Low False Positive Rate Research Articles

Methodology of cfHPV-DNA Detection in Head and Neck Cancer: A Systematic Review and Meta-analysis.

We aim to compare the diagnostic accuracy of the different methodologies used in the detection of cell-free human papillomavirus (HPV) DNA in HPV-associated head and neck squamous cell carcinoma detection using bivariate analysis methods. Pubmed, Embase, and Scopus were queried using a broad search strategy to search for relevant studies. Test characteristics were extracted from 33 studies following literature screening, and underwent analyses utilizing a bivariate approach. Summary statistics were identified for each type of methodology, and forest plots and summary receiver operating characteristic curves were constructed. Bias was estimated using Deek's Funnel Plot and the QUADAS-2 tool. In terms of diagnostic accuracy, digital droplet polymerase chain reaction (ddPCR) based testing exhibited the highest diagnostics odds ratio at 138 (59.5, 318), followed closely by next-generation sequencing (NGS) at 120 (39.7, 362), then by polymerase chain reaction (PCR) at 31.4 (14.4, 68.6), and quantitative PCR at 8.74 (4.63, 16.5). NGS and ddPCR are comparable in overall diagnostic accuracy, bringing into question their relative roles in diagnosis and screening. Cost-effective ddPCR assays may serve as useful diagnostic and screening tests in the clinic with their low false positive rates and high sensitivity. However, NGS assays also offer high sensitivity and companion metrics, suggesting they may have a more precise role in disease monitoring. Importantly, assay development and benchmarking need further standardization to improve comparison between assays. Finally, saliva-based testing needs to be further investigated using NGS and ddPCR to further understand its limitations in disease detection and monitoring.

Anomaly Pattern Detection in High-Frequency Trading Using Graph Neural Networks

This paper presents a new method for detecting abnormal patterns in high-frequency trading (HFT) using graph neural networks (GNNs). The increasing sophistication of trading algorithms and the large volume of data have often created unprecedented challenges for traditional market analysis. Our framework addresses these challenges by introducing a GNN-based architecture that takes advantage of the physical and structural properties of business data. The proposed method transforms HFT data into graphical models where the nodes represent market conditions and the edges capture their physical and price relationships. A specialized GNN architecture, incorporating attention mechanisms and temporal convolution modules, is developed to learn complex trading patterns and identify potential anomalies. The model is evaluated on high-frequency trading data from five major stocks listed on NASDAQ, spanning six months of trading activity with over 10 million events. Experimental results demonstrate superior performance compared to existing approaches, achieving a 15% improvement in detection accuracy and maintaining robust performance across different market conditions. The framework exhibits particular strength in identifying complex manipulation patterns while maintaining low false positive rates. Our approach processes large volumes of trading data in real time with significantly reduced computational requirements compared to traditional methods. This research contributes to the development of more effective market surveillance systems and provides valuable insights for regulatory authorities in maintaining market integrity.

Integration of ion mobility-quadrupole time-of-flight mass spectrometry and triple quadrupole-ion trap mass spectrometry coupled with ultra-high performance liquid chromatography for characterizing and quantitatively assay the saponins of Panax quinquefolius flower

