K-nearest Neighbor Classifier Research Articles

Fall Detection Based on Data-Adaptive Gaussian Average Filtering Decomposition and Machine Learning

Falls are a significant health concern leading to increased morbidity and healthcare costs, especially for the elderly. Early and accurate detection of fall events is critical for timely intervention and preventing severe complications. This study presents a novel approach to triaxial accelerometer signals by employing data-adaptive Gaussian average filtering (DAGAF) decomposition in conjunction with machine learning techniques for fall detection. The triaxial accelerometer signals from the FallAllD dataset were decomposed into intrinsic mode functions (IMFs) and a residual component, from which feature vectors were extracted to train support vector machine (SVM) and k-nearest neighbor (kNN) classifiers. Experimental results demonstrate that the combination of the first and the third IMFs with the residual component yields the highest classification accuracy of 96.34%, with SVM outperforming kNN across all performance metrics. This approach significantly improves fall detection accuracy compared to using raw accelerometer signals, highlighting its potential in enhancing wearable fall detection systems. The proposed DAGAF decomposition method not only enhances feature extraction but also provides a promising advancement in the field, suggesting its potential to increase the reliability and accuracy of fall detection in practical applications.

An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection

Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.

Enhancing brain cancer type prediction through machine learning algorithms and feature selection techniques

Abstract The manual identification of brain cancer types is often fraught with inaccuracies, leading to potential delays in diagnosis and treatment planning. This study presents a novel approach to predict brain cancer types using advanced machine learning (ML) algorithms integrated with sophisticated feature selection techniques. A multi-class classification framework was developed and evaluated, incorporating six ML models: Bernoulli Naive Bayes, K-nearest neighbors classifier, decision tree classifier, Gaussian process classifier (GPC), passive aggressive classifier, and perceptron. To enhance model performance, feature selection methods including the Gini index, mutual information, and principal component analysis (PCA) were employed. A comprehensive case study was conducted to assess the predictive accuracy of these models. The GPC, when trained and validated on features derived via PCA, outperformed other models in terms of predictive accuracy and generalization. Specifically, the dimensions identified by PCA (d1, d2, d3, and d4) were most effective in distinguishing between different brain cancer types. This methodology resulted in a significant improvement across various performance metrics. Compared to the baseline GPC model using all original features, the PCA-enhanced GPC achieved remarkable increases in Accuracy, Precision, Recall, and F1 Score by 294.31%, 22.14%, 294.31%, and 878.18%, respectively. These findings underscore the potential of combining ML algorithms with targeted feature selection techniques to advance the accuracy of brain cancer type prediction, offering substantial benefits for clinical decision-making and patient outcomes.

A machine learning model based on results of a comprehensive radiological evaluation can predict the prognosis of basal ganglia cerebral hemorrhage treated with neuroendoscopy.

Spontaneous intracerebral hemorrhage is the second most common subtype of stroke. Therefore, this study aimed to investigate the risk factors affecting the prognosis of patients with basal ganglia cerebral hemorrhage after neuroendoscopy. Between January 2020 and January 2024, 130 patients with basal ganglia cerebral hemorrhage who underwent neuroendoscopy were recruited from two independent centers. We split this dataset into training (n = 79), internal validation (n = 22), and external validation (n = 29) sets. The least absolute shrinkage and selection operator-regression algorithm was used to select the top 10 important radiomic features of different regions (perioperative hemorrhage area [PRH], perioperative surround area [PRS], postoperative hemorrhage area [PSH], and postoperative edema area [PSE]). The black hole, island, blend, and swirl signs were evaluated. The top 10 radiomic features and 4 radiological features were combined to construct the k-nearest neighbor classification (KNN), logistic regression (LR), and support vector machine (SVM) models. Finally, the performance of the perioperative hemorrhage and postoperative edema machine learning models was validated using another independent dataset (n = 29). The primary outcome is mRS at 6 months after discharge. The mRS score greater than 3 defined as functional independence. A total of 12 models were built: PRH-KNN, PRH-LR, PRH-SVM, PRS-KNN, PRS-LR, PRS-SVM, PSH-KNN, PSH-LR, PSH-SVM, PSE-KNN, PSE-LR, and PSE-SVM, with corresponding areas under the curve (AUC) values in the internal validation set of 0.95, 0.91, 0.94, 0.52, 0.91, 0.54, 0.67, 0.9, 0.72, 0.92, 0.92, and 0.95, respectively. The AUC values of the PRH-KNN, PRH-LR, PRH-SVM, PSE-KNN, PSE-LR, and PSE-SVM in the external validation were 0.9, 0.92, 0.89, 0.91, 0.92, and 0.88, respectively. The model built based on computed tomography images of different regions accurately predicted the prognosis of patients with basal ganglia cerebral hemorrhage treated with neuroendoscopy. The models built based on the preoperative hematoma area and postoperative edema area showed excellent predictive efficacy in external verification, which has important clinical significance.

Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography–Computed Tomography Image-based Radiomics and Clinical Features

Abstract Background: In this study, we want to evaluate the response to Lutetium-177 (177Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features. Methods: The total volume of tumor areas was segmented into 61 SPECT and 41 SPECT-CT images from 22 patients with NETs. A total of 871 radiomics and clinical features were extracted from the SPECT and SPECT-CT images. Subsequently, a feature reduction method called maximum relevance minimum redundancy (mRMR) was used to select the best combination of features. These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. For the SPECT and SPECT-CT images, ten and eight features, respectively, were selected using the mRMR algorithm. Results: The results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classification accuracies of 64% and 83% for the SPECT and SPECT-CT images, respectively. The accuracy of the classifications of DT, KNN, and SVM for SPECT-CT images is 79%, 74%, and 67%, respectively. The poor accuracy obtained from different classifications in SPECT images (≈64%) showed that these images are not suitable for predicting treatment response. Conclusions: Modeling the selected features of SPECT-CT images based on their anatomy and the presence of extensive gray levels makes it possible to predict responses to the treatment of 177Lu-DOTATATE for patients with NETs.

Bispectrum Analysis of Thermal Images for the Classification of Retinal Vascular Diseases

In the field of medicine, thermal imaging provides a non-invasive means to diagnose diseases by displaying temperature variations. Specifically, thermal images of the ocular region are proving effective in diagnosing retinal diseases like diabetic retinopathy (DR), diabetic macular edema (DME), age-related macular degeneration (AMD), and wet AMD. This study explores the use of bispectrum analysis on ocular thermal images. The bispectrum, derived from third-order statistics (cumulants), is estimated using parametric (ARMA) and non-parametric methods (direct and indirect). Various entropy measures are extracted as features from these bispectrum images. The effectiveness of these features is evaluated through experiments employing different machine learning algorithms: discriminant analysis (DA), K-nearest neighbour (KNN), decision tree (DT), and Naïve Bayes Classifier (NBC). The results indicate that the KNN classifier achieves the highest accuracy when using features extracted via the indirect method of bispectrum estimation. Meanwhile, the DT classifier performs exceptionally well with bispectrum features extracted through the direct method, outperforming other feature extraction approaches.

Algorithm Identification and Integrated with Push Service for Telemedicine System

Telemedicine systems, while overcoming physical space constraints, often lack personalized interactions. By incorporating a push service and leveraging prediction-oriented algorithms, these systems can offer an improved user experience. Such enhancements enable timely treatment options and reduce unnecessary resource usage in on-site outpatient clinics. This research work starts by creating a robust algorithm using data mining techniques. Next, it establishes the foundation for a telemedicine push service. The service includes essential modules for disease differentiation, doctor recommendations, and diagnosis predictions. To optimize these modules, a merged algorithm combining k-nearest neighbor classification, nearest neighbor recommendation, and FP-growth is needed. This work aims to enhance treatment options for patients and streamline resource usage in on-site outpatient clinics. Moreover, this work has carried out empirical research for identification of algorithm by using available data at a public Chinese telemedicine system. The results of data analysis show the follows: 1. For disease diagnosis, the KNN model (k=1) is more accurate but less efficient, SVM and LibSVM are more efficient but less accurate than the KNN model; 2. In terms of doctor recommendation, nearest neighbor recommendation performs better but is not as efficient as matrix factorization; 3. in diagnostic prediction, the combination of introducing association mining and data segmentation can play a better role. The developed algorithm and its conclusions from this study could make easier and more efficient to provide treatment options for undecided-condition patients.

