Efficiency Of Machine Learning Algorithms Research Articles

Integrating machine learning techniques with IoT sensors and connected vehicles to enable real-time traffic monitoring and Adaptive signal control systems

This research investigates machine learning algorithms to traffic monitoring and adaptive signal control coordinated in the context of the Internet of things sensors and connected vehicles for real-time traffic supervision. It measures the efficiency of machine learning algorithms, simulates adaptive control strategies for signal systems, and addresses the challenges of deploying signal system changes. The performance metrics suggest better traffic flow, leading to a decrease in traffic congestion. Further study should be made towards parametrization of algorithms, improvement of communication standards and training of integration models.

Real-Time Jaundice Detection in Neonates Based on Machine Learning Models

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

BackgroundThe accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program.ResultsOur results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction.ConclusionsThe dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

Transfer learning-based English translation text classification in a multimedia network environment.

In recent years, with the rapid development of the Internet and multimedia technology, English translation text classification has played an important role in various industries. However, English translation remains a complex and difficult problem. Seeking an efficient and accurate English translation method has become an urgent problem to be solved. The study first elucidated the possibility of the development of transfer learning technology in multimedia environments, which was recognized. Then, previous research on this issue, as well as the Bidirectional Encoder Representations from Transformers (BERT) model, the attention mechanism and bidirectional long short-term memory (Att-BILSTM) model, and the transfer learning based cross domain model (TLCM) and their theoretical foundations, were comprehensively explained. Through the application of transfer learning in multimedia network technology, we deconstructed and integrated these methods. A new text classification technology fusion model, the BATCL transfer learning model, has been established. We analyzed its requirements and label classification methods, proposed a data preprocessing method, and completed experiments to analyze different influencing factors. The research results indicate that the classification system obtained from the study has a similar trend to the BERT model at the macro level, and the classification method proposed in this study can surpass the BERT model by up to 28%. The classification accuracy of the Att-BILSTM model improves over time, but it does not exceed the classification accuracy of the method proposed in this study. This study not only helps to improve the accuracy of English translation, but also enhances the efficiency of machine learning algorithms, providing a new approach for solving English translation problems.

Study of the efficiency of machine learning algorithms based on data of various rocks

Background: Absolute permeability plays an important role in studying the fluids flow in porous media during the development of oil and gas reservoirs, the injection of CO2 into reservoirs for storage, the monitoring of pollutants migration in underground aquifers, and the modeling of catalytic systems. Therefore, an accurate and fast evaluation of its values is an important task.
 Aim: The purpose of this article is to study the applicability of machine learning methods for predicting the absolute permeability of carbonate samples, as well as ways to improve the prediction of permeability.
 Materials and methods: The input data is 408 small volumes extracted from four cylindrical carbonate samples composed almost entirely of calcite. Input data includes total and connected porosity, specific surface area, radii of all and only connected pores, coordination number, throat radius and length, tortuosity, and absolute permeability. Permeability prediction is carried out using regression machine learning methods such as random forest, extremely random trees and extended gradient boosting. Parameters (data) of small volumes were determined using pore-scale modeling of water flow in their pore space applying a specialized Avizo software.
 Results: Data of small volumes extracted from fractured and non-fractured samples were analyzed, and the results showed that there are good relationships between many parameters of small volumes. For example, the connected and total porosity have a second-order polynomial relationship with a high correlation coefficient. Using the above-mentioned regression machine learning methods, absolute permeability values were predicted when input data divided into training and testing data in a ratio of 80/20 and 70/30.
 Conclusion: Using the logarithm of permeability instead of permeability itself and considering fractured and non-fractured samples separately, can increase the accuracy of absolute permeability prediction using the above-mentioned machine learning methods up to 90%. The extremely random trees method is the most accurate among the three machine learning methods considered for our task.

