A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social robotics.It enhances systems’ability to interpret and respond to human behavior precisely.This research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse actions.The overall purpose of this study is to develop a robust and accurate system for human interaction recognition.This research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out scenes.Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method.Feature extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)techniques.The application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification process.It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities.The impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed technique.The proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM classification.This enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

A Novel Approach for Network Vulnerability Analysis in IIoT

Industrial Internet of Things(IIoT)offers efficient communication among business partners and customers.With an enlargement of IoT tools connected through the internet,the ability of web traffic gets increased.Due to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in IIoT.The proposed MPDQDJREBC technique includes feature selection and categorization.First,the network traffic features are collected from the dataset.Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption.After the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost technique.Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output.Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack.Thus,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error.This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage.Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection time.The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

SPEECH EMOTION RECOGNITION USING MODIFIED QUADRATIC DISCRIMINATION FUNCTION

Quadratic Discrimination Function (QDF) is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normalize the emotional features, then derivate a Modified QDF (MQDF) to speech emotion recognition. Features based on prosody and voice quality are extracted and Principal Component Analysis Neural Network (PCANN) is used to reduce dimension of the feature vectors. The results show that voice quality features are effective supplement for recognition, and the method in this paper could improve the recognition ratio effectively.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

Sammon Quadratic Recurrent Multilayer Deep Classifier for Legal Document Analytics

In recent years,machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain.The legal field is strongly affected by the problem of information overload,due to the large amount of legal material stored in textual form.Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions.With an increasing number of digitally available documents,legal text processing is essential to analyze documents which helps to automate various legal domain tasks.Legal document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document review.In this paper,we propose Sammon Keyword Mapping-based Quadratic Discriminant Recurrent Multilayer Perceptive Deep Neural Classifier(SKM-QDRMPDNC),a system that applies deep neural methods to the problem of legal document classification.The SKM-QDRMPDNC technique consists of many layers to perform the keyword extraction and classification.First,the set of legal documents are collected from the dataset.Then the keyword extraction is performed using SammonMapping technique based on the distance measure.With the extracted features,Quadratic Discriminant analysis is applied to performthe document classification based on the likelihood ratio test.Finally,the classified legal documents are obtained at the output layer.This process is repeated until minimum error is attained.The experimental assessment is carried out using various performance metrics such as accuracy,precision,recall,F-measure,and computational time based on several legal documents collected from the dataset.The observed results validated that the proposed SKM-QDRMPDNC technique provides improved performance in terms of achieving higher accuracy,precision,recall,and F-measure with minimum computation time when compared to existing methods.

Ridge-Forward Quadratic Discriminant Analysis in High-Dimensional Situations

Quadratic discriminant analysis is a classical and popular classification tool,but it fails to work in high-dimensional situations where the dimension p is larger than the sample size n.To address this issue,the authors propose a ridge-forward quadratic discriminant(RFQD) analysis method via screening relevant predictors in a successive manner to reduce misclassification rate.The authors use extended Bayesian information criterion to determine the final model and prove that RFQD is selection consistent.Monte Carlo simulations are conducted to examine its performance.

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

一种基于特征筛选的原核生物启动子判别分析方法

启动子识别是研究基因转录调控的重要环节,但目前方法的识别正确率偏低。在深入分析原核启动子特征的基础上,提出了一种基于特征筛选的原核启动子判别分析方法,首先在启动子序列的组成特征、信号特征和结构特征中选取备选特征,为每个特征建立适当的描述模型,并对主要的保守模式采用复合模式模型;再通过模型计算对备选特征进行逐步筛选,优化特征集,将序列表示为组合特征向量;最终利用二次判别分析实现识别。对大肠杆菌和枯草杆菌实际启动子数据进行的刀切法测试验证了方法的有效性和通用性。对于大肠杆菌非编码区(70启动子,识别的平均正确率达到了85.8%,优于其它几种典型识别方法;对于大肠杆菌编码区内部)70启动子和其它几种原核启动子,平均正确率也都超过了80%。方法框架还具有良好的可扩展性,能够方便地容纳新特征,使识别性能不断提高。

