Stratified 10-fold Cross-validation Research Articles

A preliminary prediction model of pediatric Mycoplasma pneumoniae pneumonia based on routine blood parameters by using machine learning method

BackgroundThe prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of children. In this study, we aim to systematically evaluate the value of routine blood parameters in predicting MPP and develop a robust and generalizable ensemble artificial intelligence (AI) model to assist in identifying patients with MPP.MethodsWe collected 27 features, including routine blood parameters and hs-CRP levels, from patients admitted to The Affiliated Dazu’s Hospital of Chongqing Medical University with or without MPP between January, 2023 and January, 2024. A classification model was built using seven machine learning (ML) algorithms to develop an integrated prediction tool for diagnosing MPP. It was evaluated on both an internal validation set (982 individuals) and an external validation set (195 individuals). The primary outcome measured the accuracy of the model in predicting MPP.ResultsThe GBDT is state-of-the-art based on 27 features. Following inter-laboratory cohort testing, the GBDT demonstrated an AUC, accuracy, specificity, sensitivity, PPV, NPV, and F1-score of 0.980 (0.938–0.995), 0.928 (0.796–0.970), 0.929 (0.717-1.000), 0.926 (0.889–0.956), 0.922 (0.727-1.000), 0.937 (0.884–0.963), and 0.923 (0.800-0.966) in stratified 10-fold cross-validation. A GBDT-based AI Lab was developed to facilitate the healthcare providers in remote and impoverished areas.ConclusionsThe GBDT-based AI Lab tool, with high sensitivity and specificity, could help discriminate between pediatric MPP infection and non-MPP infection based on routine blood parameters. Moreover, a user-friendly webpage tool for AI Lab could facilitate healthcare providers in remote and impoverished areas where advanced technologies are not accessible.

Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.

To provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks. A total of 634 fundus images of 317 premature infants born at 23-34 weeks of gestation were evaluated. After image pre-processing, we obtained a rectangular region (ROI). RegNetY002 was used for algorithm training, and stratified 10-fold cross-validation was applied during training to evaluate and standardize our model. The model's performance was reported as accuracy and specificity and described by the receiver operating characteristic (ROC) curve and area under the curve (AUC). The model achieved 0.98 accuracy and 0.98 specificity in detecting Type 2 ROP versus Type 1 ROP and A-ROP. On the other hand, as a result of the analysis of ROI regions, the model achieved 0.90 accuracy and 0.95 specificity in detecting Stage 2 ROP versus Stage 3 ROP and 0.91 accuracy and 0.92 specificity in detecting A-ROP versus Type 1 ROP. The AUC scores were 0.98 for Type 2 ROP versus Type 1 ROP and A-ROP, 0.85 for Stage 2 ROP versus Stage 3 ROP, and 0.91 for A-ROP versus Type 1 ROP. Our study demonstrated that ROP classification by DL-based analysis of fundus images can be distinguished with high accuracy and specificity. Integrating DL-based artificial intelligence algorithms into clinical practice may reduce the workload of ophthalmologists in the future and provide support in decision-making in the management of ROP.

A Study on the Screening of Children at Risk for Developmental Disabilities Using Facial Landmarks Derived From a Mobile-Based Application

Objective Early detection and intervention of developmental disabilities (DDs) are critical to improving the long-term outcomes of afflicted children. In this study, our objective was to utilize facial landmark features from mobile application to distinguish between children with DDs and typically developing (TD) children.Methods The present study recruited 89 children, including 33 diagnosed with DD, and 56 TD children. The aim was to examine the effectiveness of a deep learning classification model using facial video collected from children through mobile-based application. The study participants underwent comprehensive developmental assessments, which included the child completion of the Korean Psychoeducational Profile-Revised and caregiver completing the Korean versions of Vineland Adaptive Behavior Scale, Korean version of the Childhood Autism Rating Scale, Social Responsiveness Scale, and Child Behavior Checklist. We extracted facial landmarks from recorded videos using mobile application and performed DDs classification using long short-term memory with stratified 5-fold cross-validation.Results The classification model shows an average accuracy of 0.88 (range: 0.78–1.00), an average precision of 0.91 (range: 0.75–1.00), and an average F1-score of 0.80 (range: 0.60–1.00). Upon interpreting prediction results using SHapley Additive exPlanations (SHAP), we verified that the most crucial variable was the nodding head angle variable, with a median SHAP score of 2.6. All the top 10 contributing variables exhibited significant differences in distribution between children with DD and TD (p<0.05).Conclusion The results of this study provide evidence that facial landmarks, utilizing readily available mobile-based video data, can be used to detect DD at an early stage.

Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons in Polyolefins.

Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers (PLs), which are small organic molecules, are added to the polymer matrix. The miscibility of these PLs has a large impact on their effectiveness and, therefore, their interactions with the polymer matrix must be carefully considered. Many PL characteristics, including their size, topology, and flexibility, can impact their miscibility and, because of the exponentially large number of PLs, the current trial-and-error approach is very ineffective. In this work, we show that using coarse-grained molecular simulations of a small dataset of 48 PLs, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using ad-hoc molecular dynamics simulation setups that are relatively computationally inexpensive, we establish correlations between the PLs' topology, internal flexibility, thermodynamics of aggregation, and degree of miscibility, and use these descriptors to classify the molecules as miscible or immiscible. With all available data, we also construct a decision tree model, which achieves a F1 score of 0.86 ± 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method can be a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows that can efficiently screen PLs with a variety of topological features. The approach is used here to screen for apolar PLs in polyisoprene melts, but similar proxies would be valid for other polyolefins, while, in cases where polar interactions drive the miscibility, other descriptors are likely to be needed.

Meta-learning for vessel time series data imputation method recommendation

A missing data problem is inevitable when collecting time series datasets from marine sensors. Due to this, sensor data is not reliable enough to assist decision-making. To impute missing values, a number of methods are available. Choosing the best imputation method, however, is not a trivial task, as it usually involves domain expertise and trial-and-error iterations. Additionally, if imputations are done carelessly, they produce a high error, resulting in incorrect assumptions by stakeholders. In this paper, a meta-learning approach is presented that can be used to extract characteristics of the underlying data, and based on that, a less error-prone imputation method is recommended. Ten commercial ocean-going vessel datasets are used to evaluate our proposed method. A total of 29,527 data samples were generated, comprising 22 inputs and 1 target. The proposed method achieves a weighted F1-Score of 87.5% when utilizing stratified 10-fold cross-validation. Our approach can improve the average imputation score up to 86%, with the worst-case improvement being 5%. This demonstrates that our proposed approach is efficient and effective in recommending the best imputation methods.

Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence

Accurate assessment of Attention Deficit Hyperactivity Disorder (ADHD) is crucial for the effective treatment of affected individuals. Traditionally, psychometric tests such as the WISC-IV have been utilized to gather evidence and identify patterns or factors contributing to ADHD diagnosis. However, in recent years, the use of machine learning (ML) models in conjunction with post-hoc eXplainable Artificial Intelligence (XAI) techniques has improved our ability to make precise predictions and provide transparent explanations. The objective of this study is twofold: firstly, to predict the likelihood of an individual receiving an ADHD diagnosis using ML algorithms, and secondly, to offer interpretable insights into the decision-making process of the ML model. The dataset under scrutiny comprises 694 cases collected over the past decade in Spain, including information on age, gender, and WISC-IV test scores. The outcome variable is the professional diagnosis. Diverse ML algorithms representing various learning styles were rigorously evaluated through a stratified 10-fold cross-validation, with performance assessed using key metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Models were compared using both the full set of initial features and a well-suited wrapper-type feature selection algorithm (Boruta). Following the identification of the most suitable model, Shapley additive values were computed to assign weights to each predictor based on their additive contribution to the outcome and to elucidate the predictions. Strikingly, a reduced set of 8 out of the initial 20 variables produced results comparable to using the full feature set. Among the ML models tested, the Random Forest algorithm outperformed others on most metrics (ACC = 0.90, AUC = 0.94, Sensitivity = 0.91, Specificity = 0.92). Notably, the principal predictors, ranked by importance, included GAI – CPI, WMI, CPI, PSI, VCI, WMI – PSI, PRI, and LN. Individual case examples exhibit variations in predictions depending on unique characteristics, including instances of false positives and negatives. Our ML model adeptly predicted ADHD diagnoses in 90% of cases, with potential for further enhancement by expanding our database. Furthermore, the use of XAI techniques enables the elucidation of salient factors in individual cases, thereby aiding inexperienced professionals in the diagnostic process and facilitating comparison with expert assessments. It is important to note that this tool is designed to support the ADHD diagnostic process, where the medical professional always has the final say in decision-making.

