Intelligent Assessment Method Research Articles

A deep learning-based method for assessing tricuspid regurgitation using continuous wave Doppler spectra

Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human errors leading to measurement variability, with outcomes critically dependent on the operators’ diagnostic expertise. In this study, we present an innovative assessment methodology for evaluating TR severity utilizing an end-to-end deep learning system. This deep learning system comprises a segmentation model of single cardiac cycle TR continuous wave (CW) Doppler spectra and a classification model of the spectra, trained on the TR CW Doppler spectra from a cohort of 11,654 patients. The efficacy of this intelligent assessment methodology was validated on 1500 internal cases and 573 external cases. The receiver operating characteristic (ROC) curves of the internal validation results indicate that the deep learning system achieved the areas under curve (AUCs) of 0.88, 0.84, and 0.89 for mild, moderate, and severe TR, respectively. The ROC curves of the external validation results demonstrate that the system attained the AUCs of 0.86, 0.79, and 0.87 for mild, moderate, and severe TR, respectively. Our study results confirm the feasibility and efficacy of this novel intelligent assessment method for TR severity.

Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning

The interference effects of taller high-rise buildings significantly impact the nearby shorter high-rise buildings in dense urban areas, potentially leading to severe wind-induced disasters. This study aims to develop a universal intelligent assessment method to predict the aerodynamic forces and wind-induced responses of buildings, considering various interfering and coupling factors. A database containing 61440 samples is established through a series of synchronous pressure measurement wind tunnel tests. The aerodynamic forces and wind-induced responses were predicted using six machine learning models under varying wind fields, interfering building locations, interfering building sizes, and dynamic characteristics of the principal building. The performance of the models was enhanced through Bayesian hyperparameter optimization and evaluated using the R2 and NRMSE. Additionally, the SHapley Additive exPlanations (SHAP) method was applied to evaluate the impact of each factor on the aerodynamic force and wind-induced response coefficients. Eight specific cases were examined using local SHAP explanations to explore the influence of seven input variables on maximum wind-induced response coefficients. Results indicated that the XGB model exhibited superior predictive performance, with R2 exceeding 0.99 in both training and testing sets. The SHAP analysis revealed significant impacts from wind direction angle, reduced wind speed, and damping ratio on wind-induced response. Consequently, irrelevant or minimally impactful input variables were removed from the XGB models. The R2 and NRMSE variation rate with reduced inputs for the optimized XGB model was less than 0.45 %. The graphical user interface (GUI) was designed can effectively guide intelligent urban planning and building design.

Few shot intelligent assessment method for compaction quality of earthwork considering uncertainty

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices.

An intelligent assessment method of criminal psychological attribution based on unbalance data

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

This study aims to use the BP neural network model to predict the monthly total number of reported symptoms in preschool children, thereby achieving early warning for the total number of cases in the current month. By analyzing the training results of the neural network and the fitting of the model, the feasibility and effectiveness of the BP neural network in monitoring the physical health of preschool children are validated. The predictive model is of great significance for the prevention of infectious diseases in a border region of a certain province, providing important information for the local incidence situation. The research results indicate that the established BP neural network model demonstrates good accuracy and practicality in predicting the monthly total number of reported symptoms in preschool children’s physical health.

Intelligent Assessment Method of Structural Reliability Driven by Carrying Capacity Sustainable Target: Taking Bearing Capacity as Criterion

Large-scale building structures are subject to numerous uncertain loads during their service life, leading to a decrease in structural reliability. Real-time analysis and accurate prediction of structural reliability is a key step to improve the bearing capacity of buildings. This study proposes an intelligent assessment method for structural reliability driven by a sustainability target, which incorporated digital twin technology to establish an intelligent evaluation framework for structural reliability. Under the guidance of the evaluation framework, the establishment method of a structural high-fidelity twin model is formed. The mechanical properties and reliability analysis mechanism are established based on the high-fidelity twin model. The theoretical method was validated by experimental analysis of a rigid cable truss construction. The results showed the simulation accuracy of the high-fidelity twin model formed by the modeling method is up to 95%. With the guidance of the proposed evaluation method, the mechanical response of the structure under different load cases was accurately analyzed, and the coupling relationship between component failure and reliability indicators was obtained. The twinning model can be used to analyze the reliability of the structure in real time and help to set maintenance measures of structural safety. By analyzing the bearing capacity and reliability index of the structure, the safety of the structure under load is guaranteed. The sustainability of structural performance is achieved during the normal service period of the structure. The proposed reliability assessment method provides a new approach to improving the sustainability of building bearing capacity.

