Evaluation Of Machine Learning Algorithms Research Articles

Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection.

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables.

Performance evaluation of machine learning algorithms in predicting machining responses of superalloys

This study explores the application of machine learning algorithms—gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN)—to predict machining responses during the milling of Inconel 690, a superalloy known for its exceptional mechanical properties and oxidation resistance. Machining Inconel 690 presents significant challenges due to its toughness and work-hardening tendencies, which can lead to rapid tool wear and poor surface finish. Traditional optimization methods often rely on empirical models and trial-and-error approaches, which are time-consuming and costly. In contrast, machine learning techniques can effectively model complex, nonlinear relationships between machining parameters and performance outcomes, such as surface roughness, cutting force, and cutting temperature. This study employs statistical metrics, including Root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), to determine the predictive performance of the models. The results show that the GEP model achieved an R2 ranging from 0.944 572 to 0.992 999, with an RMSE between 0.015 527% and 0.694 523% and a MAPE ranging from 1.452 397% to 4.947 892%. ANFIS and ANN also demonstrated strong predictive capabilities, although GEP outperformed them. The importance of this study lies in its demonstration of advanced AI techniques as effective tools for optimizing machining processes, ultimately contributing to improved efficiency and quality in manufacturing superalloys.

Performance Evaluation of Machine Learning Algorithms in Estimating Taxi Times at Istanbul Airport

Performance Evaluation of Machine Learning Algorithms in Estimating Taxi Times at Istanbul Airport

Performance Evaluation of Machine Learning Algorithms for Iris Dataset Classification

The problem of classifying iris flowers is a well-known issue in the fields of machine learning and pattern recognition. The Iris dataset is widely recognized as a standard for evaluating the effectiveness of classification algorithms in machine learning. This paper offers a comparative assessment of different machine learning algorithms when applied to the Iris dataset. We implement and assess the performance of k-Nearest Neighbors, Logistic Regression, Decision Tree, Linear SVC methods, Random Forest, Gaussian Naïve Bayes and AdaBoost algorithms based on their accuracy in classification. We examine diverse classification methods, appraise their effectiveness, and discuss our findings. The outcomes reveal the accuracy of these models in correctly categorizing the Iris species. To achieve high accuracy, k-Nearest Neighbors, Logistic Regression, Linear SVC methods,Decision Tree,, Random Forest and AdaBoost classifiers are utilised.

Performance Evaluation of Machine Learning Algorithms in Reduced Dimensional Spaces

Performance Evaluation of Machine Learning Algorithms in Reduced Dimensional Spaces

Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources.

Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.

Performance Evaluation of Machine Learning Algorithms in Smart Agriculture

Performance Evaluation of Machine Learning Algorithms in Smart Agriculture

A Comprehensive Evaluation of Machine Learning Algorithms for Web Application Attack Detection with Knowledge Graph Integration

A Comprehensive Evaluation of Machine Learning Algorithms for Web Application Attack Detection with Knowledge Graph Integration

Comparative evaluation of machine learning algorithms for rainfall prediction to improve rice crops production

Rainfall has a huge impact on agriculture because it's one of the key causes of crops devastation. Farmers face a slew of issues when unexpected heavy rains fall, as their planted crops are washed away or damaged. Pakistan is an agricultural country where new methods and techniques are needed to improve the traditional farming methods. This research intends to provide aid in the protection of crops from severe rains using machine learning to accurately anticipate the possibility of rainfall, which is a well-known agricultural problem. Various weather factors such as temperature, humidity, and atmospheric pressure can be used to predict rainfall patterns. Rainfall prediction can be used to identify and furnish future rainfall descriptions for agricultural planning for food security, allowing farmers to take precautionary measures to safeguard rice fields. Naïve Bayes, LogitBoost, RIPPER, Decision Stump, AdaBoost, Random Forest, Artificial Neural network, and K* were evaluated for rainfall prediction based on accuracy, precision, recall, F1-measure, Root Mean Squared Error, area under receiver operating characteristic curve, elapsed training time and elapsed testing time. The results obtained indicate that the best performance is achieved by Random forest with maximum accuracy of 83.2%, followed by ANN (82.5%), LogitBoost (82.2%), RIPPER (82%), naïve Bayes (80.3%), AdaBoost (80.2%), and K*(79.2%) respectively. K* The lazy approach involved a minimum of training time and a maximum of test time. Maximum training time was consumed by Random Forest and minimum testing time was taken by Decision Stump.

Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study

Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.

