Improving ZigBee Device Network Authentication Using Ensemble Decision Tree Classifiers With Radio Frequency Distinct Native Attribute Fingerprinting

Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.

Reviewing ensemble classification methods in breast cancer

Recently, there has been increased attention in the literature on the use of ensemble methods in multivariate regression and classification. These methods have been shown to have interesting properties for both regression and classification. In particular, they can improve the accuracy of unstable predictors. Ensemble methods have so far been little studied in situations that are common for calibration and prediction in chemistry, i.e. situations with a large number of collinear x‐variables and few samples. These situations are often approached by data compression methods such as principal component regression (PCR) or partial least squares regression (PLSR). The present paper is an investigation of the properties of different types of ensemble methods used with PLSR in situations with highly collinear x‐data. Bagging and data augmentation by simulated noise are studied. The focus is on the robustness of the calibrations. Real and simulated data are used. The results show that ensembles trained on data with added noise can make PLSR robust against the type of noise added. In particular, the effects of sample temperature variations can be eliminated. Bagging does not seem to give any improvement over PLSR for small and intermediate numbers of components. It is, however, less sensitive to overfitting. Copyright © 2005 John Wiley & Sons, Ltd.

The subject of the research is methods and means of identifying the state of a computer system . The purpose of the article is to improve the quality of computer system state identification by developing a method based on ensemble classifiers. Task: to investigate methods for constructing bagging classifiers based on decision trees, to configure them and develop a method for identifying the state of the computer system. Methods used: artificial intelligence methods, machine learning, ensemble methods. The following results were obtained: the use of bagging classifiers based on meta-algorithms were investigated: Pasting Ensemble, Bootstrap Ensemble, Random Subspace Ensemble, Random Patches Ensemble and Random Forest methods and their accuracy were assessed to identify the state of the computer system. The research of tuning parameters of individual decision trees was carried out and their optimal values were found, including: the maximum number of features used in the construction of the tree; the minimum number of branches when building a tree; minimum number of leaves and maximum tree depth. The optimal number of trees in the ensemble has been determined. A method for identifying the state of the computer system is proposed, which differs from the known ones by the choice of the classification meta-algorithm and the selection of the optimal parameters for its adjustment. An assessment of the accuracy of the developed method for identifying the state of a computer system is carried out. The developed method is implemented in software and investigated when solving the problem of identifying the abnormal state of the computer system functioning. Conclusions. The scientific novelty of the results obtained lies in the development of a method for identifying the state of the computer system by choosing a meta-algorithm for classification and determining the optimal parameters for its configuration.

An intelligent ensemble learning approach for energy efficient and interference aware dynamic virtual machine consolidation

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function RBF and Support Vector Machine SVM as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of direct marketing. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for standard datasets of direct marketing. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of direct marketing.

The COVID-19 pandemic has necessitated the development of accurate and efficient classification models for diagnosis and prognosis. While deep learning has shown promising results in various medical applications, its combination with ensemble methods, which amalgamate the predictions of multiple classifiers, can further enhance the model's accuracy. This research paper introduces a novel approach called Deep Neural Ensemble Classification (DNEC) to tackle the challenge of Developing an enhanced ensemble model using deep learning algorithms and comparing its performance with existing ensemble methods. The research problem stems from the literature gap, where existing studies primarily focus on single-model approaches, lacking in-depth exploration of ensemble methods for COVID-19 classification. Motivated by the potential improvement in classification accuracy through ensemble methods, this study aims to create a deep neural ensemble classification model to improve the classification accuracy tailored for COVID-19 data. A set of diverse classifiers, including k-nearest neighbour (IBK), decision tree (J48), naïve bayes (NB), support vector machine (SVM), and sequential minimal optimization (SMO), are utilized in the proposed ensemble method. The accuracy improvement of the ensemble classifiers is evaluated using various metrics such as precision, recall, F1 score, confusion matrix, and processing time. The proposed method demonstrates that IBK+SVM+NB emerges as the top-performing deep neural ensemble classifier with an accuracy score of 99.29% and a total run time of 27.61 seconds. The innovative ensemble techniques introduced in this research contribute to the existing body of knowledge by filling the identified literature gap and offering a novel and highly accurate approach for COVID-19 classification.

Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble methods, including unweighted averaging, majority voting, the Bayes Optimal Classifier, and the (discrete) Super Learner, for image recognition tasks, with deep neural networks as candidate algorithms. We designed several experiments, with the candidate algorithms being the same network structure with different model checkpoints within a single training process, networks with same structure but trained multiple times stochastically, and networks with different structure. In addition, we further studied the overconfidence phenomenon of the neural networks, as well as its impact on the ensemble methods. Across all of our experiments, the Super Learner achieved best performance among all the ensemble methods in this study.

