Extra Trees Classifier Research Articles

Breast cancer identification using machine learning and hyperparameter optimization

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.

Novel maternal risk factors for preeclampsia prediction using machine learning algorithms

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Preeclampsia and eclampsia are the most common obstetric disorders associated with poor outcomes for women, fetuses, and newborns throughout the perinatal period. The study’s primary objective was to assess the accuracy of novel high-risk factors using MLA in predicting preeclampsia. The study included 400 pregnant women and used 27 novel high-risk factors to predict preeclampsia. The target variables for predicting preeclampsia are systolic and diastolic blood pressures. Various algorithms, including DT, RF, Gradient Boosting, SVM, K-Neighbors, LGBM, MLP, Ada Boost Classifier, and Extra Trees Classifier are used in the analysis. The accuracy and precision of the LGBM Classifier (0.85 and 0.9583 with F1 0.7188), Support Vector Classifier (0.8417 and 0.92 with F1 0.7077), Decision Tree (0.825 and 0.913 with F1 0.6667), and Extra Trees (0.8167 and 0.9091 with F1 0.6452) are found to be better algorithms for prediction of preeclampsia than those of the other models used in the study. According to the novel high-risk factors score, 17.5% of pregnant women were identified as being at high risk for preeclampsia during the first trimester, which increased to 18.7% in 3rd trimester; in addition, 16% of pregnant women had a BP of 140/90 mmHg and the above. Novel, high-risk scores and MLA can effectively predict preeclampsia at an early period.</p></td></tr></tbody></table></div>

Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques

This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar pressure data were collected and the plantar temperature was measured both immediately before and after the walk. Each participant completed the trial three times, and the average data between the trials were calculated. The research was divided into three experiments: the first evaluated the correlations between the plantar pressure and temperature data; the second focused on predicting diabetes using each data type independently; and the third combined both data types and assessed the effect of such to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification algorithms were employed, and the performance was evaluated using a five-fold cross-validation strategy. The outcomes of the initial set of experiments indicated that the machine learning models were significant correlations between the thermal data and pressure estimates. This was consistent with the findings from the prior correlation analysis, which showed weak relationships between these two data modalities. However, a shift in focus towards predicting diabetes by aggregating the temperature and pressure data led to encouraging results, demonstrating the effectiveness of this approach in accurately predicting the presence of diabetes. The analysis revealed that, while several classifiers demonstrated reasonable metrics when using standalone variables, the integration of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at 68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model’s predictive accuracy. This can indicate the importance of multimodal data integration and their potentials in diabetes prediction.

Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.

Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diagnose pneumonia. However, chest x-ray-based diagnosis requires expert radiologists which is time-consuming and laborious. Moreover, COVID-19 and pneumonia have similar symptoms which leads to false positives. Machine learning-based solutions have been proposed for the automatic prediction of pneumonia from chest X-rays, however, such approaches lack robustness and high accuracy due to data imbalance and generalization errors. This study focuses on elevating the performance of machine learning models by dealing with data imbalanced problems using data augmentation. Contrary to traditional machine learning models that required hand-crafted features, this study uses transfer learning for automatic feature extraction using Xception and VGG-16 to train classifiers like support vector machine, logistic regression, K nearest neighbor, stochastic gradient descent, extra tree classifier, and gradient boosting machine. Experiments involve the use of hand-crafted features, as well as, transfer learning-based feature extraction for pneumonia detection. Performance comparison using Xception and VGG-16 features suggest that transfer learning-based features tend to show better performance than hand-crafted features and an accuracy of 99.23% can be obtained for pneumonia using chest X-rays.

Enhancing early detection of autistic spectrum disorder in children using machine learning approaches

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

A Data-Driven Approach for Classifying and Predicting DDoS Attacks with Machine Learning

