Machine Learning Algorithms: A Comparative Study on Efficiency, Effectiveness, and Practical Applications Using the GRA Method

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

Machine learning (ML) is the most common technique for predicting the future or for classifying information, to help people make the required decisions. ML techniques are based on algorithms – sets of mathematical procedures that explain the relationship between variables. ML algorithms are trained in situations where they learn from past data and can even evaluate historical data. After extensive training, the algorithm can recognize patterns sufficiently to make predictions. ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g., for prediction of disease progression, for the extraction of medical information to predict research outcomes, for therapy planning and support, and for overall patient management. ML is also being used for data analysis, such as detection of patterns in the data by dealing appropriately with imperfect data, interpretation of continuous data generated in the Intensive Care Unit, and for intelligent alarms, resulting in effective and efficient monitoring. Successful implementation of ML methods can help the integration of computer-based systems into the healthcare environment, providing opportunities to facilitate and enhance the work of medical experts, and ultimately to improve the efficiency and quality of medical care. Most ML methods can be categorized into one of two types of learning techniques, namely supervised or unsupervised algorithms. A supervised learning algorithm takes a known set of input data (the training set) and known responses to the data (the output) and trains a model to generate reasonable predictions for the response to new input data. In medicine, supervised learning involves training a model to relate a person’s characteristics (e.g., height, weight, smoking status) to a certain outcome (onset of diabetes within five years, for example). Once the algorithm is successfully trained, it will be capable of making outcome predictions when supplied with new data. Predictions which are made by models trained using supervised learning can be either discrete (e.g., positive or negative, benign or malignant) or continuous (e.g., a score from 0 to 100). A supervised model which produces discrete categories (sometimes referred to as classes) is referred to as a classification algorithm. Examples of classification algorithms include those which predict whether a tumor is benign or malignant, or to establish whether comments written by a patient convey a positive or negative sentiment. In practice, classification algorithms return the probability of a class (between 0 for impossible and 1 for certain). Common classification algorithms are Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Neural Network, and Naïve Bayes. A supervised model which returns a prediction of a continuous value is known as a regression algorithm, which might be used by ML to predict an individual’s life expectancy or the tolerable dose of chemotherapy. Supervised ML algorithms are typically developed using a dataset, which contains several variables and a relevant outcome. In contrast with supervised learning, unsupervised learning does not involve a predefined outcome. In unsupervised learning, patterns are sought by algorithms without any input from the user. Unsupervised techniques are thus exploratory and used to find undefined patterns or clusters which occur within datasets. In this chapter, the use of different types of supervised and unsupervised ML algorithms for various applications in health care are discussed.

Exploring sex classification from earprints - A comparison of supervised machine learning algorithms and conventional linear discriminant analysis.

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

Objectives: To facilitate painless and easy method for prediction of diabetes with high accuracy and to measure blood glucose by noninvasive method using Photoplethysmography (PPG). Method: In this study, diabetes prediction is done using different machine learning algorithms on a dataset created by using samples from PIMA Indian Diabetes dataset and in vivo diabetes dataset. Machine learning algorithms used are Support Vector Machine (SVM), Decision Tree, Naïve Bayes Classifier and K Nearest Neighbor (KNN). A PPG data of 182 individuals is recorded over 1 minute duration each. Various frequency and time domain features of PPG single are extracted using single pulse wave analysis. Neural network is trained using extracted features and glucose measurement is performed. Findings: With decision tree algorithm we got highest accuracy of 89.97% for diabetes prediction and it proves to be good algorithm for consideration for measurement in diabetes treatment.Clarke Error Grid analysis for blood glucose prediction is clinically accepted, so we performed similar analysis. Using time and frequency domain features, we got 94.27 % data points in clinically accepted regions (Region A and Region B). Novelty: Based on the data collected or samples analyzed, and accuracy of our results, it is encouraging to see that further research may lead to affordable noninvasive method for detection of diabetes at early stage. Keywords: Noninvasive; blood glucose; NIR; machine learning algorithms; photoplethysmography

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Classification using machine learning algorithms in physical fitness tests carried out by students in educational centers can help prevent obesity and other related diseases. This research aims to evaluate physical fitness using percentiles of the tests and machine learning algorithms with hyperparameter optimization. The process followed was knowledge discovery in databases (KDD). Data were collected from 1525 students (784 women, 741 men) aged 6 to 17, selected non-probabilistically from five public schools. For the evaluation, anthropometric parameters such as age, weight, height, sitting height, abdominal circumference, relaxed arm circumference, oxygen saturation, resting heart rate, and maximum expiratory flow were considered. Physical Fitness tests included sitting flexibility, kangaroo horizontal jump, and 20-meter fly speed. Within the percentiles observed, we took three cut-off points as a basis for the present research: > P75 (above average), p25 to p75 (average), and < P25 (below average). The following machine learning algorithms were used for classification: Random Forest, Support Vector Machine, Decision tree, Logistic Regression, Naive Bayes, K-nearest neighbor, XGBboost, Neural network, Cat Boost, LGBM, and Gradient Boosting. The algorithms were hyperparameter optimized using GridSearchCV to find the best configurations. In conclusion, the importance of hyperparameter optimization in improving the accuracy of machine learning models is highlighted. Random Forest performs well in classifying the “High” and “Low” categories in most tests but struggles to correctly classify the “Normal” category for both male and female students.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

