Data Classification Algorithms Research Articles

Noncontact measurement of rectangular splines shaft based on line-structured light

Abstract The rectangular spline shaft, a typical type of shaft-tooth component, plays a significant role in mechanical transmissions. Existing methods for detecting size and positional tolerance in spline shafts often rely on contact-based measurement tech-niques, including specialized gauges and coordinate measuring machines (CMMs). To enhance the measurement efficiency, this paper proposes a method for measuring based on line-structured light. Firstly, a classification algorithm for data points on large and small cylindrical surfaces and keyway surface of spline shaft is established, contributing to the automatic measurement. Secondly, a coaxiality error measurement model is established based on the overall least squares method, improving the measurement accuracy. Finally, a measurement model for key width and positional tolerance is established through the rotation of the spline axis. In experiments, the size and positional tolerance of the spline shaft obtained using this method are compared with measurements by CMM, meeting the general machining accuracy requirements.

Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy.

Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols.

A Statistical and AI Analysis of the Frequency Spectrum in the Measurement of the Center of Pressure Track in the Seated Position in Healthy Subjects and Subjects with Low Back Pain.

Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and consideration of the results in the spectrum in conjunction with an AI-supported evaluation could represent an alternative diagnostic method quantifying neuromuscular control. In a prospective cross-sectional study, 188 subjects aged between 18 and 60 years were recruited and divided into two groups: "LowBackPain" vs. "Healthy". Subsequently, measurements of postural control in a seated position were carried out for 60 s using a modified balance board. A spectrum per trail was calculated using the measured CoP tracks in the range from 0.01 to 10 Hz. Various algorithms for data classification and prediction of these classes were tested for the parameter combination with the highest proven static influence on the parameter pain. The best results were found in a frequency spectrum of 0.001 Hz and greater than 1 Hz. After transforming the track from the time domain to the image domain for representation as power density, the influence of pain was highly significant (effect size 0.9). The link between pain and gender (p = 0.015) and pain and height (p = 0.012) also demonstrated significant results. The assessment of postural control in a seated position allows differentiation between "LowBackPain" and "Healthy" subjects. Using the AI algorithm of neural networks, the data set can be correctly differentiated into "LowBackPain" and "Healthy" with a probability of 81%.

Real And Fake Job Post Detection Using Machine Learning

The transition to online platforms has streamlined processes, reducing manual efforts. Job postings, now predominantly online, offer companies a broader reach for talent acquisition. However, amidst legitimate postings, fraudulent ones exist. This study aims to distinguish between real and fake job postings using machine learning techniques with optimal accuracy. Employing various data mining methods and classification algorithms—Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron—we predict job authenticity. Supervised machine learning techniques guide dataset analysis, including variable identification and univariate, bivariate, and multivariate analyses, alongside handling missing values. Comprehensive data validation, cleaning, preparation, and visualization are conducted. Our experimentation, utilizing the Employment Scam Aegean Dataset (EMSCAD) with 17,881 samples, informs our approach. Ultimately, this research aims to enhance job seekers' security by effectively identifying fraudulent job postings. remove the writing issues. Key Words: Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron.

Feature selection based on dynamic crow search algorithm for high-dimensional data classification

High-dimensional biomedical data plays an important role in disease diagnosis and classification. To analyze the high-dimensional biomedical data, machine learning algorithms are widely used. However, a great amount of redundant, irrelevant or weak correlation features are prevalent in these datasets, which significantly deteriorate the capability of machine learning techniques. It is necessary to select the most informative features and discard the useless ones for enhancing the classification accuracy and reducing the dimensionality. To address this issue, an improved Crow Search Algorithm (CSA) which is known as the Dynamic CSA (DCSA) is proposed to find the optimal feature subset in high-dimensional classification tasks. In DCSA, three modifications are investigated. Firstly, we propose a dynamic bi-level awareness probability to guide the transition between global search and local search. Consequently, the balance between exploration and exploitation is improved. Secondly, levy flight with a unfixed step length control parameter is employed as the global search and the random search mechanism of the original CSA is abandoned. Thirdly, a dynamic flight length strategy is adopted to enhance the local search and speed up convergence. All in all, DCSA is developed as a wrapper feature selection model, where KNN classifier is applied as the feature subset evaluator. The performance of DCSA is measured on seven high-dimensional biomedical datasets. Experimental results show that DCSA outperforms other state-of-the-art methods. Specifically, DCSA achieved the highest average accuracy in six data. In terms of numbers of selected features, DCSA gained the smallest subset size in a seven data. And for SRBCT data, the prediction accuracy of DCSA is at 100%.

