
Machine learning model
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The basic characteristics of sensor were investigated, and these experimental data were used for a machine learning. The results of the model validation proved to be a reliable way between the experiment and prediction values.
10p
viengfa
28-10-2024
3
2
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Recently, machine learning (ML) algorithms have proven to be highly effective tools for predicting structural damage. However, the data used in structural health monitoring often consists primarily of normal operational conditions or slight deviations from the original state, with a scarcity of data representing potentially dangerous conditions.
21p
viyamanaka
06-02-2025
1
1
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This paper presents a novel approach to enhancing the accuracy of precipitation estimation in Central Vietnam using the Extreme Gradient Boosting (XGBoost) machine learning model. The proposed method integrates multi-source data, combining satellite imagery from Himawari-8, atmospheric reanalysis from ERA-5, and digital elevation models from ASTER DEM to train the model.
9p
tuetuebinhan000
23-01-2025
1
1
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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
17
5
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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
4
1
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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models
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
6
2
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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
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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
14
2
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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
17
3
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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
6
2
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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
18
3
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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
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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
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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
9
3
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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
43
1
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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
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(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
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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
54
2
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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
41
5
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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
89
7
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