755 Pathology in the Digital Era of Neuro-oncology: A Pathomics-Based Approach to Modeling Outcomes in Medulloblastoma

INTRODUCTION: Pathomics is an emerging data science technique that may be used to extract intricate, sub-visual histopathologic features using high-throughput digital image analysis. METHODS: Using the Children’s Brain Tumor Network database, pediatric medulloblastoma patients were evaluated for inclusion in the study. After tumor segmentation, 49 quantitative pathomic features were extracted using a digital pathology software package (Qupath). LASSO cox proportional hazard model with stratified 5-fold cross-validation was used to identify high-performing features and create a predictive survival model by identifying low-, medium- and high-risk OS cohorts. A classification model was developed using histogram gradient boosting classifier to perform binary classification of Group3/4 vs. SHH/WNT with leave-one-subject-out cross validation. RESULTS: 84 patients with median age at diagnosis of 8.61 years (range 0.31-21.72), and median OS of 45.6 months (range 0.76-195.87) were included in the study. A survival model built using only clinical features yielded a concordance index (c-index) of 0.76 compared to actual outcomes (stratification: p < 0.001). However, a survival model built by combining clinical and high-performing pathomic features yielded the best performance, showing disparate outcomes between low-, medium- and high-risk groups (p < 0.001) with resultant c-index of 0.85. Furthermore, pathomic features classified subjects into Group 3/4 or SHH/WNT with classification accuracy of 71% (SEM = 0.06), and t-test against stratified chance accuracy (57%) showed significantly above-chance performance (p = .019). CONCLUSIONS: This study represents the largest pathomic-based machine learning analysis for predicting survival and molecular subtypes in medulloblastoma. The results demonstrate success in pathomic analysis using machine learning-based modeling to predict survival and molecular subtypes for patients with medulloblastoma. Further work will aim to perform our analysis on an external validation dataset to enhance model performance and improve its clinical applicability.

The pre-trained explainable deep learning model with stacked denoising autoencoders for slope stability analysis

In this work, we proposed a deeply-integrated explainable pre-trained deep learning framework with stacked denoising autoencoders in the assessment of slope stability. The deep learning model consists of a deep neural network as a trunk net for prediction and autoencoders as branch nets for denoising. A comprehensive review of machine learning algorithms in slope stability evaluation is first given in the introduction section. A series of 530 data is then collected from real slope records, which are visualized and investigated in feature engineering and further preprocessed for model training. To ensure reliable and trustworthy model interpretability, a unified model from both local and global perspectives is integrated into the deep learning model, which incorporated the ad hoc back-propagation based Deep SHAP, perturbation based Kernel SHAP and PDPs, and distillation based LIME and Anchors. For a fair evaluation, repeated stratified 10-fold cross-validation is adopted in model evaluation. The obtained results manifest that the constructed model outperforms commonly used machine learning methods in terms of accuracy and stability on the real-world slope data. The explainable model provides a reasonable explanation and validates the capability of the proposed model, and reflects the causes and dependencies of model predictions for a given sample.

3D CNN-based Deep Learning Model-based Explanatory Prognostication in Patients with Multiple Myeloma using Whole-body MRI.

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers. We defined the occurrence of progressive disease after MRI evaluation within 12 months as a poor prognosis and constructed a 3D CNN-based deep learning model to predict prognosis. Images from 111 cases were used as the training and internal validation data; images from 31 cases were used as the external validation data. Internal validation of the AI model with stratified 5-fold cross-validation resulted in a significant difference in progression-free survival (PFS) between good and poor prognostic cases (2-year PFS, 91.2% versus [vs.] 61.1%, P = 0.0002). The AI model clearly stratified good and poor prognostic cases in the external validation cohort (2-year PFS, 92.9% vs. 55.6%, P = 0.004), with an area under the receiver operating characteristic curve of 0.804. According to Grad-CAM, the MRI signals of the spleen and bones of the vertebrae and pelvis contributed to prognosis prediction. This study is the first to show that image analysis of whole-body MRI using a 3D CNN without any other clinical data is effective in predicting the prognosis of patients with MM.

