F-measure Research Articles

Enhancing Web Security through Machine Learning-based Detection of Phishing Websites

The rise of cyberattacks has led to an increase in the creation of fake websites by attackers, who use these sites for advertising products, transmit malware, or steal valuable login credentials. Phishing, the act of soliciting sensitive information from users by masquerading as a trustworthy entity, is a common technique used by attackers to achieve their goals. Spoofed websites and email spoofing are often used in phishing attacks, with spoofed emails redirecting users to phishing websites in order to trick them into revealing their personal information. Traditional solutions for detecting phishing websites rely on signature-based approaches that are not effective in detecting newly created spoofed websites. To address this challenge, researchers have been exploring machine-learning methods for detecting phishing websites. In this paper, we suggest a new approach that combines the use of blacklists and machine learning techniques such that a variety of powerful features, including domain-based features, abnormal features, and abnormal features based on URLs, HTML, and JavaScript, to rank web pages and improve classification accuracy. Our experimental results show that using the proposed approach, the random forest classifier offers the best accuracy of 93%, with FPR and FNR as 0.12 and 0.02, with a Precision of 90%, Recall of 97% an F1 Score of 93%, and MCC of 0.85.

An integrated framework for aspect category-based sentiment analysis using adaptive feature selection and category-aware decision fusion strategies

Purpose Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework. Design/methodology/approach The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks. Findings Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines. Originality/value This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.

Transformer-based fusion model for mild depression recognition with EEG and pupil area signals.

Early detection and treatment are crucial for the prevention and treatment of depression; compared with major depression, current researches pay less attention to mild depression. Meanwhile, analysis of multimodal biosignals such as EEG, eye movement data, and magnetic resonance imaging provides reliable technical means for the quantitative analysis of depression. However, how to effectively capture relevant and complementary information between multimodal data so as to achieve efficient and accurate depression recognition remains a challenge. This paper proposes a novel Transformer-based fusion model using EEG and pupil area signals for mild depression recognition. We first introduce CSP into the Transformer to construct single-modal models of EEG and pupil data and then utilize attention bottleneck to construct a mid-fusion model to facilitate information exchange between the two modalities; this strategy enables the model to learn the most relevant and complementary information for each modality and only share the necessary information, which improves the model accuracy while reducing the computational cost. Experimental results show that the accuracy of the EEG and pupil area signals of single-modal models we constructed is 89.75% and 84.17%, the precision is 92.04% and 95.21%, the recall is 89.5% and 71%, the specificity is 90% and 97.33%, the F1 score is 89.41% and 78.44%, respectively, and the accuracy of mid-fusion model can reach 93.25%. Our study demonstrates that the Transformer model can learn the long-term time-dependent relationship between EEG and pupil area signals, providing an idea for designing a reliable multimodal fusion model for mild depression recognition based on EEG and pupil area signals.

Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.

Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisition gradient echo" (MPRAGE). We hypothesize that this better conspicuity leads to high-quality annotation (HAQ), enhancing deep learning (DL) algorithm detection of BMs on MPRAGE images. Retrospective contrast-enhanced (gadobutrol 0.1 mmol/kg) SPACE and MPRAGE data of 157 patients with BM were used, either annotated on MPRAGE resulting in normal annotation quality (NAQ) or on coregistered SPACE resulting in HAQ. Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients). The SPACE-HAQ model reached 0.978 PPV, 0.882 sensitivity, and 0.916 F1-score. The MPRAGE-HAQ reached 0.867, 0.839, and 0.840, the MPRAGE NAQ 0.964, 0.667, and 0.798, respectively (p ≥ 0.157). Relative to MPRAGE-NAQ, the MPRAGE-HAQ F1-score detection increased on all additional test datasets by 2.5-9.6 points (p < 0.016) and sensitivity improved on three datasets by 4.6-8.5 points (p < 0.001). Moreover, volumetric instance sensitivity improved by 3.6-7.6 points (p < 0.001). HAQ improves DL methods without specialized imaging during application time. HAQ alone achieves about 40% of the performance improvements seen with SPACE images as input, allowing for fast and accurate, fully automated detection of small (< 1 cm) BMs. Training with higher-quality annotations, created using the SPACE sequence, improves the detection and delineation sensitivity of DL methods for the detection of brain metastases (BMs)on MPRAGE images. This MRI cross-technique transfer learning is a promising way to increase diagnostic performance. Delineating small BMs on SPACE MRI sequence results in higher quality annotations than on MPRAGE sequence due to enhanced conspicuity. Leveraging cross-technique ground truth annotations during training improved the accuracy of DL models in detecting and segmenting BMs. Cross-technique annotation may enhance DL models by integrating benefits from specialized, time-intensive MRI sequences while not relying on them. Further validation in prospective studies is needed.