Study on intelligent fuzzy assessment of failure effects for marine power plants

Due to its advantages like zero carbon dioxide emissions, high power density, and long life cycle, power devices show great application prospects in civil cargo ships and icebreakers. The equipment composition and structure configuration of power plants are complex, with harsh operating conditions, which will yield to increased failure rates and serious security threats. Given that the relative importance relying on precise data, an intelligent assessment method of failure effects based on fuzzy logic is proposed. Marine power plant hierarchy division is descripted, suitable for the failure effect assessment process. The fuzzy sets and fuzzy numbers are used to get the fuzzy evaluations and relative weights. The alpha-level sets of fuzzy risk priority numbers (RPNs) are calculated with the proposed benchmark adjustment search strategy. Meanwhile, using the synthesized alpha-level set and centroid defuzzification, the fuzzy RPNs are defuzzied. The comparative analysis of examples reveals that the suggested assessment method can improve the confidence level of risk prioritization for marine power plants.

Retracted: An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

Retracted: An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

In order to fully exploit the effect of intelligent evaluation of English teaching ability and explore the quality of English teaching, an intelligent evaluation method of English teaching ability based on improved machine learning algorithm is proposed, which can ensure the rational allocation of English teaching resources, analyze the big data of constraint parameters of English teaching ability evaluation, and obtain frequent item sets of English teaching quality based on big data mining technology. The particle swarm optimization (PSO) method was used to improve the parameters of the SVM, and the optimal parameters of the support vector machines (SVM) were obtained, which were input into the sample of English teaching effect evaluation, and a method was constructed to maximize the SVM. The optimal parameters are introduced into the decision function, so as to achieve the purpose of English teaching quality assessment. The experimental results show that the proposed method has a high convergence speed and can achieve rapid convergence with only 30 network trainings. At the same time, the evaluation accuracy of English teaching quality is as high as 95%, the correlation coefficient of the results is as high as 0.95, and the evaluation time is low. It is less than 150 ms, and the keyword frequency identification is better and better, which can realize the objective evaluation of online teaching quality.

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Enterprise tax risk involves many aspects. In order to accurately realize enterprise tax risk assessment, an intelligent assessment method of enterprise tax risk based on deep learning is proposed. Collect enterprise tax risk information; reasonably distinguish the same type of risk information according to category, type, and appearance; extract the correlation characteristics of enterprise tax risk assessment; use the correlation characteristics to build a deep learning enterprise tax risk assessment model; search the best parameters of the automatic encoder; build a stacked automatic encoder based on the automatic encoder; combine it with the classifier; and complete the design of enterprise tax risk intelligent evaluation model. The experimental results show that the deviation ratio of Receiver Operating Characteristic (ROC), the risk assessment index of the proposed method, is less than 0.38 as a whole. Therefore, the enterprise tax risk intelligent assessment method based on deep learning is better.

A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket.

Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.

Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery

ABSTRACTRapid identification of post-earthquake collapsed buildings can be used to conduct immediate damage assessments (scope and extent), which could potentially be conducive to the formulation of emergency response strategies. Up to the present, the assessments of earthquake damage are mainly achieved through artificial field investigations, which are time-consuming and cannot meet the urgent requirements of quick-response emergency relief allocation. In this research study, an intelligent assessment method based on deep-learning, super-pixel segmentation, and mathematical morphology was proposed to evaluate the damage degrees of earthquake-damaged buildings. This method firstly utilized the Deeplab v2 neural network to obtain the initial damaged building areas. Then, the simple linear iterative cluster (SLIC) method was employed to segment the test images so as to accurately extract the area boundaries of the earthquake-damaged buildings. Next, the images subdivided by SLIC can be merged according to the initial damaged building areas identified by Deeplab v2 neural network. Finally, a mathematical morphological method was introduced to eliminate the background noise. Experimental results demonstrated that the proposed algorithm was superior to others in both convergent speed and accuracy. Besides, its parameter selection was flexible and easily realized which was of great significance to earthquake damage assessments and provided valuable guidance for the formulation of future emergency response plans after earthquake events.

