Machine Learning - Science topic

Se ha demostrado empíricamente que el nivel promedio de divulgación de información de las universidades latinoamericanas es bajo y muy heterogéneo (Abello 2018; Abello et al. 2018). Los atributos de los gobiernos corporativos universitarios pueden explicar el nivel de divulgación. Sin embargo, actualmente solo se comprende el sentido del efecto de...

There is increasing interest in predicting traffic measures by modeling big data-driven complex scenarios with data mining and machine learning methods. In this study, the parameters of the traffic analysis model were created using 35,697 Twitter traffic notifications. The relationships and effects between the parameters of hour, day, month, season...

Radiomics has emerged as a promising non-invasive approach to personalized medicine for diagnoses, outcome prediction, and treatment planning based on machine learning or deep learning techniques. Despite the high performances of reported models, most approaches are still based on ad hoc methods that are specific to a single dataset. This raises q...

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing extreme learning machine and neural networks to Earth and environmental data. The book provides guided examples using real world data for numerous novel and mathematically detailed machine learning techniques...

The theme of this textbook revolves around how machine learning (ML) can help civil and environmental engineers transform their domain. This textbook hopes to deliver the knowledge and information necessary to educate engineering students and practitioners on the principles of ML and how to integrate these into our field. This textbook is about nav...

Many forms of machine learning (ML) and artificial intelligence (AI) techniques are adopted in communication networks to perform all types of optimizations, security management, and decision making tasks. Instead of using conventional black-box models the tendency is moving towards using explainable ML models that provide transparency and accountab...

Purpose: To assess the capability of deep convolutional neural networks to classify anatomical location and projection from a series of 48 standard views of racehorse limbs. Materials and methods: Radiographs (N = 9504) of horse limbs from image sets made for veterinary inspections by 10 independent veterinary clinics were used to train, validat...

In the computer vision field, human action recognition depending on pose estimation recently made considerable progress, especially by using deep learning, which improves recognition performance. Therefore, it has been employed in various applications, including sports and physical activity follow-up. This paper presents a technique for recognizing...

Fractional calculus is an incredible tool for understanding the complex world. The significance of fractional calculus has been shown to be exceptionally compelling in various phenomena, such as diffusion processes, viscoelastics, long-range interactions, materials, and so on. It turns out that fractional calculus provides numerous advantageous fea...

High-entropy alloy nanoparticles (HEA-NPs) hold unrestricted promise for various applications in an unlimited chemical compositional space, as shown in recent years. These HEA-NPs have received widespread attention in the design and exploration of catalysts with excellent activity because of their multiple elements with different compositions and u...

Hate speech may be the research focus of the interdisciplinary field of hate studies, but it is also a difficult phenomenon to define. Internationally, there are several detection studies on automatically detecting hate speech. They can be grouped according to two approaches: the first includes searching using only machine learning methods, while t...

Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques...

Open radio access network (O-RAN) has been recognized as a revolutionized architecture to support the multi-class wireless services required in fifth-generation (5G) and beyond 5G networks. The openness and the distributed nature of the O-RAN architecture have created new forms of threat surfaces than the conventional RAN architecture and require c...

The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data avai...

Generalized Linear Models (GLMs) and XGBoost are widely used in insurance risk pricing and claims prediction, with GLMs dominant in the insurance industry. The increasing prevalence of connected car data usage in insurance requires highly accurate and interpretable models. Deep learning (DL) models have outperformed traditional Machine Learning (ML...

This Special Issue focuses on exploring new techniques for the data-to-information process used to acquire remote sensing data from coastal and littoral areas. Deep learning approaches, pattern recognition, machine learning methods built on suitable models closely linked to the data, image processing techniques (for instance segmentation and classi...

The integration of distributed energy resources requires the implementation of control and automation functionalities in distribution networks, which allow them to operate in a more flexible, efficient, and reliable way. The operation of these functionalities causes topological changes on the network that must be identified since these affect prote...

Backbone curve, as a nonlinear response analysis method, can be used for performance assessment of residual resistance and performance prediction during the preliminary design of structures. In this study, a backbone curve model of reinforced concrete (RC) walls based on Genetic programming-based symbolic regression (GP-SR) was proposed, which can...

Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its ea...

combining digital image correlation (DIC) and infra-red thermography (IRT) are now common experimental techniques. In this study, we applied these methods at very high temperature so as to analyze thermo-mechanical fatigue micro-cracking. In addition to what, machine learning has been systematically applied to improve crack network detection. Final...

In recent times, surgical data science has emerged as an important research discipline in interventional healthcare. There are many potential applications for analysing endoscopic surgical videos using machine learning (ML) techniques such as surgical tool classification, action recognition, and tissue segmentation. However, the efficacy of ML algo...