Situating speakers in change: a methodology for quantifying degree and direction of change over the lifespan

Abstract Observations of late adopters catching up with early adopters of language change in a community indicate a potential connection between an individual’s earlier linguistic behaviour and their real-time response to language change. This article provides a means of empirically testing whether such a connection exists, for a given change, by introducing a methodology for obtaining measurements of individuals’ position relative to the change at an earlier time point (a “Cohort Index”) and the direction and extent to which they change over time (a “Difference Index”). Trend and panel data from Australians of Greek and Italian background is used to illustrate both indices for F1 and F2 measurements of fleece, a vowel that has undergone change in Australian English in recent decades. Individuals’ positions in the change for fleece as teenagers are revealed to significantly correlate with the direction and extent to which they have moved in the vowel space over a forty-year period. Specifically, speakers who were originally behind in the change for their generational and ethnic cohort have moved in the direction of change over time more than those who were already ahead. The analysis therefore lays the foundation for the Cohort and Difference Indices to be applied elsewhere.

Research on a phonocardiogram and electrocardiogram signal classification algorithm based on global group coordinate attention mechanism and multi-scale feature fusion

Purpose In recent years, the incidence of cardiovascular disease has continued to rise, and early screening and prevention are especially critical. Phonocardiography (PCG) and electrocardiography (ECG), as simple, cost-effective and non-invasive tests, are important tools for clinical analysis. However, it is difficult to fully reflect the complexity of the cardiovascular system using PCG or ECG tests alone. Combining the multimodal signals of PCG and ECG can provide complementary information to improve the detection accuracy. Therefore, the purpose of this paper is to propose a multimodal signal classification method based on continuous wavelet transform and improved ResNet18. Design/methodology/approach The classification method is based on the ResNet18 backbone, and the ResNet18 network is improved by embedding the global grouped coordinate attention mechanism module and the improved bidirectional feature pyramid network. Firstly, a data acquisition system was built using a MEMS-integrated PCG-ECG sensor to construct a private data set. Second is the time-frequency transformation of PCG and ECG synchronized signals on public and private data sets using continuous wavelet transform. Finally, the time-frequency images are categorized. Findings The global grouped coordinate attention mechanism and bidirectional feature pyramid network modules proposed in this paper significantly enhance the model’s performance. On public data sets, the method achieves precision, sensitivity, specificity, accuracy and F1 score of 97.96%, 98.51%, 97.58%, 98.08% and 98.23%, respectively, which represent improvements of 3.54%, 3.92%, 4.18%, 4.03% and 3.72% compared to ResNet18. Additionally, it demonstrates a clear advantage over existing mainstream algorithms. On private data sets, the method’s five metrics are 98.15%, 98.76%, 98.08%, 98.42% and 98.45%, further validating the model’s generalization ability. Originality/value The method proposed in this paper not only improves the accuracy and efficiency of the test but also provides an effective solution for early screening and prevention of cardiovascular diseases.

