度森林开源项目gcForest和MNIST数据集上设计并进行了一组实验.实验结果表明,所提出的4种覆盖指标均能有效评价测试数据集对深度森林模型的测试充分性.此外,与基于随机选择的遗传算法相比,使用覆盖信息制导的测试数据生成方法DeepRanger能达到更高的模型覆盖率.

能源资源开发区域大气CO_(2)时空变化及影响因素分析

分析能源资源开发区域大气碳浓度的时空变化和影响因素,对于探索“碳达峰”“碳中和”背景下能源资源开发高质量发展路径至关重要。新疆维吾尔自治区是我国重要的能源和战略资源基地,本文面向新疆维吾尔自治区的能源资源开发现状,采集并预处理了2015—2021年轨道碳观测卫星-2(Orbiting Carbon Observatory-2,OCO-2)二氧化碳L3数据产品,分析研究区大气碳浓度的时间变化趋势和空间分布格局,构建深度森林回归模型,并分析各影响因素对碳浓度时空变化的驱动作用。结果表明:(1)新疆维吾尔自治区、准噶尔盆地、吐哈盆地和塔里木盆地XCO_(2)浓度在2015—2021年均呈周期性上升趋势,增长率呈“先减后增”,且季节变化趋势呈现明显的“春季高冬季低”;(2)在春、秋和冬季,新疆XCO_(2)浓度空间格局呈现“北高南低”的趋势,在盆地区域及能源资源开发区域出现XCO_(2)高浓度积聚现象,夏季则呈现“北低南高”趋势;(3)地形起伏、风场流速、NDVI、地表温度、降水量、10 mV风、10 mU风和能源开发强度对区域XCO_(2)浓度时空分布有显著影响,各因素呈现明显的空间异质性和显著差异。研究结果有助于理解能源资源开采区域的大气碳浓度时空演变机制,在国家碳减排目标的实现、指导碳中和策略、追踪碳减排效果等方面具有深远意义。

基于互信息和融合加权的并行深度森林算法

针对大数据环境下并行深度森林算法中存在不相关及冗余特征过多、多粒度扫描不平衡、分类性能不足以及并行化效率低等问题,提出了基于互信息和融合加权的并行深度森林算法(parallel deep forest algorithm based on mutual information and mixed weighting,PDF-MIMW)。首先,在特征降维阶段提出了基于互信息的特征提取策略(feature extraction strategy based on mutual information,FE-MI),结合特征重要性、交互性和冗余性度量过滤原始特征,剔除过多的不相关和冗余特征;接着,在多粒度扫描阶段提出了基于填充的改进多粒度扫描策略(improved multi-granularity scanning strategy based on padding,IMGS-P),对精简后的特征进行填充并对窗口扫描后的子序列进行随机采样,保证多粒度扫描的平衡;其次,在级联森林构建阶段提出了并行子森林构建策略(sub-forest construction strategy based on mixed weighting,SFC-MW),结合Spark框架并行构建加权子森林,提升模型的分类性能;最后,在类向量合并阶段提出基于混合粒子群算法的负载均衡策略(load balancing strategy based on hybrid particle swarm optimization algorithm,LB-HPSO),优化Spark框架中任务节点的负载分配,降低类向量合并时的等待时长,提高模型的并行化效率。实验表明,PDF-MIMW算法的分类效果更佳,同时在大数据环境下的训练效率更高。

A Survey of the Machine Learning Models for Forest Fire Prediction and Detection

Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.

An artificial intelligence diabetes management architecture based on 5G

Along with the development of 5G network and Internet of Things technologies,there has been an explosion in personalized healthcare systems.When the 5G and Artificial Intelligence(Al)is introduced into diabetes management architecture,it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G.In this article,we propose a 5G-based Artificial Intelligence Diabetes Management architecture(AIDM),which can help physicians and patients to manage both acute complications and chronic complications.The AIDM contains five layers:the sensing layer,the transmission layer,the storage layer,the computing layer,and the application layer.We build a test bed for the transmission and application layers.Specifically,we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer.In application layer,we build a prediction model using a deep forest algorithm.Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well.

