Machine learning model

Bài viết tập trung vào việc đánh giá và so sánh hiệu quả của các mô hình học máy dựa trên cây (Tree-based machine learning models) trong việc dự báo gian lận thẻ tín dụng. Các mô hình được xét gồm Decision Tree, Random Forest, Gradient Boosting Machines (GBM) và Extreme Gradient Boosting (XGBoost).

17p gaupanda068 02-01-2025 0 0   Download

Machine learning and computer vision play pivotal roles in detecting product defects across various industries, enhancing effectiveness, precision, and minimizing labor expenditures. This journalutilizes image manipulation through the OpenCV, coupled with machine learning employing the ResNet-50 model, to specifically identify surface defects and dimensions in bearings.

5p vibenya 31-12-2024 3 0   Download

Modern civilization is heavily dependent on energy, which burdens the energy sector. Therefore, a highly accurate energy consumption forecast is essential to provide valuable information for efficient energy distribution and storage. This study proposed a hybrid deep learning model, called I-CNN-JS, by incorporating a jellyfish search (JS) algorithm into an ImageNetwinning convolutional neural network (I-CNN) to predict weekahead energy consumption.

6p vibenya 31-12-2024 2 0   Download

Bài 5 cung cấp những kiến thức về mô hình Markov ẩn (Hidden Markov Model). Các nội dung chính được trình bày trong chương này gồm có: Các khái niệm, ba bài toán cơ bản của HMM, thuộc tính Markov, thuật toán lan truyền xuôi,...và những nội dung khác.

28p youcanletgo_04 17-01-2016 111 21   Download

The research subject is the process of economic and mathematical modelling of time series characterizing the bitcoin exchange rate volatility, based on the use of artificial neural networks. The purpose of the work is to search and scientifically substantiate the tools and mechanisms for developing prognostic estimates of the crypto currency market development. The paper considers the task of financial time series trend forecasting using the LSTM neural network for supply chain strategies.

5p longtimenosee09 08-04-2024 11 1   Download

One of the limitations of the BDI (Belief-Desire-Intention) model is the lack of any explicit mechanisms within the architecture to be able to learn. In particular, BDI agents do not possess the ability to adapt based on past experience. This is important in dynamic environments as they can change, causing previously successful methods for achieving goals to become inefficient or ineffective.

108p runthenight07 01-03-2023 13 3   Download

This paper focuses on exploring Machine learning methods to automate this process. The main challenge we face is in generating adequate training datasets to train the Machine learning model. Creating training data by manually segmenting real images is very labour-intensive, so we have instead tested methods of automatically creating synthetic training datasets which have the same attributes of the original images. The generated synthetic images are used to train a U-net Model, which is then used to segment the original bread dough images.

75p runordie3 06-07-2022 4 1   Download

Research Aims: The thesis aims to develop deep neural networks for analyzing security data. These techniques improve the accuracy of machine learning-based models applied in NAD. Therefore, the thesis attempts to address the above challenging problems in NAD using models and techniques in deep neural networks. Specifically, the following problems are studied.

128p armyofthedead 23-06-2021 15 3   Download

Pricing and guessing the right prices are vital for both hosts and renters on homesharing plat-form from internet based companies. To contribute the growing interest and immense literatureon applying Artificial Intelligence on predicting rental prices, this paper attempts to build ma-chine learning models for that purpose using the Luxstay listings in Hanoi. R2 score is used as the main criterion for the model performance and the results show that Extreme GradientBoostings (XGB) is the model with the best performance with R2= 0.

44p hoanghung9393 28-08-2020 12 3   Download

The resulting model is a Document Retriever, called QASA, which is then integrated with a machine reader to form a complete open-domain QA system. Our system is thoroughly evaluated using QUASAR-T dataset and shows surpassing results compared to other state-of-the-art methods.

67p tamynhan1 13-06-2020 22 4   Download

The goal of the simulation strategies is to model complex multi-physics and multi-scale phenomena specific to nuclear reactors. The use of machine learning combined with such advanced simulation tools is also demonstrated to be capable of providing useful information for the detection of anomalies during operation.

14p christabelhuynh 29-05-2020 7 3   Download

In this research, we put the development of a no-wait flow-shop scheduling model alongside with the effect of learning into consideration to minimize the cost of consumption of resources.

20p tohitohi 22-05-2020 42 1   Download

The present study introduces a set of machine learning-based models to predict the heating and cooling loads in buildings. This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines.

6p tohitohi 22-05-2020 21 1   Download

(BQ) The study sheds light on the powerful learning capability of ANFIS models and its superiority over the conventional polynomial models in terms of modelling complex non-linear machining processes

15p xuanphuongdhts 27-03-2017 42 2   Download

In this tutorial I will introduce ‘learning to rank’, a machine learning technology on constructing a model for ranking objects using training data. I will first explain the problem formulation of learning to rank, and relations between learning to rank and the other learning tasks. I will then describe learning to rank methods developed in recent years, including pointwise, pairwise, and listwise approaches.

1p hongphan_1 15-04-2013 53 2   Download

The newly emerging field of systems biology involves a judicious interplay between high-throughput ‘wet’ experimentation, computational modelling and technology development, coupled to the world of ideas and theory. This interplay involves iterative cycles, such that systems biology is not at all confined to hypothesis-dependent studies, with intelligent, principled, hypothesis-generating studies being of high importance and consequently very far from aimless fishing expeditions.

22p inspiron33 26-03-2013 38 5   Download

Web search engine: Markov chain theory Data Mining, Machine Learning: Data mining, Machine learning: Stochastic gradient, Markov chain Monte Carlo, Image processing: Markov random fields, Design of wireless communication systems: random matrix theory, Optimization of engineering processes: simulated annealing, genetic algorithms, Finance (option pricing, volatility models): Monte Carlo, dynamic models, Design of atomic bomb (Los Alamos): Markov chain Monte Carlo.

16p quangchien2205 30-03-2011 88 7   Download