The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature

In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.

Genome-wide Identification,Classification and Expression Analysis of Lhc Supergene Family in Castor Bean(Ricinus communis L.)

Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to the reaction center, maintenance of thylakoid membrane structure, photoprotection and response to en- vironmental conditions, etc. Although/dw supergene family is well characterized in model plants such as Arabidopsis, rice and poplar, little information is available in castor bean （Ricinus communis L. ）. In this study, a genome-wide search was carried out for the first time to identify castor bean L/w genes and analyze the gene structures, biochemical properties, evolutionary relationships and expression characteristics based on the published data of castor bean genome and ESTs. According to the results, a total of 28 Rclhcs genes representing 13 gene families （ l_hca , l_hcb , Elip , Ohpl , Ohp2 , SEP1, SEP2 , SEP3 , SEP4 , SEP5 , PsbS , Rieske and FCII） and 25 subgene families were identified in castor bean genome; to be specific, 25 and 5 genes were found to have corresponding ESTs in NCBI and have al- ternative splicing isoforlns, respectively. These RcLhcs contain 0 to 9 introns and distribute on 26 of the 25 878 released scaffolds. All RcLhcs genes were found to be expressed in all examined tissues, i.e. leaf, flower, II/III stage endosperm, V/VI stage endosperm and seed, with the highest expression level in leaf tissue.

Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

A Survey on Acute Leukemia Expression Data Classification Using Ensembles

Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Identity-aware convolutional neural networks for facial expression recognition

Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.

Video expression recognition based on frame-level attention mechanism

Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.

New substituted molecular classifications of advanced gastric adenocarcinoma:characteristics and probable treatment strategies

Background:Gastric adenocarcinoma(GA)is a heterogeneous tumor,and the accurate classification of GA is important.Previous classifications are based on molecular analysis and have not focused on GA with the primitive enterocyte phenotype(GAPEP),a unique subtype with a poor prognosis and frequent liver metastases.New substituted molecular(SM)classifications based on immunohistochemistry(IHC)are needed.Methods:According to the IHC staining results,we divided 582 cases into six types:mismatch repair deficient(dMMR),Epstein-Barr virus associated(EBVa),the primitive enterocyte phenotype(PEP),the epithelial mes-enchymal transition(EMT)phenotype,not otherwise specified/P53 mutated(NOS/P53m)and not otherwise specified/P53 wild-type(NOS/P53w).We analyzed the clinicopathological features,the immune microenviron-ment(PD-L1,CD8)and expression of HER2 and VEGFR2 of those types.Results:There were 31(5.3%)cases of the dMMR type,13(2.2%)cases of the EBVa type,44(7.6%)cases of the PEP type,122(21.0%)cases of the EMT type,127(21.8%)cases of the NOS/P53m type and 245(42.1%)cases of the NOS/P53w type.Patients with the dMMR type had the best survival(P<0.001).Patients with the EBVa type were younger(P<0.001)and had higher PD-L1 and CD8 expression(P<0.001)than other patients.Patients with the EMT type exhibited poor differentiation and a higher rate of abdominal metastasis.Patients with the NOS/P53m and PEP types had the worst survival rates and the highest PD-L1/HER2/VEGFR2 expression levels among all patients(P<0.001).Conclusion:Different SM classifications have different clinicopathological features and expression patterns,which indicate the probable clinical treatment strategies for these subtypes.

Multi-class cancer classification through gene expression profiles: microRNA versus mRNA

Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.

Expression profiling-based clustering of healthy subjects recapitulates classifications defined by clinical observation in Chinese medicine

Differences between healthy subjects and associated disease risks are of substantial interest in clinical medicine. Based on clinical presentations, Traditional Chinese Medicine （TCM） classifies healthy people into nine constitutions： Balanced, Qi, Yang or Yin deficiency, Phlegm-dampness, Damp-heat, Blood stasis, Qi stagnation, and Inherited special constitutions. In particular, Yang and Yin deficiency constitutions exhibit cold and heat aversion, respectively. However, the intrinsic molecular characteristics of unbal- anced phenotypes remain unclear. To determine whether gene expression-based clustering can reca- pitulate TCM-based classification, peripheral blood mononudear cells （PBMCs） were collected from Chinese Han individuals with Yang/Yin deficiency （n = 12 each） and Balanced （n = 8） constitutions, and global gene expression profiles were determined using the Affymetrix HC-UI33A Plus 2.0 array. Notably, we found that gene expression-based classifications reflected distinct TCM-based subtypes. Consistent with the clinical observation that subjects with Yang deficiency tend toward obesity, series-clustering analysis detected several key lipid metabolic genes （diacylglycerol acyltransferase （DGAT2）, acyl-CoA synthetase （ACSL1）, and ATP-hinding cassette subfamily A member 1 （ABCAI）） to be down- and up- regulated in Yin and Yang deficiency constitutions, respectively. Our findings suggest that Yin]Yang deficiency and Balanced constitutions are unique entities in their mRNA expression profiles. Moreover, the distinct physical and clinical characteristics of each unbalanced constitution can be explained, in part, by specific gene expression signatures.

