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Lev Utkin and Andrei Konstantinov
The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a larg...
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Guido Bologna
The explainability of connectionist models is nowadays an ongoing research issue. Before the advent of deep learning, propositional rules were generated from Multi Layer Perceptrons (MLPs) to explain how they classify data. This type of explanation techn...
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Shangkun Deng, Yingke Zhu, Xiaoru Huang, Shuangyang Duan and Zhe Fu
Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minorit...
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Guido Bologna
In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation. A n...
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Phan Khoa Cuong,Tran Thi Bich Ngoc,Bui Thanh Cong DOI: 10.26459/hueuni-jed.v128i5C.5082
Pág. 127?140
Abstract: This research investigates the impacts of economic integration on endogenous growth by an application of the AK learning-by-doing model. Assuming that the knowledge that increases the productivity of labor will be created by accumulated capital...
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Chengchu Yan, Xiuxiu Yang and Yizhe Xu
Low delta-T syndrome often occurs in building chilled water systems, which makes systems fail to operate as efficiently as originally anticipated. Extensive studies have been conducted on the subject of low delta-T syndrome with the aims of investigating...
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Wang Qingshi,Li Naiqian,Hashmat Ali
Pág. 16 - 23
According to characteristics of revenue and risks involved in film investments, this paper expounds that movies can also be treated as a common tradable asset in capital markets. Consequently, this study employs the famous Arbitrage Pricing Theory (APT) ...
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Xuerui Liu, Yanqi Wu and Yisong Zhou
Axial bearing capacity is the key index of circular concrete-filled steel tubes (CCFST). A hybrid PSO-ANN model consisting of an artificial neural network (ANN) optimized with particle swarm algorithm (PSO) was proposed to reliably and accurately predict...
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Xiaoyi Ge, Shuai Hao, Yuxiao Li, Bin Wei and Mingshu Zhang
Social media fake news has become a pervasive and problematic issue today with the development of the internet. Recent studies have utilized different artificial intelligence technologies to verify the truth of the news and provide explanations for the r...
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Benjamin Burrichter, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 9...
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Khaqan Baluch, Heon-Joon Park, Kyuchan Ji and Sher Q. Baluch
Whilst numerical modelling is commonly used for simulation to check the design of water conveyance, sluicing and spillway structure design, the numerical modelling has rarely been compared with the physical model tests. The objective of this research pre...
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Yichi Chen, Wangqiang Niu, Yanhua Yang and Yassine Amirat
The eddy current loss caused by the conductivity of seawater results in a relatively low transfer efficiency of underwater wireless power transfer (WPT). And the transfer distance of the current WPT system is relatively short. Considering that most of th...
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Chengxi Wu, Yuewei Dai, Liang Shan and Zhiyu Zhu
This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a f...
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Abdullah Alnutayfat, Sophia Hassiotis, Dong Liu and Alexander Sutin
This paper presents a new method of signal processing for vibro-acoustic modulation (VAM) methods in order to detect damage accumulation in steel samples. Damage in the tested samples was produced by cycle loading, which, with a small amplitude, was used...
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George B. Tawadros and Imad A. Moosa
In this paper, a structural time series model is estimated to analyse the effect of quantitative easing (QE) on stock prices for the US, UK and Japan. The model is estimated by maximum likelihood in a time-varying parametric framework, using the DJIA, S&...
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Eui-Nam Huh and Md Imtiaz Hossain
Over the decades, robotics technology has acquired sufficient advancement through the progression of 5G Internet, Artificial Intelligence (AI), Internet of Things (IoT), Cloud, and Edge Computing. Though nowadays, Cobot and Service Oriented Architecture ...
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Julián Miranda, Angélica Flórez, Gustavo Ospina, Ciro Gamboa, Carlos Flórez and Miguel Altuve
This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modul...
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Boeun Kim, Saim Shin and Hyedong Jung
Image captioning is a promising research topic that is applicable to services that search for desired content in a large amount of video data and a situation explanation service for visually impaired people. Previous research on image captioning has been...
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Yipeng Yang and Allanus Tsoi
In this paper, a level set analysis is proposed which aims to analyze the S&P 500 return with a certain magnitude. It is found that the process of large jumps/drops of return tend to have negative serial correlation, and volatility clustering phenome...
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Florina Popa
One of the relevant components of the contemporary economic science is the economic growth theory, the economic background of the time leading to new guidelines of the research. The neoclassical growth theory - the core of modern analysis - explains how ...
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