Analysis Of Machine Learning Techniques Research Articles

Comparative Analysis of Machine Learning Techniques in Air Quality Index (AQI) prediction in smart cities

Comparative Analysis of Machine Learning Techniques in Air Quality Index (AQI) prediction in smart cities

Analysis and Comparison of Machine Learning Techniques for DDoS Attack Classification in Network Environments

This research presents a comparative analysis of machine learning techniques for classifying Distributed Denial of Service (DDoS) attacks within network traffic. We evaluated the performance of three algorithms: Logistic Regression, Decision Tree, and Random Forest, including their scaled-feature counterparts. The study utilized a robust methodology incorporating advanced data preprocessing, feature engineering, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The models were rigorously tested using a cross-validation framework, assessing their accuracy, precision, recall, and F1 score. Results indicated that the Random Forest algorithm outperformed the others, demonstrating superior predictive accuracy and consistency, albeit with higher computational costs. Logistic Regression, when feature-scaled, showed significant improvement in performance, highlighting the importance of data normalization in models sensitive to feature scaling. Decision Trees provided a quick and interpretable model, though slightly less accurate than the Random Forest. The research findings highlight the trade-offs between predictive performance and computational efficiency in selecting machine learning models for cybersecurity applications. The study contributes to the cybersecurity domain by elucidating the efficacy of ensemble techniques in DDoS attack classification and underscores the potential for model improvement through scaling and data balancing.

A comparative study of ensemble learning algorithms for high-frequency trading

High-Frequency Trading utilizes powerful mathematical algorithms to execute transactions at an extremely rapid pace, which makes the use of machine learning techniques for prediction necessary. This paper evaluates the effectiveness of various ensemble learning algorithms, including Boosting (Adaboost and XGBoost), Bagging (Random Forest and Bagging-LSVM), and Stacking, in predicting stock prices using High-Frequency Trading (HFT) data from the Casablanca Stock Exchange. The data contains 311.812 transactions at the millisecond precision level and was obtained under a non-disclosure agreement. Three performance metrics are used: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean of the Squared Errors (MSE) and accuracy of predictions is assessed daily, monthly and annually. The study shows that the Stacking Model outperforms other algorithms in forecasting prices across different periods due to its ability to generate profit from multiple learners and construct a robust model. These results provide valuable insights for improving High-Frequency Trading (HFT) strategies, including the accuracy and robustness of the algorithms in predicting stock prices, which are crucial for effective HFT strategies. The findings highlight how each algorithm characteristics and performance metrics can enhance decision-making in HFT. The novelty of this study lies in its unique combination of a focus on an emerging market, the application of advanced ensemble learning algorithms to high-frequency data, and its comprehensive approach to evaluating algorithm performance. Also, this paper is the first study that focuses on applying ensemble learning algorithms to high-frequency data in Casablanca Stock Exchange and one of the rare that focuses on African stock markets. Thus, this study is pioneering in its comparative analysis of machine learning techniques for HFT in Morocco, introducing sophisticated and more accurate tools for analyzing and predicting Casablanca Stock Exchange trends.

Time Period Categorization in Fiction: A Comparative Analysis of Machine Learning Techniques

This study investigates the automatic categorization of time period metadata in fiction, a critical but often overlooked aspect of cataloging. Using a comparative analysis approach, the performance of three machine learning techniques, namely Latent Dirichlet Allocation (LDA), Sentence-BERT (SBERT), and Term Frequency-Inverse Document Frequency (TF-IDF) were assessed, by examining their precision, recall, F1 scores, and confusion matrix results. LDA identifies underlying topics within the text, TF-IDF measures word importance, and SBERT measures sentence semantic similarity. Based on F1-score analysis and confusion matrix outcomes, TF-IDF and LDA effectively categorize text data by time period, while SBERT performed poorly across all time period categories.

