Accuracy Scores Research Articles

Hyperspectral Band Selection with Unique Pixel Extraction and Adaptive Neighbor Clustering

Band selection is an effective way to reduce redundant information, while preserving the physical properties of hyperspectral images (HSI). However, most band selection methods merely consider the relevance and separability between pairs of bands and ignore those for different ground objects. To solve these issues, we propose a Unique Pixel extraction and Adaptive Neighbor Clustering (UPANC) band selection method in this theoretical study. First, in consideration of the characteristics of HSI data and tasks, unique pixels are obtained with a low-rank representation, where the importance of bands is analyzed from both spectral and spatial perspectives. Second, an adaptive neighbor clustering method is designed based on the unique pixels, which groups bands into several clusters through optimizing the graph structure under label smoothness. With support vector machines (SVM) as the classifier, the UPANC method achieved good performance, where the overall accuracy scores were 89.05%, 82.62%, and 92.07% on the Houston, IndianPines, and Pavia University datasets, respectively. The experimental results illustrated the advantages of the UPANC method, which could select optimal bands to enhance the performance in land cover observation.

Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization

Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with “curse of dimensionality” due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue. The proposed technique, initially, extracts features using histrogram of gradient (HOG) approach and then reduces dimensions of extracted features using unsupervised autoencoder and subsequently refining the feature set with an improved GWO algorithm. A handcrafted artificial neural network serves as the classifier within this integrated framework, denoted as AEGWO-Net. Exhaustive experimentations were conducted on six different datasets namely ASL, ASL MNIST, ISL, ArSL, MNIST Digits, and IEEE-ISL containing gestures of different languages to demonstrate the performance of AEGWO-Net. The AEGWO-Net demonstrates superior performance improving accuracy and F1 score by 6% and 4% respectively compared to PCA-IGWO and KPCA-IGWO algorithms. Achieving high accuracy (98.40%), F1-score (96.59%), MCC (97.14%), and AUC (96.21%) indicates the robustness and generalizability of the AEGWO-Net method even with reduced dimensionality. Furthermore, a comparison between AEGWO-Net with other existing swarm intelligence techniques is also made to demonstrate its superiority.

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

The purpose of this study was to develop a robust deep learning approach trained with a small in-vivo MRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis.
Approach: A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study. The MRI data was variable with respect to the Field Of View (FOV), wide range of image intensity, and joint pose. A two-stage segmentation pipeline using modified 3D U-Net was proposed. In the first stage, a novel architecture, introduced as Expansion Transfer Learning (ETL), cascades the use of a focused Region Of Interest (ROI) cropped around ground truth for pretraining and a subsequent transfer by an expansion to the original FOV for a primary prediction. The bounding box around the ROI generated was utilized in the second stage for high-accuracy,
labeled segmentations of eight carpal bones. Different metrics including Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Hausdorff Distance (HD) were used to evaluate performance between proposed and four state-of-the-art approaches.
Main results: With an average DSC of 87.8 %, an ASD of 0.46 mm, an average HD of 2.42mm in all datasets (96.1 %, 0.16 mm, 0.38mm in 12 datasets after exclusion criteria, respectively), the proposed approach showed an overall strongest performance than comparisons.
Significance: To our best knowledge, this is the first CNN-based multi-label segmentation approach for MRI human carpal bones. The ETL introduced in this work improved the ability to localize a small ROI in a large FOV. Overall, the interplay of a two-stage approach and ETL culminated in convincingly accurate segmentation scores despite a very small amount of image data.

Machine Learning Models For Preventative Mobile Health Asthma Control

Introduction Asthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers. Methods Lightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split. The models were measured on Precision Score, Accuracy Score, Recall Score, F1 Score and model speed. Results The best model, XGBoost, obtained an Accuracy score of 0.902, Recall score of 0.904, Precision score of 0.835, and F1 score of 0.860 and a model training speed of 14 seconds. Conclusion As proved by the XGBoost model, predicting asthma triggers can be a viable option for asthma control using machine learning. In addition, the actionable information of triggers, allows patients to make behavior changes. However there will still need to be work testing the system in a mobile health system.

