Subaxial cervical spine injury classification system: is it most appropriate for classifying cervical injury?

The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity.

METHOD OF CLASSIFYING GRASPS BY ROBOT HANDS

This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.

The Motion Trace of Particles in Classifying Flow Field

According to the theory of the stochastic trajectory model of particle in the gas-solid two-phase flows, the two-phase turbulence model between the blades in the inner cavity of the FW-Φ150 horizontal turbo classifier was established, and the commonly-used PHOENICS code was adopted to carried out the numerical simulation. It was achieved the flow characteristics under a certain condition as well as the motion trace of particles with different diameters entering from certain initial location and passing through the flow field between the blades under the correspondent condition. This research method quite directly demonstrates the motion of particles. An experiment was executed to prove the accuracy of the results of numerical simulation.

Classifying Abdominal Fat Distribution Patterns byUsing Body Measurement Data

This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors(BSDs),and to develop a classifier to predict the fat distribution clusters using the BSDs.In the study,66 male and 54 female participants were scanned by MRI and a stereovision body imaging(SBI)to measure participants’abdominal VAT and SAT volumes and the BSDs.A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions.A support-vector-machine(SVM)classifier,with an embedded feature selection scheme,was employed to determine an optimal subset of the BSDs for predicting internal fat distributions.A fivefold cross-validation procedure was used to prevent over-fitting in the classification.The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry(DXA)measurements.Four clusters were identified for abdominal fat distributions:(1)low VAT and SAT,(2)elevated VAT and SAT,(3)higher SAT,and(4)higher VAT.The cross-validation accuracies of the traditional anthropometric,DXA and BSD measurements were 85.0%,87.5% and 90%,respectively.Compared to the traditional anthropometric and DXA measurements,the BSDs appeared to be effective and efficient in predicting abdominal fat distributions.

Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space

To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges.One of them is the need for high-quality training samples.This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space.We migrated training samples collected for Washington,D.C.in 1979 to classify the city’s land covers in 1982 and 1984.The classifier trained with Washington,D.C.’s samples were used in classifying Boston’s and Tokyo’s land covers.The results showed that the overall accuracies achieved using migrated samples in 1982(66.67%)and 1984(65.67%)for Washington,D.C.were comparable to that of 1979(68.67%)using a random forest classifier.Migration of training samples between cities in the same urban ecoregion,i.e.Washington,D.C.,and Boston,achieved higher overall accuracy(59.33%)than cities in the different ecoregions(Tokyo,50.33%).We concluded that migrating training samples across time and space in the same urban ecoregion are feasible.Ourfindings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale.

Indoor metabolites and chemicals outperform microbiome in classifying childhood asthma and allergic rhinitis

Indoor microorganisms impact asthma and allergic rhinitis(AR),but the associated microbial taxa often vary extensively due to climate and geographical variations.To provide more consistent environmental assessments,new perspectives on microbial exposure for asthma and AR are needed.Home dust from 97 cases(32 asthma alone,37 AR alone,28 comorbidity)and 52 age-and gender-matched controls in Shanghai,China,were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry.Homes of healthy children were enriched with environmental microbes,including Paracoccus,Pseudomonas,and Psychrobacter,and metabolites like keto acids,indoles,pyridines,and flavonoids(astragalin,hesperidin)(False Discovery Rate<0.05).A neural network co-occurrence probability analysis revealed that environmental microorganisms were involved in producing these keto acids,indoles,and pyridines.Conversely,homes of diseased children were enriched with mycotoxins and synthetic chemicals,including herbicides,insecticides,and food/cosmetic additives.Using a random forest model,characteristic metabolites and microorganisms in Shanghai homes were used to classify high and low prevalence of asthma/AR in an independent dataset in Malaysian schools(N=1290).Indoor metabolites achieved an average accuracy of 74.9%and 77.1%in differentiating schools with high and low prevalence of asthma and AR,respectively,whereas indoor microorganisms only achieved 51.0%and 59.5%,respectively.These results suggest that indoor metabolites and chemicals rather than indoor microbiome are potentially superior environmental indicators for childhood asthma and AR.This study extends the traditional risk assessment focusing on allergens or air pollutants in childhood asthma and AR,thereby revealing potential novel intervention strategies for these diseases.

The 2024 Compendium of Physical Activities and its expansion

First developed 30 years ago,the Compendium of Physical Activities(Compendium)was created to provide a standardized way of measuring and classifying specific physical activities(PAs),allowing researchers and health professionals to assess the energy expenditure and health benefits associated with different PA.1Since its inception,the Compendium has been widely utilized and recognized as a fundamental PA and health resource.

Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis

A machine learning(ML)-based random forest(RF)classification model algorithm was employed to investigate the main factors affecting the formation of the core-shell structure of BaTiO_(3)-based ceramics and their interpretability was analyzed by using Shapley additive explanations(SHAP).An F1-score changed from 0.8795 to 0.9310,accuracy from 0.8450 to 0.9070,precision from 0.8714 to 0.9000,recall from 0.8929 to 0.9643,and ROC/AUC value of 0.97±0.03 was achieved by the RF classification with the optimal set of features containing only 5 features,demonstrating the high accuracy of our model and its high robustness.During the interpretability analysis of the model,it was found that the electronegativity,melting point,and sintering temperature of the dopant contribute highly to the formation of the core-shell structure,and based on these characteristics,specific ranges were delineated and twelve elements were finally obtained that met all the requirements,namely Si,Sc,Mn,Fe,Co,Ni,Pd,Er,Tm,Lu,Pa,and Cm.In the process of exploring the structure of the core-shell,the doping elements can be effectively localized to be selected by choosing the range of features.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Forecasting the Academic Performance by Leveraging Educational Data Mining

The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.

Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images

Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .

Computation of the cohomology rings of Kac-Moody groups, their flag manifolds and classifying spaces

In this paper we introduce the history and present situation of the computation of the cohomology rings of Kac-Moody groups,their flag manifolds and classifying spaces,and give some problems and conjectures that deserve further study.

A New Stochastic Model for Classifying Flexible Measures in Data Envelopment Analysis

The way to deal with flexible data from their stochastic presence point of view as output or input in the evaluation of efficiency of the decision-making units(DMUs)motivates new perspectives in modeling and solving data envelopment analysis(DEA)in the presence of flexible variables.Because the orientation of flexible data is not pre-determined,and because the number of DMUs is fixed and all the DMUs are independent,flexible data can be treated as random variable in terms of both input and output selection.As a result,the selection of flexible variable as input or output for n DMUs can be regarded as binary random variable.Assuming the randomness of choosing flexible data as input or output,we deal with DEA models in the presence of flexible data whose input or output orientation determines a binomial distribution function.This study provides a new insight to classify flexible variable and investigates the input or output status of a variable using a stochastic model.The proposed model obviates the problems caused by the use of the large M number and using its different values in previous models.In addition,it can obtain the most appropriate efficiency value for decision-making units by assigning the chance of choosing the orientation of flexible variable to the model itself.The proposed method is compared with other available methods by employing numerical and empirical examples.

A method for classifying whistles of bottlenose dolphin(Tursiops truncates) based on syntactic pattern reorganization

A method based on syntactic pattern recognition was presented to automatically classify whistles of bottlenose dolphin. Dolphin whistles have typically been characterized in terms of their instantaneous frequency as a function of time, which is also known as ＂whistle contour＂. The frequency variation features of a whistle were extracted according to its contour. Then, the frequency variation features were used for learning grammatical patterns. A whistle was classified according to grammatical pattern of its frequency variation features. The exper- imental results showed that the classification accuracy of the proposed method was 95%. The method can provide technical support for acoustic study of dolphins＇ biological behavior.

Teaching Reform of “Probability Theory and Mathematical Statistics” Under the Background of New Engineering

Probability theory and mathematical statistics are fundamental courses for various majors in science and engineering.In response to the current teaching situation,we should integrate theory with practice,implement teaching reform,and carry out teaching innovation.The article carries out blended teaching with deep integration of online and offline modes and within and outside of class,constructing innovative measures of“four integrations and four reshaping.”The article conducts diversified evaluations to stimulate learning motivation and help achieve talent cultivation goals.Through the close integration of probability theory and mathematical statistics course teaching with professional education and practical application,the“three-in-one”teaching goal of value shaping,ability cultivation,and knowledge exploration is achieved.The fundamental task of“cultivating morality and talents”is implemented.

Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible.PMUs are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid.This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data.The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid.As a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper.The proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies.The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies.The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

Adapting the Concept Attainment Strategy to Teach Math

This paper argues for the adoption of concept attainment strategy when teaching math and shows the structure of this curriculum design that can substantially improve math instruction and comprehension in K-12 education.Initial findings based on informal surveys of teacher candidates indicate many of them do not have a clear understanding of the concepts they are expected to teach.The concept attainment strategy is a proven effective method used in social studies for teaching powerful concepts like democracy and liberty.One reason for many students feeling inadequate about their math skills stem from their lack of understanding of the key math concepts like area,perimeter,percent,and others.Poor understanding of the math fundamentals in early grades if not rectified,develops into a dislike for an incomprehensible subject.The concept attainment strategy is an inductive approach that allows the students to participate in knowledge construction and master the fundamental math skills.This paper shows how the structure of this social studies curriculum design can be adapted for teaching mathematics and invites practitioners and scholars to consider this approach to improve math instruction.

Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions.This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,etc.The proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and love.The key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and LED.The proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and time.While comparing with existing technologies,the proposed algorithm gave a better error rate and less time.Error rate and time is decreased by 19.79%,5.13 s.for the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT database.The proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

BS-SC Model:A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time,legal entities have developed and used crime prediction methodologies.The techniques are frequently updated based on crime evaluations and responses from scientific communities.There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level.Child maltreatment is not adequately addressed because children are voiceless.As a result,the possibility of developing a model for predicting child abuse was investigated in this study.Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events.The data set was balanced using the Borderline-SMOTE technique,and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse.The proposed approach successfully predicted crime types with 93%of accuracy,precision,recall,and F1-Score.The AUC value of the same was 0.989.However,when compared to the Extra Trees model(17.55),which is the second best,the proposed model’s execution time was significantly longer(476.63).We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse.The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model(BS-SC Model)would be effective in the real-time child abuse prediction and prevention process.