Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Somatic mutations in FAT cadherin family members constitute an underrecognized subtype of colorectal adenocarcinoma with unique clinicopathologic features

BACKGROUND The FAT cadherin family members(FAT1,FAT2,FAT3 and FAT4)are conserved tumor suppressors that are recurrently mutated in several types of human cancers,including colorectal carcinoma(CRC).AIM To characterize the clinicopathologic features of CRC patients with somatic mutations in FAT cadherin family members.METHODS We analyzed 526 CRC cases from The Cancer Genome Atlas PanCancer Atlas dataset.CRC samples were subclassified into 2 groups based on the presence or absence of somatic mutations in FAT1,FAT2,FAT3 and FAT4.Individual clinicopathological data were collected after digital slide review.Statistical analysis was performed using t tests and chi-square tests.RESULTS This CRC study cohort had frequent mutations in the FAT1(10.5%),FAT2(11.2%),FAT3(15.4%)and FAT4(23.4%)genes.Two hundred CRC patients(38.0%)harbored somatic mutations in one or more of the FAT family genes and were grouped into the FAT mutated CRC subtype.The FAT-mutated CRC subtype was more commonly located on the right side of the colon(51.0%)than in the rest of the cohort(30.1%,P<0.001).It showed favorable clinicopathologic features,including a lower rate of positive lymph nodes(pN1-2:33.5%vs 46.4%,P=0.005),a lower rate of metastasis to another site or organ(pM1:7.5%vs 16.3%,P=0.006),and a trend toward an early tumor stage(pT1-2:25.0%vs 18.7%,P=0.093).FAT somatic mutations were significantly enriched in microsatellite instability CRC(28.0%vs 2.1%,P<0.001).However,FAT somatic mutations in microsatellite stable CRC demonstrated similar clinicopathologic behaviors,as well as a trend of a better diseasefree survival rate(hazard ratio=0.539;95%confidence interval:0.301-0.967;log-rank P=0.073).CONCLUSION FAT cadherin family genes are frequently mutated in CRC,and their mutation profile defines a subtype of CRC with favorable clinicopathologic characteristics.

Study on Isomerous CAD Model Exchange Based on Feature

A model-exchange method based on feature between isomerous CAD systems is put forward in this paper.In this method,CAD model information is accessed at both feature and geometry levels and converted according to standard feature opera- tion.The feature information including feature tree,dimensions and constraints,which will be lost in traditional data conver- sion,as well as geometry are converted completely from source CAD system to destination one.So the transferred model can be edit- ed through feature operation,which cannot be implemented by general model-exchange interface.

Feature-based Integrated CAD/CAPP/CAM System For Concurrent Engineering

This paper presents methodologies and technologies of feature_based integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The product information is represented on the basis of hierarchical and dynamic structure of feature representation. The Object_Oriented feature modeling method is adopted to represent the feature classification, feature relationship and feature interaction. The set of knowledge_based rule for process planing and manufacturiability evaluation is provided and can be shared by all stages of full product life_cycle. The feature_based machining operation and machining sequence can be determined automatically. The machining process of the machining feature can be determined according to the set of knowledge_based rule.

APPLICATION RESEARCH OF COMBINATION PRINCIPLE IN CAD FEATURE RECOGNIZATION

The method of extracting the basic features of part from the file of STEPAP214 of 3-D model is proposed. All faces in the file are the minimal elements. The combination isdone for the faces with geometry restrictions and some attributes by the theory of the bestalphabetic tree which is constructed by HU--TUCKER algorithm in combination principle. So the basicfeatures could be attained. This provides the research basis to the more share and integration ofCAD information in the virtual enterprises. Finally, a case is used to illustrate the validity ofthe approach.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

Merging Functional Form Feature into GT Codes to Enhance the CAD/CAM Integration

The TangShan Research Institute of Group Technology has been engaged in the develop- ment of GT for the light industry machinery building factories since 1980.Under its overall supervision more than 8 factories have applied GT to drawing control and process planning,while 4 of them reorga- nized their machine shops into GT cells and the throughput capabilities were raisd immediately by 15 to even 49%. Since the existing GT coding systems predict only the global characteristics of machine parts,their application alone is unable to transfer all the information needed for the CAD/CAM integration in a CIM environment.From the very beginning of 1989,we started to study the feasibility of introducing concepts of Functional Form Features into GT codes.Based on the exhaustive study of nearly 100,000 part drawings from machineries for making pottery and porcelain appliances,cigarette,paper pulp, glass bottles,plastics moulding,beer and beverage filling etc.,a new GT/FFF coding system was worked out and a corresponding set of standard drawings assembled from FFF primitives Was formulat- ed which can cover up to 65—75% of existing parts from light industry machineries.FFF based GT codes greatly facilitate the standardization control of released drawings,accelerate the engineering de- sign and process planning,and certainly will play an important role in the future CIM organization.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

