Classification Model Research Articles

Decision model of public opinion risk in campus social network based on hybrid dynamic deletion and shortest path algorithm

Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment classification model based on the variant of convolutional neural network and recurrent neural network, and secondly, uses attention mechanism to classify the model. improvement of the classification model by using the attention mechanism. The research results show that the average precision rate, recall rate, and F-value of the dynamic deletion-shortest path algorithm are 97.30%, 79.55%, and 87.53%, and the classification speed is 397 KB/s, which is better than the traditional shortest path algorithm. In the classification effect measurement of long text, the accuracy and F-value of the recurrent neural network variant model are above 84%, and the accuracy of the text sentiment classification model with the introduction of the attention mechanism is improved by 3.89% compared to the pre-improvement period. In summary, the dynamic deletion-shortest path algorithm proposed in the study and the sentiment classification model with the introduction of the attention mechanism have superior performance and can provide certain application value for campus social network opinion risk decision-making.

NO classifier prediction of anti neuroinflammatory agents using text mining of 3D molecular fingerprints.

CNS Drug discovery has been challenging due to the lack of clarity on CNS diseases' basic biological and pathological mechanisms. Despite the difficulty, some CNS drugs have been developed based on phenotypic effects. Herein, we propose a phenotype-structure relationship model, which predicts an anti-neuroinflammatory potency based on 3D molecular structures of the phenotype-active or inactive compounds without specifying targets. For this chemo-centric study, a predictive model of the nitric oxide (NO) inhibitory potency in hyper-activated microglia is built from the 548 agents, which were collected from 95 research articles (28 substructures consisting of natural products and synthetic scaffolds) and doubly externally validated by the agents of 9 research articles as third set. 3D Structures (multi-conformer ensemble) of every agent were encoded into the E3FP molecular fingerprint of the Keiser group as a 3D molecular representation. The location information of the molecular fingerprints could be learned and validated to classify the inhibitory potency of compounds (IC50 cut-off between the active and inactive: 37.1 µM): (1) multi-layer perceptron (MLP) (AUC-CV: 0.997, AUC-Test: 0.992), (2) recurrent neural network (RNN) (AUC-CV: 0.999, AUC-Test: 0.995), and (3) convolutional neural network (CNN) (AUC-CV: 0.998, AUC-Test: 0.994). The high performance of these models was compared with that of four classical machine classification models (Logistic, Ridge, Lasso, and Naïve Bayes). We named the binary classification models NO-Classifier. Independent test set validation and decision region analysis of the independent test set doubly demonstrated NO-Classifier effectively discerned the anti-inflammatory potency of testing compounds in inflammatory cell phenotype with the webserver in https://no-classifier.onrender.com.

Gaussian mixture model and Bayesian convolutional neural network for abnormal musculoskeletal radiographs classification

Gaussian mixture model and Bayesian convolutional neural network for abnormal musculoskeletal radiographs classification

Untargeted metabolomics to detect and identify plasma metabolic signatures associated with intracranial aneurysm and its rupture.

The biological basis for metabolic differences between unruptured and ruptured intracranial aneurysm (UIA and RIA) populations and their potential role in triggering IA rupture remain unclear. The aim of this study was to analyze the plasma metabolic profiles of patients with UIA and RIA using an untargeted metabolomic approach and to develop a model for early rupture classification. Plasma samples were analyzed using an ultra-high-performance liquid chromatography high-resolution tandem mass spectrometry-based platform. Least absolute shrinkage and selection operator regression and random forest machine learning methods were employed for metabolite feature selection and predictive model construction. Among 49 differential plasma metabolites identified, 31 were increased and 18 were decreased in the plasma of RIA patients. Five key metabolites-canrenone, piperine, 1-methyladenosine, betaine, and trigonelline-were identified as having strong potential to discriminate between UIA and RIA patients. This combination of metabolites demonstrated high diagnostic accuracy, with an area under the curve exceeding 0.95 in both the training and validation datasets. Our finding highlights the significance of plasma metabolites as potential biomarkers for early detection of IA rupture risk, offering new insights for clinical practice and future research on IA management.