Analyzing the Performance and Efficiency of Machine Learning Algorithms, such as Deep Learning, Decision Trees, or Support Vector Machines, on Various Datasets and Applications

This research endeavors to comprehensively evaluate and compare the performance of three prominent machine learning algorithms—Deep Learning (DL), Decision Trees (DT), and Support Vector Machines (SVM)—across a spectrum of diverse datasets and applications. The study is driven by specific objectives, including the quantitative analysis of accuracy, precision, recall, and F1 Score for each algorithm to discern their nuanced strengths and weaknesses in varied contexts. Additionally, the research aims to investigate the impact of algorithmic factors, such as complexity and interpretability, on the performance of these machine learning models. By exploring the trade-offs associated with sophisticated models and interpretable alternatives, the study contributes valuable insights to algorithm selection criteria. Another crucial objective is to analyze the effect of dataset characteristics, including size, complexity, and class imbalance, on algorithmic behavior, offering insights into challenges posed by different datasets and potential strategies for addressing issues such as imbalances and biases. Furthermore, the research seeks to assess the generalization capabilities of machine learning algorithms across diverse application domains, encompassing image classification, natural language processing, and numerical prediction. Lastly, the study delves into ethical considerations, specifically focusing on bias assessment and transparency measures in algorithmic decision-making. By emphasizing responsible AI deployment, the research addresses potential biases and ensures transparency through the availability of code and datasets. This structured approach to the research objectives provides a clear roadmap for an in-depth investigation into algorithmic performance, influential factors, and ethical considerations in the deployment of machine learning algorithms.

Fast and accurate excited states predictions: machine learning and diabatization.

The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We utilize the proposed diabatization scheme to achieve higher prediction accuracy for adiabatic states and we show its performance by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data.

An Extensive Analysis of Feature Selection in Machine Learning

Abstract: Researchers and engineers working in the disciplines of data mining and machine learning have difficulties while analysing high-dimensional data. A dimension reduction method called feature selection is used to pick characteristics that are pertinent to machine learning tasks. Improving the efficiency of machine learning algorithms, hastening the learning process, and creating basic models all depend critically on reducing the size of the dataset by removing superfluous and useless information. Numerous feature selection techniques have been put forth in the literature to find the pertinent feature or feature subsets in order to accomplish the goals of clustering and classification. The purpose of this study is to review the state of the art for these methods.

The Efficiency of Machine Learning Algorithms in the Prediction of Drug Reactions in Clinical Settings

The Efficiency of Machine Learning Algorithms in the Prediction of Drug Reactions in Clinical Settings

Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem

In the field of machine learning, the issue of class imbalance is a common problem. It refers to an imbalance in the quantity of data collected, where one class has a significantly larger number of data compared to another class, which can negatively affect the classification efficiency of algorithms. Under-sampling methods address class imbalance by reducing the quantity of data in the majority class, thereby achieving a balanced dataset and mitigating the class imbalance problem. Traditional under-sampling methods based on k-means clustering either set the unified value of k (number of clusters) or determine it directly based on the quantity of data in the minority or majority class. This paper proposes an adaptive k-means clustering under-sampling algorithm that calculates an appropriate k for each dataset. After clustering the majority class dataset into k clusters, our algorithm calculates the distances between the data within each cluster and the cluster centroids from two perspectives and selects data based on these distances. Subsequently, the subset of the majority class dataset are combined with the minority class dataset to generate a new balanced dataset, which is then used for classification algorithms. The performance of our algorithm is evaluated on 45 datasets. Experimental results demonstrate that our algorithm can dynamically determine appropriate k for different datasets and output a balanced dataset, thus enhancing the classification efficiency of machine learning algorithms. This work can provide new algorithmic ensemble strategies for addressing class imbalance problem.

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.