MUMA: a multi-omics meta-learning algorithm for data interpretation and classification.

Multi-omics data integration is a promising field combining various types of omics data, such as genomics, transcriptomics, and proteomics, to comprehensively understand the molecular mechanisms underlying life and disease. However, the inherent noise, heterogeneity, and high dimensionality of multi-omics data present challenges for existing methods to extract meaningful biological information without overfitting. This paper introduces a novel Multi-Omics Meta-learning Algorithm (MUMA) that employs self-adaptive sample weighting and interaction-based regularization for enhanced diagnostic performance and interpretability in multi-omics data analysis. Specifically, MUMA captures crucial biological processes across different omics layers by learning a flexible sample reweighting function adaptable to various noise scenarios. Additionally, MUMA incorporates an interaction-based regularization term, encouraging the model to learn from the relationships among different omics modalities. We evaluate MUMA using simulations and eighteen real datasets, demonstrating its superior performance compared to state-of-the-art methods in classifying biological samples (e.g., cancer subtypes) and selecting relevant biomarkers from noisy multi-omics data. As a powerful tool for multi-omics data integration, MUMA can assist researchers in achieving a deeper understanding of the biological systems involved. The source code for MUMA is available at https://github.com/bio-ai-source/MUMA.

An ensemble method with a hybrid of genetic algorithm and K-prototypes algorithm for mixed data classification

Due to challenges posed by mixed data clustering, this study aims to introduce an innovative clustering-based classification algorithm that possesses the advantages of both classification and clustering techniques for mixed data analysis. The proposed algorithm employs the K-prototypes algorithm with a genetic algorithm to optimize weights and centroids and utilizes the bagging method to build multiple classifiers, thereby enhancing classification performance. Furthermore, it incorporates four mutation mechanisms, including Gaussian, Cauchy, Levy, and single-point mutations, to explore optimal solutions. This study suggests using a 20% sampling ratio for the bootstrap sampling in the proposed algorithm. This ratio has been proven to be sufficient for achieving good classification performance while reducing computational time. Experimental results indicate that the proposed algorithm outperforms benchmark classifiers, demonstrating superior classification performance across five performance indicators. In addition, the loan eligibility case study offers valuable insights into applying the proposed algorithm in real-world scenarios, demonstrating that the proposed algorithm can achieve superior classification performance compared to other algorithms. It also offers managerial implications to help different industries and fields understand the appropriate timing and scenarios for implementing the algorithm.

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

Terahertz time-domain spectroscopy (THz-TDS) has been widely used for food and drug identification. The classification information of a THz spectrum usually does not exist in the whole spectral band but exists only in one or several small intervals. Therefore, feature selection is indispensable in THz-based substance identification. However, most THz-based identification methods empirically intercept the low-frequency band of the THz absorption coefficients for analysis. In order to adaptively find out important intervals of the THz spectra, an interval-based sparse ensemble multi-class classifier (ISEMCC) for THz spectral data classification is proposed. In ISEMCC, the THz spectra are first divided into several small intervals through window sliding. Then the data of training samples in each interval are extracted to train some base classifiers. Finally, a final robust classifier is obtained through a nonnegative sparse combination of these trained base classifiers. With l1 -norm, two objective functions that based on Mean Square Error (MSE) and Cross Entropy (CE) are established. For these two objective functions, two iterative algorithms based on the Alternating Direction Method of Multipliers (ADMM) and Gradient Descent (GD) are built respectively. ISEMCC transforms the problem of interval feature selection and decision-level fusion into a nonnegative sparse optimization problem. The sparse constraint ensures only a few important spectral segments are selected. In order to verify the performance of the proposed algorithm, comparative experiments on identifying the origin of Bupleurum and the harvesting year of Tangerine peel are carried out. The base classifiers used by ISEMCC are Support Vector Machine (SVM) and Decision Tree (DT). The experimental results demonstrate that the proposed algorithm outperforms six typical classifiers, including Random Forest (RF), AdaBoost, RUSBoost, ExtraTree, and the two base classifiers, in terms of classification accuracy.