A Hybrid Fuzzy Evaluation Method for Quantitative Risk Classification of Barrier Lakes Based on an AHP Method Extended by D Numbers

The risk classification of barrier lakes is the key to conducting emergency treatment in a scientific manner. However, risk classification faces difficulties such as a short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly, and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an eight-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lakes and intelligent identification of particles on the surface of barrier bodies were developed, which realized the rapid acquisition of eight-factor evaluation index information in an emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of eight factors. The risk assessment on 15 barrier lakes such as Tangjiashan barrier lake shows that the conclusions drawn for the risk classification method proposed in this paper are basically consistent with those of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss impacts on the risk level of barrier lakes and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

Initial Development of Automated Machine Learning-Assisted Prediction Tools for Aryl Hydrocarbon Receptor Activators

Background: The aryl hydrocarbon receptor (AhR) plays a crucial role in immune and metabolic processes. The large molecular diversity of ligands capable of activating AhR makes it impossible to determine the structural features useful for the design of new potent modulators. Thus, in the field of drug discovery, the intricate nature of AhR activation necessitates the development of novel tools to address related challenges. Methods: In this study, quantitative structure–activity relationship (QSAR) models of classification and regression were developed with the objective of identifying the most effective method for predicting AhR activity. The initial dataset was obtained by combining the ChEMBL and WIPO databases which contained 978 molecules with EC50 values. The predictive models were developed using the automated machine learning platform mljar according to a 10-fold cross validation (10-CV) testing procedure. Results: The classification model demonstrated an accuracy value of 0.760 and F1 value of 0.789 for the test set. The root-mean-squared error (RMSE) was 5444, and the coefficient of determination (R2) was 0.208 for the regression model. The Shapley Additive Explanations (SHAP) method was then employed for a deeper comprehension of the impact of the variables on the model’s predictions. As a practical application for scientific purposes, the best performing classification model was then used to develop an AhR web application. This application is accessible online and has been implemented in Streamlit. Conclusions: The findings may serve as a foundation in prompting further research into the development of a QSAR model, which could enhance comprehension of the influence of ligand structure on the modulation of AhR activity.

AdaBoost model for rockburst intensity prediction considering class differences and quantitative characterization of misclassification difference.

With the resource development gradually into the deep, rock explosion phenomenon is more and more frequent. The suddenness and harmfulness of rockbursts threaten the safe development of underground resources. In order to more accurately predict the possible intensities of rockbursts in specific rock conditions and stress environments, the rock mechanical and stress parameters between different intensities of rockbursts are further explored, an AdaBoost model considering the differences in the hierarchy is established, and a Flash Hill-Climbing method is proposed to optimize the hyperparameters in the classification model. Finally, a misclassification difference index (MCDI) is defined to quantitatively characterize the severity of misclassification. The results show that the accuracy of the Improved AdaBoost model is about 2.3% higher than that of the Normal AdaBoost model, and the Misclassification Difference Index (MCDI) of the Normal AdaBoost model is 05, while the the Improved AdaBoost model is 00. The model can provide a theoretical reference for rockburst prediction.

Deep active learning for multi label text classification.

Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC. Training a high-quality deep MLTC model typically demands large-scale labeled data. And comparing with annotations for single-label data samples, annotations for multi-label samples are typically more time-consuming and expensive. Active learning can enable a classification model to achieve optimal prediction performance using fewer labeled samples. Although active learning has been considered for deep learning models, there are few studies on active learning for deep multi-label classification models. In this work, for the deep MLTC model, we propose a deep Active Learning method based on Bayesian deep learning and Expected confidence (BEAL). It adopts Bayesian deep learning to derive the deep model's posterior predictive distribution and defines a new expected confidence-based acquisition function to select uncertain samples for deep MLTC model training. Moreover, we perform experiments with a BERT-based MLTC model, where BERT can achieve satisfactory performance by fine-tuning in various classification tasks. The results on benchmark datasets demonstrate that BEAL enables more efficient model training, allowing the deep model to achieve training convergence with fewer labeled samples.

Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: aretrospective study.

The aim of this study was to develop and assess aradiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). An analysis was performed retrospectively on agroup of 121 patients who received ahistological diagnosis of GIST. They were then divided into two sets, with85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing arandom forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with apathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct aprognostic prediction model. The clinical model (ModelC) displayed limited predictive efficacy in the context of GIST. Conversely, aradiomics model (ModelR) incorporating five parameters exhibited robust discriminative capabilities across both the training and validation sets, yielding area under the ROC curve (AUC) values of 0.893 (95% confidence interval [CI]: 0.807-0.949) and 0.855 (95% CI: 0.732-0.978), respectively. The F1max scores derived from the P‑R curves were 0.741 and 0.842 for the training and validation sets, respectively. Noteworthy was the exclusion of the two-dimensional tumor diameter and tumor location when constructing ahybrid model (ModelCR) that amalgamated radiomics and clinical features. ModelR demonstrated asubstantially enhanced discriminative capacity in the training set compared with ModelC (p < 0.005). The net reclassification improvement (NRI) corroborated the superior performance of ModelR over ModelC, thereby enhancing diagnostic accuracy and clinical applicability. Patients in the high-risk group had significantly worse recurrence-free survival (RFS, p < 0.001) and overall survival (OS, p = 0.004), and the radiomics signature is an independent risk factor for RFS. The extended model incorporating the radiomics signature outperformed the baseline model in terms of risk assessment accuracy (p < 0.001). Our investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.

Comparative Performance Analysis of Ensemble Models for Breast Cancer Classification

Breast cancer remains a prevalent global health issue, accounting for approximately 2.3 million new cases and 670,000 deaths worldwide in 2022. Early detection and accurate diagnosis are crucial to improving patient outcomes, as delayed identification can lead to severe complications. Advances in machine learning (ML) have facilitated improvements in cancer diagnosis, with various algorithms enhancing predictive accuracy. This study proposes a novel ensemble model for breast cancer classification, utilizing 31 features from the University of Wisconsin Breast Cancer dataset. We applied six algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and XGBoost—and combined them with ensemble techniques, specifically Hard Voting, to develop a high-accuracy model. The model was evaluated on classification performance metrics, achieving improvements in accuracy, precision, recall, and F1 score. Results indicate that the proposed ensemble model outperforms individual classifiers and other ensembles, showing potential as a reliable tool for early breast cancer detection.

An intellectual model of pest detection and classification using enhanced optimization-assisted single shot detector and graph attention network

An intellectual model of pest detection and classification using enhanced optimization-assisted single shot detector and graph attention network

BCDNet: A Deep Learning Model with Improved Convolutional Neural Network for Efficient Detection of Bone Cancer Using Histology Images

Among the several types of cancer, bone cancer is the most lethal prevailing in the world. Its prevention is better than cure. Besides early detection of bone cancer has potential to have medical intervention to prevent spread of malignant cells and help patients to recover from the disease. Many medical imaging modalities such as histology, histopathology, radiology, X-rays, MRIs, CT scans, phototherapy, PET and ultrasounds are being used in bone cancer detection research. However, hematoxylin and eosin stained histology images are found crucial for early diagnosis of bone cancer. Existing Convolutional Neural Network (CNN) based deep learning techniques are found suitable for medical image analytics. However, the models are prone to mediocre performance unless configured properly with empirical study. Within this article, we suggested a framework centered on deep learning for automatic bone cancer detection. We also proposed a CNN variant known as Bone Cancer Detection Network (BCDNet) which is configured and optimized for detection of a common kind of bone cancer named Osteosarcoma. An algorithm known as Learning based Osteosarcoma Detection (LbOD). It exploits BCDNet model for both binomial and multi-class classification. Osteosarcoma-Tumor-Assessment is the histology dataset used for our empirical study. Our the outcomes of the trial showed that BCDNet outperforms baseline models with 96.29% accuracy in binary classification and 94.69% accuracy in multi-class classification.