Endoscopic-Assisted Mandibular Angle Revision Using Patient-Specific PEEK Implants: Surgical Accuracy and Aesthetic Outcomes in Over-Resected Mandibles.

Mandibular angle osteotomy (MAO) is a popular procedure to improve facial aesthetics, however, over-resection of the mandibular angle can lead to both functional and aesthetic challenges. Precision is essential in restoring these over-resected mandibles to achieve balanced outcomes. Polyetheretherketone (PEEK) implants offer biocompatibility, durability, and customization potential, making them valuable for achieving precise and predictable results. This study examines the efficacy of PEEK patient-specific implants (PSIs) in endoscopic-assisted mandibular angle revision (MAR) surgery, assessing improvements in surgical accuracy and aesthetic outcomes. We retrospectively analyzed 24 patients (39 mandibles) who underwent MAR from January 2019 to December 2023. Surgical accuracy was measured by comparing planned and achieved PSI positions using root mean square error (RMSE) and maximum deviation (MaxD). Patient satisfaction was assessed using the FACE-Q questionnaire, administered preoperatively and at least six months postoperatively, with correlations drawn between implant fit accuracy and FACE-Q scores changes. The average RMSE ranged from 0.1166 to 0.3149mm, with 96% of PSIs showing deviations under 0.3 mm. FACE-Q scores for lower facial appearance and psychological well-being improved significantly, form 42.50 ± 5.28 to 56.08 ± 4.58 and 61.50±4.31 to 70.58 ± 6.46, respectively. A strong negative correlation was observed between implant fit accuracy (RMSE, MaxD) and FACE-Q score improvements related to appearance. Endoscopic-assisted MAR with PEEK PSIs offers precise correction and significant aesthetic improvements for patients with over-resected mandibles. This technique demonstrated high surgical accuracy and favorable patient-reported outcomes. Future studies on a larger scale are recommended to validate these findings. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition

Accurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. Artificial Intelligence (AI) models have shown the potential to automate this process. This study evaluates the accuracy of publicly available Large Language Models (LLMs)—ChatGPT-4, ChatGPT-4o, and Gemini—and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.0), in identifying surgical instruments from images. The study utilized a dataset of 92 high-resolution images of 25 surgical instruments (retractors, forceps, scissors, and trocars) photographed from multiple angles. Model performance was evaluated using accuracy, weighted precision, recall, and F1 score. ChatGPT-4o exhibited the highest accuracy (89.1%) in categorizing instruments (e.g., scissors, forceps). SID 2.0 (77.2%) and ChatGPT-4 (76.1%) achieved comparable accuracy, while Gemini (44.6%) demonstrated lower accuracy in this task. For precise subtype identification of instrument names (like “Mayo scissors” or “Kelly forceps”), all models had low accuracy, with SID 2.0 having an accuracy of 39.1%, followed by ChatGPT-4o (33.69%). Subgroup analysis revealed ChatGPT-4 and 4o recognized trocars in all instances. Similarly, Gemini identified surgical scissors in all instances. In conclusion, publicly available LLMs can reliably identify surgical instruments at the category level, with ChatGPT-4o demonstrating an overall edge. However, precise subtype identification remains a challenge for all models. These findings highlight the potential of AI-driven solutions to enhance surgical-instrument management and underscore the need for further refinements to improve accuracy and support patient safety.

