Over the past decade, neurorobotics-integrated machine learning has emerged as a new methodology to investigate and address related problems. The combined use of machine learning and neurorobotics allows us to solve problems and find explanatory mode...
Sentiment analysis is a key technique in natural language processing that enables computers to understand human emotions expressed in text. It is widely used in applications such as customer feedback analysis, social media monitoring, and product rev...
Machine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learnin...
Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investig...
The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliv...
Background: Essential tremor (ET) is among the most common neurological disorders, requiring precise diagnosis and severity assessment for personalized and effective management. Methods: This study explores an innovative approach to evaluate ET sever...
Data Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (c...
Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper...
In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results i...
The biological investigation of a population’s shape diversity using digital images is typically reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress...
The recent proliferation of novel machine learning techniques in quantitative marker-based 3D gait analysis (3DGA) has shown promise for improving interpretations of clinical gait analysis. The objective of this study was to characterize the state of...
Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory...
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehens...
Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from whic...
Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynam...
Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S...
This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine le...
Implementing machine learning is imperative for enhancing advanced cybersecurity practices globally. The current cybersecurity landscape needs further investigation into the potential impasse. This scientometric study aims to comprehensively analyse...
The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool s...
12 November 2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis wher...
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; techn...
Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road...
As various research fields continue to evolve, new technologies emerge constantly, making it challenging for scholars to keep up with the latest and most promising research directions. To address this issue, we propose TrendFlow, a framework that lev...
In many fields, data-driven decision making has become essential due to machine learning (ML), which provides insights that improve productivity and quality of life. A basic machine learning approach called clustering helps find comparable data point...
Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and int...
The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially...
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the...
The emissions of greenhouse gases, such as carbon dioxide, into the biosphere have the consequence of warming up the planet, hence the existence of climate change. Sentiment analysis has been a popular subject and there has been a plethora of researc...
Water quality assessment plays a vital role in environmental monitoring and resource management. This study aims to enhance the predictive modeling of the Water Quality Index (WQI) using a combination of statistical diagnostics and machine learning t...
Machine learning (ML) is increasingly used to address the computational complexity and multiscale nature of mechanical analysis in nanomaterials and nanostructures. Traditional analytical, numerical, and atomistic approaches, such as continuum mechan...
X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has...
During industrial production activities, industrial products serve as critical resources whose performance is subject to various external factors and usage conditions. To ensure uninterrupted production processes and to guarantee the safety of the pr...
The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for gene...
This study advances crime analysis methodologies in Maryland by leveraging sophisticated machine learning (ML) techniques designed to cater to the state’s varied urban, suburban, and rural contexts. Our research utilized an enhanced combination...
Exhaled air contains volatile molecular compounds of endogenous origin, being products of current metabolic pathways. It can be used for medical express diagnostics through control of these compounds in the patient’s breath using molecular abso...
This study evaluated the generalization and reliability of machine learning models for multiclass classification of retinal pathologies using a diverse set of images representing eight disease categories. Images were aggregated from two public datase...
Automated Machine Learning (AutoML) has become an important area of modern artificial intelligence, enabling computers to automate the selection, training, and tuning of machine learning models and offering exciting opportunities for enhanced decisio...
Rootkits are malicious programs designed to conceal their activities on compromised systems, making them challenging to detect using conventional methods. As the threat landscape continually evolves, rootkits pose a serious threat by stealthily conce...
This study investigates the influence of spatial heterogeneity on urban transport demand forecasting. A two-stage framework combining cluster analysis and machine learning was applied to origin-destination trip data generated by a calibrated transpor...
Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation...
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to e...
Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the...
While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. T...
With the rapid advancement of machine learning (ML) technologies, their innovative applications in enhancing building energy efficiency are increasingly prominent. Utilizing tools such as VOSviewer and Bibliometrix, this study systematically reviews...
This article compares the predictive capabilities of six models, namely, linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), XGBoost, random forest (RF), and deep neural network (DNN), to predict the default be...
This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root...
Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges...
We present our latest research work on the development of a skin image analysis tool by using machine-learning algorithms. Skin imaging is very import in skin research. Over the years, we have used and developed different types of skin imaging techni...
Formation of a hierarchy within an organization is a natural way of assigning the duties, delegating responsibilities and optimizing the flow of information. Only for the smallest companies the lack of the hierarchy, that is, a flat one, is possible....
Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses o...
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