Binary Classification Task Research Articles

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

C3NN: Cosmological Correlator Convolutional Neural Network an Interpretable Machine-learning Framework for Cosmological Analyses

Modern cosmological research in large-scale structure has witnessed an increasing number of machine-learning applications. Among them, convolutional neural networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference, and various other tasks. However, many models based on CNNs are criticized as “black boxes” due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN)—a fusion of CNN architecture and cosmological N-point correlation functions (NPCFs). We demonstrate that its output can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we can open the “black box” by quantitatively ranking different orders of the interpretable outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and log-normal random fields and relating its outputs to the NPCFs describing the two fields. Furthermore, we exhibit the model’s ability to distinguish different dark energy scenarios (w 0 = −0.95 and −1.05) using N-body simulated weak-lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool to extract physical insights in a robust and explainable way from observational data.

Instance Selection via Voronoi Neighbors for Binary Classification Tasks

Instance Selection via Voronoi Neighbors for Binary Classification Tasks

Unveiling anomalies: harnessing machine learning for detection and insights

The rise of Internet of Things (IoT) devices has brought about an increase in security risks, emphasizing the need for effective anomaly detection systems. Previous research introduced a dynamic voting classifier to overcome overfitting or inaccurate accuracies caused by dataset imbalance. This article introduces a new method for IoT anomaly detection that employs a hybrid voting classifier, which combines several machine learning models. To solve the overfitting and class weight issues, an adaptive voting classifier is used that adjusts weights according to the highest preference for accuracy. The developing voting system increases the effectiveness of more accurate classifiers, enhancing the group’s overall capability. A proposed combined classifier combines Logistic Regression, AdaBoost, Gradient Boosting, and Multi-Layer Perceptron models using a soft voting method. To develop and assess this method, the CIC-IoT-2023 dataset is utilized, which contains 33 types of IoT attacks across 7 categories. This process includes thorough data preprocessing and feature selection from a pool of 42 available attributes. The performance of this approach is measured against individual classifiers across binary, 8-class, and 34-class classification tasks. The results highlight the effectiveness of the hybrid model. It achieves 98.95% accuracy, 76.72% recall, and 72.01% F1-score in the 34-class problem, surpassing the performance of all individual models. For the 8-class task, the hybrid classifier attains 99.39% accuracy, 90.89% recall, and an 83.01% F1-score. This demonstrates the high potential of the hybrid approach for IoT anomaly detection.

Empirical study on the performance of various machine learning models in predicting stock price movements as a binary classification task

Empirical study on the performance of various machine learning models in predicting stock price movements as a binary classification task

Development and External Validation of a Machine Learning–based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study

ObjectivesTo develop and externally validate a machine learning–based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool. DesignProspective cohort study. Setting and ParticipantsA total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China. MethodsPotential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP). ResultsThe 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram. Conclusions and ImplicationsThe machine learning–based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study

IntroductionBreast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.Material and methodsFour-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.ResultsThe dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.ConclusionDeep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.Relevance statementDeep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.Key points• We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms.• VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts.• Visual explanations using Grad-CAM++ highlighted VGG16’s superior performance in localizing BAC.Graphical

A micro Reinforcement Learning architecture for Intrusion Detection Systems

This paper proposes an Intrusion Detection System (IDS) that utilizes Deep Reinforcement Learning (DRL) in a fine-grained manner to enhance the performance of binary and multiclass intrusion classification tasks. The proposed system, named Micro Reinforcement Learning Classifier (MRLC), is evaluated using three standard datasets. MRLC architecture utilizes a fine-grained learning approach to enhance IDS accuracy. Simulation studies demonstrate that MRLC has a high efficiency in discriminating different intrusion classes, outperforming state-of-the-art RL-based methods. The average accuracy of MRLC is 99.56%, 99.99%, 99.01% for NSL-KDD, CIC-IDS2018, and UNSW-NB15 datasets respectively. The implementation codes are available at https://github.com/boshradarabi/MICRO-RL-IDS.

