Classification-based Method Research Articles

Multifractal Structures and the Energy-Economic Efficiency of Chinese Cities: Using a Classification-Based Multifractal Method

Improper urban spatial structure can lead to problems such as traffic congestion, long commuting times, and diseconomies of scale. Evaluating the efficiency of urban spatial structure is an important means to enhance the sustainable development of cities. The fractal method has been widely used in the identification and efficiency evaluation of urban spatial structure due to its sufficient characterization of urban complexity. However, the identification of urban fractal structures has expanded from monofractal structures to multifractal structures, while the efficiency evaluation of urban fractal structures remains limited to the single-dimensional efficiency evaluations of single fractals, seriously affecting the reliability of urban fractal structure evaluation. Therefore, this study identifies and evaluates urban spatial structure within the unified framework of multifractal analysis. Specifically, a classification-based multifractal method is introduced to identify the multifractal structure of 290 cities in China. An iterative application of the geographic detector method is used to evaluate the comprehensive energy-economic efficiency of urban multifractal structures. The results indicate that the 290 Chinese cities include 6 typical multifractal structures. The explanatory power of these six typical multifractal structures for urban energy-economic efficiency is 16.27%. The advantageous multifractal structures of cities that achieve higher energy-economic efficiency rates satisfy a cubic polynomial form. By comparing them with the advantageous multifractal structures, the main problems affecting the efficiency of urban multifractal structures in the other five types of cities are shown to include overly strong or weak concentration capacity of high-level centers, weak hierarchical structures among centers, and the spreading of low-level centers.

Dynamic performance assessment of offshore wind structures based on root morphology model

Assessing the dynamic performance of offshore wind turbines (OWTs) is crucial for their safe operation and maintenance. To evaluate the structural dynamic performance, traditional methods rely on the reanalysis of damage models, leading to the lack of the real-time capability. Therefore, this paper presents a structural dynamic performance assessment technology based on the root morphology model to address such issue. The approach is developed to synergize data fusion-based and environmental condition classification-based dynamic performance assessment technologies. The data fusion-based method, using optimized VMD for denoising and ARMA models for free response signals, enables real-time dynamic performance assessment by tracking changes in AR coefficients over time. In parallel, the environmental condition classification-based method employs wind speed and wave height monitoring data to classify structural response data into multiple healthy sub-databases. Subsequently, Extenics is applied to compute the correlation between the data to be evaluated and the performance state levels based on the classified data, providing a precise evaluation of the structural dynamic performance. Finally, these two technologies are integrated through root morphology models, which fully consider structural vibration response data and environmental monitoring data. To verify the real-time and reliability of the proposed method, field study has been carried out on a 4 MW monopile OWT during the passage of Typhoon 'In-fa'. Results indicate that the root morphology model-based assessment technique effectively evaluates the impact of typhoons on the dynamic performance of OWTs. Further, a rose diagram for the dynamic performance assessment of OWTs is drawn to achieve real-time evaluation of OWT dynamic performance, which has certain engineering significance to guarantee the safe operation of OWTs and reduce the cost of operation and maintenance.

A Z-number-based three-way decision method with classification-based state determination for the evaluation of new energy enterprises

New energy enterprises are important promoting factors for sustainable development of modern society. Limited budgets of governments require that new energy enterprises should be efficiently evaluated before they are founded. Existing evaluation methods ignored the hesitation of experts to some alternatives. Although the three-way decision method has been applied widely as a method to evaluate alternatives, the determination of the state set has not been deeply discussed. To solve these challenges, this paper proposes a Z-number-based three-way decision method with classification-based state determination, which can assign alternatives with hesitation to a boundary region for further consideration and compute the conditional probability with a classification-based method. First, since traditional fuzzy sets cannot ensure the reliability of decision information, an evaluation matrix based on Z-numbers is constructed. Second, a fuzzy best-worst method is applied to determine the weights of criteria. Third, the conditional probability is computed based on classification-based state sets that are obtained by a sorting method. An example regarding the evaluation and selection of new energy enterprises demonstrates the validity and stability of the proposed method. The comparison analysis shows that our proposed method can divide alternatives into different regions efficiently and is less affected by the variation of parameters.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