The importance of IoT security is growing as a result of the growing number of IoT devices and their many applications. Distributed denial of service (DDoS) assaults on IoT systems have become more frequent, sophisticated, and of a different kind, according to recent research on network security, making DDoS one of the most formidable dangers. Real, lucrative, and efficient cybercrimes are carried out using DDoS attacks. One of the most dangerous types of assaults in network security is the DDoS attack. ML-based DDoS-detection systems continue to face obstacles that negatively impact their accuracy. AI, which incorporates ML to detect cyberattacks, is the most often utilised approach for these goals. In this study, it is suggested that DDoS assaults in Software-Defined Networking be identified and countered using ML approaches. The F1-score, recall, accuracy, and precision of many ML techniques, including Cat Boost and Extra Tree classifier, are compared in the suggested model. DDoS-Net is designed to handle data imbalance effectively and incorporates thorough feature analysis to enhance the model's detection capabilities. Evaluation on the UNSW-NB15 dataset demonstrates the exceptional performance of DDoS-Net. The highest accuracy achieved by the machine learning algorithms Cat Boost and Extra Tree classifier is 90.78% and 90.27% respectively using the most familiar dataset. This work presents a strong and precise approach for DDoS attack detection, which greatly improves the cybersecurity environment and strengthens digital infrastructures against these ubiquitous threats.

Robust Decision Support System for Stress Prediction Using Ensemble Techniques

The prevalence of stress among students has become a significant concern, impacting academic performance and overall well-being. Low-level stress can be beneficial as it helps us maintain focus on our daily routines, enabling us to perform tasks efficiently. However, severe stress can lead to heart disease, diabetes, aches, pains, and numerous other serious health issues. To address this issue, this research presents a comprehensive Decision Support System (DSS) tailored for predicting student stress levels using ensemble techniques. The goal of this project is to develop an ensemble model method of voting to classify the student dataset. An ensemble model is a combination of individual machine-learning algorithms that enhance the accuracy of the model by taking different approaches to the problem and their summary output through base classifiers. Extensive experimentation and validation are conducted using real-world student data to assess the effectiveness of the proposed DSS. In this research, we employed an ensemble modeling voting approach to classify the student dataset. Ensemble models integrate individual machine learning algorithms to enhance accuracy by leveraging the combined output of the base classifiers. Gradient Boosting Classifier (GBC), Random Forest (RF), Bagging Classifier (BC), and Extra Trees Classifier (ETC) served as base learners to construct a voting ensemble model for student dataset classification. Our findings revealed that the voting classifier consisting of Support Vector Machine (SVM), Random Forest (RF), Extra Trees Classifier (ETC), and Bagging Classifier (BC) yielded the highest accuracy at 94.78%, while GBC achieved 84.39% accuracy, RF attained 94.26% accuracy, ETC achieved 94.02% accuracy, and BC obtained 93.65% accuracy. The ensemble modeling approach significantly outperformed individual machine learning algorithms, demonstrating superior accuracy by mitigating variance and classification errors. Implementing such a system not only enhances the student assessment process in the education sector but also fosters interdisciplinary research collaboration across diverse fields within the region.

Development of a machine learning model to identify intraventricular hemorrhage using time-series analysis in preterm infants

Although the prevalence of intraventricular hemorrhage (IVH) has remained high, no optimal strategy has been established to prevent it. This study included preterm newborns born at a gestational age of < 32 weeks admitted to the neonatal intensive care unit of a tertiary hospital between January 2013 and June 2022. Infants who had been observed for less than 24 h were excluded. A total of 14 features from time-series data after birth to IVH diagnosis were chosen for model development using an automated machine-learning method. The average F1 scores and area under the receiver operating characteristic curve (AUROC) were used as indicators for comparing the models. We analyzed 778 preterm newborns (79 with IVH, 10.2%; 699 with no IVH, 89.8%) with a median gestational age of 29.4 weeks and birth weight of 1180 g. Model development was performed using data from 748 infants after applying the exclusion criteria. The Extra Trees Classifier model showed the best performance with an average F1 score of 0.93 and an AUROC of 0.999. We developed a model for identifying IVH with excellent accuracy. Further research is needed to recognize high-risk infants in real time.

Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model

Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.

Using machine learning model for predicting risk of memory decline: A cross sectional study