Comparative analysis of machine learning techniques for cryptocurrency price prediction

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India

Accurate assessment of aboveground biomass (AGB) in tropical forests, particularly within a biodiversity hotspot, is vital for sustainable resource management and the preservation of ecosystems. However, estimating AGB in tropical forests is complex due to the diverse and intricate nature of vegetation, necessitating the integration of data from multiple sources. To tackle this challenge, our study utilized seven machine learning algorithms to analyze various combination of multisource datasets. We developed seven models/scenarios that incorporated Sentinel-1, Sentinel-2 as well as environmental factors such as topography, soil and climate to identify key variables for accurate estimation of AGB. For optimal performance, hyperparameters of the algorithms were fine-tuned through 10-fold cross-validation and their accuracy were assessed using the testing dataset. We found that the integrated model of satellite datasets, topography, climate, and soil variables exhibited the highest accuracy, where ensemble stacking, that combined multiple MLAs, proved to be reliable and best suited for predicting AGB (mean absolute error-3.97 Mg 0.1 ha−1, root mean square error-5.67 Mg 0.1 ha−1, and coefficient of determination - 0.82). Notably, the top predictor variables included Sentinel-2 bands (near infrared and green), soil properties (pH and soil organic carbon), and topography (elevation). The study emphasizes the significance of incorporating environmental variables (specifically topography and soil properties) along with Sentinel datasets to improve the accuracy of AGB estimation. This approach has the potential for broader applications, specifically in regions where vegetation productivity is governed by diverse environmental conditions.

Mathematical Modeling and Statistical Analysis of Elederly Fall Detection System Using Improved Support Vector Machine

This research focuses on enhancing the safety of elderly individuals through early fall detection using mathematical modelling and statistical analysis of machine learning techniques for the application and effectiveness of elderly fall detection. Falls among the elderly can lead to severe consequences, necessitating timely intervention. Leveraging machine learning algorithms, this innovative open-source project analyses sensor data from wearable sensors like accelerometers, gyroscopes, and magnetometers, along with environmental data such as temperature and humidity, to promptly identify fall patterns. The project uses a dataset containing 14 variables, including age, sex, medical indicators, and more, collected from diverse subjects and activities. The results obtained during the testing phase underscore the importance of refining the model through dataset adjustments. As the physical, cognitive, and sensory functions decline with age, the risk of falls increases, highlighting the need for fall detection and prevention systems. This research reviews the latest machine learning-based systems for fall detection and prevention, analyzing them based on various parameters. It identifies support vector machines and wearable devices as common tools, but emphasizes the need for broader studies in different contexts. The paper also visualizes the performance metrics of ML algorithms in conjunction with various wearables and outlines future research directions, including energy efficiency, sensor fusion, context awareness, and wearable design, to advance fall detection and prevention for the elderly.

Enhancing Predictive Quality in HVOF Coating Technology: A Comparative Analysis of Machine Learning Techniques

This paper presents a comparative analysis of the performance of machine learning techniques for enhancing predictive quality in thermal spray coating technology, with a focus on the high velocity oxygen fuel (HVOF) process. The study aims for accurately predicting coating properties, which are pivotal in determining coating quality in HVOF applications. By investigating five influential factors, including the fuel-to-oxygen ratio and coating velocity, this research was designed to improve the understanding and control of these properties. Through advanced statistical design of experiments, correlations between these factors and key coating properties are established. Six machine learning models are trained and evaluated to assess their predictive capabilities for coating properties. The obtained results provide valuable insights into the HVOF coating process and highlight the potential of predictive model-based techniques for accurately predicting such properties. Furthermore, this research offers guidance for selecting an effective machine learning technique in the context of thermal spray technology.

A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection

A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection

A Comparative Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection

A Comparative Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection

Telecom customer churn prediction model : Analysis of machine learning techniques for churn prediction and factor identification in telecom sector

This research project leverages exploratory data analysis (EDA) on a telecom company’s customer data to predict user churn. Utilizing Python and its libraries, including Pandas, NumPy, Matplotlib, and Scikit-Learn, it identifies key parameters crucial for accurate predictions. The results and predictions are visualized using the Flask framework and Power BI analytics tool. In the highly competitive telecommunications sector, customer churn poses a significant challenge, with an annual churn rate of 15-25%. This study addresses the escalating phenomenon of consumers freely switching between providers, leading to financial losses for businesses. By employing machine learning models, it aims to forecast potential churners, enabling companies to focus targeted retention efforts and mitigate losses. The tool’s efficacy lies in its ability to analyze patterns among churned users, offering a solution to a pressing issue in the telecom industry. Beyond its immediate application, this tool can be extended to other industries, providing valuable insights for customer retention strategies.