Panax quinquefolius (American ginseng) is globally popular serving as herbal medicine or tonifying agent. Compared with the root, studies on the flower of Panax quinquefolius (PQF) in respect of the multicomponent characterization and quality evaluation is scarce. This work was designed, by feat of two liquid chromatography–mass spectrometry platforms, to characterize and quantitatively assay the saponins of PQF. Ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) combined with data-independent high-definition MSE (HDMSE) was developed to acquire the structure information of PQF saponins. Consequently, efficient and more accurate identification (higher identification accuracy and much lower false positives rate) of the involved saponins were accomplished by the intelligent peak annotation workflows of the UNIFI bioinformatics platform incorporated with a “Multi-dimensional Information Library of Ginsenosides” (GinMIL). Totally 128 ginsenosides were identified or tentatively characterized, and 45 thereof were identified by comparing with the reference standards. Additionally, an efficient UHPLC/QTrap-MS (triple quadrupole-ion trap mass spectrometry) method enabling quantitative assay of 21 ginsenosides in PQF was established and validated by scheduled multiple reaction monitoring (sMRM). It gave low limit of detection (0.011–0.65 pg) and limit of quantification (0.053–4.1 pg), good linearity (R2 > 0.99), intra-/inter-day precision (RSD < 13.6 %), stability (RSD < 3.5 %), repeatability (RSD < 7.5 %), and recovery (RSD < 13.5 %). It application to quantify 21 ginsenosides in 14 batches of PQF samples revealed differences in their saponin content. Conclusively, systematic multi-component characterization and multi-indicator quantitation of PQF were achieved, thus providing the methodological reference for the quality investigation of functional herbs.

AI-Enhanced Secure Mobile Banking System Utilizing Multi-Factor Authentication

The increasing reliance on mobile banking has significantly heightened the need for robust security mechanisms to protect users from unauthorized access and fraudulent activities. As mobile banking continues to grow in popularity, safeguarding financial transactions and personal data becomes a top priority. This paper introduces an AI-enhanced secure mobile banking system that leverages Multi-Phase Authentication (MPA) to strengthen the authentication process. In this system, artificial intelligence is integrated with traditional authentication methods, creating a dynamic framework that assesses the risk level associated with each user interaction. Based on this real-time risk assessment, the system adjusts the authentication requirements, making them more stringent when higher risks are detected and more lenient when the risk is lower. This adaptive mechanism not only enhances the security of mobile banking by providing multiple layers of protection but also improves the user experience by reducing unnecessary authentication steps that can cause frustration and delay. The proposed system's effectiveness is validated through a series of simulations and case studies, which demonstrate significant improvements in key security metrics. These include a marked reduction in instances of fraud and lower false positive rates, which indicate that the system can accurately distinguish between legitimate and suspicious activities without imposing undue burden on users. Overall, the results of this study highlight the potential of AI-enhanced multi-phase authentication to provide a scalable and user-friendly solution for secure mobile banking. This approach represents a promising direction for the future of digital financial services, offering a balance between rigorous security and seamless user experience.

Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

The biological relevance and dynamics of mRNA modifications have been extensively studied; however, whether rRNA modifications are dynamically regulated, and under which conditions, remains unclear. Here, we systematically characterize bacterial rRNA modifications upon exposure to diverse antibiotics using native RNA nanopore sequencing. To identify significant rRNA modification changes, we develop NanoConsensus, a novel pipeline that is robust across RNA modification types, stoichiometries and coverage, with very low false positive rates, outperforming all individual algorithms tested. We then apply NanoConsensus to characterize the rRNA modification landscape upon antibiotic exposure, finding that rRNA modification profiles are altered in the vicinity of A and P-sites of the ribosome, in an antibiotic-specific manner, possibly contributing to antibiotic resistance. Our work demonstrates that rRNA modification profiles can be rapidly altered in response to environmental exposures, and provides a robust workflow to study rRNA modification dynamics in any species, in a scalable and reproducible manner.

HawkEye: An end-host method to detect the Low-rate Denial-of-Service attack of cross-traffic over bottleneck links

The adaptive mechanisms of the Transmission Control Protocol (TCP) address network congestion and other unpredictable network conditions. They ensure the reliability of data transmission and the stability of the network. Unfortunately, the vulnerabilities in these adaptive mechanisms are targeted explicitly by low-rate denial-of-service (LDoS) attacks, which severely degrade network service quality. Only by modifying these protocols and addressing their vulnerabilities can one entirely prevent LDoS attacks. Although various improved TCP algorithms exist, they often fail to identify LDoS attacks accurately and, in some cases, may even reduce TCP performance. Furthermore, traditional LDoS attack detection methods rely on intermediate devices, which do not meet TCP’s end-to-end performance optimization needs. We introduce HawkEye, which moves the detection mechanism to the end hosts to address this issue. HawkEye uses an improved genetic algorithm to fine-tune the parameters of the LightGBM on the sending host, integrating multiple network traffic features to detect LDoS attacks. Experimental results show that our proposed method achieves the high accuracy, high true positive rate, and low false positive rate, successfully addressing the limitations of end-host detection of LDoS attacks and providing an innovative and effective solution for enhancing LDoS attack detection.