Comparative analysis of preprocessing methods for molecular descriptors in predicting anti-cathepsin activity

Quantitative Structure-Activity Relationship (QSAR) is a powerful tool for investigating the correlation between the chemical and biological properties of molecules. It employs mathematical and statistical modeling techniques that focus on molecular descriptors, which represent various characteristics of the molecules. However, the extensive use of descriptors in QSAR modeling introduces complexities in data analysis and computation. To address these challenges, data preprocessing techniques, such as data reduction and feature selection, are critical for directing the inputs of statistical models. Feature selection plays a crucial role in improving the accuracy and efficiency of machine learning algorithms by identifying relevant features that significantly influence the target response. In this study, a comprehensive comparison of preprocessing methods in QSAR modeling is carried out. The methods under investigation include filtering through Recursive Feature Elimination (RFE) and wrapping methods such as Forward Selection (FS), Backward Elimination (BE), and Stepwise Selection (SS). To evaluate the effectiveness of these preprocessing methods, both linear regression and nonlinear regression models were utilized. The research findings reveal the usefulness of feature selection methods in reducing the number of descriptors required for the accurate assessment of anti-cathepsin activity. Remarkably, the FS, BE, and SS methods, particularly when coupled with nonlinear regression models, exhibit promising performance relating to R-squared scores. This research emphasizes the significance of data preprocessing, particularly feature selection, in QSAR modeling. The comparative analysis of different preprocessing methods provides a valuable understanding of their effectiveness in reducing descriptor complexity and improving model performance. These findings contribute to the improvement of QSAR modeling techniques, supporting accurate predictions of anti-cathepsin activity and enabling the exploration of structure-activity relationships in drug discovery and design.

Assessing the Efficiency of Machine Learning Algorithms in Detecting Cyberbullying on Twitter

Cyberbullying represents one of the unwanted behaviors which has gotten more and more common since the use of social media, particularly Twitter, has expanded quickly. Cyberbullying can have serious repercussions for its victims. Due to large volumes of data and the intricate structure of online interactions, detecting cyberbullying in real-time is an exhausting task. The purpose of this research proposal is to utilize machine learning techniques that will enhance precision and recall in recognizing instances of cyberbullying on Twitter. In the study, Twitter data is evaluated, trends and patterns are identified, and a wide range of cooperative behaviors are thoroughly assessed. The creation of a framework for collaboration, empirical analysis of patterns of collaboration, evaluation of performance indicators, and improvement of real-time detection capabilities are the main goals.

Investigating the efficiency of machine learning algorithms in classifying pulse-like ground motions

Investigating the efficiency of machine learning algorithms in classifying pulse-like ground motions

Analysis of the Performance Impact of Fine-Tuned Machine Learning Model for Phishing URL Detection

Phishing leverages people’s tendency to share personal information online. Phishing attacks often begin with an email and can be used for a variety of purposes. The cybercriminal will employ social engineering techniques to get the target to click on the link in the phishing email, which will take them to the infected website. These attacks become more complex as hackers personalize their fraud and provide convincing messages. Phishing with a malicious URL is an advanced kind of cybercrime. It might be challenging even for cautious users to spot phishing URLs. The researchers displayed different techniques to address this challenge. Machine learning models improve detection by using URLs, web page content and external features. This article presents the findings of an experimental study that attempted to enhance the performance of machine learning models to obtain improved accuracy for the two phishing datasets that are used the most commonly. Three distinct types of tuning factors are utilized, including data balancing, hyper-parameter optimization and feature selection. The experiment utilizes the eight most prevalent machine learning methods and two distinct datasets obtained from online sources, such as the UCI repository and the Mendeley repository. The result demonstrates that data balance improves accuracy marginally, whereas hyperparameter adjustment and feature selection improve accuracy significantly. The performance of machine learning algorithms is improved by combining all fine-tuned factors, outperforming existing research works. The result shows that tuning factors enhance the efficiency of machine learning algorithms. For Dataset-1, Random Forest (RF) and Gradient Boosting (XGB) achieve accuracy rates of 97.44% and 97.47%, respectively. Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) achieve accuracy values of 98.27% and 98.21%, respectively, for Dataset-2.