Predicting the outcome of regional development projects using machine learning

<p><span lang="EN-US">Morocco, in its pursuit of inclusive and sustainable territorial development, initiated the advanced regionalization experiment over six years ago. The primary challenge facing government officials today is the management of a burgeoning number of regional development projects. In this article we developed a predictive model based on artificial intelligence and Machine Learning to predict the outcomes of regional development projects, in order to identify the risks associated with their potential failure, and anticipate their impact. To accomplish this, we implemented various data mining techniques and classification algorithms. We collected and analyzed data from past and ongoing regional development projects, considering diverse factors that influence their success or failure. Through rigorous experimentation, we assessed the effectiveness of different predictive models. Our findings reveal that the Random Forest classifier stands out as an efficient algorithm for predicting the outcomes of regional development projects. This research contributes to the broader discourse on the practical implementation of artificial intelligence in public policy and regional development, showcasing its potential to optimize resource allocation, and alleviate the burden of repetitive administrative tasks for organizationsoperating with limited resources.</span></p>

An Image Classification Method of Unbalanced Ship Coating Defects Based on DCCVAE-ACWGAN-GP

Affected by the improper operation of the workers, environmental changes during drying and curing or the quality of the paint itself, diverse defects are produced during the process of ship painting. The traditional defect recognition method relies on expert knowledge or experience to detect defects, which is not conducive to ensuring the effectiveness of defect recognition. Therefore, this paper proposes an image generation and recognition model which is suitable for small samples. Based on a deep convolutional neural network (DCNN), the model combines a conditional variational autoencoder (DCCVAE) and auxiliary conditional Wasserstein GAN with gradient penalty (ACWGAN-GP) to gradually expand and generate various coating defect images for solving the overfitting problem due to unbalanced data. The DCNN model is trained based on newly generated image data and original image data so as to build a coating defect image classification model suitable for small samples, which is conducive to improving classification performance. The experimental results showed that our proposed model can achieve up to 92.54% accuracy, an F-score of 88.33%, and a G mean value of 91.93%. Compared with traditional data enhancement methods and classification algorithms, our proposed model can identify various defects in the ship painting process more accurately and consistently, which can provide effective theoretical and technical support for ship painting defect detection and has significant engineering research value and application prospects.

Classified Volatile Organic Compound Detection using Data Classification Algorithms

Sensors are becoming smaller and less expensive, sparking interest in assessing vast volumes of sensor data. Meanwhile, the emergence of machine learning has led to the development of technologies that have a substantial impact on our lives. Machine learning models are often used to produce accurate, real-time predictions even in the presence of noisy sensed data. In this study, a Volatile Organic Compound (VOC) categorization system based on sensor data collected from a sensor array was developed. The most difficult challenge posed in the sensor array was the detection of the type of VOC. It is feasible to categorize VOCs brought on by applying data classification algorithms to data collected from sensor devices. In this work, we used data from the classification algorithms Decision Tree (DT), Naive Bayes (NB), and Linear Regression (LR) on a developed linear sensor array and their classification accuracy was compared. Four different VOCs were evaluated: acetone (C3H6O), benzene (C6H6), ethanol (C2H5OH), and toluene (C6H5CH3). The acquired classification accuracy reached 95.65% with the LR algorithm.

Quantum algorithms for state preparation and data classification based on stabilizer codes

Quantum algorithms for state preparation and data classification based on stabilizer codes

Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture

<span>Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.</span>

Research and Application of Data Classification Algorithm Based on Deep Learning

Research and Application of Data Classification Algorithm Based on Deep Learning

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models.

Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network- Gradient Penalty Model.

Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.

