A comparative evaluation of machine learning algorithms for network anomaly detection

Information and communication technology usage in the healthcare sector is not perceptible due to various challenges with increased healthcare needs. With the outburst of COVID-19, when the different countries announced lockdown and social distancing rules, it is crucial to predict a person's symptoms, which will help in the early diagnosis. In such situations, there is a tremendous growth seen in the usage of various technologies, such as remote health monitoring, Wireless Body Area Networks (WBANs), Machine Learning (ML), and Decision Support system (DSS). Hence, the chapter focuses on detecting diseases and associated symptoms using various ML algorithms. A total of 3073 patient data (heartbeat, snore, and body temperature) has been collected. The collected data were preprocessed to remove empty cells and zero values by replacing the mean of the cells. Later, the extracted features were used in Empirical Mode Decomposition (EWD) and Discrete Wavelet Transformation (DWT). Then, the optimized algorithms with the threshold values were identified by consulting doctors for accurate disease prediction. With the testing performance of various ML algorithms, such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), Gradient Descent (SGD), Naive Bayes (NB), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF), was compared. Performance evaluation parameters are accuracy, precision, F1 score, and recall. The results showed an average of 100% accuracy with SGD and SVM with DWT, whereas EMD, SVM, and MLP outperformed the state-of-the-art algorithms with 99.83% accuracy.

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

Breast cancer is one of the world's leading causes of mortality in women and is due to uncontrollable breast cell growth. Early detection and proper care are the only means of avoiding deaths from breast cancer. The precise characterization of tumors is a critical task in the medical profession. Because of their high precision and accuracy, machine learning methods are commonly used in identifying and classifying various forms of cancer. In this review article, the authors have tested different machine learning algorithms and implemented them, which can be used by doctors to identify cancer cells in an early and accurate way. This article introduces several algorithms, including support vector machine (SVM), Nave Bayes Classifier (NBC), artificial neural network (ANN), Random Forest (RF), decision tree (DT), and k-Nearest-Neighbor (KNN). These algorithms are trained with a collection of data containing tumor parameters for a person with breast cancer. After comparing the results, we found the highest accuracy of the Support Vector Machine and Random Forest and the highest precision of the Naive Bayes Classifier (NBC). In addition, we review the number of researches that provide machine learning algorithms for detecting breast cancer

Land surface temperature (LST) is described as one of the most important environmental parameters of the land surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m, and downscaling results would be validated by the resampled LST product of the advanced spaceborne thermal emission and reflection radiometer. The results are further compared with the classical algorithm—thermal sharpening algorithm (TsHARP), using images derived from two representatives kind of areas of Beijing city. The result shows that: 1) all machine learning algorithms produce higher accuracy than TsHARP; 2) the performance of TsHARP on urban area is unsatisfactory than rural because of the weak indication of impervious surface by normalized difference vegetation index, however, machine learning algorithms get the desired results on both two areas, in which ANN and RF models perform well with high accuracy and fast arithmetic, SVM also gets a good result but there is a smoothing effect on land surface; and 3) additionally, machine learning algorithms are promising to achieve a universal framework which can downscale LST for any area within the training data from long spatiotemporal sequences.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

Depression is a critical global mental health challenge that often remains undiagnosed due to the limitations and subjectivity of conventional screening techniques. The growing use of social media platforms offers new avenues for understanding human emotions, as individuals increasingly share their thoughts, moods, and experiences online. Leveraging this vast digital footprint, the present study introduces a machine learning (ML)-driven approach for the automated detection of depression using Twitter data. A comprehensive dataset comprising 205,271 posts was collected and carefully preprocessed through multiple natural language processing (NLP) techniques, including tokenization, stop-word elimination, lemmatization, and sentiment polarity assessment, to extract meaningful textual features. Six distinct ML models were trained and evaluated: Support Vector Classifier (SVM), Logistic Regression, Decision Tree, AdaBoost, Na"{i}ve Bayes, and K-Nearest Neighbors (KNN). Various performance metrics, including accuracy, precision, recall, and F1-score, were employed to assess the efficiency of each developed model. Among the tested models, Logistic Regression achieved the highest accuracy (92%), followed by SVM with 90%, while KNN performed comparatively lower with 70%. The results indicate that linear and ensemble-based classifiers are more effective than distance-based models in managing high-dimensional text data. Overall, this study offers a robust comparative evaluation of ML algorithms for depression detection and underscores the transformative potential of NLP and social media analytics in scalable, data-driven mental health monitoring systems.

Significant advancements have been achieved recently in the use of artificial intelligence (AI) to solve healthcare-related problems. A potential method for identifying and treating many illnesses is machine learning (ML). The “Breast Cancer Dataset” was used in this work to perform classification tasks using ML techniques including Support Vector Machine (SVM), Random Forest (RF) and MultiLayer Perceptron (MLP) for identifying the forms of breast cancer, either Benign or Malignant. The SVM, RF, and MLP achieved impressive PR scores exceeding 0.98. However, MLP exhibited superior performance among these algorithms, achieving an exceptional AUC value of 99.71. Conversely, SVM slightly outperformed the other algorithms in terms of training accuracy, with a value of 97.90%. This work used shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) to shed light on how these categorization algorithms made judgments. These interpretability methodologies showed that the majority of characteristics consistently predicted the presence of malignant or benign cancer types. This study demonstrates how ML algorithms' decision-making procedures may be understood, possibly promoting the use of ML in clinical settings for medical diagnosis.