User Behavior Analysis and Security Strategy Optimization of College English Learning Platform Using Dynamic Graph Convolutional Network

This paper aimed to address the shortcomings of current college English learning platforms in user behavior analysis and security protection, especially the lack of accurate abnormal behavior detection and dynamic identity authentication measures. By adopting DGCN (Dynamic Graph Convolutional Network) and XGBoost (eXtreme Gradient Boosting) classification models for abnormal behavior detection, a dynamic identity authentication optimization strategy based on behavior risk scoring is proposed to improve the security of the platform. The user embedding features output from DGCN are input into XGBoost for user behavior classification. Based on the predicted probability of the XGBoost classification model, a risk score is generated for each user, and different identity authentication strategies are defined according to the risk score range. Experimental results show that by modeling multi-layer dynamic graph structures, the accuracy, precision, recall and F1 score of the XGBoost model reached 97.6%, 97.5%, 95.3%, and 96.4% respectively. In addition, when the step size of the DGCN model increases from 1 to 10, its accuracy only slightly decreases from 97.6% to 97.5%, and the user behavior classification performance is stable. The incidence of account theft with dynamic identity authentication is maintained between 3% and 8%, and the average authentication time is increased by 2.1 seconds compared with the traditional password authentication method. The proposed DGCN can optimize the security strategy of the college English learning platform based on the user behavior risk score, and it can select targeted identity authentication strategies according to the user's risk score.

Development and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke

ObjectiveTo develop and validate an explainable machine learning (ML) model predicting the risk of hemorrhagic transformation (HT) after intravenous thrombolysis.MethodsWe retrospectively enrolled patients who received intravenous tissue plasminogen activator (IV-tPA) thrombolysis within 4.5 h after symptom onset to form the original modeling cohort. HT was defined as any hemorrhage on head CT scan completed within 48 h after IV-tPA administration. We utilized the Random Forest (RF), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GauNB) algorithms to develop ML-HT models. The models' predictive performance was evaluated using confusion matrix (including accuracy, precision, recall, and F1 score), and discriminative analysis (area under the receiver-operating-characteristic curve, ROC-AUC) in the original cohort, followed by validation in an independent external cohort. The models' explainability was assessed using SHapley Additive exPlanations (SHAP) global feature plot, SHAP Summary Plot, and Partial Dependence Plot.ResultsA total of 1,007 patients were included in the original modeling cohort, with an HT incidence of 8.94%. The RF-based ML-HT model showed metrics of 0.874 (accuracy), 0.972 (precision), 0.890 (recall), 0.929 (F1 score); with ROC-AUC of 0.7847 in the original cohort and 0.7119 in the external validation cohort. The MLP model showed 0.878, 0.967, 0.989, 0.978, 0.7710, and 0.6768, respectively. The AdaBoost model showed 0.907, 0.967, 0.989, 0.978, 0.7798, and 0.6606, respectively. The GauNB model showed 0.848, 0.983, 0.598, 0.716, 0.6953, and 0.6289, respectively. The explainable analysis of the RF-based ML model indicated that the National Institute of Health Stroke Scale (NIHSS) score, age, platelet count, and atrial fibrillation were the primary determinants for HT following IV-tPA thrombolysis.ConclusionThe RF-based explainable ML model demonstrated promising predictive ability for estimating the risk of HT after IV-tPA thrombolysis and may have the potential to assist the clinical decision-making in emergency settings.

Advancing Rice Grain Impurity Segmentation with an Enhanced SegFormer and Multi-Scale Feature Integration

During the rice harvesting process, severe occlusion and adhesion exist among multiple targets, such as rice, straw, and leaves, making it difficult to accurately distinguish between rice grains and impurities. To address the current challenges, a lightweight semantic segmentation algorithm for impurities based on an improved SegFormer network is proposed. To make full use of the extracted features, the decoder was redesigned. First, the Feature Pyramid Network (FPN) was introduced to optimize the structure, selectively fusing the high-level semantic features and low-level texture features generated by the encoder. Secondly, a Part Large Kernel Attention (Part-LKA) module was designed and introduced after feature fusion to help the model focus on key regions, simplifying the model and accelerating computation. Finally, to compensate for the lack of spatial interaction capabilities, Bottleneck Recursive Gated Convolution (B-gnConv) was introduced to achieve effective segmentation of rice grains and impurities. Compared with the original model, the improved model’s pixel accuracy (PA) and F1 score increased by 1.6% and 3.1%, respectively. This provides a valuable algorithmic reference for designing a real-time impurity rate monitoring system for rice combine harvesters.

Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning

Nailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes associated with a wide range of systemic diseases, such as diabetes, cardiovascular disorders, and rheumatological conditions like systemic sclerosis, can manifest as observable microvascular changes in the terminal capillaries of nailfolds. The current gold standard relies on experts performing manual evaluations, which is an exhaustive time-intensive, and subjective process. In this study, we demonstrate the viability of a deep learning approach as an automated clinical screening tool. Our dataset consists of NFC images from a total of 225 participants, with normal images accounting for 6% of the dataset. This study introduces a robust framework utilizing cascade transfer learning based on EfficientNet-B0 to differentiate between normal and abnormal cases within NFC images. The results demonstrate that pre-trained EfficientNet-B0 on the ImageNet dataset, followed by transfer learning from domain-specific classes, significantly enhances the classifier’s performance in distinguishing between Normal and Abnormal classes. Our proposed model achieved superior performance, with accuracy, precision, recall, F1 score, and ROC_AUC of 1.00, significantly outperforming both models of single transfer learning on the pre-trained EfficientNet-B0 and cascade transfer learning on a convolutional neural network, which each attained an accuracy, precision, recall, and F1 score of 0.67 and a ROC_AUC of 0.83. The framework demonstrates the potential to facilitate early preventive measures and timely interventions that aim to improve healthcare delivery and patients’ quality of life.

Machine Learning-Aided Diagnosis Enhances Human Detection of Perilunate Dislocations.

Perilunate/lunate injuries are frequently misdiagnosed. We hypothesize that utilization of a machine learning algorithm can improve human detection of perilunate/lunate dislocations. Participants from emergency medicine, hand surgery, and radiology were asked to evaluate 30 lateral wrist radiographs for the presence of a perilunate/lunate dislocation with and without the use of a machine learning algorithm, which was used to label the lunate. Human performance with and without the machine learning tool was evaluated using sensitivity, specificity, accuracy, and F1 score. A total of 137 participants were recruited, with 55 respondents from emergency medicine, 33 from radiology, and 49 from hand surgery. Thirty-nine participants were attending physicians or fellows, and 98 were residents. Use of the machine learning tool improved specificity from 88% to 94%, accuracy from 89% to 93%, and F1 score from 0.89 to 0.92. When stratified by training level, attending physicians and fellows had an improvement in specificity from 93% to 97%. For residents, use of the machine learning tool resulted in improved accuracy from 86% to 91% and specificity from 86% to 93%. The performance of surgery and radiology residents improved when assisted by the tool to achieve similar accuracy to attendings, and their assisted diagnostic performance reaches levels similar to that of the fully automated artificial intelligence tool. Use of a machine learning tool improves resident accuracy for radiographic detection of perilunate dislocations, and improves specificity for all training levels. This may help to decrease misdiagnosis of perilunate dislocations, particularly when subspecialist evaluation is delayed.

The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings

IntroductionAccurate measurement of dietary intake without interfering in natural eating habits is a long-standing problem in nutritional epidemiology. We explored the applicability of hyperspectral imaging and machine learning for dietary assessment of home-prepared meals, by building a proof-of-concept, which automatically detects food ingredients inside closed sandwiches.MethodsIndividual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.ResultsThe resulting best performing models had an accuracy score of ~80% for predicting type of bread, ~60% for butter, and ~ 28% for filling type. We see that the main struggle in predicting the fillings lies with the spreadable fillings, meaning the model may be focusing on structural aspects and not nutritional composition.DiscussionFurther analysis on non-homogeneous mixed food items, using computer vision techniques, will contribute toward a generalizable system. While there are still significant technical challenges to overcome before such a system can be routinely implemented in studies of free-living subjects, we believe it holds promise as a future tool for nutrition research and population intake monitoring.