Smart Water Management with Digital Twins and Multimodal Transformers: A Predictive Approach to Usage and Leakage Detection

Effective water management is crucial in urban and rural settings, requiring efficient usage and timely detection of issues like leakages for sustainability. This paper introduces an integrated framework that combines Digital Twin technology with a multimodal transformer-based model for accurate water usage prediction and leakage detection. The system synchronizes real-time data from various sensors including flow meters, pressure sensors, and thermal imaging devices with a Digital Twin of the water network. Advanced transformer models, specifically the Informer model for long-term time-series prediction and a Water Multimodal Transformer for anomaly detection, process these data to capture complex patterns and dependencies. Experimental results demonstrate the framework’s effectiveness: the Informer model achieved an R2 score of 0.9995 and a Mean Squared Error (MSE) of 2.2, outperforming traditional models. For leakage detection, the model attained 98.4% accuracy and precision, an F1 score of 97.6%, a low False Positive Rate of 0.0019, and an Area Under the Curve (AUC) of 0.984. By fusing diverse sensor data and utilizing advanced transformer architectures, the framework provides a comprehensive view of the water network, enabling real-time decision-making, enhancing forecasting accuracy, and reducing water waste. This scalable solution supports sustainable water management practices in both urban and industrial contexts.

Evaluation of the mindray CL900i CLIA HIV Ag/Ab combo assay for sensitive and specific HIV screening compared to established methods

Architect-HIV Ag/Ab combo chemiluminescence assay is globally recognized for its sensitivity but has a notable false-positive rate. In this study, we aim to evaluate the performance of a new cost-effective screening alternative, the chemiluminescence Ag/Ab combo assay (CL-900i-HIV) from Mindray, China. We selected 195 archived samples categorized according to the INNO-LIA™ HIV I/II, the gold standard confirmatory assay. These samples included true positive (n = 38; positive by Architect-HIV & INNO-LIA-HIV), true negative (n = 101; negative by Architect-HIV & INNO-LIA-HIV), false positive (n = 20; positive by Architect-HIV & negative by INNO-LIA-HIV), and indeterminate results (n = 26). We tested all samples using the Mindray CL-900i-HIV and all positive Architect-HIV samples (n = 80) were confirmed by PCR. Compared to INNO-LIA™ HIVI/II line immunoassay confirmatory assay, Mindray CL-900i-HIV demonstrated a sensitivity of 100% (95% CI 90.7–100), specificity of 100% (95% CI 97.0–100), overall percent agreement (OPA) of 100% (95% CI 97.7–100.0), and perfect agreement with the INNO-LIA confirmatory assay (κ = 1.00). Additionally, Mindray’s CL-900i-HIV exhibited a significantly lower false-positive rate (8.75%) compared to Architect-HIV’s (55%). Mindray CL900i demonstrated high sensitivity and very low false-positive rate, thus, has the potential to serve as an excellent, cost-effective surrogate for HIV screening, overcoming the limitations of existing automated assays.

XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection

With the proliferation of web applications, cross-site scripting (XSS) attacks have increased significantly and now pose a significant threat to users' information security and privacy. To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. Moreover, existing approaches often prioritize detection accuracy over real-time processing capabilities, which are essential for effective defense. To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extensive evaluations of our dataset demonstrate that the proposed model outperforms other baseline ML models across various metrics, including processing rate. Notably, our model achieves an accuracy of 99.27% while maintaining a low false positive rate of 0.06% and high processing rate of exceeding 1000 samples per second. These results highlight its high accuracy and robustness in detecting XSS, and suitability for real-time applications. Our work presents a comprehensive solution for enhancing web application security by providing a diverse dataset and a high-accuracy detection model with low latency.