Traditionally, the approach that has been used in the design of pattern classification was to experimentally assess the performance of several classifiers so that the best one would be chosen. Ensemble classifiers is developed to design a such reliable system with improved accuracy. Recognising that each classifier may make different or complementary errors, the idea is to pool the results from all classifiers to find a composite system that would out-perform any individual (base) classifier. In this way, a single complex classifier may be replaced by a set of relatively simple classifiers. Similarly, the idea of ensemble learning is to employ multiple learners and combine their predictions. Most ensemble methods use a single base learning algorithm to produce homogeneous base learners to produce homogeneous ensembles. Some methods use heterogeneous learners to create heterogeneous ensembles. For ensemble methods to be more accurate than any of its members, the base learners must be as accurate and as diverse as possible. Motivated by this ensemble approach, a heterogeneous ensemble model with different algorithms for members' training is proposed. More specifically, three types of classifiers namely the ELM, SVM and RF have been adopted for classification of Malay syllables. In the process of improving ensemble accuracy and stability, different combination methods are explored. These methods vary in their approach to treat the training data, the type of algorithms used, and the combination methods followed. Diversity among the performance of each single classifier in the ensemble classifiers is also analysed and compared with SD, SI, FI, FD, MI and MD configurations. Based on the obtained results, heterogeneous ensemble method has been successfully proven in improving recognition accuracy on the selected Malay syllables.

Chapter 2 - ZigBee/IEEE 802.15.4 Networking Examples

Background: The accurate evaluation of patient prognosis, prediction of mortality rates, and anticipation of long-term illness sequelae continue to pose significant challenges for clinicians managing COVID-19. Objectives: This study sought to ascertain the association between serum electrolyte levels at hospitalization and mortality in patients infected with the Delta variant of COVID-19. Methods: This retrospective study included 316 hospitalized COVID-19 patients. Demographic data, clinical characteristics, and admission serum electrolyte and albumin levels were collected. Receiver Operating Characteristic (ROC) curve analysis was performed to assess the predictive value of individual biomarkers. Multivariate logistic regression was then used to identify independent predictors of mortality, adjusting for potential confounders including age. Results: The mean age of the study population was 59.53 ± 15.92 years, with deceased patients significantly older (68.86 ± 15.92 years) than discharged patients (56.80 ± 16.82 years). Initial comparisons showed significant differences in sodium (Na), potassium (K), and albumin levels between the two groups. The ROC curve analysis indicated moderate predictive power for Na [area under the curve (AUC) = 0.637] and K (AUC = 0.605), while calcium (Ca), magnesium (Mg), and albumin showed poor or no predictive value. However, multivariate logistic regression revealed that only advanced age (OR = 1.033, 95% CI: 1.001 - 1.066, P = 0.040) and longer hospital stay (OR = 1.130, 95% CI: 1.022 - 1.250, P = 0.017) were independently associated with increased mortality. Serum electrolyte and albumin levels did not remain significant predictors after adjustment for these factors. Conclusions: This study confirms advanced age and longer hospital stay as independent risk factors for mortality in COVID-19 patients. While initial analyses suggested an association between elevated serum Na and K levels at admission and higher mortality, these correlations did not remain statistically significant after adjusting for age and length of hospital stay in multivariate analysis. Further research in larger, diverse populations is recommended to explore these relationships.

Clinical utility of complete blood count indices in pediatric MOG antibody-associated disease.

Introduction: Preeclampsia (PE) is one of the leading obstetric diseases with significant morbidity and mortality in both mother and foetus. The etiology of preeclampsia is unknown. It may result from several reasons with imbalance between angiogenic regulatory factors in maternal circulation as one of the factor. High circulatory levels of Soluble Fms-like Tyrosine Kinase-1 (sFlt-1) are detectable several weeks before clinical presentation of preeclampsia. Aim: To determine association of serum levels of sFlt-1 with preeclampsia in second and third trimester of pregnancy. Materials and Methods: A prospective cohort study was conducted in the Department of Biochemistry in collaboration with the Department of Obstetrics and Gynaecology in a tertiary care hospital (Sri Guru Ram Das Institute of Medical Sciences and Research, Amritsar, Punjab, India), from November 2018 to March 2021. The study participants were divided into two groups i.e, normotensive (group 1) and preeclamptic (group 2). The enrollment of participants was done during second trimester. Serum sFlt-1 concentration was measured in second trimester (24-28 weeks) and in third trimester (beyond 28 weeks) using Enzyme Linked Immunoassay (ELISA) kits. Receiver Operating Characteristics (ROC) curve analysis was done for evaluation of Area Under Curve (AUC), sensitivity and specificity using software defined cut-off values. Results: Total 60 participants were there in each group with maternal mean age of 25.8±3.2 years in group 1 and 30.6±5.5 years in group 2 (p-value <0.001). sFlt-1 levels were significantly higher in preeclampsia group both in second and third trimesters when compared with normotensive group with median 313.07 versus 65.150 (p-value <0.001) and 337.875 versus 76.925 (p-value <0.001), respectively. The ROC curve analysis using 190.5 ng/mL as cut-off point in second trimester showed sensitivity 90%, specificity 85%, AUC was 0.832, 95% CI (0.745-0.918) and in third trimester at cut-off point 271.5 ng/mL showed sensitivity 90%, specificity 90%, AUC was 0.884, 95% CI (0.817-0.951). Conclusion: The soluble Fms-Like Tyrosine Kinase-1-1 (sFlt-1), may serve as biomarker for early diagnosis and can improve prediction of preeclampsia.