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.&#x0D;

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

&amp;lt;p&amp;gt;Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites propagate through the atmosphere before being received on Earth. Thereby, the signal is delayed and tropospheric parameters can be estimated. The good global coverage of GNSS receivers, combined with the high temporal resolution and the high accuracy, make GNSS a suitable tool for studies on the atmosphere.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Atmospheric delays are differentiated into a zenith hydrostatic (ZHD) and a non-hydrostatic, or zenith wet delay (ZWD). The hydrostatic part has a larger contribution (causing a delay of roughly 2.4 meters in the zenith direction) but can be modeled with sufficient accuracy using analytical methods. The ZWD has a smaller contribution (causing a delay between 0 to 40 centimeters) and depends mainly on the water vapour content in the atmosphere. However, due to the variable nature of water vapour, the ZWD is difficult to model and is therefore typically estimated. Its quantification is essential since it drives weather systems and climate change to a great extent. For many applications, such as weather forecasting or positioning using low-cost GNSS receivers such as smartphones, global real-time monitoring or even predictions of ZWD would be required and be beneficial.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the last decade, machine learning (ML) algorithms have gained a lot of interest and are successfully utilized in many different fields. Thereby, ML algorithms have proven to be able to efficiently process and combine large amounts of data and solve problems of various kinds.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This motivated us to investigate the feasibility of ML algorithms for the prediction of tropospheric parameters, in particular ZWD, with the help of meteorological data such as the water vapour content. The work aims to develop a global model capable of predicting ZWD in space and time. Therefore, different ML algorithms are used to train a model based on meteorological features. The performance of the utilized algorithms is evaluated based on commonly used performance metrics, such as Root Mean Squared Error (RMSE) and R&amp;amp;#178;.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Preliminary investigations are carried out utilizing 3000 GNSS stations distributed over Europe. The performance of various ML methods, such as Linear Regression methods, Random Forest, (Extreme) Gradient Boosting, and Multilayer Perceptron is compared. Furthermore, different feature combinations, as well as training sample sizes are investigated. It is revealed that linear methods are not able to properly reflect the observations. Instead, our Random Forest approach provides, so far, the highest model accuracy with an RMSE of 1.7 centimeters and an R&amp;amp;#178; value of 0.88.&amp;lt;/p&amp;gt;

Periodontitis is increasingly associated with heart failure, and the goal of this study was to develop and validate a prediction model based on machine learning algorithms for the risk of heart failure in middle-aged and elderly participants with periodontitis. We analyzed data from a total of 2876 participants with a history of periodontitis from the National Health and Nutrition Examination Survey (NHANES) 2009 to 2014, with a training set of 1980 subjects with periodontitis from the NHANES 2009 to 2012 and an external validation set of 896 subjects from the NHANES 2013 to 2014. The independent risk factors for heart failure were identified using univariate and multivariate logistic regression analysis. Machine learning algorithms such as logistic regression, k-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron were used on the training set to construct the models. The performance of the machine learning models was evaluated using 10-fold cross-validation on the training set and receiver operating characteristic curve (ROC) analysis in the validation set. Based on the results of univariate logistic regression and multivariate logistic regression, it was found that age, race, myocardial infarction, and diabetes mellitus status were independent predictors of the risk of heart failure in participants with periodontitis. Six machine learning models, including logistic regression, K-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron, were built on the training set, respectively. The area under the ROC for the 6 models was obtained using 10-fold cross-validation with values of 0 848, 0.936, 0.859, 0.889, 0.927, and 0.666, respectively. The areas under the ROC on the external validation set were 0.854, 0.949, 0.647, 0.933, 0.855, and 0.74, respectively. K-nearest neighbor model got the best prediction performance across all models. Out of 6 machine learning models, the K-nearest neighbor algorithm model performed the best. The prediction model offers early, individualized diagnosis and treatment plans and assists in identifying the risk of heart failure occurrence in middle-aged and elderly patients with periodontitis.

With the exponential growth of streaming services, the demand for effective recommender systems has become paramount. Recommender systems, powered by machine learning algorithms, play a critical role in analyzing user behavior and preferences to deliver personalized content recommendations. However, the abundance of available algorithms poses a challenge for streaming platforms in determining the most suitable approach for their recommender systems. This research paper presents an evaluation of various machine learning algorithms used in recommender systems for streaming services. Through a comparative analysis of collaborative filtering, content-based filtering, matrix factorization techniques, deep learning algorithms, and emerging approaches such as reinforcement learning and hybrid models, this study aims to provide insights into the strengths, limitations, and performance of each approach. By assessing the effectiveness of these algorithms, exploring their applications, and discussing recent advancements, this research contributes to the advancement of recommendation technology in streaming services, ultimately enhancing user satisfaction and engagement.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. Feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.