Intelligent Assessment method of Air-Defense & Anti-Missile Command Model Based on Genetic Algorithm Optimization

In order to solve the problems existing in the traditional assessment methods of Air - Defense and Anti-Missile command models, based on the requirements of the command model assessment, an intelligent Air-Defense and Anti-Missile command model intelligence assessment method based on genetic algorithm optimization was proposed. On the one hand, in order to meet the needs of the intelligent assessment, the evaluation knowledge bases is established based on the Air-Defense and Anti-Missile operational command process; on the other hand, basing on the historical data the objectivity of the assessment is improved by adjusting the index weights dynamically with the entropy weighting method and the intelligent optimization method. At the same time, the fuzzy relation matrix is obtained by real-time data processing of the command model and then using the multiple fuzzy comprehensive evaluation method to get the result. Finally, the feasibility of the method is verified by examples.

Stacked-GRU Based Power System Transient Stability Assessment Method

With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency control.

A novel intelligent assessment method for SCADA information security risk based on causality analysis

At present, the oil and gas SCADA system is facing severe information security threats, and the information security assessment for the existing SCADA system is an important response measure. In order to solve the problem that the standard of information security assessment of industrial control system are not perfect and the evaluation objects are fuzzy, this study put forward a novel method of oil and gas SCADA security assessment based on causality analysis. Firstly, defense security assessment technology route with pre-defense,post-defense was introduced and the evaluation indexes were mapped to the pre-defense and post-defense factors, and the pre-defense and post-defense scoring tables for oil and gas SCADA system information security were developed, the evaluation factors were formed as causality diagram table; then the factor space theory was used to evaluate and model the information security of oil and gas SCADA system, and the model was formally described. Finally causality analysis evaluation method of fuzzy Mamdani reasoning was adopted to evaluate factors neurons in the proposed model. The results show that the average accuracy of the causality analysis and the decision tree algorithm is 92.06 and 91.45% respectively. The causality analysis method based on factor space has a good effect on SCADA information security assessment. The conclusions of this study can not only be applied to oil and gas SCADA security risk assessment, but also provide a reference for other industry security assessment.

Intelligent assessment of subsurface cracks in optical glass generated in mechanical grinding process

Grinding process of optical glass has been reported to be related with the creation of subsurface cracks. However, for the time being, most measurement methods have been depended on human operations. In this paper, an intelligent assessment method based on image processing technique is proposed. Grinding trials proved that, the proposed method can accurately (with the biggest relative error of 3.53%) and quickly (nearly 1.6 seconds per micrographs) recognize and measure the subsurface crack depths. More importantly, the proposed method has good robustness to different-sized images. Besides, the method does not require any input parameters or any adjustment of thresholds, therefore the method does not require any prior knowledge of either mechanical grinding process or brittle material behaviors relating with subsurface cracks. Based on above, the proposed method is expected to be meaningful to both metrology equipment companies and optical glass manufacturers.

Application of Fuzzy Analytic Hierarchy Process to Bridge State Comprehensive Assessment and its Improvement Using Intelligent Control Technology

According to Ji-Shao Expressway ,based on the method of AHP, do the fuzzy evaluation and use Symbolic Math Toolbox based MATLAB established was grades assessment diagnostic model for the Intelligent Control Technology system, make rating of the damage status of each part with accumulative score method. Intelligent assessment method and the traditional AHP get Results are consistent，Show that soft computing based on Matlab modern engineering can be effectively applied to active duty bridges’ fuzzy evaluation. At the same time, it can provide reference and guidance for the general survey of other existing bridges.

A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion

The development of efficient on-line data processing and decision support algorithms is one of future trends of expert systems for machine condition monitoring research. This paper contributes to a lean model for machine performance assessment by combining an efficient signal processing algorithm, an effective feature selection criterion, and an intelligent assessment method. In the proposed model, firstly, a second generation wavelet packet transform is used to project raw signals into the wavelet domain; secondly, the Fisher criterion is applied to reduce redundant dimensions; eventually, a fuzzy c-means clustering method is used to assess and classify the performance of mechanical systems. The vibration signals from a rolling element bearing experiment has been used to verify both efficiency and effectiveness of the lean model. Compared with conventional methods, the lean model can reduce the time consumption of feature extraction by 49.7% and storage space or data transfer load related to the feature dimensionality by 97.7%, which indicates a great improvement in efficiency.