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

In the ever-evolving realm of technology, the identification of human activities using intelligent devices such as smartwatches, fitness bands, and smartphones has emerged as a crucial area of study. These devices, equipped with inertial sensors, gather a wealth of data and provide insights into users' movements and behaviors. These data not only serve practical purposes, but also hold significant implications for domains such as healthcare and fitness tracking. Traditionally, these devices have been employed to monitor various health metrics such as step counts, calorie expenditure, and real-time blood pressure monitoring. However, recent research has shifted its focus to leveraging the data collected by these sensors for user authentication purposes. This innovative approach involves the utilization of Machine Learning (ML) models to analyze the routine data captured by sensors in smart devices employing ML algorithms, which can recognize and authenticate users based on their unique movement patterns and behaviors. This introduces a paradigm shift from traditional one-time authentication methods to continuous authentication, adding an extra layer of security to protect users against potential threats. Continuous authentication offers several advantages over its conventional counterparts. First, it enhances security by constantly verifying a user's identity through their interaction with the device, thereby mitigating the risk of unauthorized access. Second, it provides a seamless and nonintrusive user experience, eliminating the need for repetitive authentication prompts. Moreover, it offers robust protection against various threats such as identity theft, unauthorized access, and device tampering. The application of continuous authentication extends beyond individual devices and encompasses interconnected systems and networks. This holistic approach ensures a comprehensive security across digital platforms and services. The experiments demonstrate that the logistic regression model achieves an accuracy of 82.32% on the test dataset, highlighting its robustness for binary classification tasks. Additionally, the random forest model outperforms with a 92.18% accuracy, emphasizing its superior capability in handling complex feature interactions. In the study, the sequential neural network achieved an accuracy of 92% on the HAR dataset, outperforming traditional machine learning models by a significant margin. The model also demonstrated robust generalization capabilities with a minimal drop in performance across various cross-validation folds.

O-246 A cutting-edge artificial intelligence system tracking embryo development and pinpointing the optimal day for blastocyst utilization

Abstract Study question Can an AI model predict at which specific day (D) of development a blastocyst will reach expansion at a usable stage for biopsy/cryopreservation/transfer? Summary answer Our model achieved a high accuracy tracking embryos’ developmental times while forecasting the day when they would reach a usable blastocyst stage. What is known already Forecasting the optimal day for blastocyst utilization is crucial. It can facilitate informed decisions on transfer/biopsy/cryopreservation, while streamlining the laboratory workflow. Previous prediction models on this task relied solely on embryo morphological and morphokinetic data. However, emerging approaches, incorporating computer image analysis, offer an opportunity to enhance their accuracy and objectivity. To our knowledge, none has specifically predicted the precise day of embryo development when the blastocyst has expanded enough to become usable. This information is invaluable to plan workload distribution on D5/6/7 of development. Moreover, with the rise of PGT-A, optimizing the time allocated for biopsy becomes essential. Study design, size, duration Retrospective study conducted at a tertiary IVF centre, utilizing data from June 2015 to January 2023. A total of 14,704 time-lapse videos (TLV) from embryos of 1,805 patients, cultured across 2,475 cycles, were analyzed split into train/validation/test by patient (to avoid data leaking) with 60/20/20 proportion. Outcome parameters encompassed the times for all embryo developmental stages or morphokinetic parameters, blastocyst formation and utilization, as well as the specific day of blastocyst utilization. Participants/materials, setting, methods Embryos from patients with primary/secondary infertility were included. Laboratory culture conditions followed normal clinical routine. All embryos were monitored using embryoscope time-lapse systems and annotated by experienced embryologists. Blastocysts graded ≥BL3CC (Gardner) were biopsied/cryopreserved/transferred on D5/6/7. Only videos depicting embryos with normal fertilization were considered in the model. Predictions for the utilization day were made 48-hours and 24-hours ahead of the blastocyst usable stage. Main results and the role of chance We developed a customized variant of the Temporal Fusion Transformer model, modified for analyzing time-sequence-images from TLV. This model architecture allows the analysis of the sequence and clinical variables, identifying its most influential aspects from the model’s perspective. In the first two tasks of blastocyst prediction and forecasting blastocyst utilization day, data was split into training-set: 8,802 TLV (1,083 patients); validation-set: 2,925 TLV (361 patients); and test-set: 2,977 TLV (361 patients). Our trained model achieved a strong performance on ‘early warning’ 48-hour ahead forecasting of embryo reaching a usable blastocyst stage. Utilizing only unlabelled time-lapse image observations of the embryo, it reached 90 AUROC score on the test-set. For ‘late warning’, 24-hour ahead, we reached 97 AUROC score on the test-set, almost ideal prediction. Finally, 91 AUROC score was achieved on the binary blastocyst stage classification task, just considering the blastocyst formation at any expansion. In the embryo morphokinetic stage-labelling task, data was split into training-set: 8,420 TLV (1,078 patients); validation-set: 2,777 TLV (360 patients); and test-set: 2,780 TLV (360 patients). We achieved competitive results with 95.5 macro-averaged AUROC score for multi-class (17 classes) prediction for each time-lapse-image. Each individual parameter from tPB2 to tHB reached a AUROC higher than 90. Limitations, reasons for caution The model needs to undergo an extensive cross-validation, to evaluate its performance, followed by a prospective validation. Wider implications of the findings This model belongs to the realm of IVF lab automation, offering substantial assistance in daily operations. It aids embryologists not only in annotating developmental stages with high accuracy, but also predicts the blastocyst utilization day, thereby providing crucial support for efficient workload planning and streamlined operations. Trial registration number NA

Covert Task Embedding: Turning a DNN Into an Insider Agent Leaking Out Private Information.

