Top Research Papers on Machine Learning Projects

This dissertation examines the machine learning issues raised by the domain of anomaly detection for computer security by focusing here on learning models of normalcy at the user behavioral level.

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This project created two models which are trained to detect neurons and axons using a modified version of the U-Net which uses a VGG pre-trained network as its encoder, and analyzed the directionality of the axons depending on the orientation of the grid formed by the nanostructure.

Training neural audio classifiers with few data used prototypical networks and transfer learning to train the neural network and predicted the future trending on the smart devices.

The research paper, entitled " Machine Learning ", has been successfully published in the International Scientific Journal of Engineering and Management (ISJEM) on Volume 02 Issue 04 April 2023.

Initialize G to the set of maximally general hypotheses in H Initialize S to theset of maximically specific hypotheses inH For each training example d, remove from G any hypothesis inconsistent with d.

Introduction and overview of machine learning and its applications, including Discriminative and generative models, unsupervised and supervised learning, and decision trees.

El sector salud tiene involucrado una gran cantidad de procesos y procedimientos generadores de todo tipo de información que en muchos casos no están disponibles de forma libre para los profesionales de diferentes áreas y en especial de las ciencias computacionales.¿Qué sucedería si toda esta información pudiera estar disponible? La medicina preventiva y predictiva podría desarrollarse con mayor rapidez, desarrollando modelos predictivos a través de algoritmos de Machine Learning, como apoyo a los profesionales de la salud en la toma de decisiones. Este artículo permite conocer la convergencia...

Ever since computers were invented, the authors have wondered whether they might be made to learn and if they could understand how to program them to learn-to improve automatically with experience-the impact would be dramatic.

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach.

Machine Learning (ML) is a form of Artificial Intelligence (AI) that uses data to train a computer to perform tasks. Unlike traditional programming, in which rules are programmed explicitly, machine learning uses algorithms to build rulesets automatically. At a high level, machine learning is a collection of techniques borrowed from many disciplines including statistics, probability theory, and neuroscience combined with novel ideas for the purpose of gaining insight through data and computation.

Image recognition is a well known for identify an object as a digital image, one of the reason it work so well is because a learning algorithm that has based on the intensity of the pixels black & white images and color images.

The successive lessons will present the theoretical settings of machine learning in the regression and in the classification framework and also in the clustering framework and the implementation of these methods on real applications using the R software.

With MATLAB® you can use clustering, regression, classification, and deep learning to build predictive models and put them into production.

A methodology for estimating electricity consumption for rice crops that use flood irrigation, in the city of Uruguaiana, Rio Grande do Sul, implementing classification using artificial intelligence techniques (clustering, k-means and random forest) is presented.

With MATLAB® you can use clustering, regression, classification, and deep learning to build predictive models and put them into production.

The wide range of new developments in the combination of synchrotron radiation and machine learning discussed in this special issue will extend synch Rotron radiation experiments to more advanced measurements, bring about more efficient and automatedsynchroton radiation experiments, and increase the amount of information obtained from these experiments.

A historical perspective on artificial intelligence is provided and a light, semi-technical overview of prevailing tools and techniques are given to help understand where real value ends and speculative hype begins.

This paper discusses learning algorithms together with some example applications, as well as the current challenges and research areas in machine learning.

It is concluded that the best compromise was a model using SSD detection and MOSSE tracking : the authors managed to achieve 31 fps and 63% of accuracy.