Introduction: The aim of this study was to compare various machine learning algorithms for constructing a diabetic retinopathy (DR) prediction model among type 2 diabetes mellitus (DM) patients and to develop a nomogram based on the best model. Methods: This cross-sectional study included DM patients receiving routine DR screening. Patients were randomly divided into training (244) and validation (105) sets. Least absolute shrinkage and selection operator regression was used for the selection of clinical characteristics. Six machine learning algorithms were compared: decision tree (DT), k-nearest neighbours (KNN), logistic regression model (LM), random forest (RF), support vector machine (SVM), and XGBoost (XGB). Model performance was assessed via receiver-operating characteristic (ROC), calibration, and decision curve analyses (DCAs). A nomogram was then developed on the basis of the best model. Results: Compared with the five other machine learning algorithms (DT, KNN, RF, SVM, and XGB), the LM demonstrated the highest area under the ROC curve (AUC, 0.894) and recall (0.92) in the validation set. Additionally, the calibration curves and DCA results were relatively favourable. Disease duration, DPN, insulin dosage, urinary protein, and ALB were included in the LM. The nomogram exhibited robust discrimination (AUC: 0.856 in the training set and 0.868 in the validation set), calibration, and clinical applicability across the two datasets after 1,000 bootstraps. Conclusion: Among the six different machine learning algorithms, the LM algorithm demonstrated the best performance. A logistic regression-based nomogram for predicting DR in type 2 DM patients was established. This nomogram may serve as a valuable tool for DR detection, facilitating timely treatment.

Background: Despite major advances in diagnoses and treatments, cardiovascular disease (CVD) continues to be the leading cause of morbidity and mortality worldwide. To improve and optimize CVD results, AI techniques have the potential to radically revolutionize the way we practice cardiology, especially in imaging and provide with new tools to interpret data and make clinical decisions. Aim: Establishing strategies are necessary to improve the diagnosis and treatment of CVD in the future. Nowadays, artificial intelligence (AI) may have the potential to solve this problem. The application of AI in heart diseases aims to facilitate the detection of radiology patients. Methods: The machine learning algorithms used in this study are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Naive Bayes, and Decision Tree. In our study, a total of 600 patients, 300 female and 300 male, who were diagnosed with IHD as a result of the findings obtained from the reports of the patients who underwent CAG in the Fırat University Hospital were included in our study. Accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained by the classification. Results: Among the algorithms we have used, KNN had the highest success rate. It was followed by SVM in the second success rate. The success rate of RCA was 83% in KNN, and it was 75% in SVM. While the success rate of LCx in KNN was 76%, it was 68% in SVM. Similarly, the success rate of LAD in KNN was 73%, and it was 71% in SVM. Conclusion: The demand for CAG will be rising in the coming years, owing to an increase in HR. Therefore, new strategies will be sought to reduce the duration of CAG. We consider the application of AI in routine clinical practice. Keywords: Right coronary artery, Left coronary artery, Stenosis, Artificial intelligence

Deep-sea ecosystems, harbouring extraordinary biodiversity and geological wonders, remain largely unexplored due to their remote and inhospitable nature. Recent technological advancements, particularly in the realm of machine learning (ML), offer promising avenues for unravelling the mysteries of the deep and safeguarding its fragile ecosystems. This abstract presents an overview of the manifold applications of ML in deep-sea ecosystem analysis, focusing on key areas such as species identification, habitat characterization, and event detection. Primarily, ML algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests, are deployed for automated species identification, leveraging vast repositories of underwater imagery. By discerning intricate visual patterns, these algorithms facilitate rapid biodiversity assessment and aid in monitoring shifts in species distributions. Moreover, ML techniques, such as Decision Trees, K-Nearest Neighbour (KNN), and deep learning architectures, are instrumental in habitat classification endeavours. Through the analysis of multifaceted sensor data, these algorithms enable the delineation of distinct seafloor habitats, empowering conservationists with invaluable insights into ecosystem structure and function. Furthermore, ML-based anomaly detection algorithms, Hidden Markov Models (HMMs), and reinforcement learning (RL) strategies contribute to event detection efforts in the deep sea. By scrutinizing temporal data streams from underwater sensors, these algorithms detect aberrations indicative of natural phenomena or anthropogenic disturbances, thus facilitating timely interventions and mitigative actions. In conclusion, the integration of ML techniques into deep-sea ecosystem analysis represents a paradigm shift in our approach to understanding and preserving these enigmatic realms. By automating labor-intensive tasks, enhancing data processing capabilities, and fostering real-time monitoring, ML empowers scientists and conservationists to unlock the secrets of the deep and embark on a journey towards sustainable stewardship of these invaluable ecosystems.

6 - Machine learning and deep learning algorithms for fault diagnosis of photovoltaic systems