Part-level 3-D object classification with improved interpretation tree

For classifying unknown 3-D objects into a set of predetermined object classes, a part-level object classification method based on the improved interpretation tree is presented. The part-level representation is implemented, which enables a more compact shape description of 3-D objects. The proposed classification method consists of two key processing stages： the improved constrained search on an interpretation tree and the following shape similarity measure computation. By the classification method, both whole match and partial match with shape similarity ranks are achieved; especially, focus match can be accomplished, where different key parts may be labeled and all the matched models containing corresponding key parts may be obtained. A series of experiments show the effectiveness of the presented 3-D object classification method.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

西安地区3~6岁儿童脚型分类及特征规律分析

针对当前市场童鞋设计不合理和缺乏儿童脚型理论研究参考问题,本研究首先使用三维足部扫描仪获取到3~6岁的52名男童和71名女童三维脚型数据,进行儿童脚型特点和差异的对比分析;其次,采用相关性分析和主成分分析将26组多维指标降维处理至3个主成分:脚长因子、宽围因子和高度因子;然后,以提取的3个主成分作为分类标准进行H-K聚类分析,将儿童脚型分为3类:细瘦足、适中足、肥壮足,其中左脚细瘦足占比最多,右脚适中足占比最多;最后,将脚长尺寸标准化,发现儿童脚型存在显著的性别及左右脚差异.该研究结论可为童鞋设计和鞋楦三维数字化构建提供理论参考.

Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.

LU Invariants and Canonical Forms and SLOCC Classification of Pure 3-Qubit States

In this paper the entanglement of pure 3-qubit states is discussed. The local unitary （LU） polynomial invariants that are closely related to the canonical forms are constructed and the relations of the coefficients of the canonical forms are given. Then the stochastic local operations and classlcal communication （SLOCC） classification of the states are discussed on the basis of the canonical forms, and the symmetric canonical form of the states without 3-tangle is discussed. Finally, we give the relation between the LU polynomial invariants and SLOCC classification.

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

The deep learning models are identified as having a significant impact on various problems.The same can be adapted to the problem of brain tumor classification.However,several deep learning models are presented earlier,but they need better classification accuracy.An efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this issue.The method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple levels.The noise-removed image has been equalized for its quality by using histogram equalization.Further,the features like white mass,grey mass,texture,and shape are extracted from the images.Extracted features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality reduction.The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution layer.The neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images.Based on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

The lithofacies classification is essential for oil and gas reservoir exploration and development.The traditional method of lithofacies classification is based on"core calibration logging"and the experience of geologists.This approach has strong subjectivity,low efficiency,and high uncertainty.This uncertainty may be one of the key factors affecting the results of 3 D modeling of tight sandstone reservoirs.In recent years,deep learning,which is a cutting-edge artificial intelligence technology,has attracted attention from various fields.However,the study of deep-learning techniques in the field of lithofacies classification has not been sufficient.Therefore,this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks(CNN)and the excellent ability to describe time-dependent features of long short-term memory networks(LSTM)to conduct lithological facies-classification experiments.The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3%and the best classification effect compared to the CNN,LSTM or the three commonly used machine learning models(Support vector machine,random forest,and gradient boosting decision tree).In addition,the borderline synthetic minority oversampling technique(BSMOTE)is introduced to address the class-imbalance issue of raw data.The results show that processed data balance can significantly improve the accuracy of lithofacies classification.Beside that,based on the fine lithofacies constraints,the sequential indicator simulation method is used to establish a three-dimensional lithofacies model,which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area.According to this comprehensive analysis,the proposed CNN-LSTM model,which eliminates class imbalance,can be effectively applied to lithofacies classification,and is expected to improve the reality of the geological model for the tight sandstone reservoirs.

EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.

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.

Support vector classification for SAR of 5-HT3 receptor antagonists

In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.