Cancer classification based on microarray gene expression data using a principal component accumulation method

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.

基于Multi-Agent技术的三层信息融合系统研究

针对传统传感器网络管理复杂,系统信息融合智能化不高、精度低和模式单一、结构不清晰等不足,首先分析了Multi-Agent技术、传感器网络技术以及信息融合技术的独特优势,然后采用计算机网络分层结构思想和基于人工智能本体的知识表达理念,在信息融合过程中采用改进的SVM分类方法,构建了一种基于Multi-Agent技术的多传感器三层信息融合系统并对其具体融合过程进行了分析。最后对分类过程用MATLAB进行了分析。实验结果表明:系统分类精度较高,一定程度上不仅明显弥补了传统传感器的诸多不足,而且为后期决策提供了较为精准的目标参数。

新旧世界葡萄酒质量表达演变及形成

葡萄酒等级是全球葡萄酒行业的关注点。在旧世界葡萄酒等级表达中,产品等级不仅反映产品内在质量,还与具有质量风格的地理特征紧密相关;新世界葡萄酒等级表达中,葡萄酒地理特征仅表示产品来源真实性,葡萄酒质量以产品口感为基础并满足消费者消费偏好需求。葡萄酒官方等级分级具有从产区到列级酒庄的纵向化结构,葡萄酒等级分类具有从品种、年份到酒庄的横向化结构,前者分级以生产者视角下“为何分级”并同时聚焦分级内生形成关键要素,后者分类以消费者视角下“为何分类”并锚定产品质量的某一关键要素,两者之间关注点存在差异。这种差异给国际葡萄酒市场创造竞争空间,形成一种质量表达的实践共创、智慧共存的竞争格局。

基于耐破度的快递包装纸盒分析研究

为建立一种通过耐破机测试结果快速检验快递包装纸盒的方法。遵循ISO2759标准,利用耐破机对收集到的100个不同来源、不同规格的快递纸盒进行抗压测试。结果显示依据样品在1平方厘米面积上承受的千克力的不同可以将样品分为四大类,准确率可达100%。该方法简便易行,可为快递包装纸盒的耐破性分类提供科学的依据,为公安机关实际办案提供了一种速度快、成本低的可推断包装用途或所包装物品的新手段。

汉语复句的分类问题

汉语复句的分类是汉语语法研究中的基本问题,也是难点问题之一。根据复句小句的表达功能与信息地位,可以对汉语复句进行重新分类。据此,以复句中小句能否被提问、能被怎样提问作为形式测试手段,将汉语复句分为并列类复句和主从类复句两个大类,其中主从类复句又有时间复句、因果复句、条件复句三个次类。相较以前的汉语复句分类,新的分类具有更强的可操作性和解释力。

基于MobileNet V2模型迁移学习的垃圾图像分类算法

垃圾分类结合人工智能图形识别技术,是对传统垃圾处理方式的改革,旨在实现垃圾的有效分类处置,是一种科学管理方法[1]。本文基于MobileNet V2网络结构,使用迁移学习提高模型特征表达的能力,改进并选择合适的损失函数和优化方案,使得模型能够区分不同种类的垃圾,训练完毕的模型导出后可以部署在嵌入式系统或者APP中。

基于人脸表情特征的高校课堂教学质量在线评估模型

针对高校课堂教学质量在线评估模型识别率较低,评估过程主观性较强的问题,提出基于人脸表情特征的高校课堂教学质量在线评估模型。提取高校课堂人脸表情特征,利用图像层、S1层、C1层、S2层和C2层进行特征匹配和评选,使用贝叶斯分类模型对特征图像的平滑参数进行优化,确定使用率先验概率,判断学生的状态,评估课堂质量。实验结果表明,提出评估模型的评估率优于传统评估模型,在10~30 min内,学生的听课率最高,因此可以将重点问题和难点问题在第10~30 min内讲解,提高教学质量。