DETERMINING STUDENT'S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES

Predicting student's academic performance during online learning has been considered a major task during the pandemic period. During the online mode of learning, academic activities have been affected in such a way that the management of educational institutions has planned to design support systems for predicting the student's performance to reduce the dropout ratio of the students and bring improvement in academic activities. During COVID-19, the main challenge is maintaining student's grades by predicting their academic performance using different techniques such as Education Data Mining and Learning Analytics. Different features have been identified related to the teaching mechanisms in online learning, which have a great impact on the improvement of academic performance. A high-quality dataset helps us to generate productive results, which in turn helps us to make effective decisions for promoting high-quality education. In this research, five prediction models for predicting academic performance have been proposed by collecting an imbalanced dataset of 350 students from the same computer science domain. After applying pre-processing techniques for cleaning the data, machine learning models have been applied, including K-Nearest Neighbor Classifier, Decision Tree, Random Forest, Support Vector Classifier, and Gaussian Naive Bayes. Results have been predicted for an imbalanced and balanced dataset after feature selection. Support Vector classifier has produced the best results in a balanced dataset with selected features by giving an accuracy of 96.89%.

Analysis on pulse rate variability for pilot workload assessment based on wearable sensor

AbstractThe workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real‐time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA‐TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA‐TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k‐nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing > takeoff > cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals

Background and ObjectiveParkinson’s Disease (PD) is one of the most commonly observed neurodegenerative disorders worldwide. Many researchers have utilized machine learning (ML) models to detect PD and understand its underlying causes automatically. In this research, our primary objective is to automatically detect PD and extract meaningful results using the proposed ML model. Materials and MethodsIn this study, an FNIRS dataset collected from PD patients and control participants under three conditions—(i) rest, (ii) walking, and (iii) finger tapping—was utilized. A new explainable feature engineering (XFE) model was proposed to detect PD and automatically extract meaningful information under these conditions. The XFE model consists of four main phases: (i) feature extraction using the proposed channel transformation and transition pattern (TPat), (ii) feature selection employing cumulative weighted neighborhood component analysis (CWNCA), (iii) classification using the k-nearest neighbors (kNN) classifier, and (iv) channel network extraction to obtain explainable results. ResultsThe suggested TPat-based XFE model was applied to the FNIRS dataset. This dataset included three distinct cases. Our model achieved over 94% classification accuracy using leave-one-subject-out cross-validation (LOSO CV) and 100% classification accuracy using 10-fold cross-validation. Additionally, channel transitions for each case were identified and discussed. ConclusionsBased on the results and findings, the proposed model demonstrated high accuracy in FNIRS signal classification and provided explainable results. In this regard, the presented TPat-based XFE model contributed significantly to both ML and neuroscience.

An efficient approach for EMG controlled pattern recognition system based on MUAP identification and segregation

An Electromyography (EMG) based pattern recognition system constitutes various steps of signal processing and control engineering from signal acquisition to real-time control. Efficient control of external devices largely depends on the signal processing steps executed before the final output. This work presents a new approach to signal processing using Motor Unit Action Potential (MUAP) based signal decomposition and segmentation. An MUAP is a neurological response during muscle contraction. Due to the higher contact area of surface electrodes, MUAPs from multiple muscles are captured. An MUAP generated from a single muscle usually has identical waveshapes and similar discharging rates and usually lasts for 8–15 ms. These are known as primary MUAPs. The proposed algorithm identifies and uses the primary observed MUAPs for feature extraction and classification. Firstly, noise signals are eliminated by a determined noise margin, which also separates the active muscle movement signals. Next, a novel MUAP identification algorithm is implemented to detect the MUAP trains. Then, identified primary MUAPs are used to make segments with variable widths to extract feature vectors. Based on the correlation score of all the primary MUAPs, the segmentation is performed, which results in segmentation width varying from 110–200 ms. The achieved segmentation width is lesser than the conventional overlapping and non-overlapping methods — the proposed approach results in a 20 to 50% reduction in the segmentation width. Four different classifiers are tested during the machine learning stage to investigate the performance of the proposed approach. The obtained feature sets are then used to train the Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The classifiers are tested with precision, recall, F1 score, and accuracy. The kNN and DT classifiers performed better than the LDA and RF classifiers. The maximum precision and recall are 100% while the maximum achieved accuracy is 98.56%. The comparative results show higher accuracy even at lower segmentation widths than the conventional constant window scheme. The kNN and DT classifiers provide a 5% to 15% increment in accuracy compared to the constant window segmentation-based approach.