Skin microbiome differences in pancreatic adenocarcinoma, other cancers, and healthy controls: a pilot study

IntroductionMany studies have reported the importance of the human microbiome in relationship to the overall health of its host. While recent studies have explored the microbiome’s role in various types of cancer compared to healthy patients, this pilot study is the first to investigate differences in the skin microbiome composition among pancreatic adenocarcinoma patients, individuals with other cancers, and cancer-free controls.MethodsThe study characterizes the skin microbiome’s potential associations with cancer status by analyzing skin swabs from the forehead and cheek of 58 participants using Next Generation Sequencing (NGS), differential abundance analysis, and machine learning techniques.ResultsThe study results indicated that the cancer group displayed a significantly higher mean alpha diversity compared to the control group. Additionally, a machine learning classification model achieved a mean F1 Score of 0.943 in predicting cancer status, indicating measurable differentiation in the skin microbiome between the study groups. This differentiation is supported by differential abundance methods, including ANCOM-BC and MaAsLin2.DiscussionThis pilot study suggests that skin microbiome profiling could serve as a non-invasive biomarker for cancer detection and monitoring, which warrants a larger, longitudinal study to validate these results.

Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation

This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task—classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. On the General Language Understanding Evaluation (GLUE) benchmark, comprising paraphrase identification, sentiment analysis, and text classification, the student model maintained strong performance across many tasks despite this reduction. The model was also used on a real-world ADHD dataset with an accuracy of 85%, F1 score of 85%, precision of 85%, and recall of 85%. When compared to DistilBERT (66 million parameters) and ClinicalBERT (110 million parameters), LastBERT demonstrated comparable performance, with DistilBERT slightly outperforming it at 87%, and ClinicalBERT achieving 86% across the same metrics. These findings highlight the LastBERT model’s capacity to classify degrees of ADHD severity properly, so it offers a useful tool for mental health professionals to assess and comprehend material produced by users on social networking platforms. The study emphasizes the possibilities of knowledge distillation to produce effective models fit for use in resource-limited conditions, hence advancing NLP and mental health diagnosis. Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. Especially using readily available computational tools like Google Colab and Kaggle Notebooks. This study shows the accessibility and usefulness of advanced NLP methods in pragmatic world applications.

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus more on the local features of pulmonary nodules and lack the extraction of global features of pulmonary nodules. To solve this problem, a Cross fusion attention ViT (Cross-ViT) network that fuses local features extracted by CNN and global features extracted by Transformer is proposed. The network first extracts different features independently through two branches and then performs feature fusion through the Cross fusion attention module. Cross-ViT can effectively capture and process both local and global information of lung nodules, which improves the accuracy of classifying the benign and malignant nature of pulmonary nodules. Experimental validation was performed on the LUNA16 dataset, and the accuracy, precision, recall and F1 score reached 91.04%, 91.42%, 92.45% and 91.92%, respectively, and the accuracy, precision, recall and F1 score with SENet as CNN branch reached 92.43%, 94.27%, 91.68% and 92.96%, respectively. The results show that the accuracy, precision, recall and F1 score of the proposed method are 0.3%, 0.11%, 4.52% and 3.03% higher than those of the average optimal method, respectively, and the performance of Cross-ViT network for benign and malignant classification is better than most classification methods.

Comparative Analysis of Kolmogorov-Inspired CNN and Traditional CNN Models for Pneumonia Detection: A Study on Chest CT Images

Aim: In this study, our goal is to compare the effectiveness of Kolmogorov Inspired Convolutional Neural Networks (KAN) with traditional Convolutional Neural Networks (CNN) models in pneumonia detection and to contribute to the development of more efficient and accurate diagnostic tools in the field of medical imaging. Methods: Both models are structured with the same layers and hyperparameters to ensure a fair comparison of their performance. For a robust evaluation, the relevant dataset was divided into 80% for training and 20% for testing. Results and Conclusion: Performance metrics of KAN; 95.2% sensitivity, 97.6% specificity, 94.1% precision, 96.9% accuracy (Acc), 0.9466 F1 score (F1) and 0. 9251 Matthews Correlation Coefficient (MCC), while the CNN model was found 92.5%, 96.4%, 91.2%, 95.3%, 0.9188 and 0.8858 for the same criteria, indicating that KAN outperformed. This comparison emphasizes that KAN has the potential to be a more effective model for pneumonia detection in chest CT images.

CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability

Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. This study introduces a novel CART-ANOVA (Cartesian-ANOVA) hyperparameter tuning framework, which differs from traditional optimization methods by systematically integrating statistical significance testing (ANOVA) with the Cartesian product of hyperparameter values. This approach ensures robust and precise parameter tuning by evaluating the interaction effects between hyperparameters, such as batch size and learning rate, rather than relying solely on grid or random search. Additionally, it implements seven distinct classification schemes for brain tumors, aimed at improving diagnostic accuracy and robustness. Methods: The proposed framework employs a ResNet18-based knowledge transfer learning (KTL) model trained on a primary dataset, with 20% allocated for testing. Hyperparameters were optimized using CART-ANOVA analysis, and statistical validation ensured robust parameter selection. The model’s generalization and robustness were evaluated on an independent second dataset. Performance metrics, including precision, accuracy, sensitivity, and F1 score, were compared against other pre-trained CNN models. Results: The framework achieved exceptional testing accuracy of 99.65% for four-class classification and 98.05% for seven-class classification on the source 1 dataset. It also maintained high generalization capabilities, achieving accuracies of 98.77% and 96.77% on the source 2 datasets for the same tasks. The incorporation of seven distinct classification schemes further enhanced variability and diagnostic capability, surpassing the performance of other pre-trained models. Conclusions: The CART-ANOVA hyperparameter tuning framework, combined with a ResNet18-based KTL approach, significantly improves brain tumor classification accuracy, robustness, and generalization. These advancements demonstrate strong potential for enhancing diagnostic precision and informing effective treatment strategies, contributing to advancements in medical imaging and AI-driven healthcare solutions.

Infrared dim tiny-sized target detection based on feature fusion

Detection of infrared objects is essential for applications ranging from remote sensing to thermal imaging. In certain instances, such as when the infrared target is situated at a considerable distance from the detector, the detected object exhibits diminutive dimensions with a concurrently low signal intensity, leading to a challenge in achieving precision in object detection. In this paper, we propose a dual-layer omnidirectional target enhancement (DODTE) module to address the issue of low contrast, which causes the target to be submerged in the background and lose its position information. The module aims to extract and enhance the position information of the target, serving as a feature map for subsequent guidance. Furthermore, to tackle the problem of the fuzzy and ambiguous shape of the target caused by a weak signal and tiny dimension, a residual-based pyramid-like (RBPL) module is designed, which extracts the deep information (i.e. the shape information) from the images to compensate for the lack of expressive ability of the fixed convolution kernel for shape information. These two main modules are employed as the core to form a feature fusion network to realize the detection of infrared dim tiny-sized targets. The comparison of the proposed network with other algorithms are performed on open-source dataset and experimentally generated infrared images. The quantitative evaluation metrics, including IOUs and F1 scores, validate the outperformance of the proposed network. Furthermore, ablation experiments demonstrate that the proposed two modules can effectively tackle the tiny-size, low contrast and dark intensity issues, providing a solution for detecting dim and small-size infrared targets.

Machine learning insights into patient satisfaction following lateral lumbar interbody fusion.

Lateral Lumbar Interbody Fusion (LLIF) has become a minimally invasive procedure for treating degenerative lumbar conditions. While it offers reduced blood loss and faster recovery, patient satisfaction following LLIF surgery shows significant variability. Identifying the factors influencing satisfaction is crucial for optimizing surgical outcomes and improving patient care. This study aims to determine key factors affecting patient satisfaction after LLIF surgery using machine learning (ML) models, including Random Forest, Logistic Regression, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). Additionally, the study evaluates the predictive performance of these models to identify the most influential factors contributing to postoperative satisfaction. A retrospective analysis was conducted on 149 patients who underwent LLIF surgery. Preoperative, intraoperative, and postoperative variables were collected, including patient demographics, clinical measures, surgical details, and functional outcomes. Based on these variables, ML models were used to predict patient satisfaction vs. not satisfied) satisfaction (s. Model performance was evaluated using fivefold cross-validation, with metrics including accuracy, precision, recall, and F1 score. Of the 149 patients, 85.2% reported satisfaction with the surgical outcome. Random Forest achieved the highest predictive performance, with an average accuracy of 82.6%, precision of 83.6%, recall of 99.2%, and an F1 score of 90.7%. Key factors influencing patient satisfaction included the preoperative low back pain score, social life function, and postoperative improvements in walking ability and mental health. Surgical factors, such as the number of fused segments and endplate injury, had less influence on satisfaction. Functional outcomes, particularly improvements in low back pain, walking ability, and mental health, are the primary determinants of patient satisfaction following LLIF surgery. In contrast, surgical factors play a less significant role. Mental health emerged as a critical factor, underscoring the importance of addressing psychological recovery through preoperative counseling and personalized postoperative care. The analysis demonstrated that ML models, especially Random Forest, are effective tools for identifying the factors most predictive of postoperative satisfaction. These findings highlight the potential of ML techniques to enhance personalized treatment planning and improve outcomes by focusing on both physical and mental recovery. Further research, including multi-center studies and the integration of psychological variables, is needed to provide a more comprehensive understanding of patient satisfaction after LLIF surgery.