Deep learning-based algorithm for automatic quantification of nigrosome-1 and Parkinsonism classification using susceptibility map-weighted MRI.

To develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism using susceptibility map-weighted imaging (SMwI). We retrospectively collected 450 participants (210 with idiopathic Parkinson's disease [IPD] and 240 individuals in the control group) for training data between November 2022 and May 2023, and 237 participants (168 with IPD, 58 with essential tremor, and 11 with drug-induced Parkinsonism) for validation data between July 2021 and January 2022. SMwI data were reconstructed from multi-echo GRE. Diagnostic performance for diagnosing IPD was assessed using deep learning-based automatic quantification (Heuron NI) and classification (Heuron IPD) model. Reference standard for IPD was based on 18F-FP-CIT PET finding. Additionally, the correlation between the H&Y stage and volume of nigral hyperintensity in patients with IPD was assessed. Quantification of nigral hyperintensity using Heuron NI showed AUC of 0.915 (95% CI, 0.872-0.947) and 0.928 (95% CI, 0.887-0.957) on the left and right, respectively. Classification of nigral hyperintensity abnormality using Heuron IPD showed AUC of 0.967 (95% CI, 0.881-0.991) and 0.976 (95% CI, 0.948-0.992) on the left and right, respectively. H&Y score ≥ 3 showed significant smaller nigral hyperintensity volume (1.43 ± 1.19 mm3) compared to H&Y score 1-2.5 (1.98 ± 1.63 mm3; p = 0.008). Our deep learning-based model proves rapid, accurate automatic quantification of nigral hyperintensity, facilitating IPD diagnosis, symptom severity prediction, and patient stratification for personalized therapy. Further study is warranted to validate the findings across various clinical settings. IPD = Idiopathic Parkinson's disease; SN = substantia nigra; SMwI = susceptibility map weighted imaging; QSM = quantitative susceptibility mapping; CNN = convolutional neural network; ICV = intracranial volume.

Surface Roughness Prediction Using ANN Regression and Classification Model for S30C Alloy Metals Parts Manufactured by Laser Powder Bed Fusion

Surface Roughness Prediction Using ANN Regression and Classification Model for S30C Alloy Metals Parts Manufactured by Laser Powder Bed Fusion

Identifying Alpine Lakes with Shoreline Features

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these areas pose significant challenges to accurate detection. This paper proposes a method that leverages the high precision of deep learning for small lake and lake boundary extraction combined with deep learning to eliminate noise and errors in the identification results. Using Sentinel-2 data, we accurately identified and delineated alpine lakes in the eastern Himalayas. A total of 2123 lakes were detected, with an average lake area of 0.035 km². Notably, 76% of these lakes had areas smaller than 0.01 km². The slope data is crucial for the lake classification model in eliminating shadow noise. The accuracy of the proposed lake classification model reached 97.7%. In the identification of small alpine lakes, the recognition rate of this method was 96.4%, significantly surpassing that of traditional deep learning approaches. Additionally, this method effectively eliminated most shadow noise present in water body detection results obtained through machine learning techniques.

Survival prediction in patients with head and neck squamous cell carcinoma and novel mechanistic insights of S100A8/A9.