R-DDQN: Optimizing Algorithmic Trading Strategies Using a Reward Network in a Double DQN

Algorithmic trading is playing an increasingly important role in the financial market, achieving more efficient trading strategies by replacing human decision-making. Among numerous trading algorithms, deep reinforcement learning is gradually replacing traditional high-frequency trading strategies and has become a mainstream research direction in the field of algorithmic trading. This paper introduces a novel approach that leverages reinforcement learning with human feedback (RLHF) within the double DQN algorithm. Traditional reward functions in algorithmic trading heavily rely on expert knowledge, posing challenges in their design and implementation. To tackle this, the reward-driven double DQN (R-DDQN) algorithm is proposed, integrating human feedback via a reward function network trained on expert demonstrations. Additionally, a classification-based training method is employed for optimizing the reward function network. The experiments, conducted on datasets including HSI, IXIC, SP500, GOOGL, MSFT, and INTC, show that the proposed method outperforms all baselines across six datasets and achieves a maximum cumulative return of 1502% within 24 months.

A Novel Communication Time-Delay Cooperative Control Method with Switching Event-Triggered Strategy

A novel communication time-delay classification-based method is designed for nonlinear multiagent systems with the finite-time prescribed performance function. The time-delay phenomenon for communication channels between agents is discussed. Then, an improved time-delay classification method is proposed to broaden the standard of classification mechanism by considering the degree of deviation and relative variation of neighbor agents, rather than classifying the delay time into large time-delay and small time-delay. Based on this, the unified Lyapunov-Krasovskii functional and the finite-time performance function are used to solve the large time-delay phenomenon and ensure that the error is within the preset boundary, respectively. Furthermore, a modified switching event-triggered strategy is put forward to reduce the transmission burden, which considers the impact of tracking error to adjust the threshold condition in real-time. Additionally, all signals of the closed-loop systems are bounded. Eventually, two simulation examples verify the validity of the control strategy.

A Classification-Based Product Selection Method Based on Online Reviews on Multifaceted Attributes

A Classification-Based Product Selection Method Based on Online Reviews on Multifaceted Attributes

Automated fingerprint analysis as a diagnostic tool for the genetic disorder Kabuki syndrome

PurposeEmerging therapeutic strategies for Kabuki syndrome (KS) make early diagnosis critical. Fingerprint analysis as a diagnostic aid for KS diagnosis could facilitate early diagnosis and expand the current patient base for clinical trials and natural history studies. MethodFingerprints of 74 individuals with KS, 1 individual with a KS-like phenotype, and 108 controls were collected through a mobile app. KS fingerprint patterns were studied using logistic regression and a convolutional neural network to differentiate KS individuals from controls. ResultsOur analysis identified 2 novel KS metrics (folding finger ridge count and simple pattern), which significantly differentiated KS fingerprints from controls, producing an area under the receiver operating characteristic curve value of 0.82 [0.75; 0.89] and a likelihood ratio of 9.0. This metric showed a sensitivity of 35.6% [23.73%; 47.46%] and a specificity of 96.04% [92.08%; 99.01%]. An independent artificial intelligence convolutional neural network classification-based method validated this finding and yielded comparable results, with a likelihood ratio of 8.7, sensitivity of 76.6%, and specificity of 91.2%. ConclusionOur findings suggest that automatic fingerprint analysis can have diagnostic use for KS and possible future utility for diagnosing other genetic disorders, enabling greater access to genetic diagnosis in areas with limited availability of genetic testing.

A Modelling Framework of an Intelligent News Recommendation Engine for Financial Analysts