基于深度森林算法的返贫风险预警及防范

为巩固拓展脱贫攻坚成果同乡村振兴有效衔接,打好实施乡村振兴战略的基础,文章选取2020年中国乡村振兴综合调查(CRRS)数据为样本,以可持续生计理论为指导构建返贫风险评价指标体系,使用集成学习的多种算法构建返贫风险预警模型。结果表明:各模型经过参数优化后,深度森林算法识别效果最佳,对返贫风险识别整体准确率为0.98,对构建动态返贫风险预警系统有一定的技术帮助;各地区不同风险等级生计资本存在显著差异性,应因地制宜出台返贫帮扶政策,并为精准帮扶防范返贫提出相应建议。

基于Spark和NRSCA策略的并行深度森林算法

针对并行深度森林在大数据环境下存在冗余及无关特征过多、两端特征利用率过低、模型收敛速度慢以及级联森林并行效率低等问题,提出了基于Spark和NRSCA策略的并行深度森林算法——PDF-SNRSCA。首先,该算法提出了基于邻域粗糙集和Fisher score的特征选择策略(FS-NRS),通过衡量特征的相关性和冗余度,对特征进行过滤,有效减少了冗余及无关特征的数量;其次,提出了一种随机选择和等距提取的扫描策略(S-RSEE),保证了所有特征能够同概率被利用,解决了多粒度扫描两端特征利用率低的问题;最后,结合Spark框架,实现级联森林并行化训练,提出了基于重要性指数的特征筛选机制(FFM-II),筛选出非关键性特征,平衡增强类向量与原始类向量维度,从而加快模型收敛速度,同时设计了基于SCA的任务调度机制(TSM-SCA),将任务重新分配,保证集群负载均衡,解决了级联森林并行效率低的问题。实验表明,PDF-SNRSCA算法能有效提高深度森林的分类效果,且对深度森林并行化训练的效率也有大幅提升。

Research and Implementation of Cancer Gene Data Classification Based on Deep Learning

Cancer has become a cause of concern in recent years. Cancer genomics is currently a key research direction in the fields of genetic biology and biomedicine. This paper analyzes 5 different types of cancer genes, such as breast, kidney, colon, lung and prostate through machine learning methods, with the goal of building a robust classification model to identify each type of cancer, which will allow us to identify each type of cancer early, thereby reducing mortality.

面向复杂场景的森林防火监测技术研究

为解决森林防火工作中监测预警不及时、不准确的问题,基于YOLOv8模型提出了一种面向复杂场景的监测技术,实现了森林火情的快速准确监测。在Backbone部分引入动态蛇形卷积(Dynamic Snake Convolution, DySConv)模块和全局注意力机制(Global Attention Mechanism, GAM)模块,显著增强了模型的特征提取能力,提升了烟火特征的表现力。采用WIoU(Wise Intersection over Union)损失函数,增强网络对普通质量锚框的关注度,从而提供更准确的目标检测评估。通过将DyHead模块集成到检测头中,增强了检测头的尺寸、空间和任务感知能力,优化了整体性能。为了对模型性能进行严格评估,设计了消融实验和主流模型对比实验,结果表明,提出的方法是有效的,该方法的权重大小为14.4 MB,平均精度均值(mean Average Precision, mAP)@.5:.95达到80.3%。相较于YOLOv8,mAP@5:.95提高了8.7%。该模型可为远程火情监控与预警提供技术支持,在森林防火工作中具备实用价值和现实意义。

基于改进随机森林和深度学习的学生就业预测模型

为促进学校和学生更好地应对动态就业市场,提高教育与就业的匹配度,提出一种结合改进随机森林算法(RF)和深度置信网络(DBN)的学生就业预测混合模型。首先,对学生数据进行预处理,去除不相关的属性以实现数据一致性。其次,为了提高预测模型的准确性,采用结合主成分分析(PCA)和RF算法的特征选择模型,从原始特征中选择最优子集。最后,将选定特征作为DBN的输入,学习高级特征表示并完成学生就业预测。实验结果表明,所提混合模型的预测准确度达到了92%以上,相比循环神经网络(RNN)和多层感知机(MLP)模型分别高出16.8%和14%,能够帮助教育机构了解不同专业和课程的就业趋势,改善学生的职业发展路径规划。