A large-scale evaluation of machine learning algorithms in mid-term water demand forecasting

ABSTRACT Water utilities currently face challenges in accurately measuring user-end consumption as they have to visit every conventional meter that is installed in their service area. This study explores the use of accessible machine learning models to forecast water demand at two crucial temporal scales, addressing practical needs such as customer billing. To draw meaningful conclusions, these models were tested and trained from actual user-end water consumption data from over 2.1 million customers over a 10-year period. These data were provided by the Water and Sewage Company of Greece (EYDAP). The large-scale experiment included the use of statistical models (ARIMA and SARIMA), deep learning [long short-term memory (LSTM)], and clustering techniques (k-NN algorithm). The model with the best performance was ARIMA, outperforming all the other models including the more complex ones. The LSTM model did not perform as expected in predicting water consumption, it excelled only in predicting the total amount of water consumed. These forecasts can be valuable tools for water utility companies, aiding in tasks such as customer billing and water balance calculations. This paper covers all the necessary steps that must be taken to achieve meaningful user-end water consumption forecasts, from raw data preprocessing to hyperparameter tuning for machine learning algorithms.

Comparative evaluation of machine learning algorithms for phishing site detection.

The advent of Internet technologies has resulted in the proliferation of electronic trading and the use of the Internet for electronic transactions, leading to a rise in unauthorized access to sensitive user information and the depletion of resources for enterprises. As a consequence, there has been a marked increase in phishing, which is now considered one of the most common types of online theft. Phishing attacks are typically directed towards obtaining confidential information, such as login credentials for online banking platforms and sensitive systems. The primary objective of such attacks is to acquire specific personal information to either use for financial gain or commit identity theft. Recent studies have been conducted to combat phishing attacks by examining domain characteristics such as website addresses, content on websites, and combinations of both approaches for the website and its source code. However, businesses require more effective anti-phishing technologies to identify phishing URLs and safeguard their users. The present research aims to evaluate the effectiveness of eight machine learning (ML) and deep learning (DL) algorithms, including support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), logistic regression (LR), convolutional neural network (CNN), and DL model and assess their performances in identifying phishing. This study utilizes two real datasets, Mendeley and UCI, employing performance metrics such as accuracy, precision, recall, false positive rate (FPR), and F-1 score. Notably, CNN exhibits superior accuracy, emphasizing its efficacy. Contributions include using purpose-specific datasets, meticulous feature engineering, introducing SMOTE for class imbalance, incorporating the novel CNN model, and rigorous hyperparameter tuning. The study demonstrates consistent model performance across both datasets, highlighting stability and reliability.

Arabic Music Genre Identification

Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.

Predictive modeling in geoarchaeology: An evaluation of machine learning algorithms and topographic variables on the Serranópolis City - Brazil

The search for predictive models in archaeology using geographical data and artificial intelligence (AI) has made progress, but there is a scarcity of studies focusing on South America. Serranópolis, in Goiás, Central-West Brazil, emerges as an ideal landscapefor applying AI and geographical data to predict archaeological sites. This study aimed to predict potential archaeological locations in Serranópolis using topographic variables and four supervised classification algorithms: C5.0, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM). The methodology involved sample points representing open-air archaeological sites and rock shelters, along with randomly selected pseudo-absence samples. Eighteen topographic variables from a digital elevation model were considered. RF outperformed other algorithms, producing a probability map of site occurrence. Key predictors included sky view factor, roughness, topographic depression, and slope. The findings enhance our understanding of archaeological potential in this region, highlighting the effectiveness of RF in predictive modeling.

Evaluation of Machine Learning Algorithms for Classification of Infrastructure Elements in Complex Structures

Evaluation of Machine Learning Algorithms for Classification of Infrastructure Elements in Complex Structures

INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION

In the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.

An Evaluation of Machine Learning Algorithms Used for Recommender Systems in Streaming Services

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.

Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems

Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems

Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 μg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.

Secure and Efficient Wireless Sensor Network and Machine Learning -based Monitoring System for Student Physical and Mental Health

In this research we have reviewed the performance of machine learning algorithms and WSNs for the monitoring student physical and mental health. We have found that logistic regression is a stronger alternative after an exhaustive evaluation of machine learning algorithms that consistently produce superior performance metrics than Gaussian mixture models (GMM). This has demonstrated average accuracy of 87.1% across 10 experiments, besting the average accuracy of 83.8% of GMM. It also illustrates greater efficacy for assessing system security, producing average accuracy of 86.6%, with 91.9% for GMM. The findings demonstrate the significance of select a machine learning algorithms that meets the specific needs of monitoring student health systems. They are also an illustration of logistic regressions dependability, and adeptness at identifying subtle changes in student health indicators that allow for proactive initiation of interventions and support mechanisms tailored to individual students’ needs. Going forward, monitoring systems should next be further refined and optimised so that they can more effectively support both student well-being and academic success in the complex and diverse educational settings of Indian universities.