Machine learning model
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This modeling is performed on an experimental data set of complexes, where the metal ions of these complexes include transition ion metals and lanthanide ion metals. We use these models to develop a series of new thiosemicarbazone and their complexes; simultaneously, the complexes are worked out the stability constants from the novel models.
9p dianmotminh02 03-05-2024 6 2 Download
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Forest fires present a significant threat to the tropical forest ecosystem in the northwestern region of Vietnam. Our study aimed to assess the impacts of environmental factors on forest fire occurrence and to map forest fire probability for the whole region. The forest fire occurrence data over the period 2003–2016, environmental factors (climate, fuel condition, topography, and human activity), and the MaxEnt approach were used for this study.
21p dianmotminh02 03-05-2024 7 3 Download
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Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model
n this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model.
10p dianmotminh02 03-05-2024 9 2 Download
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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 7 1 Download
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The article "A review of metaheuristic optimized machine learning regression with applications in construction engineering" aims at reviewing state-of-the-art research works involving the use of metaheuristic optimized machine learning regression models. Recent research articles published in the time period of 2019-2021 are surveyed. Research areas of construction material, construction management, structural engineering, geotechnical engineering, hydraulic engineering, and structural health monitoring are taken into account.
7p nhanchienthien 25-07-2023 6 4 Download
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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 9 3 Download
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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 3 1 Download
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Bài viết đưa ra phương án tạo ra model để xác định, phân loại dữ liệu này. Nội dung của bài viết tập trung vào việc ứng dụng machine learning trong việc phân loại dữ liệu và sử dụng bài toán logistic regressiong để thiết lập Model, thiết lập Loss Function, tối ưu loss Function và dự đoán mô hình.
4p vijenchae2711 21-07-2021 56 5 Download
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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 12 2 Download
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We present machine learning models for fast estimating atomic forces and energies. In our method, the total energy of a system is approximated as the summation of atomic energy which is the interaction energy with its surrounding chemical environment within a certain cutoff radius.
7p tamynhan6 14-09-2020 15 1 Download
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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 11 2 Download
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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 19 3 Download
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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 7 3 Download
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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 41 0 Download
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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 20 1 Download
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This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data.
16p dieutringuyen 07-06-2017 36 2 Download
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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 41 2 Download
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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 Download
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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 53 2 Download
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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 36 4 Download