Design and implementation of a smoke alarm system using YOLOv8

Abstract. This paper present a smoke alarm system built using Yolov8, a state-of-the-art object detection model. The system is designed to detect smoke in real-time environments and alert users in the event of a fire or smoke detection. The use of Yolov8 in this system offers several advantages over other object detection models, making it a suitable choice for building an effective smoke alarm system. One of the critical advantages of Yolov8 is its high accuracy in detecting objects with a small size, such as smoke. This is achieved through its efficient and robust architecture, which allows it to process images quickly and accurately. In comparison to other object detection models, Yolov8 demonstrates superior performance in terms of speed and accuracy. Furthermore, Yolov8s ability to detect objects with high precision and low false positives is crucial for a smoke alarm system. This is because smoke detection needs high accuracy to prevent false alarms and promptly detect real threats. The smoke alarm system built using Yolov8 can be implemented in various environments, including homes, offices, and public buildings. It can provide early warnings for fires and smoke, enabling timely evacuation and reducing potential injuries or damages. The implementation of this system can greatly contribute to improving safety in various environments by providing early warnings of fires and smoke.

A robust distance-based approach for detecting multidimensional outliers

ABSTRACT Identifying outliers in data analysis is a critical task, as outliers can significantly influence the results and conclusions drawn from a dataset. This study explores the use of the Mahalanobis distance metric for detecting outliers in multivariate data, focusing on a novel approach inspired by the work of M. Falk, [On mad and comedians, Ann. Inst. Stat. Math. 49 (1997), pp. 615–644]. The proposed method is rigorously tested through extensive simulation analysis, where it demonstrates high True Positive Rates (TPR) and low False Positive Rates (FPR) when compared to other existing outlier detection techniques. Through extensive simulation analysis, we empirically evaluate the affine equivariance and breakdown properties of our proposed distance measure and it is evident from the outputs that our robust distance measure demonstrates effective results with respect to the measures FPR and TPR. The proposed method was applied to seven different datasets, showing promising true positive rates (TPR) and false positive rates (FPR), and it outperformed several well-known outlier identification approaches. We can effectively use our proposed distance measure in fields demanding outlier detection.

Dye-based chromoendoscopy versus i-scan virtual chromoendoscopy in long-standing ulcerative colitis: Multicenter prospective RCT.

Background and study aims Long-standing ulcerative colitis (UC) is associated with an increased risk of developing colorectal neoplasia. Both dye-based chromoendoscopy (DCE) and virtual chromoendoscopy (VCE) increase detection of neoplastic lesions. In this prospective randomized controlled trial (RCT), we compared the neoplasia detection rate between DCE and i-scan VCE in patients with long-standing UC. Patient and methods In four European hospitals, 131 patients with long-standing UC (disease duration > 8 years) were randomized to either DCE with methylene blue 0.1% (n = 66) or i-scan VCE (n = 65). All procedures were performed by trained endoscopists. Biopsies were taken from all visible lesions and the surrounding mucosa. Results The mean number of neoplastic lesions detected per colonoscopy was not significantly different between DCE (0.27) and i-scan VCE (0.37) ( P = 0.41). Similarly, there was no significant difference in neoplasia detection rate between DCE (19.7%) and VCE (27.7%) (odds ratio0.64, 95% confidence interval 0.28-1.50, P = 0.31). However, the per lesion neoplasia detection rate was significantly higher with i-scan VCE compared to DCE (27.6% vs 15.3%, P = 0.036). Both withdrawal and total procedure time were on average 10.0 and 9.9 minutes shorter using i-scan VCE (both P < 0.001). Conclusions This multicenter, prospective RCT showed no significant difference in neoplasia detection between DCE and i-scan VCE in long-standing UC. However, use of i-scan VCE was associated with a lower false-positive rate and a significantly shorter procedure time compared with DCE. I-scan VCE, therefore, could be a valid replacement for DCE in UC surveillance colonoscopies.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Application of disposable digital hysteroscope for outpatient hysteroscopy in women undergoing artificial assisted reproductive procedures: a clinical research