Objective:To investigate the role of the body roundness index（BRI） and a body shape index（ABSI） in the assessment of Obstructive Sleep Apnea-Hypopnea Syndrome（OSAHS）. Methods:A retrospective analysis was performed on the medical records of 256 adult patients with suspected OSAHS who underwent polysomnography（PSG） in the Department of Otorhinolaryngology, Peking University Third Hospital, from March 2024 to February 2025. The collected data included gender, age, Body Mass Index（BMI）, waist circumference（WC）, Apnea-Hypopnea Index（AHI）, and the lowest oxygen saturation（LSaO2）. Non-parametric correlation analysis, Receiver Operating Characteristic（ROC） curve analysis, and multiple logistic regression analysis were employed to explore the relationships between WC, BMI, BRI, ABSI, age and other indicators, as well as AHI and LSaO2. Results:Non-parametric correlation analysis showed that, among males, the indicators most strongly correlated with AHI, in descending order, were WC（r=0.476, P<0.001）, BMI（r=0.458, P<0.001）, and BRI（r=0.456, P<0.001）. Among females, the order was BRI（r=0.567, P<0.001）, WC（r=0.484, P<0.001）, BMI（r=0.449, P<0.001）, and age（r=0.352, P=0.004）. For the correlation with LSaO2, in males, the order was BMI（r=-0.445, P<0.001）, WC（r=-0.426, P<0.001）, and BRI（r=-0.415, P<0.001）; in females, it was BRI（r=-0.482, P<0.001）, BMI（r=-0.468, P<0.001）, and WC（r=-0.373, P<0.001）. ROC curve analysis indicated that the indicators of predictive performance for AHI>15/h from strongest to weakest（area under the curve, AUC） were WC（AUC=0.655, P<0.001）, BRI（AUC=0.646, P=0.001）, and BMI（AUC=0.646, P=0.001） in males; in females they were BRI（AUC=0.836, P<0.001）, WC（AUC=0.799, P<0.001）, and BMI（AUC=0.766, P=0.001）. Predictors of LSaO2<80% from strongest to weakest performance were BMI（AUC=0.701, P<0.001）, WC（AUC=0.689, P<0.001）, and BRI（AUC=0.689, P<0.001） in men; in females they were BRI（AUC=0.744, P=0.007）, BMI（AUC=0.724, P=0.013）, and WC（AUC=0.680, P=0.046）. Multifactor logistic regression analysis shows that in men WC（OR=1.049, 95%CI1.009-1.091） was an independent risk factor for AHI>15/h, and WC（OR=1.075, 95%CI1.038-1.114） was an independent risk factor for LSaO2<80%; in women BRI（OR=2.899, 95%CI 1.528-5.550） was an independent risk factor for AHI>15/h, and BMI（OR=1.276, 95%CI1.067-1.527） was an independent risk factor for LSaO2<80%. Conclusion:BRI is closely correlated with the severity of OSAHS in both adult male and female populations, with a more pronounced correlation in females. BRI may have advantages over other anthropometric measures in predicting the risk of OSAHS, playing a decisive role in predicting OSAHS in adult females, and may serve as a novel indicator for predicting the severity of OSAHS in adult women.

BackgroundPatients undergoing transurethral resection (TUR) of the prostate are at risk of TUR syndrome, generally defined as having cardiovascular and/or neurological manifestations, along with serum sodium concentrations less than or equal to 125 mmol/l. As these symptoms can also occur in patients with serum sodium greater than 125 mmol/l, this study aimed to investigate the relationship between serum sodium concentrations and neurological manifestations of TUR syndrome.MethodsData on patients who underwent TUR of the prostate under local anesthesia over an 8-year period were retrospectively reviewed. Based on their cardiovascular and neurological manifestations, patients were divided into two groups: a symptomatic and an asymptomatic group. Logistic regression analysis was used to detect the risk factors for being symptomatic. Receiver operator characteristic (ROC) curve analysis was used to determine the optimal cutoff value of estimated change in serum sodium level that could predict the development of clinical manifestation of TUR syndrome.ResultsOf the 229 patients, 60 showed symptoms. Serum sodium level correlated with neurological score (Spearman’s correlation coefficient > 0.5). Logistic regression detected that the risk factors for being symptomatic were serum sodium level variables, operation time longer than or equal 90 min, and presence of continuous drainage from the bladder. ROC curve analysis showed that a change in serum sodium level of 7.4 mmol/l was the optimal cutoff value, with a sensitivity of 0.72, a specificity of 0.87, and an area under the curve (AUC) of 0.87. ROC curve analysis also showed that a 7.0% change in serum sodium level was optimal for this parameter, with a sensitivity of 0.70, a specificity of 0.89, and an AUC of 0.87.ConclusionsChanges in serum sodium concentration of > 7 mmol/l and of > 7% could predict the development of cardiovascular and neurological manifestations, which were assumed to be symptoms of TUR syndrome.