Referring to news articles has become a predominant task in the daily routine of each financial analyst, as it helps to make accurate financial insights. However, as there are thousands of news articles generated daily, finding the most relevant articles becomes a time-consuming task. Hence, this study develops a modelling framework of an intelligent news recommendation engine for financial analysts in a particular financial company, which assists in recommending the most appropriate articles according to analysts' preferences in an efficient and effective manner without tedious browsing.In this study, the data collection phase of the recommendation engine is accomplished by retrieving news articles from online news websites using the web scraping technique. The response variable, analysts’ preference level for each article is manually acquired from a group of financial analysts at a selected financial company. In the analysis phase of the recommendation engine, a classification-based method was utilized where three machine learning models, KNN, SVM, Random Forest, and two deep learning models, LSTM and CNN with Natural Language Processing techniques are experimented to discover the best algorithm. Moreover, the synonym replacement method is employed as a text data augmentation method to address the imbalanced problem in the dataset.As CNN obtains the highest accuracy in comparison to other applied methods it is chosen as the most suitable approach in the analysis phase. Moreover, this study reveals that DL models perform considerably higher performances in the context of news recommendation engines rather than ML approaches. Lastly, this framework can be adjustable for any financial company with minor modifications.

Accurate identification of sludge contamination sources by classification-based PMF and machine learning with consideration of sewer network distribution differences

The application of source identification such as PMF for large-scale pollution source analysis frequently produces ambiguous outcomes. In this study, we utilized a classification-based method to accurately track key pollution sources in the sludge. In the study, we categorized the wastewater treatment plants into two groups: T1 and T2, according to the pipeline network. T1 sewage treatment plants are the main sewage plants in urban areas, covering a large area and connected to industrial wastewater treatment plants for secondary treatment. T2 sewage treatment plants are typically smaller in size and usually responsible for treating sewage in rural or township areas. The PMF analysis indicates that industrial pollution sources contribute 3.4 times more to T1 sludge than to T2 sludge, making industrial pollution the primary factor causing the disparity. The application of Random Forest and Adaboost based on pollutant concentrations for classification and fitting of sludge resulted in the identification of the main pollutants: Zn, Cu, Ni, and Cyanide, which align with characteristic pollutants from the electroplating industry. The GIS analysis shows a significant correlation between the distance of wastewater treatment plants with abnormal environmental risk and electroplating industrial parks, all within a 20 km radius. Indeed, when conducting large-scale pollution source identification studies, utilizing classification-based analysis can effectively improve the accuracy of pollution source identification, leading to more valuable analysis results.

Classification-based Method for Wall Crack Detection System

It is imperative to ensure that building inspectors have adequate resources and tools to conduct their inspections efficiently and effectively. Relying solely on manual labour to check for wall cracks is inconvenient and may prove inefficient and poor use of time and financial recourses. Besides, there are concerns regarding the need for skilled inspectors due to their limited accessibility and the subjective nature of their evaluations. Previously, image processing and artificial intelligence have been independently utilized to identify wall cracks and estimate their width. However, more can be done when integrating these two approaches to produce a comprehensive solution. This study presented a technique to indicate wall cracks utilizing a pre-trained Convolutional Neural Network (CNN) model called Squeezenet. Then, the following image processing can precisely estimate the width of the cracks in pixels. Based on the total models studied, 78% were successfully detected and classified into their respective crack groups. Although 22% of the remaining models were mistakenly classified, the system still managed to detect the presence of cracks in them accurately. This study only considers analyzing projected cracks categorized as minor, moderate and major. Nevertheless, the discussion does not address the translation of pixel approximations into their respective physical measurements.

Deep learning on photoacoustic tomography to remove image distortion due to inaccurate measurement of the scanning radius.

Photoacoustic tomography (PAT) is a non-invasive, non-ionizing hybrid imaging modality that holds great potential for various biomedical applications and the incorporation with deep learning (DL) methods has experienced notable advancements in recent times. In a typical 2D PAT setup, a single-element ultrasound detector (USD) is used to collect the PA signals by making a 360° full scan of the imaging region. The traditional backprojection (BP) algorithm has been widely used to reconstruct the PAT images from the acquired signals. Accurate determination of the scanning radius (SR) is required for proper image reconstruction. Even a slight deviation from its nominal value can lead to image distortion compromising the quality of the reconstruction. To address this challenge, two approaches have been developed and examined herein. The first framework includes a modified version of dense U-Net (DUNet) architecture. The second procedure involves a DL-based convolutional neural network (CNN) for image classification followed by a DUNet. The first protocol was trained with heterogeneous simulated images generated from three different phantoms to learn the relationship between the reconstructed and the corresponding ground truth (GT) images. In the case of the second scheme, the first stage was trained with the same heterogeneous dataset to classify the image type and the second stage was trained individually with the appropriate images. The performance of these architectures has been tested on both simulated and experimental images. The first method can sustain SR deviation up to approximately 6% for simulated images and 5% for experimental images and can accurately reproduce the GTs. The proposed DL-approach extends the limits further (approximately 7% and 8% for simulated and experimental images, respectively). Our results suggest that classification-based DL method does not need a precise assessment of SR for accurate PAT image formation.

