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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Maria Plotnikova,Yuliya Bogoyavlenska
Pág. 382 - 393
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Ognjen Radovic,Srdan Marinkovic,Jelena Radojicic
Credit scoring attracts special attention of financial institutions. In recent years, deep learning methods have been particularly interesting. In this paper, we compare the performance of ensemble deep learning methods based on decision trees with the b...
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Rachmawaty Rachmawaty, Jeni Irnawati, Afif Zaerofi
Pág. 413 - 425
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Nebile KORUCU GÜMÜSOGLU, Sinan ALÇIN
Pág. 21 - 34
The impact of capital flows on macroeconomic variables is widely studied in applied literature. In this context, this paper aims to analyze the impact of short-term capital flows and foreign direct investment on current account deficit for Turkey by usin...
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Charles Gitiya Njoroge
Pág. 88 - 100
The purpose of the study was to establish the effect of exchange rate on the performance of the residential property market in Kenya. The study used secondary data that was accumulated using secondary data collection sheet from first quarter of 2005 to f...
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Cumhur Ekinci and Oguz Ersan
Assuming that investors can be foreign or local, do high-frequency trading (HFT) or not, and submit orders through a bank-owned or non-bank-owned broker, we associated trades to various investors. Then, building a panel vector autoregressive model, we an...
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Stanislav Letkovský, Sylvia Jencová and Petra Va?anicová
Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial in...
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Yoga Sasmita, Heri Kuswanto and Dedy Dwi Prastyo
Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general an...
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Abdelkrim Lachgar, David J. Mulla and Viacheslav Adamchuk
One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to ...
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