椅旁IPS e.max CAD高嵌体修复后牙缺损的临床效果探析

目的为评价CEREC椅旁CAD/CAM系统制作的IPS e.max CAD玻璃陶瓷高嵌体应用于根管治疗后牙缺损病例的临床效果。方法选择42例根管治疗后牙,应用高嵌体的牙体预备方式,采用CEREC椅旁CAD/CAM修复系统和IPS e.max CAD玻璃陶瓷,即刻完成修复体并粘接;修复1年后复查,参照改良修正后的美国公众健康服务标准(US Public Health Service Criteria,USPHS),在修复体边缘染色、边缘继发龋、修复体边缘适合性、修复体崩瓷折裂或脱落、修复体颜色、牙龈健康状况、修复体邻接关系、患者满意度8个方面进行评价。结果修复体边缘染色C级病例1例,成功率97.6%;边缘继发龋C级病例1例,成功率97.6%;修复体邻接关系欠佳C级病例1例,成功率97.6%;在边缘适合性、修复体崩瓷折裂或脱落、修复体颜色、牙龈健康状况、患者满意方面表现优秀,成功率均为100%。结论椅旁CAD/CAM系统制作的IPS e.maxCAD高嵌体修复体在短期内可取得良好的修复效果。

Feature Setup Determination in Integrated CAD/CAM System For Concurrent Engineering

This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

FusionNN:A Semantic Feature Fusion Model Based on Multimodal for Web Anomaly Detection

With the rapid development of the mobile communication and the Internet,the previous web anomaly detectionand identificationmodels were built relying on security experts’empirical knowledge and attack features.Althoughthis approach can achieve higher detection performance,it requires huge human labor and resources to maintainthe feature library.In contrast,semantic feature engineering can dynamically discover new semantic featuresand optimize feature selection by automatically analyzing the semantic information contained in the data itself,thus reducing dependence on prior knowledge.However,current semantic features still have the problem ofsemantic expression singularity,as they are extracted from a single semantic mode such as word segmentation,character segmentation,or arbitrary semantic feature extraction.This paper extracts features of web requestsfrom dual semantic granularity,and proposes a semantic feature fusion method to solve the above problems.Themethod first preprocesses web requests,and extracts word-level and character-level semantic features of URLs viaconvolutional neural network(CNN),respectively.By constructing three loss functions to reduce losses betweenfeatures,labels and categories.Experiments on the HTTP CSIC 2010,Malicious URLs and HttpParams datasetsverify the proposedmethod.Results show that compared withmachine learning,deep learningmethods and BERTmodel,the proposed method has better detection performance.And it achieved the best detection rate of 99.16%in the dataset HttpParams.

Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

Endoscopic features and treatments of gastric cystica profunda

BACKGROUND Gastric cystica profunda(GCP)represents a rare condition characterized by cystic dilation of gastric glands within the mucosal and/or submucosal layers.GCP is often linked to,or may progress into,early gastric cancer(EGC).AIM To provide a comprehensive evaluation of the endoscopic features of GCP while assessing the efficacy of endoscopic treatment,thereby offering guidance for diagnosis and treatment.METHODS This retrospective study involved 104 patients with GCP who underwent endoscopic resection.Alongside demographic and clinical data,regular patient followups were conducted to assess local recurrence.RESULTS Among the 104 patients diagnosed with GCP who underwent endoscopic resection,12.5%had a history of previous gastric procedures.The primary site predominantly affected was the cardia(38.5%,n=40).GCP commonly exhibited intraluminal growth(99%),regular presentation(74.0%),and ulcerative mucosa(61.5%).The leading endoscopic feature was the mucosal lesion type(59.6%,n=62).The average maximum diameter was 20.9±15.3 mm,with mucosal involvement in 60.6%(n=63).Procedures lasted 73.9±57.5 min,achieving complete resection in 91.3%(n=95).Recurrence(4.8%)was managed via either surgical intervention(n=1)or through endoscopic resection(n=4).Final pathology confirmed that 59.6%of GCP cases were associated with EGC.Univariate analysis indicated that elderly males were more susceptible to GCP associated with EGC.Conversely,multivariate analysis identified lesion morphology and endoscopic features as significant risk factors.Survival analysis demonstrated no statistically significant difference in recurrence between GCP with and without EGC(P=0.72).CONCLUSION The findings suggested that endoscopic resection might serve as an effective and minimally invasive treatment for GCP with or without EGC.

Epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China:a hospital-based retrospective study

Traumatic spinal cord injury is potentially catastrophic and can lead to permanent disability or even death.China has the largest population of patients with traumatic spinal cord injury.Previous studies of traumatic spinal cord injury in China have mostly been regional in scope;national-level studies have been rare.To the best of our knowledge,no national-level study of treatment status and economic burden has been performed.This retrospective study aimed to examine the epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China at the national level.We included 13,465 traumatic spinal cord injury patients who were injured between January 2013 and December 2018 and treated in 30 hospitals in 11 provinces/municipalities representing all geographical divisions of China.Patient epidemiological and clinical features,treatment status,and total and daily costs were recorded.Trends in the percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department and cost of care were assessed by annual percentage change using the Joinpoint Regression Program.The percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department did not significantly change overall(annual percentage change,-0.5%and 2.1%,respectively).A total of 10,053(74.7%)patients underwent surgery.Only 2.8%of patients who underwent surgery did so within 24 hours of injury.A total of 2005(14.9%)patients were treated with high-dose(≥500 mg)methylprednisolone sodium succinate/methylprednisolone(MPSS/MP);615(4.6%)received it within 8 hours.The total cost for acute traumatic spinal cord injury decreased over the study period(-4.7%),while daily cost did not significantly change(1.0%increase).Our findings indicate that public health initiatives should aim at improving hospitals’ability to complete early surgery within 24 hours,which is associated with improved sensorimotor recovery,increasing the awareness rate of clinical guidelines related to high-dose MPSS/MP to reduce the use of the treatment with insufficient evidence.

Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.