Feasibility Evaluation of Online Classification-based Control for Gross Movement in a 2-DoF Prosthetic Arm.

Regression and classification models have been extensively studied to exploit the myoelectric and kinematic input information from the residual limb for the control of multiple degree-of-freedom (DoF) powered prostheses. The gross movement control of above-elbow prostheses is mainly based on regression models which map the available inputs to continuous prosthetic poses. However, the regression output is sensitive to the variation in the input signal. The myoelectric signal variation is usually large due to unintentional muscle contractions, which can deteriorate the user-in-the-loop performance with respect to the offline analysis. Alternatively, the classification models offer the advantage of being more robust to the input signal variation, but they were predominantly used for fine motor functions such as grasping. For gross motor functions, the discrete output may cause issues. Therefore, this work attempts to investigate the feasibility of utilising the classification model to control a 2-DoF transhumeral prosthesis for gross movement. The performance of 6 able-bodied subjects was evaluated in performing reaching and orientation matching tasks with a prosthetic arm in a virtual reality environment. The results were compared with the case of using their intact arms and existing results using the regression model. Our findings indicate that the classification-based method provides comparable performance to the regression model, making it a potential alternative for gross arm movement in multi-DoF prosthetic arms.

S-KMN: Integrating semantic features learning and knowledge mapping network for automatic quiz question annotation

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.

A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images.

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were used for water type 1; [Formula: see text], [Formula: see text], and [Formula: see text] were adopted for water type 2; and [Formula: see text], [Formula: see text], and [Formula: see text] were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05mg/L and 2.24mg/L to 1.75mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.

Conceptual Hierarchy in Child-Directed Speech: Implicit Cues are More Reliable

ABSTRACT In order for children to understand and reason about the world in an adult-like fashion, they need to learn that conceptual categories are organized in a hierarchical fashion (e.g., a dog is also an animal). While children learn from their first-hand observation of the world, social knowledge transmission via language can also play an important role in this learning. Previous studies have documented several cues in parental talk that can help children learn about conceptual hierarchy. However, these studies have used different datasets and methods that have made it difficult to compare the relative usefulness of various linguistic cues to conceptual knowledge and to test whether they scale up to naturalistic speech. Here, we study a large-scale corpus of English child-directed speech – collected in North America and the UK – and used a unified classification-based evaluation method which allowed us to investigate and compare cues that vary in terms of how explicit the information they offer is. We found the more explicit cues to be too sparse or too noisy in child-directed speech, making them unlikely to support robust learning. In contrast, the implicit cues offered a more reliable source of information. Further, we investigated developmental changes from 3 to 6 years of age, and we found no differences in the availability of these cues in the input. Our work confirms the utility of caregiver talk for conveying conceptual information and supporting the development of early taxonomic knowledge. It provides a first step toward a cognitive model that would combine perceptual- and language-based mechanisms, leading to a more comprehensive account of children’s conceptual development.

A classification-based multifractal analysis method for identifying urban multifractal structures considering geographic mapping

Identifying urban multifractal structures are helpful for understanding urban spatial organization patterns as complex systems. Multifractal analysis is a powerful tool to model multifractal structures. However, due to the use of statistical moments to delineate the multifractal spectrum for multifractal analysis, the great majority of existing studies cannot map urban multifractal structures to geographic space. Lack of geographic mapping makes it difficult to interpret the causes of the anomalous scaling characteristics of urban multifractal structures. For the few mappable multifractal structure modeling methods, they model multifractal structures from a global or local perspective that generates inadequate or redundant scaling characteristics. Here, a classification-based multifractal analysis method (CMFA) was proposed to overcome the shortcomings. It classifies the urban areas into zones according to the density of urban elements and builds up the multi-scaling relationships of urban elements for each zone. The corresponding multifractal structures can be mapped according to the spatial distribution of zones across scales. A case study was conducted to identify the multifractal structures of nighttime light in Beijing, China, to verify the CMFA method. In conclusion, when there are abnormal scaling characteristics reflected by the multifractal spectrum, the multifractal structure maps can diagnose the land use problems leading to disordered spatial organization patterns. Urban planners should focus on such problem land parcels and carry out urban renewal to optimize urban spatial structures.