Ecological Thinking about the Metaverse from a Posthumanist Perspective

ABSTRACT The metaverse is a burgeoning technological domain, ranging from data-driven algorithms to advanced artificial intelligence. Immersive virtual reality does not reflect the ecological crisis in fictional worlds through interactivity. In the footsteps of Félix Guattari, this article elaborates on ecological thinking in three categories, each of which reflects the ecological risk and potential positive improvement of the metaverse. In the first category, environmental ecology, the article argues that the technological infrastructure of the metaverse is intensely power-consuming and materialises data and information fluxes. Moreover, the structure of digitised societies promotes intangible production. In the second category, social ecology, it argues that cognitive capitalism further stimulates consumerism because of big data classification and user-targeted algorithms and further amplifies climate change due to the empowered logistics industry. Additionally, capitalism causes continuous competence in the culture industry, leading to a high elimination rate of device production and low efficiency of intellectual labour. In the last category, mental ecology, the article argues that by precisely targeting users, the attention economy of the metaverse empowers individuals who are eager to pursue valorisation. These digital dwellers overlook ecological issues and the foundations of their virtual lives. In the posthumanist approach, humans are flattened by capitalism and become surplus value as natural forces. Humans’ minds are alienated from the idea that nature and humans coexist. Instead, the metaverse is directed toward individual power that immerses itself in a capital-driven network of information technology. This article proposes the merit of forming a nature-culture continuum to promote an ecological ideology in the metaverse. On the one hand, the nature-culture continuum of the metaverse spreads ecological knowledge and praxis via interactivity. On the other hand, the key mediation of nature-human metabolism through human labour contributes to environmentally friendly products, which cultivate an ecological culture of consumerism. Thus, the metaverse functions in a posthumanist context that consumes nature and humans for profit; however, the metaverse can also arouse thoughts of sustainability.

TB-MFCC multifuse feature for emergency vehicle sound classification using multistacked CNN – Attention BiLSTM

Vehicles equipped for emergencies like ambulances, fire engines, and police cruisers play a vital role in society by responding quickly to emergencies and helping to prevent loss of life and maintain order. Vehicle sound identification and classification are very important in the cities to identify emergency vehicles easily and to clear the traffic effectively. Convolutional Neural Network plays an important role in the accurate prediction of vehicles during an emergency. The main motive of this paper is to develop a suitable model and algorithms for data augmentation, feature extraction, and classification. The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. First, in the proposed signal augmentation, each audio signal uses noise injection, stretching, shifting, and pitching separately and this process increases the number of instances in the dataset. The proposed augmentation reduces the overfitting problem in the network. Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC), Root Mean Square (RMS), Chroma, and Tempogram to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE) of the model during classification. Finally, the proposed Multi-stacked Convolutional Neural Network (MCNN) with Attention-based Bidirectional Long Short Term Memory (A-BiLSTM) improves the nonlinear relationship among the features. The proposed Pooled Multifuse Feature Augmentation (PMFA) with MCNN & A-BiLSTM increases the accuracy (98.66 %), reduces the False Positive Rate (FPR) by 1.01 %, and loss (0 %). Thus the model predicts the sound without overfitting, underfitting, and vanishing gradient problems.

Economic Disruptions in Repayment of Peer Loans

Economic disruptions can alter the likelihood of defaults on peer-to-peer loans, causing those impacted to adjust. The option to declare economic hardship and temporarily reduce the payment burden can provide some relief. When this occurs, the borrower’s financial qualifications have changed. The qualities instrumental in successfully securing the original loan terms must be reanalyzed to manage risk. This is a critical point in the life of the loan because the declaration of financial hardship can signal that the borrower’s ability to repay has diminished. We present a novel default detection scheme for borrowers experiencing an economic disruption based on the Two-Class Support Vector Machine, a data classification algorithm for supervised learning problems. The method utilizes data from actual loan records (15,355 loans from 2016 through 2020), specifically from borrowers who declared economic hardship. We provide a detailed description of the default detection process and present results that show defaults among borrowers experiencing financial hardship can be predicted accurately.

Band selection using hybridization of particle swarm optimization and crow search algorithm for hyperspectral data classification

Band selection using hybridization of particle swarm optimization and crow search algorithm for hyperspectral data classification