An Extensive Analysis of Machine Learning Techniques With Hyper-Parameter Tuning by Bayesian Optimized SVM Kernel for the Detection of Human Lung Disease

An Extensive Analysis of Machine Learning Techniques With Hyper-Parameter Tuning by Bayesian Optimized SVM Kernel for the Detection of Human Lung Disease

Interpretable prediction of acute respiratory infection disease among under-five children in Ethiopia using ensemble machine learning and Shapley additive explanations (SHAP).

Although the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques. Secondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction. The XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia. The XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú

Accurate glacier mapping is crucial for assessing future water security in Andean ecosystems. Traditional accuracy assessment may be biased due to overlooking spatial autocorrelation during map validation. In recent years, spatial cross-validation (CV) strategies have been proposed in environmental and ecological modeling to reduce bias in predictive accuracy. In this study, we demonstrate the influence of spatial autocorrelation on the accuracy assessment of glacier surface predictive models. This is achieved by comparing the performance of several widely used machine learning algorithms including the gradient-boosting machines (GBM), k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) for mapping nine main Peruvian glacier regions. Spatial and non-spatial cross-validation methods were used to evaluate the model’s classification errors in terms of the Matthews correlation coefficient. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results. Regarding only spatial CV, the k-nearest neighbors were the overall best model across Huallanca (0.90), Huayhuasha (0.78), Huaytapallana (0.96), Raura (0.93), Urubamba (0.96), Vilcabamba (0.93), and Vilcanota (0.92) regions, consistently demonstrating the highest performance followed by logistic regression at Blanca (0.95) and Central (0.97) regions. Our validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

Towards Sustainable Equine Welfare: Comparative Analysis of Machine Learning Techniques in Predicting Horse Survival

Promoting sustainable equine welfare is pivotal in ensuring the well-being of horses, particularly concerning their survival based on past medical conditions. This study presents a comprehensive comparative analysis of various machine learning techniques employed to predict the survival prospects of horses using historical medical data. By leveraging a dataset encompassing diverse medical attributes and survival outcomes, this research assesses the efficacy and comparative performance of distinct machine learning algorithms. The study delves into the application of supervised learning models, including but not limited to decision trees, random forests, support vector machines, and neural networks, in predicting equine survival. Evaluative metrics such as accuracy, precision, recall, and F1 score are employed to assess the predictive capabilities and generalizability of each model. Moreover, this research emphasizes the importance of sustainable equine welfare within the broader context of responsible animal care.

Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey

This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.

A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on FormSpring in Textual Modality

Social media usage has increased tremendously with the rise of the internet and it has evolved into the most powerful networking platform of the twenty-first century. However, a number of undesirable phenomena are associated with increased use of social networking, such as cyberbullying (CB), cybercrime, online abuse and online trolling. Especially for children and women, cyberbullying can have severe psychological and physical effects, even leading to self-harm or suicide. Because of its significant detrimental social impact, the detection of CB text or messages on social media has attracted more research work. To mitigate CB, we have proposed an automated cyberbullying detection model that detects and classifies cyberbullying content as either bullying or non-bullying (binary classification model), creating a more secure social media experience. The proposed model uses Natural Language Processing (NLP) techniques and Machine Learning (ML) approaches to assess cyberbullying contents. Our main goal is to assess different machine learning algorithms for their performance in cyberbullying detection based on a labelled dataset from Formspring [1]. Nine popular machine learning classifiers namely Bootstrap Aggregation or Bagging, Stochastic Gradient Descent (SGD), Random Forest (RF), Decision Tree (DT), Linear Support Vector Classifier (Linear SVC), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Multinomial Naive Bayes (MNB) and K-Nearest Neighbour (KNN) are considered for the work. In addition, we have experimented with a feature extraction method namely CountVectorizer to obtain features that aid for better classification. The results show that the classification accuracy of AdaBoost classifier is 86.52% which is found better than all other machine learning algorithms used in this study. The proposed work demonstrates the effectiveness of machine learning algorithms in automatic cyberbullying detection as against the very intense and time-consuming approaches for the same problem, thereby by facilitating easy incorporation of an effective approach as tools across different platforms enabling people to use social media safely.