As sensors become increasingly important and popular in vehicle suspension systems for control and monitoring purposes, the sensor faults endanger the reliability and safety of suspension systems and have raised broad concerns. However, due to the complex structures and unknown inputs of suspension systems, it is difficult to detect sensor faults u...

Recently, computer vision technology has become essential for the automatic, accurate, and fast classification of fruits. Actually, there are many challenges in separating the types of fruits that are somewhat similar, such as apples, pears, and peaches. However, the challenges become more difficult if the separation is on different varieties of th...

Recent advances in robotics have allowed the introduction of robots assisting and working together with human subjects. To promote their use and diffusion, intuitive and user-friendly interaction means should be adopted. In particular, gestures have become an established way to interact with robots since they allow to command them in an intuitive m...

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through phy...

The data in this article include 10,000 synthetic patients with liver disorders, characterized by 70 different variables, including clinical features, and patient outcomes, such as hospital admission or surgery. Patient data are generated, simulating as close as possible real patient data, using a publicly available Bayesian network describing a ca...

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious a...

In this paper, we describe the three datasets that were used to train, validate, and test deep learning models to detect microfossil fish teeth. The first dataset was created for training and validating a Mask R-CNN model to detect fish teeth in the images taken using the microscope. The training set contained 866 images and one annotation file; th...

A key issue in tourism management relates to the lack of consensus regarding a theoretical and practical definition of the term “tourist.” In turn, this results in a range of methods for counting tourists and measuring tourism. This paper presents a novel non-linear model for classifying international tourists in urban settings, based on machine le...

The data stream is considered the backbone of many real-world applications. These applications are most effective when using modern techniques of machine learning like deep neural networks (DNNs). DNNs are very sensitive to set parameters, the most prominent one is the learning rate. Choosing an appropriate learning rate value is critical because i...

The mono-exponential decay has been used to describe various physical phenomena such as cavity ring-down signal, fluorescence decay, etc. In this paper, a neural network method of extreme learning machine (ELM) is adopted to efficiently extract decay time. The theoretical extraction precision, accuracy, and computation cost are all preliminarily an...

The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper,...

In this paper, we investigate forward and inverse problems of some continue, fractional, and hyperchaotic systems related to Lorenz and Rössler systems via two machine learning algorithms. For the forward problems, we can well learn the entire family of chaotic and hyperchaotic systems for the given distributions via deep Fourier neural operator al...

The security of the internet is seriously threatened by a distributed denial of service (DDoS) attacks. The purpose of a DDoS assault is to disrupt service and prevent legitimate users from using it by flooding the central server with a large number of messages or requests that will cause it to reach its capacity and shut down. Because it is carrie...

With the application of smart meters, more information is available from residential buildings for support heat load forecast. Yet, there is still a lack of an effective method to exploit the value of the high spatial granularity information, particularly for residential communities with high randomness in human behaviors. To fill this gap, this pa...

Agriculture is one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full...

The Language Model Models (LLMs) have demonstrated their ability to process and understand natural language inputs accurately. This indicates that LLMs are capable of superior natural language processing capabilities. The GPT embeddings are words generated from the GPT backend of the ChatGPTdeveloped by OpenAI, which can produce accurate outputs fo...

Introduction The notion of a single localized store of word representations has become increasingly less plausible as evidence has accumulated for the widely distributed neural representation of wordform grounded in motor, perceptual, and conceptual processes. Here, we attempt to combine machine learning methods and neurobiological frameworks to pr...

A compressor is one of the key components of a gas turbine engine and its performance and characteristics significantly affect the overall performance of the engine. Axial flow compressors are one of the most conventional types of compressors and are widely used in turbine engines for large-scale power generation. Intelligent techniques are useful...

p> A code smell is a surface indication that usually corresponds to a deeper problem in the system. Detecting and removing code smells is crucial for sustainable software development. However, manual detection can be daunting and time-consuming. Machine learning (ML) is a promising approach towards the automation of code smell detection. The first...

In this study, we attempt to anticipate annual rice production in Bangladesh (1961-2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0...

p>1st dataset 6-months randomized controlled, cross-over study 37 participants wearing CGM sensors and using MDI regimen smartphone app where to record administered insulin and meal macronutrient information diagnosed with T1D for more than 3 years 2nd dataset head-to-head glucose monitoring study, comparing CGM and flash glucose monitoring...

p>Uncertainty estimation is a critical component of building safe and reliable machine learning models. Accurate estimation of uncertainties is essential for identifying and mitigating potential risks and ensuring that machine learning systems operate reliably in real-world scenarios. Various approaches, such as ensemble and Bayesian neural network...

Artificial Intelligence (AI) is a term used to describe the ability of a machine to mimic and display human skills. AI techniques are currently used for image recognition, speech-to-text, web search, recommendation systems... In this matter, Machine Learning (ML) are the set of techniques that enables the learning process for the machine. More spec...