We present the covert task embedding (CTE) attack, a new general threat affecting deep neural networks (DNNs). The new attack consists in hiding a malicious privacy-sensitive task within a seemingly innocuous network, in such a way that the result of the malicious task is delivered together with the legitimate output in a stealthy way. The result of the covert task is further protected by requiring that its extraction depends on a secret key shared by the embedder and the detector. We demonstrate the feasibility of the CTE attack in various settings, wherein a face-based age estimation DNN is trained in such a way as to also detect the gender (binary classification task) or ethnicity (multiclassification task) of the framed individual and stealthily pass along such information together with the estimated age. The results of the experiments we carried out show that, in all cases, the gender and ethnicity information can be reliably extracted without impairing the accuracy of the age estimation functionality. Despite the simplicity of the estting considered in the brief, our experiments show the feasibility of the CTE attack, thus calling for the development of suitable remedies against it.

Deep Learning Application as a Method for Classification Equipment Failure on Natural Gas Pipeline

Predicting maintenance and preventing failures in natural gas pipelines is critical for safety and efficiency in the oil and gas industry. This research utilizes machine learning (ML) and deep learning (DL) methods to predict failures in natural gas transmission pipelines. Using a dataset from the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA), the study examines failure incidents from 2010-2019 and non-failure data from 2020-2023. The research involves comprehensive data preprocessing, addressing missing values and dataset imbalances. Key pipeline attributes such as diameter, wall thickness, age, and operating pressure are used for failure prediction. The ML approach employs logistic regression, optimized for binary classification tasks, to establish the probability of pipeline failures. For the DL approach, an Artificial Neural Network (ANN) with Feed Forward and Back Propagation algorithms is developed. Key parameters, including activation functions and hyperparameters, are finely tuned to enhance predictive performance. ReLU activation functions are used in input and hidden layers, while the sigmoid function is applied to the output layer. Performance evaluation of both ML and DL models is based on accuracy, F1 Score, and Area Under Curve (AUC). In summary, ML showed better performance than DL, with value of accuracy is 0.74, F1 Score is 0.85, and AUC is 0.55 while the value of accuracy, F1 score, AUC from DL is 0.61, 0.003, and 0.58 respectively. DL offers superior performance in handling complex and huge amounts of data. In the other hand, ML offers more robust models and simpler adjustable models than DL. Because of the amount of computed data in this research, it is better to have ML as a predictive algorithm.

D-Net: A dual-encoder network for image splicing forgery detection and localization

Many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these methods focus on validating local patches or local objects. We regard image splicing forgery detection as a binary classification task that distinguishes tampered and non-tampered regions by forensic fingerprints rather than semantic features. As the network goes deep, its representation ability becomes strong. However, the non-semantic forensic fingerprints can hardly be retained by normal CNNs in deep layers. We proposed a novel dual-encoder network (D-Net) for image splicing forgery detection to resolve these issues, employing an unfixed and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides structural information that assists the learning and detection of the forgeries. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Net for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Net and state-of-the-art methods, D-Net, without pre-training or training on a large number of forgery images, outperformed the other methods in pixel-level forgery detection. Moreover, it is stably robust to different anti-forensic attacks.

Galaxy merger challenge: A comparison study between machine learning-based detection methods

Aims. Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. Our aim is to benchmark the relative performance of merger detection methods based on machine learning (ML). Methods. We explore six leading ML methods using three main datasets. The first dataset consists of mock observations from the IllustrisTNG simulations, which acts as the training data and allows us to quantify the performance metrics of the detection methods. The second dataset consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data to those employed for training. The third dataset is composed of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. We also compare mergers and non-mergers detected by the different methods with a subset of HSC-SSP visually identified galaxies. Results. For the simplest binary classification task (i.e. mergers vs. non-mergers), all six methods perform reasonably well in the domain of the training data. At the lowest redshift explored 0.1 < ɀ < 0.3, precision and recall generally range between ~70% and 80%, both of which decrease with increasing ɀ as expected (by ~5% for precision and ~10% for recall at the highest ɀ explored 0.76 < ɀ < 1.0). When transferred to a different domain, the precision of all classifiers is only slightly reduced, but the recall is significantly worse (by ~20–40% depending on the method). Zoobot offers the best overall performance in terms of precision and F1 score. When applied to real HSC observations, different methods agree well with visual labels of clear mergers, but can differ by more than an order of magnitude in predicting the overall fraction of major mergers. For the more challenging multi-class classification task to distinguish between pre-mergers, ongoing-mergers, and post-mergers, none of the methods in their current set-ups offer good performance, which could be partly due to the limitations in resolution and the depth of the data. In particular, ongoing-mergers and post-mergers are much more difficult to classify than pre-mergers. With the advent of better quality data (e.g. from JWST and Euclid), it is of great importance to improve our ability to detect mergers and distinguish between merger stages.