Robust estimation of the latent trait in graded response models

Aberrant responses (e.g., careless responses, miskeyed items, etc.) often contaminate psychological assessments and surveys. Previous robust estimators for dichotomous IRT models have produced more accurate latent trait estimates with data containing response disturbances. However, for widely used Likert-type items with three or more response categories, a robust estimator for estimating latent traits does not exist. We propose a robust estimator for the graded response model (GRM) that can be applied to Likert-type items. Two weighting mechanisms for downweighting “suspicious” responses are considered: the Huber and the bisquare weight functions. Simulations reveal the estimator reduces bias for various test lengths, numbers of response categories, and types of response disturbances. The reduction in bias and stable standard errors suggests that the robust estimator for the GRM is effective in counteracting the harmful effects of response disturbances and providing more accurate scores on psychological assessments. The robust estimator is then applied to data from the Big Five Inventory-2 (Ober et al., 2021) to demonstrate its use. Potential applications and implications are discussed.

A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.

Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture. The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture. Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection. Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.

Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopause transition: a pilot study.

This study aims to develop and validate a machine learning model for identifying individuals within the nursing population experiencing severe subjective cognitive decline (SCD) during the menopause transition, along with their associated factors. A secondary analysis was performed using cross-sectional data from 1,264 nurses undergoing the menopause transition. The data set was randomly split into training (75%) and validation sets (25%), with the Bortua algorithm employed for feature selection. Seven machine learning models were constructed and optimized. Model performance was assessed using area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1 score. Shapley Additive Explanations analysis was used to elucidate the weights and characteristics of various factors associated with severe SCD. The average SCD score among nurses in the menopause transition was (5.38 ± 2.43). The Bortua algorithm identified 13 significant feature factors. Among the seven models, the support vector machine exhibited the best overall performance, achieving an area under the receiver operating characteristic curve of 0.846, accuracy of 0.789, sensitivity of 0.753, specificity of 0.802, and an F1 score of 0.658. The two variables most strongly associated with SCD were menopausal symptoms and the stage of menopause. The machine learning models effectively identify individuals with severe SCD and the related factors associated with severe SCD in nurses during the menopause transition. These findings offer valuable insights for the management of cognitive health in women undergoing the menopause transition.

Sentiment Analysis for Movie Database Management System Using Naive Bayes Algorithm

Abstract: According to F. Furtado and A. Singh (2021), movie recommendation systems aim to assist viewers by suggesting movies to watch, thereby avoiding the time-consuming and complex process of selecting from a vast library of movies, which can number in the thousands or even millions. This article seeks to reduce human effort by recommending movies based on user interests. To address these challenges, we propose a technique that combines content-based and collaborative approaches. This method produces more precise results than systems that rely solely on content-based methods. Content-based recommendation systems are limited to individual preferences and do not recommend items outside the user’s immediate interests, restricting exploration. To overcome these limitations, we developed a system that addresses these concerns. The proposed movie recommendation system uses Cosine Similarity to suggest movies like the one chosen by the user. While existing algorithms generate recommendations, they often fail to answer the critical question, “Is this a film worth watching?” To enhance the overall experience, our system incorporates sentiment analysis of selected movie reviews using machine learning techniques. This study employs two supervised learning approaches, Naïve Bayes (NB) and Support Vector Machine (SVM), to improve accuracy and efficacy. A comparison of the two methods reveals that SVM achieved an accuracy score of 98.63%, while NB scored 97.33%, as reported by N. Pavitha et al. (2022). The project integrates a sentiment analysis module into the movie management system to enhance decision-making and user experience. The Naïve Bayes approach is used to classify and evaluate user reviews and feedback into positive, negative, and neutral categories. This analysis provides insights into audience preferences, overall satisfaction, and movie reception. The system utilizes this information to refine movie recommendations, analyze trends, and enhance content selection, all of which contribute to a more personalized user experience. The system is evaluated using the ISO 25010 framework, focusing on functionality, reliability, usability, effectiveness, and robustness. Findings show that respondents actively participated in the review process across multiple domains. Key results indicate high satisfaction levels in terms of ease of use, interface design, and system performance. Feedback also highlighted areas for improvement, such as optimizing loading times and enhancing specific features for a smoother user experience. Overall, the findings reflect positive engagement with the system, demonstrating its effectiveness in meeting user needs while identifying opportunities for future enhancements.