BackgroundMemory decline is the earliest symptom of various neurodegenerative disease, such as Alzheimer's disease (AD). However, accurately the prediction and identification of risk factors leading up to memory decline has remained limited. ObjectiveThe objective of this study is to create and verify a machine learning model that can accurately predict risk factors for memory decline among US adults. MethodsA total of 9971 individuals were enrolled from the National Health and Nutrition Examination Survey (NHANES) 2015–2016 database. The least absolute shrinkage and selection operator (LASSO) was used to screen for characteristic predictors. Five machine learning (ML) algorithms: including Logistic Regression, ExtraTrees classifier, Bagging classifier, eXtreme Gradient Boosting (XGBoost), and Random Forest (RF) were employed. The performance of each model was evaluated by confusion matrix, area under curve (AUC), accuracy, precision, specificity, Recall and F1 scores. ResultsThe ultimate sample comprised 4525 subjects, of whom 7.7 % (N = 347) exhibited memory deterioration. The ExtraTrees classifier model and the XGBoost model demonstrated superior prediction performance and clinical value compared to other independent machine learning models, based on the AUC value of 0.915 and 0.911. Additionally, they consistently demonstrated accurate predicting ability for memory decline in the external datasets, with an AUC of 0.851 and 0.843, respectively. ConclusionThe ExtraTrees classifier and the XGBoost models were the two outperformed models in predicting memory decline. Nevertheless, it is necessary to conduct future investigations to confirm the accuracy of our findings.

A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.

Introduction: We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Materials and Methods: Data from pediatric patients who underwent ureteroscopy for stone treatment by a single experienced surgeon, between 2010 and 2023 in Southampton General Hospital, were retrospectively collected. Fifteen ML classification algorithms were used to investigate correlations between preoperative characteristics and postoperative outcomes: primary stone-free status (SFS, defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments >2 mm at Xray kidney-ureters-bladder (XR KUB) or ultrasound kidney-ureters-bladder (US KUB) at 3 months follow-up) and complications. For the task of complication and stone status, an ensemble model was made out of Bagging classifier, Extra Trees classifier, and linear discriminant analysis. Also, a multitask neural network was constructed for the simultaneous prediction of all postoperative characteristics. Finally, explainable artificial intelligence techniques were used to explain the prediction made by the best models. Results: The ensemble model produced the highest accuracy (90%) in predicting SFS, finding correlation with overall stone size (-0.205), presence of multiple stones (-0.127), and preoperative stenting (-0.102). Complications were predicted by Synthetic Minority Oversampling Technique (SMOTE) oversampled dataset (93.3% accuracy) with relation to preoperative positive urine culture (-0.060) a1nd SFS (0.003). Training ML for the multitask model, accuracies of 83.3% and 80% were respectively reached. Conclusion: ML has a great potential of assisting health care research, with possibilities to investigate dataset at a higher level. With the aid of this intelligent tool, urologists can implement their practice and develop new strategies for outcome prediction and patient counseling and informed shared decision-making. Our model reached an excellent accuracy in predicting SFS and complications in the pediatric population, leading the way to the validation of patient-specific predictive tools.

Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease

PurposeEarly retinal disease identification is vital since symptoms are passive at initial stages but lead to irreversible vision loss at advanced stages. Globally, a substantial population is at risk of vision impairment, prompting researchers to investigate methods for efficient classification. MethodsThis work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). The top performing n (n=3,4,5) classifiers were ensembled with meta-learner using stacking strategy. The performance of the pipeline is evaluated with two distinct meta-learners, three different image sizes, and feature counts on the RFMiD dataset. ResultsThe architectures were assessed using (1) the performance metrics – accuracy, precision, recall, and F1-score, (2) statistical graphics to understand the prevalence of classifiers, Borda count voting method to identify the best CNN model, (3) Tukey's honestly significance difference test to identify best-performing architecture. Two architectures achieved a high accuracy of 92.34 percent and F1 scores of 95.11 and 95.19. ConclusionThis is the first work to experiment with 144 combinations to identify suitable deep architecture for binary retinal disease classification. The study recommends Xception for feature extraction ensembled with ExtraTreeClassifier, Light gradient boosting machine, Random Forest, AdaBoost classifiers, and meta-learner as Logistic Regression. Another best-performing architecture is similar with a variation in DenseNet121 for feature extraction and support vector as a meta-learner.

Deep Learning-Based Prediction Models for the Detection of Vitamin D Deficiency and 25-Hydroxyvitamin D Levels Using Complete Blood Count Tests

Vitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single "sufficient/deficient" classification problem using machine learning or statistics-based methods. The main objective of this study is to predict a patient's VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health laboratory was used for this purpose. CNN, RNN, LSTM, GRU and Auto-encoder algorithms were used to develop DL-based models. The top 25 features in the CBC tests were carefully selected by implementing the Extra Trees Classifier and Multi-task LASSO feature selection algorithms. The performance of the models was evaluated using metrics such as accuracy, F1-score, mean absolute error, root mean square error and R-squared. Remarkably, all three models showed satisfactory results when compared to the existing literature; however, the CNN-based prediction models proved to be the most successful.

Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.

Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system's robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance.

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry

PurposeIn today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.Design/methodology/approachA questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.FindingsThe findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.Originality/valueThis study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach

Pregnancy termination remains a complex and sensitive issue with approximately 45% of abortions worldwide being unsafe, and 97% of abortions occurring in developing countries. Unsafe pregnancy terminations have implications for women’s reproductive health. This research aims to compare black box models in their prediction of pregnancy termination among reproductive-aged women and identify factors associated with pregnancy termination using explainable artificial intelligence (XAI) methods. We used comprehensive secondary data on reproductive-aged women’s demographic and socioeconomic data from the Demographic Health Survey (DHS) from six countries in East Africa in the analysis. This study implemented five black box ML models, Bagging classifier, Random Forest, Extreme Gradient Boosting (XGB) Classifier, CatBoost Classifier, and Extra Trees Classifier on a dataset with 338,904 instances and 18 features. Additionally, SHAP, Eli5, and LIME XAI techniques were used to determine features associated with pregnancy termination and Statistical analysis were employed to understand the distribution of pregnancy termination. The results demonstrated that machine learning algorithms were able to predict pregnancy termination on DHS data with an overall accuracy ranging from 79.4 to 85.6%. The ML classifier random forest achieved the highest result, with an accuracy of 85.6%. Based on the results of the XAI tool, the most contributing factors for pregnancy termination are wealth index, current working experience, and source of drinking water, sex of household, education level, and marital status. The outcomes of this study using random forest is expected to significantly contribute to the field of reproductive healthcare in East Africa and can assist healthcare providers in identifying individuals’ countries at greater risk of pregnancy termination, allowing for targeted interventions and support.

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.

Descending Stairs Detection Using Digital Image Processing to Guide Visually Impaired

Blindness refers to a condition in which an individual experiences limitations in their visual ability. Individuals with visual impairments require specific assistance to facilitate their movement from one location to another. The need for this assistance arises due to various obstacles scattered throughout their environment. One of the most significant challenges is navigating descending stairs. To address this issue, a descending stairs detection system based on digital image processing has been developed. Through this approach, the mobility of individuals with visual impairments can be enhanced. The descending stairs detection system is designed using the Gray Level Co-occurrence Matrix (GLCM) method to extract distinctive features of descending stairs and the surrounding floor surfaces. Seven GLCM features are incorporated into the development of this system, allowing it to differentiate between descending stairs and floors using the Extra Tree Classifier classification method. Through a series of tests, the system's accuracy is measured at 84%, demonstrating its adequate ability to identify descending stairs. Additionally, the average computation time for detecting descending stairs and floors is recorded at 0.121 (s), indicating the efficient performance of this system in supporting the mobility of individuals in need.

IMPLICATIONS OF COMPUTE AND INTERPRETABILITY FOR THE DIFFERENT SDN SECURITY MODELS

The main focus of this project is to design and implement an IDS for SDNs that are currently causing great interest in the networking society. Making the control plane and the data plane modularity, SDNs provide a higher level of flexibility and programming capabilities to manage the network but concurrently budding novel security risks. Several machine learning algorithms are in use such as the Logistic Regression, Decision Tree, Random Forest, etc. , which are used to identify malice within the network. As for the dataset sdn_intrusion. csv, preprocessing steps included were cleaning where missing values were managed, categorical data was encoded as well as handling for outliers existed. This paper applied feature selection by analysing correlation between the input features and the target variable with the aim of enhancing the model accuracy. The models were assessed with cross validation and several metrics like accuracy, Precision, Recall, F1 score, AUC and the results are quantitatively and qualitatively presented in the form of confusion matrices, ROC and Precision Recall curves. As the findings indicate, Extended Trees (Extra Trees Classifier) and Histograms Gradient Boosting models are efficient in identifying intrusions within SDN architectures. In conclusion, the study highlights the necessity of the proper choice of the machine learning algorithm and preliminary data processing for the effective IDS construction in SDNs, which enhances the readiness level of the networks against cyber threats. KEYWORDS: Software Defined Networks, Intrusion Detection System, Machine Learning, Network Security, Logistic Regression, Extra Trees Classifier, Random Forest, LightGBM, Precision-Recall Curve, ROC Curve.