基于机器视觉技术的单粒葡萄质量与果径预测分级研究

【目的】采用机器视觉技术,针对新疆无核白和红提单粒葡萄的质量和果径大小进行预测和分级研究。【方法】在不同的颜色特征空间模型,预处理原始图像,采用最大类间方差法分割目标区域;采用数学形态学方法去除二值图像中部分果梗及噪声点,获得最佳二值图像;基于二值图像,分析获取单粒葡萄的几何特征;最后,分别采用一元线性回归法和偏最小二乘回归法预测单粒葡萄的质量和果径,采用二次判别分析法对单粒葡萄的质量和果径进行分级。【结果】利用短轴与果形指数特征相结合建立的偏最小二乘回归模型可有效预测单粒葡萄的质量和果径,预测决定系数达到0.98和0.945;基于该特征组合的二次判别分析法可用于单粒葡萄的质量和果径分级,准确率超过85%。【结论】机器视觉技术能够较准确预测单粒葡萄的质量和果径,并能对质量和果径进行分级。

人类polⅡ启动子的识别

依据基因启动子区和非启动子区碱基分布的特征,应用基于多样性增量的二次判别分析(IDQD),对人类polⅡ启动子进行识别,识别精度达到90%以上的水平,优于其他已发表的(包括SVM分类器等)识别算法.使用IDQD算法也能对转录起始位点(TSS)进行较准确的预测,10-fold交叉检验结果的敏感性和特异性分别为86%和91%.这些结果表明IDQD是一个有效的分类器.

人类基因组中可变和组成性剪接位点的预测

根据剪接位点的核酸序列保守特征,以及邻近位点的碱基组成和关联特性,结合一对可变剪接位点之间的距离参数和受体端剪接位点前30位碱基的GC和TC含量,利用结合多样性指标的二次判别方法(IDQD),预测了人类基因组中可变和组成性内含子的供体端和受体端的剪接位点,对可变的供体端和受体端剪接位点,阈值!选择-2时,总的预测精度分别为87.9%和89.9%,对组成性的供体端和受体端剪接位点,阈值!选择-1,总的预测精度分别为92.8%和94.3%.

核小体定位与RNA剪接

根据核小体定位序列和缺失序列的碱基分布特征,应用多样性增量二次判别方法(IDQD)构建模型对这两类序列进行了区分,受试者操作特性曲线下的面积达到了0.958.应用这一模型研究了核小体在人类基因组剪接位点(GT/AG)邻近序列中的分布方式,发现外显子所对应的DNA序列通常倾向参与核小体的形成,并且由它所转录的RNA统计上具有较强的刚性,而剪接位点及其邻近的内含子对应的DNA序列则避免参与核小体的形成,所转录的RNA统计上具有较强的柔性.进一步还发现,DNA序列的核小体定位/缺失和RNA的刚性/柔性具有统计相关性,为从机制上解释为何前体RNA剪接事件与DNA序列中的核小体定位信息有关提供了依据.

使用多样性增量预测磷酸化位点

磷酸化是蛋白质最重要的翻译后修饰之一.应用基于多样性增量的二次判别分析(Increment of Diversity with Quadratic Discriminant analysis,IDQD)方法对CK2,PKA和PKC三种类型磷酸化位点进行预测,k-fold交叉检验的正确率分别为86%,90%和85%,独立测试集检验的正确率分别为86%,88%和84%.所得结果高于包括支持向量机在内的现有预测方法.

一种基于组合特征的大肠杆菌σ^(70)启动子识别算法

启动子识别是研究基因转录调控的重要环节,但目前算法的识别正确率偏低。在深入分析启动子生物特征的基础上,提出了一种基于多种特征组合的大肠杆菌7σ0启动子识别算法,在启动子序列的组成特征、信号特征和结构特征中选取10种典型特征,以此为依据,对位于非编码区和编码区内部的启动子分别加以识别。首先通过特征描述模型分别计算各种特征在启动子序列和非启动子序列中的得分,将特征得分组合成10维特征向量,再利用二次判别分析法在特征向量集上进行训练和识别。在实际数据集中进行的刀切法测试验证了算法的有效性。对位于非编码区的启动子,平均正确率达到了86.7%,明显优于其它算法;对位于编码区内部的启动子,平均正确率也达到了82.4%。算法还具有良好的可扩展性,能够方便地容纳新特征,使识别性能不断提高。

