1,286 Results Found

Robust learning is an important issue in Scientific Machine Learning (SciML). There are several works in the literature addressing this topic. However, there is an increasing demand for methods that can simultaneously consider all the different uncer...

Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis...

This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environment...

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science...

Scientific machine learning (SciML) offers an emerging alternative to the traditional modeling approaches for wave propagation. These physics-based models rely on computationally demanding numerical techniques. However, SciML extends artificial neura...

Cultivating scientific literacy is a goal widely shared by educators and students around the world. Many studies have sought to enhance students’ proficiency in scientific literacy through various approaches. However, there is a need to explore...

The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with r...

One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in...

Squamous-cell carcinoma of the anus (ASCC) is a rare disease. Barriers have been encountered to conduct clinical and translational research in this setting. Despite this, ASCC has been a prime example of collaboration amongst researchers. We performe...

The design of biodegradable polymeric materials is of increasing scientific interest due to accelerating levels of plastics pollution. One area of increasing interest is the design of biodegradable polymer films based on seaweed as a raw material. Th...

Chronic dermatologic diseases are characterized by pathophysiologic complexity and the existence of many unmet patient management needs that can contribute to treatment failure, with poor adherence being a major issue. This study aims to identify key...

Polymeric materials are ubiquitous in modern technology, from structural composites and membranes to responsive elastomers, yet their design remains challenging due to complex, multi-scale behaviors [...]

In this paper we propose an open source application called LDAShiny, which provides a graphical user interface to perform a review of scientific literature using the latent Dirichlet allocation algorithm and machine learning tools in an interactive a...

Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a criti...

Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation o...

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well...

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analy...

The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this...

Scientific creativity is a crucial indicator of adolescents’ potential in science and technology, and its automated evaluation plays a vital role in the early identification of innovative talent. To address challenges such as limited sample siz...

With the popularity of higher education and the evolution of the workplace environment, graduate education has become a key choice for students planning their future career paths. Therefore, this study proposes to use the data processing ability and...

The influence of scientific papers is measured by their citations. Although predicting the papers’ citation impact based on non-content factors has garnered extensive attention, the influence of such factors is rarely compared. In this article,...

Large language models (LLMs) are tools that help us in a variety of activities, from creating well-structured texts to quickly consulting information. But as these new technologies are so easily accessible, many people use them for their own benefit...

Video services are meant to be a fundamental tool in the development of oceanic research. The current technology for underwater networks (UWNs) imposes strong constraints in the transmission capacity since only a severely limited bitrate is available...

This study presents a new way to investigate comprehensive trends in cancer nanotechnology research in different countries, institutions, and journals providing critical insights to prevention, diagnosis, and therapy. This paper applied the qualitati...

This paper deals not only with pointwise and uniform convergence but also Y-valued fractional approximation results by univariate symmetrized neural network (SNN) operators on Banach space Y,.. Moreover, our main motivation in this work is to compare...

Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to i...

The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that...

Evidence-based medicine integrates results from randomized controlled trials (RCTs) and meta-analyses, combining the best external evidence with individual clinical expertise and patients’ preferences. However, RCTs of surgery differ from those...

This paper introduces a parameter-efficient transformer-based model designed for scientific literature classification. By optimizing the transformer architecture, the proposed model significantly reduces memory usage, training time, inference time, a...

Trajectory planning aims to compute an optimal path and velocity of an agent through the minimization of a cost function. This paper proposes a just-in-time routing method, incorporating the stochastic minimization of a cost function, which ingests t...

In this study, the numerical solutions to the Elder problem are analyzed using Big Data technologies and data-driven approaches. The steady-state solutions to the Elder problem are investigated with regard to Rayleigh numbers (Ra), grid sizes, pertur...

This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional st...

Machine learning (ML) technologies are currently widely used in many domains of science and technology, to discover models that transform input data into output data. The main advantages of such a procedure are the generality and simplicity of the le...

In the context of hybrid twins, a data-driven enrichment is added to the physics-based solution to represent with higher accuracy the reference solution assumed to be known at different points in the physical domain. Such an approach enables better p...

Neural Ordinary Differential Equations (Neural ODEs) have emerged as a promising approach for learning the continuous-time behaviour of dynamical systems from data. However, Neural ODEs are black-box models, posing challenges in interpreting and unde...

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle mul...

Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bri...

Food loss is a major challenge for global food security, resource use efficiency, and sustainability. However, collecting primary food loss data is costly. This study explores a neural network-based approach to estimate food loss in the postharvest s...

We introduce CAESAR, a new framework for scientific data reduction that stands for Conditional AutoEncoder with Super-resolution for Augmented Reduction. The baseline model, CAESAR-V, is built on a standard variational autoencoder with scale hyperpri...

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further e...

In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components....

This paper presents the design and implementation of a system for semi-automating the literature review process based on machine learning. By using machine learning algorithms, the system determines whether scientific papers belong to the topic that...

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of t...

The concept of using wastewater as a substitute for limited water resources and environmental protection has enabled this sector to make major technological advancements and, as a result, has given us an abundance of physical data, including chemical...

Over the past decade, focused ultrasound (FUS) has emerged as a promising therapeutic modality for various medical conditions. However, the exponential growth in the published literature on FUS therapies has made the literature review process increas...

A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis...

Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a signif...

Machine learning methods are widely used in commercial applications and in many scientific areas [...]

The aim of the work is to investigate the effectiveness of selected classification algorithms and their extensions in assessing microstructure of castings. Experiments were carried out in which the prepared algorithms and machine learning methods wer...

The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI)...