血清sTim-3、Gal-9及IL-17水平与冠心病患者冠脉狭窄程度的关系及对预后的预测价值

目的探讨血清可溶性T细胞免疫球蛋白黏蛋白-3(sTim-3)、半乳糖凝集素-9(Gal-9)、白介素-17(IL-17)水平与冠心病患者冠脉狭窄程度及预后的关系,以期为冠心病患者的治疗及预后预测提供参考。方法选取2020年4月—2022年4月于延安市人民医院就诊的182例冠心病患者,根据Gensini评分将患者分为轻度狭窄组(49例)、中度狭窄组(76例)和重度狭窄组(57例);根据纽约心脏病协会(NYHA)心功能分级将患者分为心功能Ⅰ级组(32例)、心功能Ⅱ级组(85例)和心功能Ⅲ级组(65例);根据预后情况分为预后不良组(43例)和预后良好组(139例)。比较不同冠脉狭窄程度、不同心功能分级及不同预后冠心病患者血清sTim-3、Gal-9、IL-17水平,分析这几个指标与冠脉狭窄程度的相关性及其对患者预后的预测价值。结果患者Gensini评分及血清sTim-3、Gal-9、IL-17水平随冠脉狭窄程度的加重而升高,且不同冠脉狭窄程度患者Gensini评分及血清sTim-3、Gal-9、IL-17水平比较,差异有统计学意义(P<0.05)。患者血清sTim-3、Gal-9、IL-17水平随心功能分级的增加而升高,且不同心功能分级患者血清sTim-3、Gal-9、IL-17水平比较,差异有统计学意义(P<0.05)。Pearson相关分析结果显示,冠心病患者血清sTim-3、Gal-9、IL-17水平与Gensini评分均呈正相关(P<0.05)。预后良好组患者血清sTim-3、Gal-9、IL-17水平均低于预后不良组,差异均有统计学意义(P<0.05)。ROC曲线分析结果显示,血清sTim-3、Gal-9、IL-17水平以及三者联合预测冠心病患者预后的曲线下面积(AUC)分别为0.834、0.781、0.789、0.852(P<0.05)。结论冠心病患者血清sTim-3、Gal-9、IL-17水平随冠脉狭窄程度加重以及心功能分级增加而升高,且在冠心病预后不良患者中呈高表达水平,对冠心病患者预后具有一定预测价值,三者联合检测的预测价值更高。

基于改进ID3算法的非结构化大数据分类优化方法

针对非结构化大数据在分类过程中,由于其数据中存在大量的冗余数据,若不能及时清洗大数据中的冗余数据,会降低数据分类精度的问题,提出一种基于改进ID3(Iterative Dichotomiser 3)算法的非结构化大数据分类优化方法。该方法针对非结构化大数据集合中冗余数据多以及维度繁杂的问题,对数据进行清洗处理,并结合有监督辨识矩阵完成数据降维;根据数据降维结果,采用改进ID3算法建立用于数据分类的决策树分类模型,通过该模型对非结构化大数据进行分类处理,从而实现数据的精准分类。实验结果表明,使用该方法对非结构化大数据分类时,分类效果好,精度高。

Directional Point Net:3D Environmental Classification for Wearable Robots

A subject who wears a suitable robotic device will be able to walk in complex environments with the aid of environmental recognition schemes that provide reliable prior information of the human motion intent.Researchers have utilized 1 D laser signals and 2 D depth images to classify environments,but those approaches can face the problems of self-occlusion.In comparison,3 D point cloud is more appropriate for depicting the environments.This paper proposes a directional PointNet to directly classify the 3 D point cloud.First,an inertial measurement unit(IMU)is used to offset the orientation of point cloud.Then the directional PointNet can accurately classify the daily commuted terrains,including level ground,climbing up stairways,and walking down stairs.A classification accuracy of 98%has been achieved in tests.Moreover,the directional PointNet is more efficient than the previously used PointNet because the T-net,which is utilized to estimate the transformation of the point cloud,is not used in the present approach,and the length of the global feature is optimized.The experimental results demonstrate that the directional PointNet can classify the environments in robust and efficient manner.