BackgroundHysteroscopy is the preferred method for the diagnosis and treatment of uterine diseases. With the increasing demand for outpatient hysteroscopy, disposable digital hysteroscopy has emerged, which can meet the requirements of diagnosis and treatment integration, and greatly reduce the labour and storage costs.MethodsA total of 108 outpatients who underwent hysteroscopy using disposable digital hysteroscope were divided into two groups: the non-porous group (group A, n=49) and the married with child group (group B, n=59). The patients’ general information and clinical data were recorded and statistical analysis was performed.ResultsNo differences were found between the age, weight, body mass index, menopausal status and indication for hysteroscopy of the two groups. The most common indication was embryo transfer failure. The surgical success rate of 108 patients was 100%, the concordance rate between hysteroscopic diagnosis of chronic endometritis and pathological diagnosis was 94.44% (68/72), while the false positive rate stood at 38.89% (14/36). Additionally, incidental findings rate for endometrial polyps was 92.00% (23/25) with a low false positive rate of only 6% (5/83). The mean visual analogue scale score of group A was 2.285±0.180, which was not significantly different from group B (p=0.483, 95% CI(−0.60-0.44)). The pregnancy rate among patients who received assisted reproductive technology and were followed up for 1 year after surgery was 66.67% (40/60), while the abortion rate stood at 5.00% (3/60).ConclusionsThe disposable digital hysteroscope is a valuable and effective tool for outpatient hysteroscopy.

Use of ST/HR hysteresis decreases false positive rate in exercise electrocardiography test of middle-aged asymptomatic women

BackgroundExercise electrocardiography (ECG) is a common diagnostic and prognostic method for the detection of coronary artery disease (CAD). However, its accuracy in asymptomatic women has not been comprehensively investigated and the diagnostic criteria may require refinements. This study evaluated the performance of ECG-derived ST/HR-index, ST/HR hysteresis and ST-segment depression parameters among asymptomatic middle-aged women. Methods108 women (mean age 56 ± 4 years) performed exercise ECG test on treadmill until exhaustion three times within a nine-month period. False-positive rates of maximum ST/HR-index, ST/HR hysteresis, and ST-segment depression values measured from 12 leads at peak exercise and after one-minute recovery were evaluated with recommended diagnostic partition values. Repeatability was assessed with intraclass correlation (ICC) and Bland-Altman plot analysis. ResultsFalse-positive rate was lower for all variables when a two‑lead configuration was used instead of a single‑lead configuration. Using a two‑lead configuration, ST/HR hysteresis (0–1.9 %) and ST-segment depression after one-minute recovery (0–2.8 %) had lower false-positive rates compared to ST/HR index (3.7–20.4 %) and ST-segment depression at peak exercise (9.3–27.8 %). ICC values indicated moderate repeatability for ST/HR hysteresis while ST/HR index, ST-segment at peak exercise, and ST-segment after one-minute recovery had moderate-to-good repeatability. Bland-Altman analysis indicated poor repeatability for all evaluated ECG variables. ConclusionIn asymptomatic middle-aged women, practitioners should prefer the use of ST/HR hysteresis and ST-segment after one-minute recovery over the conventional ST-segment depression at peak exercise or ST/HR index, and evaluate the ECG data from a two‑lead configuration instead of single‑lead.