Rectifier Fault Diagnosis Based on Euclidean Norm Fusion Multi-Frequency Bands and Multi-Scale Permutation Entropy

With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher power factor. However, due to the complex control and harsh environment, it can easily fail. Faults can cause current and voltage distortion, harmonic increases and other problems, which can threaten the safety of the power system and the train. In order to ensure the stable operation of the rectifier, incidences of faults should be reduced. A fault diagnosis technique based on Euclidean norm fusion multi-frequency bands and multi-scale permutation entropy is proposed. Firstly, by the optimal wavelet function, information on the optimal multi-frequency bands of the fault signal is selected after wavelet packet decomposition. Secondly, the multi-scale permutation entropy of each frequency band is calculated, and multiple fault feature vectors are obtained for each frequency band. To reduce the classifier’s computational cost, the Euclidean norm is used to fuse the multi-scale permutation entropy into an entropy value, so that each frequency band uses an entropy value to characterize the fault information features. Finally, the optimal multi-frequency bands and multi-scale permutation entropy after fusion are used as the fault feature vector. In the simulation system, it is shown that the method’s average accuracy is 78.46%, 97.07%, and 99.45% when the SNR is 5 dB, 10 dB, and 15 dB, respectively. And the fusion of multi-scale permutation entropy can improve the accuracy, recall rate, precision, and F1 score and reduce the False Alarm Rate (FAR) and the Missing Alarm Rate (MAR). The results show that the fault diagnosis method has high diagnosis accuracy, is a simple feature fusion method, and has good robustness to working conditions and noise.

Mobile Botnet Attacks Detection Using Supervised Learning Algorithms

ABSTRACTIn today's era of increased smart device usage, the prevalence of malicious attacks targeting mobile devices has risen significantly. Malware developers aim to infect as many mobile devices as possible, capitalizing on the vulnerabilities present in different operating systems that often host sensitive programs such as educational and banking applications. To combat this issue, this article introduces a mobile botnet detection system that leverages machine learning algorithms such as random forest, k‐nearest neighbor, logistic regression, and decision trees. Additionally, the system incorporates the use of the golden ratio evolutionary algorithm for dimension reduction and feature selection. The proposed method's performance is evaluated using detection accuracy, Precision, F1 Score, and Recall as evaluation criteria on NSL‐KDD, Drebin, and ISCX datasets. The results are compared with evaluation values obtained without applying the golden ratio feature selection algorithm and once again with the proposed method. The findings demonstrate that the proposed method, particularly when combined with the random forest algorithm, achieves the highest accuracy in the desired dataset. Moreover, the decision tree and k‐nearest neighbor algorithms also exhibit superior detection accuracy within the proposed method's framework. By utilizing the proposed method, the training time is reduced through feature selection, enabling swift mobile botnet identification.

Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.

Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using deep learning on multi-tracer, multi-scanner, and multi-center datasets. In the current study, we employed six datasets (from 12 cameras) for various tasks and purposes. Dataset #1 (93 patients, 99mTc-MDP) was used to develop the 2D-planar segmentation and localization models. Dataset #2 (216 patients, 99mTc-DPD) was used for the detection (grade 0 vs. grades 1, 2, and 3) and scoring (0 and 1 vs. grades 2 and 3) of ATTR-CM. Datasets #3 (41 patients, 99mTc-HDP), #4 (53 patients, 99mTc-PYP), and #5 (129 patients, 99mTc-DPD) were used as external centers. ATTR-CM detection and scouring were performed by two physicians in each center. Moreover, Dataset #6 consisting of 3215 patients without labels, was employed for retrospective model performance evaluation. Different regions of interest were cropped and fed into the classification model for the detection and scoring of ATTR-CM. Ensembling was performed on the outputs of different models to improve their performance. Model performance was measured by classification accuracy, sensitivity, specificity, and AUC. Grad-CAM and saliency maps were generated to explain the models' decision-making process. In the internal test set, all models for detection and scoring achieved an AUC of more than 0.95 and an F1 score of more than 0.90. For detection in the external dataset, AUCs of 0.93, 0.95, and 1 were achieved for datasets 3, 4, and 5, respectively. For the scoring task, AUCs of 0.95, 0.83, and 0.96 were achieved for these datasets, respectively. In dataset #6, we found ten cases flagged as ATTR-CM by the network. Out of these, four cases were confirmed by a nuclear medicine specialist as possibly having ATTR-CM. GradCam and saliency maps showed that the deep-learning models focused on clinically relevant cardiac areas. In the current study, we developed and evaluated a fully automated pipeline to detect and score ATTR-CM using large multi-tracer, multi-scanner, and multi-center datasets, achieving high performance on total body images. This fully automated pipeline could lead to more timely and accurate diagnoses, ultimately improving patient outcomes.

Developing an artificial intelligence model for English grammar correction: A computational linguistics approach

This article aimed to explore the development of an artificial intelligence model for English grammar correction based on computational linguistics methods. Traditional grammar correction systems suffer from problems such as complex rules, sparse data, and insufficient utilization of contextual information. To address these issues, this article adopted Transformer’s pre-trained language model, utilizing its powerful context understanding and automatic feature extraction capabilities to improve the processing performance of complex syntax structures and long-distance dependencies. Meanwhile, by constructing large-scale and diverse datasets and combining them with data augmentation techniques, the model’s generalization ability and robustness were enhanced. This article also investigated hyperparameter tuning, model integration, and continuous optimization strategies in the process of model training optimization, and provided a detailed description of model evaluation and experimental validation. In the evaluation, the average precision, average recall, and average F1 score for most common grammar errors were 0.8, 0.805, and 0.801, respectively. The model in this article has excellent grammar correction ability. Through comprehensive experiments and evaluations, the potential and advantages of a new grammar correction artificial intelligence model developed based on computational linguistics methods in improving grammar correction effectiveness and practicality have been demonstrated.

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015–2018

BackgroundAlveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logistic regression and Bayesian kernel machine regression. Overfitting using kernel function, long calculation period, the definition of prior distribution and lack of rank of heavy metal will affect the performance of the statistical model. Optimal model on this topic still remains controversy. This study aimed: (1) to develop an algorithm for exploring the association between heavy metal exposure and ABL; (2) filter the actual causal variables and investigate how heavy metals were associated with ABL; and (3) identify the potential risk factors for ABL.MethodsData were collected from National Health and Nutrition Examination Survey (NHANES) between 2015 and 2018 to develop a machine learning (ML) model. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation. The selected data were balanced using the Synthetic Minority Oversampling Technique (SMOTE) and divided into a training set and testing set at a 3:1 ratio. Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), and XGboost were used to construct the ML model. Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC), Precision, Recall, and F1 score were used to select the optimal model for further analysis. The contribution of the variables to the ML model was explained using the Shapley Additive Explanations (SHAP) method.ResultsRF showed the best performance in exploring the association between heavy metal exposure and ABL, with an AUC (0.88), accuracy (0.78), precision (0.76), recall (0.83), and F1 score (0.79). Age was the most important factor in the ML model (mean| SHAP value| = 0.09), and Cd was the primary contributor. Sex had little effect on the ML model contribution.ConclusionIn this study, RF showed superior performance compared with the other five algorithms. Among the 12 heavy metals, Cd was the most important factor in the ML model. The relationship of Co & Pb and ABL are weaker than that of Cd. Among all the independent variables, age was considered the most important factor for this model. As for PIR, low-income participants present association with ABL. Mexican American and Non-Hispanic White show low association with ABL compared to Non-Hispanic Black and other races. Gender feature demonstrates a weak association with ABL. In the future, more advanced algorithms should be developed to validate these results and related parameters can be tuned to improve the accuracy of the model.Clinical trial numbernot applicable.