利用光谱趋势参数快速判定小麦粉DON等级的研究

脱氧雪腐镰刀菌烯醇(DON)是一种经常发生在谷物及其衍生产品中的霉菌毒素,危害人类和家畜的生命健康。开发快速、准确、经济、无环境危害的检测方法是一个亟待解决的问题。本研究定义了可见光-近红外(Vis-NIR)光谱的趋势参数TP(trend parameter),利用TP确定与DON浓度最相关的特征波段。文中校正集样本的光谱矩阵行按样本DON浓度逐渐增加的顺序排列,矩阵每一列(每一个波段)都对应一个TP值,所有样本在某波段下的吸光度在列方向上的递增趋势越强(即TP值越大),则此波段下的吸光度与DON浓度的相关性就越强,该波段便可以作为评估DON浓度的特征波段。研究发现在666, 1 238和1 660 nm处TP出现局部最大值,利用此三个特征波段下的光谱进行二次判别分析Quadratic Discriminant Analysis(QDA),以此构建的TP-QDA模型可以将小麦粉按DON污染水平分成轻度(0<DON<1 000μg·kg-1)、中度(1 000≤DON<2 000μg·kg-1)、和重度(DON≥2 000μg·kg-1)三个等级。该模型的整体分类准确率在校正集和验证集中分别为88.24%和86.27%。对比了传统的主成分分析PCA(principal component analysis)特征波段选取方法,其所构建的PCA-QDA模型也将相同的小麦样品分成三个污染等级,整体分类准确率在校正集和验证集中分别为68.62%和72.55%。这些研究结果证实了TP选择特征波段的方法在判断DON污染水平时优于PCA特征波段选取方法,并且TP-QDA模型可有效地用于快速对小麦粉的污染等级进行分类,从而减少收储运过程中分析筛选小麦的时间和经济成本。研究结果还有待在更广泛的小麦品种中进行普适性验证。

多样性指标用于基因中剪切位点的识别

根据基因剪切位点处的碱基保守性特征 ,和附近位点的碱基组成和关联特征 ,应用多样性指标和二次判别分析 ,对几类模式生物的基因结构进行统一的分析和预测 ,能够较好地识别外显子和内含子及其边界 .计算结果表明 ,对于 4类物种 ,线虫 (C .elegans) ,拟南芥 (A .thaliana ) ,果蝇 (D .melanogaster)和人类 (human) ,核苷酸水平的识别精度为 92 5 %～ 97 1 % ,外显子水平的识别敏感性为 83 7%～ 94 5 % ,特异性为 87 8%～97 1 % .

大肠杆菌σ^(70)启动子的识别

应用多样性增量结合二次判别分析(Increment of Diversity with Quadratic Discriminant analysis,IDQD)方法,对大肠杆菌σ70启动子进行识别。使用受试者操作特性(receiver operating characteristic,ROC)曲线和精度召回率曲线(Precision Recall Curves,PRC)进行性能评估。10-fold交叉检验给出,在正负集之比为1∶1时,ROC曲线下面积和PRC曲线下面积均为95%。结果表明,IDQD算法有能力应用于原核启动子的识别。识别精度高于现有算法。

采用虚拟训练样本的二次判别分析方法

小样本问题会造成各类协方差矩阵的奇异性和不稳定性.本文采用对训练样本进行扰动的方法来生成虚拟训练样本,利用这些虚拟训练样奉克服了各类协方差矩阵的奇异性问题,从而可以直接使用二次判别分析(Quadratic discriminant analysis,QDA)方法.本文方法克服了正则化判别分析(Regularized discriminant analysis,RDA)需要进行参数优化的问题.实验结果表明,QDA的模式识别率优于参数最优化时RDA算法的识别率.

FUDT在苹果近红外光谱分类中的应用

苹果的分类是苹果采收后商品化处理的重要环节。为了快速、无损和有效地实现苹果的分类,利用近红外光谱技术采集四种苹果的近红外反射光谱,用主成分分析对高维的近红外光谱进行降维处理,分别运行线性判别分析,二次判别分析,模糊非相关判别转换和Foley-Sammon判别分析提取鉴别信息,用k-近邻分类器进行分类。分类结果表明,模糊非相关判别转换能更好地提取苹果近红外光谱的品种鉴别信息,达到了最高的分类准确率。