Pneumothorax Detection System in Thoracic Radiography Images Using CNN Method

Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition. Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics. Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed. Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.

Hybrid Deep Learning Model to Predict Students’ Sentiments in Higher Educational Institutions

Sentiment analysis has been widely used in various fields of social media, education, and business. Specifically, in the education domain, the usage of sentiment analysis is difficult due to the huge amount of information, the nature of language, and processing the diverse perceptions of students. Deep learning emerges as an advanced concept in the realm of machine learning that learns features automatically from raw text data, making them well-suited for sentiment analysis tasks. In recent years, deep learning has been used in analyzing the sentiments. Deep learning architectures have surpassed other machine learning paradigms for performing sentiment analysis. The ability to analyze automatically the students’ sentiments enables HEI to process huge amounts of unstructured data quickly, efficiently, and cost-effectively. The paper aims to predict the sentiments of students’ reviews posted in VLE regarding online learning that enables the educators to optimize their teaching methods for the best results. This study paper explores the usage of CNN, LSTM, and hybrid CNN-LSTM for the prediction of sentiments. The proposed hybrid CNN-LSTM architecture achieves superior performance compared to other baseline algorithms with respect to accuracy, precision, recall, and F1 score. According to outcomes, the recommended technique achieves remarkable accuracy of 97%. The findings facilitate the progress of a more efficient deep learning sentiment prediction system that gives valuable insights from a huge volume of students’ textual data.

CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer

Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extraction insufficient for achieving high segmentation accuracy. To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. This approach effectively addresses the challenge of modeling contextual information in crack feature extraction. Additionally, the Detailed-Macro Feature Fusion (DMFF) module enables multi-scale feature integration by combining detailed and coarse-grained features, mitigating the significant feature loss encountered during the encoding and decoding stages, and further improving segmentation precision. To overcome the limitations of existing public datasets, which often feature a narrow range of crack types and simplistic backgrounds, this study introduces TunnelCrackDB, a dataset encompassing diverse crack types and complex backgrounds.Experimental evaluations on both the public Crack dataset and the newly developed TunnelCrackDB demonstrate the efficacy of CGV-Net. On the Crack dataset, CGV-Net achieves accuracy, recall, and F1 scores of 73.27% and 57.32%, respectively. On TunnelCrackDB, CGV-Net attains accuracy, recall, and F1 scores of 81.15%, 83.54%, and 82.33%, respectively, showcasing its superior performance in challenging segmentation tasks.

Credit Card Fraud Detection in the Banking Sector: A Comprehensive Machine Learning Approach for Information Security

In the context of computers, cybersecurity is experiencing significant technological development, and the changes have been driven by its operations in recent years. The secret to creating an intelligent and automated security system is to extract patterns or insights from cybersecurity data and create a matching data-driven model. One of the main problems, and threats to information security, is fraud. Credit card fraud detection (CCFD) is a significant issue for consumers, businesses, and banks, mostly because of the growth of computerized financial transactions. Because of this, a methodology for detecting fraud is presented that uses state-of-the-art machine learning (ML) techniques. The methodology in this research is a carefully chosen set of state-of-the-art ML algorithms that are particularly made for accurate CCFD problems. The technique uses a wide range of ML models to handle large-scale problems with a large number of transactions. Three ML models are used in this study, such as logistic regression (LR), random forest (RF), and XGBoost. These models are trained for accurate results of CCFD. Four evaluation metrics are used in this study to evaluate the ML models, such as accuracy, precision, recall, and f1 score. The results show that the RF model has the highest accuracy of 99.65%, followed by the XGBoost, with 99.963% accuracy, and the LR model, with 99.934% accuracy. The study's summary gives banking organizations, governmental organizations, and legislators crucial knowledge to help them fight against the harm that credit card theft does to customers, businesses, and the economy at large. By offering an ML-driven solution to the fraud problem, our work solves it and opens the door for further advancements in this important field.