Comparative analysis of machine learning techniques for so2 prediction modelling

Sulphur dioxide (SO2) is produced both naturally and by human activity. The primary natural resource is derived from volcanoes. The burning of fossil fuels is the primary anthropogenic source (especially coal and diesel). Therefore, a reliable and accurate predicting method is essential for an early warning system for SO2 atmospheric concentration. There are still limited studies in Malaysia that use machine learning methods to predict SO2 concentrations. With the aid of machine learning, this study seeks to develop and predict future SO2 concentrations for the next day using the maximum daily data from Klang, Selangor. RapidMiner Studio is the data mining tool used for this research work. Based on the results, it showed that the SVM model was the best guide to be used compared with the other five models (GLM, DL, DT, GBT, and RF). The performance indicators showed that the SVM model was adequate for the next day’s prediction (R2 = 0.77, SE = 8.26, REL = 18.69%, AE = 1.46, and RMSE = 2.82). The developed model in this research can be used by Malaysian authorities as a public health protection measure to give Malaysians an early warning about the problem of air pollution. The goal of predictive modelling is to make a reasonable prediction of the variable of interest, and frequently, to determine how much the independent variable contributed to the dependent variable. The results also showed that the previous SO2 concentrations were one of the most influential parameters used to predict the future SO2 concentrations.

#5088 ARTIFICIAL INTELLIGENCE APPLIED TO VASCULAR DOPPLER ULTRASOUND MODELS FOR PREDICTING UNDERDEVELOPMENT OF VASCULAR ACCESS IN NEPHROLOGY

Abstract Background and Aims The vascular access of choice for hemodialysis patients is the arteriovenous fistula (AVF). There is a high rate of early primary failure and loss of primary AVF patency. Monitoring of vascular access is essential for early diagnosis of complications and prolonging survival. Models based on Artificial Intelligence (AI) and Machine Learning (ML) can be used for this. Method Retrospective descriptive study of the Vascular Doppler Ultrasound (VDU) in adults carried out since January 2019 to January 2022 in our AVF follow-up nephrology clinic. We analyze the results and create AI-based AVF underdevelopment prediction models. We included clinical, demographic and ultrasound variables. Patients were undergoing AVF post-surgery follow-up (VDU by protocol at 3-4 weeks after AVF surgery) or were referred to the clinic with signs of AVF dysfunction. The insufficient development of the vascular access is established as an objective variable. SPSS 20 Statistical Package. Automated Learning Analysis (ML) with Orange ML and BigML. Results 243 VDU were performed. Of the total, 139 (57%) were follow-up post-surgical VDU per protocol and 104 (43%) were AVF dysfunction VDU. Using supervised ML Analysis techniques with random sampling of 80% of the instances for Training and 20% for Test, we obtain prediction models for the underdevelopment (UD) attribute of FAV: Decision tree algorithm, Area under the curve (AUC) 89%, Classification accuracy (CA) 90%, Precision 90%. Random Forest Algorithm (RF) (AUC) 95%, (CA) 86%, Accuracy 81%. Near Neighbor Algorithm (K-NN) (AUC) 88%, CA 82%, Accuracy 78%. Convolutional Neural Networks (NNC) (AUC) 82%, CA 74%, Accuracy 60%. Algorithm with unsupervised technique of clustering in k-Means 3 clusters are obtained. The variables that best correlate with the objective variable are access flow, vein diameter, resistance index (RI) proximal, (RI) distal, and diameter of the anastomosis. Conclusion The vascular ultrasound systematized by the nephrologist facilitates the early diagnosis of complications that lead to early intervention. Analysis of the data with techniques (ML) can facilitate early diagnosis AVF poor development requiring close monitoring or intervention. The development of a nephrology clinic for monitoring vascular access could avoid invasive and unnecessary procedures for the patient.

Comparative analysis of machine learning techniques for predicting water main failures in the City of Kitchener

The resilience of water main networks highly depends on the capacity for identifying and fixing structural failures in the system as fast as possible. Given the buried nature of such systems, this will be hard and costly through manual or semi-automated inspections. In this paper, a data-driven method is applied to predict the failure of water mains in the City of Kitchener. Six machine learning prediction models were developed under two scenarios: global models, which consider the three dominant material types in the network; and the homogenous model, which considers only cast-iron pipes. The water main’s condition score, length, and criticality score were the most influential factors on the pipe failure. The random forest models outperformed the other machine learning models with an accuracy of 97.3% and an F1-score of 80.4%; the homogenous modeling showed superior performance than the global one with an F1-score of 86.0%. The results showed that more than 72% of breaks could have been potentially prevented by monitoring and upgrading only 8% of the network. The superiority of the developed models lies in their ability to predict pipe failures with the least number of false alarms.