S100A8/A9, an innate immune protein, significantly regulates inflammatory processes and immune responses. While S100A8/A9 has been linked to various diseases, its association with head and neck squamous cell carcinoma (HNSCC) remains unclear. Samples from the Cancer Genome Atlas (TCGA) were categorized into groups with low and high expression of S100A8/A9. R software, Sangerbox, UALCAN, GEPIA2, STRING, Cytoscape, TCGC Data Portal, miRcode, OncomiR and ENCORI databases were used to comprehensively study the expression, interactome and mutational profiles of S100A8/A9 and associated mechanism in HNSCC. The proteins S100A8/A9 were found to be associated with processes of 'epithelium development', 'regulating pluripotency of stem cells' and 'regulation of immune system'. The most frequent mutation observed in the S100A8 protein was E93K (3/37, MU4401889), while for the S100A9 protein, it was R10C (4/37, MU4633862). Furthermore, the group expressing high levels of S100A8/A9 showed increased infiltration by dendritic cells and neutrophils, but decreased infiltration by M2 macrophages, compared to the group with low S100A8/A9 expression. S100A8/A9 was also found to interact with a variety of mRNA transcripts, several of which were involved in initiation and progression of HNSCC. Through LASSO-Cox method, 20 genes (CALML5, MSX1, FZD3, STC2, SLC2A3, TMEM198B, DYNC1I1, SPHK2, ALMS1.IT1, SPPL2B, PDGFA, BCORL1, TEX101, SGCE, DNAJC12, RRN3P1, HOXB9, TMEM150C, METTL7B and PSPN) were screening to construct a prognostic model to distinguish favorable and poor prognosis of HNSCC patients. Besides, our prognostic model was also validated in GSE41613 cohort. S100A8/A9 may be a promising marker for the diagnostic and prognostic assessment of the HNSCC patients. Based on these insights, we have devised a new classification model for HNSCC, which has the potential to enhance the management and personalized treatment of HNSCC patients. The model should also be further optimized through the expansion of sample size and implemented experimental studies in future research.

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.

BCDPi: An Interpretable Multitask Deep Neural Network Model for Predicting Chemical Bioconcentration in Fish.

Predicting the bioconcentration of chemical compounds plays a crucial role in assessing environmental risks and toxicological impacts. This study presents a robust multitask deep learning model for predicting the bioconcentration potential. The model can predict the bioconcentration of compounds in multiple categories, including non-bioconcentrative (non-BC), weakly bioconcentrative (weak-BC), and strongly bioconcentrative (strong-BC). We also employed the SHapley Additive exPlanations (SHAP) technology for the model interpretation. The binary classification models (non-BC vs BC and weak-BC vs strong-BC) showed good predictive performance, which achieved accuracy values over 90% and area under the curve (AUC) values with 0.95. The final ternary classification model provided an overall accuracy with 91.11%. Comparative analysis of molecular physicochemical properties showed that the importance of molecular weight, polar surface area, solubility, and hydrogen bonding are important for chemical bioconcentration. Besides, we identified eight structural alerts responsible for chemical bioconcentration. We made the model available as an online tool named BCdpi-predictor, which is accessible at http://bcdpi.sapredictor.cn/. Users can predict the bioconcentration potential of chemical compounds freely. The model has significant implications for environmental policy and regulatory frameworks, such as REACH, by providing a more accurate and interpretable method for assessing chemical risks. We hope that the results of this study can provide helpful tools and meaningful information for chemical bioconcentration prediction in environmental risk assessment.

Combined graph convolutional networks with a multi-connection pattern to identify tremor-dominant Parkinson's disease and essential tremor with resting tremor.

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans.

The purpose of this study is to quantify the three-dimensional (3D) structural morphology, bone mineral density (BMD) distribution, and mechanical properties of different China-Japan Friendship Hospital (CJFH) classification types and assist clinicians in classifying necrotic femurs accurately. In this study, 41 cases were classified as types L2 and L3 based on CT images. Then, 3D Statistical Shape and Appearance Models (SSM and SAM) were established, and 80 principal component (PC) modes were extracted from the SSM and SAM as the candidate features. The bone strength of each case was also calculated as the candidate feature using finite element analysis (FEA). Support vector machine (SVM) and Extreme Gradient Boosting (XGBoost) were used to establish 10 machine learning models. Feature selection methods were used to screen the candidate features. The performance of each model was evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. This resulted in a SVM model for CJFH classification with the performance: accuracy of 87.5%, sensitivity of 85.0%, specificity of 76.0%, and AUC of 94.2%. This study provided effective machine learning models for assisting in diagnosing CJFH types, increasing the objectivity of the diagnosis. They may have great potential for application in clinical assessments of CJFH classification.