ALScA: A Framework for Using Auxiliary Learning Side-Channel Attacks to Model PUFs

Physical unclonable functions (PUFs) have emerged as potent hardware primitives owing to their intrinsic properties of being secret key-free, clone-proof, and lightweight. However, PUFs cannot avoid the threats of machine learning modeling and side-channel attacks (SCAs). Nevertheless, almost all attacks neglect the correlations between the mathematical model and side-channel models introduced by PUF internal parameters; thus, such attacks fail to exploit related data and struggle in modeling complex PUFs. To address this problem, we propose a framework for using auxiliary learning SCAs to model strong PUFs by learning multiple related tasks together. Side-channel information predictions are introduced as auxiliary tasks to facilitate the primary task of predicting response. The parameters hard for the primary task to learn can be shared by the auxiliary tasks that learn the same parameters more straightforwardly. Based on the proposed framework, we design a specific auxiliary learning power SCA that employs power level prediction as the auxiliary task. The proposed attack is implemented with the hard-parameter sharing and hierarchy sharing deep neural networks. Experimental results demonstrate that the proposed attack succeeds in modeling XOR APUF, MPUF, and iPUF and outperforms the state-of-the-art methods in modeling MPUF and iPUF. We evaluate the influences of task relatedness, architecture, and loss weight ratio. Furthermore, we propose a fine-grained classification-based method to generate the auxiliary task with an enhanced relationship to the primary task. According to the response, the class corresponding to a specific side-channel state is further divided into two subclasses. Experimental results demonstrate that the generated auxiliary task promotes performance and alleviates the adverse effects of improper architecture and parameters.

A New Radio Frequency Distortion Dynamic Range Index for Performance Evaluation

Dynamic range and spurious-free dynamic range are two of the most critical performance indexes in the field of radio frequency (RF). However, the definitions of both the indexes are ambiguous, and their characterization ability is insufficient, resulting in unfair, and even mutually incompatible, performance evaluation in practice. In this study, a new index named radio frequency distortion dynamic range and its corresponding evaluation method are proposed to achieve a fair and detailed dynamic range evaluation by unifying the existing definitions and improving the performance resolution ability. First, the sliding threshold selection method is introduced to replace the classification-based method of dynamic range definition to characterize more details of the dynamic range. Second, a dynamic range evaluation method of “performance body” is proposed to obtain a more comprehensive evaluation by generalizing the current evaluation from being based on a single condition to the one based on scanning critical conditions. Experiments show that the proposed radio frequency distortion dynamic range index with the proposed evaluation method reduces the ambiguity of dynamic range evaluation and can distinguish the performance difference that the current indexes cannot do.

Strong Synergic Growth Inhibition and Death Induction of Cancer Cells by Astragalus membranaceus and Vaccaria hispanica Extract.

We present here a new, classification-based screening method for anti-cancer botanical combinations. Using this method, we discovered that the combination of Astragalus membranaceus and Vaccaria hispanica (AV) has strong synergic anti-proliferative and killing effects on cancer cells. We showed that AV induces the hyper activation of proliferation and survival pathways (Akt and ERK1/2) and strongly downregulates the cell cycle control proteins p21 and p27. Moreover, FACS analyses revealed that AV induces accumulation of cells in G2/M phase, supported by accumulation of cyclin A. Taken together, our results suggest that AV interferes with the cell cycle in cancer cells, leading to accumulation in G2/M and apoptosis. Further studies are needed to validate the generalizability of the anti-cancer effect of the AV combination, to fully understand its mechanism of action and to evaluate its potential as a new anti-cancer treatment.