Machine Learning to Predict Disease Severity and Progression in Hospitalized COVID-19 Patients Using Laboratory Data on Admission

Background: Herein, we aimed to develop and test machine learning (ML) models to predict disease severity and/or progression in hospitalized COVID-19 patients through baseline laboratory features. Methods: In this retrospective study of hospitalized COVID-19 patients admitted to a tertiary care center, we evaluated routine admission data to determine the accuracy rates of different ML algorithms: k-nearest neighbor classifier, bagging classifier, random forest (RF), and decision tree. These models were compared over three outcomes: those who needed oxygen supplementation vs. who did not on admission (Analysis 1, n: 180), those who later developed oxygen requirement vs. those who did not (Analysis 2, n: 112), and those who needed invasive mechanical ventilation vs. those who did not during hospitalization (Analysis 3, n: 164). Results: The median age of the patients was 55 (44-68) years, with males constituting 47.2% of the subjects. At admission, 37.8% of the patients required oxygen supplementation. During hospitalization, 17.5% needed mechanical ventilation, and 8.3% died. For all analyses, RF had the highest accuracy in classifying the need for oxygen supplementation on admission (89.4%) or during hospitalization (91.1%) and for invasive mechanical ventilation (92.2%). These were followed by a bagging classifier for Analysis 1 (88.3%) and Analysis 3 (91.0%) and by a decision tree for Analysis 2 (88.4%). C-reactive protein, monocyte distribution width, and high-sensitive troponin-T were the most crucial laboratory contributors to Analysis 1, Analysis 2, and Analysis 3, respectively. Conclusion: Our study showed that ML algorithms could predict the need for oxygen supplementation and mechanical ventilation during hospitalization using baseline laboratory data, suggesting a slight superiority of RF, among others.

Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit

This study investigates four normalization methods (Min-Max, Z-Score, Decimal Scaling, MaxAbs) across prostate, kidney, and heart disease datasets for K-Nearest Neighbor (K-NN) classification. Imbalanced feature scales can hinder K-NN performance, making normalization crucial. Results show that Decimal Scaling achieves 90.00% accuracy in prostate cancer, while Min-Max and Z-Score yield 97.50% in kidney disease. MaxAbs performs well with 96.25% accuracy in kidney disease. In heart disease, Min-Max and MaxAbs attain accuracies of 82.93% and 81.95%, respectively. These findings suggest Decimal Scaling suits datasets with few instances, limited features, and normal distribution. Min-Max and MaxAbs are better for datasets with numerous instances and non-normal distribution. Z-Score fits datasets with a wide range of feature numbers and near-normal distribution. This study aids in selecting the appropriate normalization method based on dataset characteristics to enhance K-NN classification accuracy in disease diagnosis. The experiments involve datasets with different attributes, continuous and categorical data, and binary classification. Data conditions such as the number of instances, the number of features, and data distribution affect the performance of normalization and classification.

A novel K-nearest neighbor classifier for lung cancer disease diagnosis

A novel K-nearest neighbor classifier for lung cancer disease diagnosis

A Machine Learning-Based Wrapper Method for Feature Selection

This paper presents a two-stage feature selection scheme using machine learning techniques. In the first stage a wrapper method is adopted to select various combinations of subsets of features from the original dataset. The performance of the model is evaluated by three classifiers: K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF). In the second and final stage, a sequential backward feature selection Method is applied. The proposed method is demonstrated on eighteen datasets and the average classification accuracy of eighteen datasets achieved is 89.81%, 87.55%, and 89.82% using the KNN, SVM, and RF classifiers, respectively with a maximum reduced size of the subset being ten only. Comparing the proposed method to eight other feature selection methods, the former achieves better classification accuracy in terms of selecting the most useful but a smaller number of features.

The role of encodings and distance metrics for the quantum nearest neighbor

Over the past few years, we observed a rethinking of classical artificial intelligence algorithms from a quantum computing perspective. This trend is driven by the peculiar properties of quantum mechanics, which offer the potential to enhance artificial intelligence capabilities, enabling it to surpass the constraints of classical computing. However, redesigning classical algorithms into their quantum equivalents is not straightforward and poses numerous challenges. In this study, we analyze in-depth two orthogonal designs of the quantum K-nearest neighbor classifier. In particular, we show two solutions based on amplitude encoding and basis encoding of data, respectively. These two types of encoding impact the overall structure of the respective algorithms, which employ different distance metrics and show different performances. By breaking down each quantum algorithm, we clarify and compare implementation aspects ranging from data preparation to classification. Eventually, we discuss the difficulties associated with data preparation, the theoretical advantage of quantum algorithms, and their impact on performance with respect to the classical counterpart.