PsyneuroNet architecture for multi-class prediction of neurological disorders

Early detection and diagnosis of psychiatric disorders are crucial for timely intervention, improved treatment outcomes, and preventing complications. The purpose of this study is to use artificial intelligence methods to diagnose symptoms of psychiatric conditions such as depression, schizophrenia, sleep disorder, and Alzheimer’s disease in epileptic patients using EEG data. The research uses a cross-validation procedure with ten iterations and looks at three data sets: the CHB-MIT dataset with EEG recordings from neonatal subjects, the DEAP dataset containing physiological signals and emotional ratings, and the OpenNeuro dataset containing multichannel EEG. Preprocessing involves dividing the EEG signals into five brain wave frequency components and applying filtering techniques for further analysis. We introduce a novel architecture called PsyneuroNet, which aims to improve feature extraction and prediction accuracy for real-time psychiatric disorder recognition and integrates three key components: cross-normalization across convolution, a bidirectional long-short-term memory network (Bi-LSTM), and an enhanced intuition-based convolutional neural network (ICNN). The proposed methodology is evaluated based on the 99.65% prediction accuracy achieved on the CHB-MIT dataset, the 97.27% prediction accuracy achieved on the DEAP dataset, and the 98.08% prediction accuracy achieved on the OpenNeuro dataset for prediction problems. The proposed method outperforms the state-of-the-art approaches in terms of accuracy by 3.64%, with a sensitivity of 99.65%, a specificity of 99.75%, and a very low false positive rate per hour (FPR/h) of 0.3%, all achieved by using ten folds cross-validation. The PsyneuroNet’s capability to classify a diverse range of neurological disorders within a single architecture is noteworthy. This versatility makes the model suitable for addressing a broad spectrum of clinical scenarios. The model’s ability to generalize effectively to unseen data, as demonstrated by testing its performance on the DEAP and OpenNeuro datasets after training on CHB-MIT data, is a significant aspect of novelty.

15 Predictive validity of two developmental screening tools at the 18-month visit and neurodevelopmental consultation at an average age of 8 years

Abstract Background The Canadian Paediatric Society (CPS) recommends an enhanced 18-month well-baby visit incorporating a developmental screening tool to stimulate discussion with parents, and notes that a widely used tool is the Nipissing District Developmental Screen (NDDS). For early detection of autism spectrum disorder, the CPS notes that a commonly used screening tool is the Infant Toddler Checklist (ITC). Objectives To examine the predictive validity of the ITC and NDDS at the 18-month visit using later neurodevelopmental consultation as the criterion measure; and examine whether positive developmental screening was associated with health care utilization (HCU). Design/Methods Using a prospective design, parents completed the ITC and the NDDS at the 18-month visit in primary care practices. Seven types of HCU were collected from health administrative databases, via linkage using the child’s health insurance number, including: neurodevelopmental consultation, special paediatric consultation, scheduled primary care visit billings, unscheduled primary care or minor visit billings, other non-primary care visit billings, hospitalizations, and emergency department visits. Covariates included child and family characteristics. We calculated screening test properties (using neurodevelopmental consultation as the criterion) and used multivariable negative binomial regression to estimate rate ratios (RR) for each of the 7 types of HCU. Results For both the ITC (n=1460) and NDDS (n=802) cohorts, mean age at screening was 18 months and mean age at follow up was 8 years. Of the ITC cohort, 11% were screen positive, 2.6% had a neurodevelopmental consultation. A positive ITC had 40% sensitivity (95% CI 24%-57%), 90% specificity (95% CI 88%-91%), 10% false positive rate (95% CI 9%-12%). A positive ITC was associated with 6 of 7 types of HCU, including neurodevelopmental consultation (RR=2.78, 95% CI 1.37-5.67). Of the NDDS cohort, 36% were screen positive, 2.5% had a neurodevelopmental consultation. A positive NDDS had a sensitivity of 50% (95% CI 27%-73%), specificity of 65% (95% CI 61%-68%), false positive rate of 35% (95% CI 31%-40%). A positive NDDS was associated with only 1 of 7 types of HCU: unscheduled primary care or minor visit billings 1.24 (95% CI 1.01, 1.53, p=0.04). Conclusion The predictive validity of the ITC was stronger than the NDDS due to high specificity and low false positive rates. The low sensitivity of the ITC is similar to other screening tools used at 18 months, highlighting the importance of ongoing developmental surveillance. The false positive rate of the NDDS is unacceptably high for a screening tool.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