Comparison of the efficiency of machine learning algorithms for phishing detection from uniform resource locator

We are using cyberspace for completing our daily life activities because of the growth of Internet. Attackers use some approachs, such as phishing, with the use of false websites to collect personal information of users. Although, software companies launch products to prevent phishing attacks, identifying a webpage as legitimate or phishing, is a very defficult and these products cannot protect from attacks. In this paper, an anti-phishing system has been introduced that can extract feature from website’s URL as instant basis and use four classification algorithms named as K-Nearest neighbor, decision tree, support vector machine, random forest on these features. According to the comparison of the experimental results from these algorithms, random forest algorithm with the selected features gives the highest performance with the 95.67% accuracy rate. Then we have used one deep learning algorithm as enhanced of our experiment named as deep neural decision forests which have given performance with the 92.67% accuracy rate. Then we have created a system which can extract the features from raw URL and pass the features to our deep neural decision forest trained model and can classify the URL as Phishing or legitimate.

Mapping of the loose sediments of glacial and periglacial formations in areas with boreal vegetation using remote sensing

The southeastern Fennoscandian Shield is a specific region dominated by glacial and periglacial originated Quaternary deposits. In dense boreal vegetation zones, direct remote decoding of Quaternary sediments is impossible. Our aim is to assess the efficiency of machine learning algorithms for identification of types of Quaternary deposits based on spectral data of vegetation cover and digital elevation model data. A comparative analysis of two classification methods, discriminant analysis (DA) and classification and regression trees (CARTs), was performed. Two models, DA and CART, were constructed based on a set of spectral variables. They included principal components and spectral indices, such as the normalized difference vegetation index, the normalized difference moisture index, and the clay minerals ratio. When the absolute height and the topographic position index (DA+ and CART+ models) are added to a set of independent variables, the classification of Quaternary sediments becomes more accurate. The nonparametric CART method was shown to be more accurate in differentiating loose deposits. The correctness of the CART+ model for the reference sample of observation points was as high as 90.5%. According to the trained data, mapping of Quaternary deposits was carried out. Kappa analysis showed that the agreement with the map of Quaternary deposits for the CART+ and DA+ models was 0.5 and 0.44, respectively. For the DA and CART models, the agreement was much lower. It is noted that the sets of spectral data for the vegetation cover more clearly show the grain-size composition of loose sediments. To make the classification of Quaternary deposits more correct, morphometric indicators were added. Furthermore, the CART+ method was used to estimate the maximum height of glaciolacustrine sediments (117.5 m), which corresponds to the maximum absolute mark of the periglacial reservoir.

Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach

Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories.

Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance

The banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is usually achieved through human resources, which stand as the core element in sustaining the organization in the highly competitive marketplace. It becomes essential to effectively manage human resources strategically and align its strategies with organizational strategies. We adopted a survey research design using a quantitative approach, distributing a structured questionnaire to 305 respondents utilizing efficient sampling techniques. The prediction of bank performance is very crucial since bad performance can result in serious problems for the bank and society, such as bankruptcy and negative influence on the country’s economy. Most researchers in the past adopted traditional statistics to build prediction models; however, due to the efficiency of machine learning algorithms, a lot of researchers now apply various machine learning algorithms to various fields, including performance prediction systems. In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. The results of the analysis clearly show that human resources outcomes are crucial in achieving organizational performance, and the models built from the eight machine learning classifier algorithms in this study predict the bank performance as superior with the accuracies of 74–81%. The feature importance was computed with the package in Scikit-learn to show comparative importance or contribution of each feature in the prediction, and employee attitude is rated far more than other features. Nigeria’s bank industry should focus more on employee attitude so that the performance can be improved to outstanding class from the current superior class.

Atomtransmachine: An atomic feature representation model for machine learning

In this study, a self-monitoring model is proposed to extract the atomic characteristics of the main group elements and transition metals from several molecular structures. Different from previous studies, we use a spatial convolution layer to extract the spatial features of atoms and a multi-attention mechanism to screen their important features in forming new crystal structures. Extensive numerical analyses show that the features extracted using the proposed model are effective and can improve the efficiency of machine learning algorithms.