Rapid discrimination of xiaokeng green tea geographical origin using surface-enhanced Raman spectroscopy coupled with chemometrics

Xiaokeng green tea (XKGT), named for its origin, brings high economic benefits to the region due to its superior quality, but it is susceptible to fraud. In this study, 131 roasted green teas from different origins in Anhui Province were used as samples to investigate the ability of surface-enhanced Raman spectroscopy (SERS) combined with chemometrics to accurately discriminate green tea origins in narrow regions. First, spherical Ag nanoparticles (AgNPs) were prepared as SERS substrates. A stratified 5-fold cross-validation method was used to divide the modelling samples. The radial basis function neural network (RBFNN), convolutional neural network (CNN), and random forest (RF) models were built, and four different preprocessing methods were compared. The results showed that the optimised RBFNN model with normalisation, as the preprocessing method, had an average prediction set accuracy of 97.69% in distinguishing samples from the Xiaokeng tea area from other tea areas. The RBFNN model was further used to differentiate tea samples from four different origins within Anhui Province, namely Xiaokeng Village (XK), Chizhou City except Xiaokeng Village (CZ), Lu'an City (L'A) and Huangshan City (HS), with an average accuracy of 91.85% for the prediction set. These findings point to the potential of combining SERS with chemometrics as an effective method for discriminating the geographic origins of XKGT in narrow regions.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.

The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments. This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results. Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity. This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease

Parkinson’s disease (PD) is a neurological condition that millions of people worldwide suffer from. Early symptoms include a slight sense of weakness and a propensity for involuntary tremulous motion in body limbs, particularly in the arms, hands, and head. PD is diagnosed based on motor symptoms. Additionally, scholars have proposed various remote monitoring tests that offer benefits such as early diagnosis, ease of application, and cost-effectiveness. PD patients often exhibit voice disorders. Speech signals of the patients can be used for early diagnosis of the disease. This study proposed an artificial intelligence–based approach for PD diagnosis using speech signals. Scalogram images, generated through the Continuous Wavelet Transform of the speech signals, were employed in deep learning techniques to detect PD. The scalograms were tested with various deep learning techniques. In the first part of the experiment, AlexNet, GoogleNet, ResNet50, and a majority voting-based hybrid system were used as classifiers. Secondly, a deep feature fusion method based on DenseNet and NasNet was investigated. Several evaluation metrics were employed to assess the performance. The deep feature fusion system achieved an accuracy of 0.95 and an F1 score with stratified 10-fold cross-validation, improving accuracy by 38% over the ablation study. The key contributions of this study include the investigation of scalogram images with a comprehensive analysis of deep learning models and deep feature fusion for PD detection.

Machine Learning for Predicting Personality using Facebook-Based Posts

Social media contributes a lot to human life. People can share their thoughts through text, photos, and voice through social media. Information from social media can be useful, including in personality research. Personality can generally be known through personality tests. In this research, personality prediction is formed to determine personality through Facebook posts without using a personality test. We create a model based on big five personality traits using 5 machine learning algorithms: Support Vector Machine (SVM), Multinomial Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression. Data augmentation was also used for balancing the dataset value and trained using stratified 10-fold cross-validation. This research yields the highest f1 score on Openness using Multinomial Naive Bayes algorithm of 82.31% and the highest average is 68.62%. So the five supervised Machine Learning algorithms used in this research produced Multinomial Naive Bayes as the best algorithm to predict personality based on big five personality traits from user postings on Facebook.

Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression

Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline’s plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

A PRECISION HEALTH APPROACH FOR OSTEOARTHRITIS: PREDICTION OF RAPID KNEE OSTEOARTHRITIS PROGRESSION USING AUTOMATED MACHINE LEARNING

Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA). Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development.To create a reliable and interpretable precision health tool that predicts rapid knee OA progression over a 2-year period from baseline patient characteristics using an advanced automated machine learning (autoML) framework, “Autoprognosis 2.0”.All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class predictions (categorising disease progression into pain and/or radiographic progression) and binary predictions. Models were developed using a training set of 1352 instances and all available variables (including clinical, X-ray, MRI, and biochemical features), and validated through both stratified 10-fold cross-validation and hold-out validation on a testing set of 339 instances. Model performance was assessed using multiple evaluation metrics. Interpretability analyses were carried out to identify important predictors of progression.Our final models yielded higher accuracy scores for multi-class predictions (AUC-ROC: 0.858, 95% CI: 0.856-0.860) compared to binary predictions (AUC-ROC: 0.717, 95% CI: 0.712-0.722). Important predictors of rapid disease progression included WOMAC scores and MRI features. Additionally, accurate ML models were developed for predicting OA progression in a subgroup of patients aged 65 or younger.This study presents a reliable and interpretable precision health tool for predicting rapid knee OA progression. Our models provide accurate predictions and, importantly, allow specific predictors of rapid disease progression to be identified. Furthermore, the transparency and explainability of our methods may facilitate their acceptance by clinicians and patients, enabling effective translation to clinical practice.

MolToxPred: small molecule toxicity prediction using machine learning approach.

Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.

CYBERSECURITY: RESEARCH ON METHODS FOR DETECTING DDOS ATTACKS

This article describes the problem of DDoS attacks, analyzing their nature and consequences. The paper covers common DDoS attack types, such as SYN flood, ICMP flood, UDP flood. Existing methods for detecting attacks from literature are reviewed, including machine learning approaches, including artificial neural networks, support vector machines and decision trees. The paper introduces a decision tree-based machine learning model for the detection of DDoS attacks. The model is trained and tested on a publicly available dataset. The dataset consists of 1,04,345 rows of data, where every row includes 23 features, such as source IP, destination IP, port number, number of bytes transferred from the switch port, etc. A similar set of characteristics can be obtained on a real network hardware using simple calculations, which makes it possible to approximate the model evaluation to real operating conditions. SYN flood, ICMP flood and UDP flood attack types are present in the data, as well as legitimate traffic. To avoid overfitting, only some columns were used, and columns such as IP addresses were discarded. The field “label” in each row of the dataset contains either 0 or 1 where 0 corresponds to legitimate traffic and 1 to malicious one. The problem of DDoS attack detection is therefore formally reduced to the task of binary classification of each row from the dataset. The constructed model achieves an average classification accuracy of 0.94 with a standard deviation at the level of 0.06 in detecting the above mentioned types of attacks. To objectively assess the effectiveness of the model and avoid distortion of the results, stratified 5-fold cross-validation was used. The developed model can be applied in the real world network hardware to filter malicious packets or as a tool for warning the administrator about an attack. This research advances cybersecurity by enhancing DDoS attack detection.

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky-Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard-Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

Categorization of Indian residential consumers electrical energy consumption pattern using clustering and classification techniques

Data Mining techniques are widely applied in power sector to analyze the electricity consumption pattern(ECP) of consumers for implementing effective demand response management. In this study, the ECP of LV consumers of Karur city is used for evaluation. The dataset consists of five years ECP. In this study, K-means clustering algorithm is used to categorize the data into binary and three classes. The results of the K-means clustering technique is used for developing eight classifier models. The hyperparameters of the classifier models are tuned using grid search optimization technique. The classification process applies hold-out partitioning and stratified 10-fold cross-validation(CV) techniques to evaluate the classifier’s efficiency in predicting a consumer’s ECP. The experimental result shows that the K-means clustering has minimal overlapping of 0.48% and 1.98% for binary and three-class clustering processes. Hence, the K-means algorithm results are used to build this study’s classification model. In the classification process, the experimental results of LR and SVM algorithms have better accuracy in binary and three class categories respectively, for the hold-out partitioning method. Meanwhile, the SVM also has better generalization ability and is validated using stratified 10-fold CV, achieving an accuracy of 99.09% and 98.19% for binary and three classes respectively, in the classification of consumer data.

Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.

Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored. To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria. Retrospective. A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria. Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery. Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis). We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05. In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis. ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation. 3 TECHNICAL EFFICACY: Stage 2.