A Method for Predicting the Impact of Labor Education at Institutions of Higher Education Based on a Two-Stage Clustering Algorithm

In order to develop more accurate teaching plans and methods based on students’ characteristics and needs, and improve the effectiveness of labor education, a two-stage clustering algorithm based method for predicting the effectiveness of labor education in universities is studied. A two-stage clustering algorithm-based method for predicting the effectiveness of labor education in universities has been proposed. Through in-depth analysis of labor education data in universities, principal component analysis (PCA) was used to select 16 most representative variables from the original features. Subsequently, self-organizing map (SOM) neural network was used for preliminary clustering, and the optimal number of clusters was determined to be 8. Furthermore, the K-means clustering algorithm performs fine clustering based on the initial centers provided by SOM, significantly improving the stability and accuracy of clustering. At the same time, processing the outliers in the clustering results has reduced their impact on the clustering effect. Finally, the processed data was input into an improved XGBoost prediction model, which achieved a true positive rate (TPR) of over 85% on the ROC curve when predicting the effectiveness of labor education in universities. While maintaining a low false positive rate (FPR), the model outperformed other comparison methods significantly. This achievement not only validates the effectiveness of the proposed method in feature selection, clustering optimization, and prediction model construction, but also confirms its ability to accurately predict the labor education effectiveness of students in different grades, providing strong support for higher education institutions to optimize teaching plans and improve educational outcomes.

The Role of Pentacam Random Forest Index in Detecting Subclinical Keratoconus in a Chinese Cohort.

Purpose: This study aimed to evaluate the diagnostic accuracy of a novel shape index, the Pentacam Random Forest Index (PRFI), in detecting keratoconus (KC), specifically subclinical keratoconus, in Chinese refractive surgery candidates. Methods: This prospective cohort study included 856 participants who were divided into four groups based on their tomographic outcomes: the KC group (n = 137), the very asymmetric ectasia (VAE) group (n = 73), the normal cornea group (n = 363) and the tomographically suspected KC (TSK) group (n = 283). The diagnostic performance of PRFI and other widely used indices, including the shape index BAD-D and the combined index TBI, was assessed using receiver operating characteristic (ROC) curve analysis and compared using DeLong's test. The area under the curve (AUC), best cutoff values, and Youden index for each parameter are reported. Additionally, the false-positive rates of BAD-D and PRFI were calculated and compared in "normal corneas". Results: All shape and biomechanical parameters collected in this study were found to be significantly different among the four groups (KC, VAE, TSK, and normal groups; p = 0.000). The AUC of PRFI was the highest in detecting any form of KC (including clinical KC eyes and VAE-NT eyes) in Chinese refractive surgery candidates, outperforming the widely used shape index BAD-D (0.919 vs. 0.890, p < 0.001). There was no significant difference in performance between the PRFI and the combined TBI index (0.919 vs. 0.916, p > 0.05). For detecting subclinical KC eyes (i.e., VAE-NT), the AUC of PRFI was 0.774, which was statistically comparable to TBI (0.774 vs. 0.776, p > 0.05), while outperforming BAD-D (0.774 vs. 0.684, p < 0.001). The best cutoff values of PRFI for detecting any KC and VAE-NT eyes were determined to be 0.37 and 0.27, respectively. Additionally, PRFI demonstrated a lower false-positive rate than BAD-D (13.8% vs. 43.8%, p < 0.001). Notably, the relatively high false-positive rate of BAD-D observed in this study might be attributed to the smaller horizontal corneal diameter in tomographically suspected eyes. Conclusions: The PRFI proved to be a superior shape index compared to BAD-D in detecting any form of keratoconus, including subclinical cases, in Chinese refractive surgery candidates. This finding may be attributed to the relatively small corneas commonly observed in Asians.