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system

Internet of Things (IoT) devices are extensively utilized but are susceptible to cyberattacks, posing significant security challenges. To mitigate these threats, machine learning techniques have been implemented for network intrusion detection in IoT environments. These techniques commonly employ various feature reduction methods, prior to inputting data into models, in order to enhance the efficiency of detection processes to meet real-time requirements. This study provides a comprehensive comparison of feature selection (FS) and feature extraction (FE) techniques for network intrusion detection systems (NIDS) in IoT environments, utilizing the TON-IoT and BoT-IoT datasets for both binary and multi-class classification tasks. We evaluated FS methods, including Pearson correlation and Chi-square, and FE methods, such as Principal Component Analysis (PCA) and Autoencoders (AE), across five classic machine learning models: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP). Our analysis revealed that FE techniques generally achieve higher accuracy and robustness compared to FS methods, with RF paired with AE delivering superior performance despite higher computational demands. DTs are most effective with smaller feature sets, while MLPs excel with larger sets. Chi-square is identified as the most efficient FS method, balancing performance and computational efficiency, whereas PCA outperforms AE in runtime efficiency. The study also highlights that FE methods are more effective for complex datasets and less sensitive to feature set size, whereas FS methods show significant performance improvements with more informative features. Despite the higher computational costs of FE methods, they demonstrate a greater capability to detect diverse attack types, making them particularly suitable for complex IoT environments. These findings are crucial for both academic research and industry applications, providing insights into optimizing detection performance and computational efficiency in NIDS for IoT networks.

Screening COVID-19 from chest X-ray images by an optical diffractive neural network with the optimized F number

The COVID-19 pandemic continues to significantly impact people’s lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection offer high accuracy but demand substantial computing resources, time, and energy. In this study, we introduce an optical diffractive neural network (ODNN-COVID), which is characterized by low power consumption, efficient parallelization, and fast computing speed for COVID-19 detection. In addition, we explore how the physical parameters of ODNN-COVID affect its diagnostic performance. We identify the F number as a key parameter for evaluating the overall detection capabilities. Through an assessment of the connectivity of the diffractive network, we established an optimized range of F number, offering guidance for constructing optical diffractive neural networks. In the numerical simulations, a three-layer system achieves an impressive overall accuracy of 92.64% and 88.89% in binary- and three-classification diagnostic tasks. For a single-layer system, the simulation accuracy of 84.17% and the experimental accuracy of 80.83% can be obtained with the same configuration for the binary-classification task, and the simulation accuracy is 80.19% and the experimental accuracy is 74.44% for the three-classification task. Both simulations and experiments validate that the proposed optical diffractive neural network serves as a passive optical processor for effective COVID-19 diagnosis, featuring low power consumption, high parallelization, and fast computing capabilities. Furthermore, ODNN-COVID exhibits versatility, making it adaptable to various image analysis and object classification tasks related to medical fields owing to its general architecture.

GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT

This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach adeptly handles the temporal dynamics inherent to botnet attacks. Key to our approach is the development of a time-stamped multi-edge graph structure that uncovers subtle temporal patterns and hidden relationships in network flows, critical for recognizing botnet behaviors. Moreover, our sequential graph learning framework incorporates time-sequenced edges and multi-edged structures into a two-layered gated graph model, which is optimized with specialized message-passing layers and aggregation functions to address the challenges of time-series traffic data effectively. Our comparative analysis with the state of the art reveals that our sequential gated graph convolutional neural network achieves substantial improvements in detecting IoT botnets. The proposed GGCN model consistently outperforms the conventional model, achieving improvements in accuracy ranging from marginal to substantial—0.01% for BoT IoT and up to 25% for Mirai. Moreover, our empirical analysis underscores the GGCN’s enhanced capabilities, particularly in binary classification tasks, on imbalanced datasets. These findings highlight the model’s ability to effectively navigate and manage the varying complexity and characteristics of IoT security threats across different datasets.

Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet

Purpose. This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN). Methods. datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values = 0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed. Results. for grade prediction, EfficientNetB3 gave the best performance with AUC = 0.924. For stage prediction, EfficientNetB0 was the best with AUC = 0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC = 0.71) and Xception (AUC = 0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC = 0.67 (grade) and AUC = 0.66 (stage). Conclusion. the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context.

Development of a grading diagnostic model for schistosomiasis-induced liver fibrosis based on radiomics and clinical laboratory indicators

To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People's Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients'radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients'radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = -5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.