A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.

Effective Breast cancer (BC) analysis is crucial for early prognosis, controlling cancer recurrence, timely medical intervention, and determining appropriate treatment procedures. Additionally, it plays a significant role in optimizing mortality rates among women with breast cancer and increasing the average lifespan of patients. This can be achieved by performing effective critical feature analysis of the BC by picking superlative features through significant ranking-based Feature Selection (FS). Various authors have developed strategies relying on single FS, but this approach may not yield excellent results and could lead to various consequences, including time and storage complexity issues, inaccurate results, poor decision-making, and difficult interpretation of models. Therefore, critical data analysis can facilitate the development of a robust ranking methodology for effective feature selection. To solve these problems, this paper suggests a new method called Aggregated Coefficient Ranking-based Feature Selection (ACRFS), which is based on tri chracteristic behavioral criteria. This strategy aims to significantly improve the ranking for an effective Attribute Subset Selection (ASSS). The proposed method utilized computational problem solvers such as chi-square, mutual information, correlation, and rank-dense methods. The work implemented the introduced methodology using Wisconsin-based breast cancer data and applied the Synthetic Minority Oversampling Technique (SMOTE) to the obtained data subset. Later, we employed models such as decision trees, support vector machines, k-nearest neighbors, random forests, stochastic gradient descent, and Gaussian naive bayes to determine the type of cancer. The classification metrics such as accuracy, precision, recall, F1 score, kappa score, and Matthews coefficient were utilized to evaluate the effectiveness of the suggested ACRFS approach. The proposed method has demonstrated superior outcomes with fewer features and a minimal time complexity.

Overall Staging Prediction for Non-Small Cell Lung Cancer (NSCLC): A Local Pilot Study with Artificial Neural Network Approach

Background: Non-small cell lung cancer (NSCLC) has been the most common cancer globally in the recent decade. CT is the most common imaging modality for the initial diagnosis of NSCLC. The gold standard for definitive diagnosis is the histological evaluation of a biopsy or surgical sample, which usually requires a long processing time for the confirmation of diagnosis. This study aims to develop artificial intelligence models to predict overall staging based on patient demographics and radiomics retrieved from the initial CT images, so as to prioritize later-stage patients for histology evaluation to facilitate cancer diagnosis. Method: Two cohorts of NSCLC patient datasets were utilized for this study. The NSCLC-radiomics dataset from The Cancer Imaging Archive (TCIA) was divided into 70% for the training group and 30% for the internal testing group. Another cohort from a local hospital was collected for the an external testing group. Patient demographics and 107 radiomic features were retrieved from the gross tumor volume delineated by clinical oncologists on CT images. Artificial neural networks were used to build models for NSCLC overall staging (stage I, II, or III) prediction. Four traditional classifiers were also adopted to build models for comparison. Result: The proposed feed-forward neural network (FFNN) model showed good performance in predicting overall staging with an accuracy of 88.84%, 76.67%, and 74.52% in overall accuracies in validation, internal cohort testing, and external cohort testing, respectively. The sensitivity and specificity are balanced in all the stages, with average precision and F1 score in each of the stages. Conclusion: The FFNN demonstrated good performance in overall staging prediction for NSCLC patients. It has the benefit of predicting multiple overall stages in a single model. The software required and the proposed model are simple. It can be operated on a general-purpose computer in the radiology department. The application will eventually be used as a prediction tool to prioritize the biopsy or surgery sample for histological analysis and molecular investigation, thus shortening the time for diagnosis by pathologists, which supports the triage of patients for further testing.