Fault detection framework in wind turbine pitch systems using machine learning: Development, validation, and results

This work develops a methodology for detecting faults in wind turbines through supervised machine-learning classification methods focusing in two modes of operation. The Normal mode will be related to the regular operation of the wind turbines and in the case of the abnormal mode, the system will be cataloged as Pitch system failure. This methodology uses real data through the supervisory control and data acquisition (SCADA) system and the recording of events and actions (LOG alarm system). Sixteen classifications artificial intelligence (AI) models were tested, such as Random Forest Classifier (RF), Gradient Boosting Classifier (GBC), Extra Trees Classifier (ET), Extreme Gradient Boosting (XGBOOST), Light Gradient Boosting Machine (LIGHTGBM), Categorical Boosting Classifier (CATBOOST), Adaptative Boosting Classifier (ADA), Decision Tree Classifier (DT), Linear Discriminant Analysis (LDA), K-Nearest Neighbors Classifier (KNN), Logistic Regression (LR), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Dummy Classifier (DC), Super vector machine (SVM), Ridge Classifier (RC). The training process model was carried out by structuring the dataset in 75% for training and validation and 25% for the final test, considering cross-validation and optimization of the hyperparameters. The Random Forest Classifier (RF) and Extra Trees Classifier (ET) classifiers models presented the best performances, and the models with the lowest performances were K-Nearest Neighbors Classifier (KNN) and Linear Discriminant Analysis (LDA) ones. The average hit effectiveness for both Healthy and Faulty operating modes was approximately 80% across most of the models developed.

N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals

The N-body problem is a remarkable research topic in physics. We propose a new feature extraction model inspired by the N-body trajectory and test its feature extraction capability. In the first part of the research, an open-access electroencephalogram (EEG) dataset is used to test the proposed method. This dataset has three classes, namely (i) Alzheimer's Disorder (AD), (ii) frontal dementia (FD), and (iii) control groups. In the second step of the study, the EEG signals were divided into segments of 15 s in length, which resulted in 4,661 EEG signals. In the third part of the study, the proposed new self-organized feature engineering (SOFE) model is used to classify the EEG signals automatically. For this SOFE, two novel methods were presented: (i) a dynamic feature extraction function using a graph of the N-Body orbital, termed N-BodyPat, and (ii) an attention pooling function. A multileveled and combinational feature extraction method was proposed by deploying both methods. A feature selection function using ReliefF and Neighborhood Component Analysis (RFNCA) was used to choose the most informative features. An ensemble k-nearest neighbors (EkNN) classifier was employed in the classification phase. Our proposed N-BodyPat generates seven feature vectors for each channel, and the utilized EEG signal dataset contains 19 channels. In this aspect,133 (=19 × 7) EkNN-based outcomes were created. To attain higher classification performance by employing these 133 EkNN-based outcomes, an iterative majority voting (IMV)-based information fusion method was applied, and the most accurate outcomes were selected automatically. The recommended N-BodyPat-based SOFE achieved a classification accuracy of 99.64 %.

Prediction of H3K27M alteration in midline gliomas of brain using radiomics- A multi-institute study

Abstract Background Non-invasive prediction of H3K27M-altered Diffuse midline gliomas is important because of the involvement of deep locations and proximity to eloquent structures. We aim to predict H3K27M alteration in midline gliomas using radiomics features of T2W images. Methods Radiomics features extracted from 124 subjects (69 H3K27M-altered/55 H3K27M-wildtype). T2W-images were resampled to 1x1x1mm voxel-size, pre-processed, and normalized for artefact correction, intensity variations. Feature-set was normalized, subjected to reduction by variance thresholding, correlation coefficient thresholding, and sequential feature selector. Adaptive synthesis oversampling technique was used to over-sample the training data. Random forest classifier (RFC), Decision tree classifier (DTC) and K-nearest neighbors classifier (KNN) were trained over the training dataset and the performance was assessed over the internal test dataset and external test data set (52 subjects: 33 H3K27M-altered/19-H3K27M-wildtype). Results DTC achieved validation score of 77.33% (5-fold cross-validation) and accuracy of 80.64%, 75% on internal and external test dataset. RFC achieved validation score of 80.7% (5-fold cross-validation) and accuracy of 80.6%, 73% on internal and external test dataset. DTC achieved validation score of 78.67% (5-fold cross-validation) and accuracy of 80.64%, 61.53% on internal and external test dataset. Accuracy score of DTC, RFC, and KNN on internal test dataset was approximately 80% while on the external test dataset, DTC achieved 75% accuracy, RFC achieved 73% accuracy and KNN achieved 65.1% accuracy. Conclusion H3K27M alteration is a potential immunotherapeutic marker and is associated with poor prognosis and radiomics features extracted from conventional T2W-images can help in identifying H3K27M-altered cases non-invasively with high precision.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.