南京市O_(3)污染时空演变特征及气象因素影响分析

利用南京市2016—2022年空气自动监测网数据,详细分析了南京市O_(3)时空分布特征及气象因素影响。结果表明:2016—2022年O_(3)日最大8 h滑动平均质量浓度(O_(3)-8 h)超标天数占总超标天数比例呈明显上升趋势,上升了32.3个百分点;O_(3)污染月份有所增加,由4—9月延长至3—11月。O_(3)-1 h日变化总体呈单峰型,峰值出现时间由15:00延后至16:00,O_(3)污染持续时间有所延长,2019—2022年各时段平均O_(3)小时质量浓度比2016—2018年高1~10μg/m^(3),污染总体抬升。2016—2022年O_(3)-8 h北部郊区点位年均增长率约为8.7μg/m^(3),南部郊区点位约为5.6μg/m^(3),均高于全市的0.5μg/m^(3);O_(3)超标时间北部郊区点位年均增长率约为7 d,南部郊区点位约为5 d,均高于全市的2 d。当气温超过27℃时O_(3)超标率增长迅速,气温区间为(30,35]℃时O_(3)超标率最高(超过50%)。当气温小于33℃、相对湿度小于40%时,O_(3)平均浓度和超标率随气温和相对湿度的升高而升高;气温大于35℃、相对湿度大于60%时,O_(3)平均浓度和超标率随气温和相对湿度的升高而降低。风速区间为(4,5]m/s、风向为SE和S时,O_(3)超标率和平均浓度最高。

Classification of Young Females' Body Shape in Jiaodong Area Based on 3D Morphological Characteristics

To improve the classification method of body type, 103 young female college students in Jiaodong area(Shandong, China) were measured by a 3 D body scanning system, and variables of upper body parts were selected and analyzed by SPSS software. According to the indices such as the chest ratio, the chest sagittal diameter ratio, and the shoulder angle, the tested population was quickly clustered into six categories by the classification method of “size feature+shape index+front and back indices”, which were divided into flat chest body, graceful body, breast augmentation body, normal body, convex back body, and flat body. The proportion of various body types and classification rules were obtained. According to the classification rules, 103 samples and 15 new females’ body data were analyzed and verified. Finally, according to the descriptive statistical analysis of upper body-related indicators of young female in this area, the height and the chest circumference were selected as independent variables, regression analysis was carried out on 11 related indicators, and the mapping relationship between height and chest circumference was studied, which provided a mathematical model for the design of fit clothing structure of young females in Jiaodong area.

3D Object Recognition by Classification Using Neural Networks

In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads to recognition of the former. 3D objects of this database are transformations of other objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group.

Texture Classification of 3D Surface Textures Via Directional Quincunx Lifting

This thesis presents a new approach to classify 3D surface textures by using lifting transform with quincunx subsampling. Feature vectors are generated from eight different lifting prediction directions. We classify 3D surface texture images based on minimum Euclidean distance between the test images and the training sets. The feasibility and effectiveness of our proposed approach can be validated by the experimental results.

构建高校金融学课程教学“3-levels”模式——基于调查问卷数据的实证研究

以金融学为研究对象,基于教学问卷调查数据,对金融学专业和非金融学专业学生及金融学课程教学情况进行了统计分析,同时结合金融学的课程特点和金融人才需求,建立了适合金融学教学的知识结构扩展型、教学手段多样型、学习成果展示型的"3-levels"教学模式。

基于Sentinel-3 OLCI影像的渤海FUI水色指数遥感提取及应用

水色是水体水文学特征的基本要素之一。水色测定主要是采用福莱尔比色表(Forel-Ule Scale)将自然水体按颜色从深蓝到红棕共分为21个级别,用以观测、记录海洋和内陆水体的颜色。基于2020—2021年大辽河口、黄河口西南及秦皇岛近岸海域的现场实测数据和同步Sentinel-3 OLCI影像,验证了FUI水色指数遥感提取结果的准确性,发现当现场测量时间与卫星过境时间接近时,FUI水色指数遥感提取结果与实测结果基本一致。利用2021年1—12月Sentinel-3 OLCI影像,提取了渤海月均FUI水色指数遥感产品,发现渤海FUI水色指数的主要变化区间为5~17,整体呈现辽东湾、渤海湾及莱州湾沿岸海域高,秦皇岛海域及其他离岸海域低的空间分布特征,且存在秋冬季高、春夏季低的时间变化规律。此外,FUI水色指数对诸多海洋生态环境问题具有显著的指示功能。尝试将其应用于海洋水色异常和海水水质类别观测,均取得了较好的应用效果。由此可见,FUI水色指数遥感提取将在今后的海洋生态环境监测与评价方面发挥重要作用。