In this study, we attempt to anticipate annual rice production in Bangladesh (1961-2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0...

Machine learning (ML) has powerful nonlinear processing and multivariate learning capabilities, so it has been widely utilised in the fatigue field. However, most ML methods are inexplicable black-box models that are difficult to apply in engineering practice. Symbolic regression (SR) is an interpretable machine learning method for determining the...

Vietnam is regularly and severely affected by flood events and there were nearly 14,000 dead people in 200 separate floods from 1989 to 2015. However, there have been limited studies specifically on flood-related mortality in Vietnam. This paper presents a longitudinal investigation of flood fatalities in Vietnam. More specifically, we use the avai...

Machine learning (ML) techniques have become one of the most successful scientific tools and changed the everyday life of people around the globe (e.g., search engines). A vast amount of digital data sources on human behaviour has emerged due to the rise of the internet and opened the door for computer scientists to apply ML on social phenomena. In...

Background: Selecting the k best features is a common task in machine learning. Typically, a few features have high importance, but many have low importance (right-skewed distribution). This report proposes a numerically precise method to address this skewed feature importance distribu-tion in order to reduce a feature set to the informative minimu...

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p>This research develops a methodology to identify transactions through data-driven tracking and analysis of ransomware-Bitcoin payment networks [30]. We demonstrate the methodology by applying the GraphSAGE embedding algorithm to the WannaCry ransomware-Bitcoin cash-out network. The paper takes a data-driven approach to building a machine learning...

Glaucoma is an eye disease that is the second leading cause of blindness.Examination of glaucoma by an ophthalmologist is usually done by observingthe retinal image directly. Observations from one doctor to another may differ,depending on their educational background, experience, and psychologicalcondition. Therefore, a glaucoma detection system ba...

Resumo Por meio deste texto, apresenta-se resultados de pesquisa na qual se utilizou algoritmos de aprendizado de máquina combinado com o GPT para prever e melhorar o desempenho dos alunos. Essa abordagem pode ter impacto inovador na educação e na experiência de aprendizado dos alunos. O estudo teve abordagem quantitativa, com base em pesquisa expe...

Machine learning applications for material science challenges have yielded promising results. Nevertheless, most of these studies trained and validated their models using geometries optimized through density functional theory (DFT), meaning that geometries calculated at the same level of theory will be available for unseen molecular data. Unfortuna...

p>Uncertainty estimation is a critical component of building safe and reliable machine learning models. Accurate estimation of uncertainties is essential for identifying and mitigating potential risks and ensuring that machine learning systems operate reliably in real-world scenarios. Various approaches, such as ensemble and Bayesian neural network...

Organizations increasingly use process mining techniques to gain insight into their processes. Process mining techniques can be used to monitor and/or enhance processes. However, the impact of processes on the people involved, in terms of unfair discrimination, has not been studied. Another neglected area is the impact of applying process mining te...

In the context of product innovation, there is an emerging trend to use Machine Learning (ML) models with the support of Design Of Experiments (DOE). The paper aims firstly to review the most suitable designs and ML models to use jointly in an Active Learning (AL) approach; it then reviews ALPERC, a novel AL approach, and proves the validity of thi...

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandem...

Emotion recognition based on a speech signal is one of intensively studied research topics in the domains of human-computer interaction and affective computing. The main idea are a new Hybrid feature set was introduced in extract features , which use the basic concept in work is the Residual Signal of the prediction procedure, which is the differen...

We show how machine learning methods can unveil the fractional and delayed nature of discrete dynamical systems. In particular, we study the case of the fractional delayed logistic map. We show that given a trajectory, we can detect if it has some delay effect or not and also to characterize the fractional component of the underlying generation mod...

Anomaly detection can identify deviations in event logs and allows businesses to infer inconsistencies, bottlenecks, and optimization opportunities in their business processes. In recent years, various anomaly detection algorithms for business processes have been proposed based on either process discovery or machine learning algorithms. While there...

Predictive process monitoring techniques leverage machine learning (ML) to predict future characteristics of a case, such as the process outcome or the remaining run time. Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another imp...

Alzheimer's disease is the most common cause of dementia. Dementia refers to brain symptoms such as memory loss, difficulty thinking and problem solving and even speaking. This stage of development of neuropsychiatric symptoms is usually examined using magnetic resonance images (MRI) of the brain. The detection of Alzheimer's disease from data such...

Customer journey analysis is important for organizations to get to know as much as possible about the main behavior of their customers. This provides the basis to improve the customer experience within their organization. This paper addresses the problem of predicting the occurrence of a certain activity of interest in the remainder of the customer...

An important practical capability of conformance checking is that organizations can use it to alleviate potential deviations from the intended process behavior. However, existing techniques only identify these deviations, but do not provide insights on potential explanations, which could help to improve the process. In this paper, we present attrib...