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A two-stage forecasting model using random forest subset-based feature selection and BiGRU with attention mechanism: Application to stock indices.

PloS one
The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market's non-stationary and volatile nature, driven by...

Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade.

International journal of health geographics
BACKGROUND: Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, brokers, and surgeons. A comprehensive understanding of these trade networks is, however, hindered by t...

Forecasting second-hand house prices in China using the GA-PSO-BP neural network model.

PloS one
While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle s...

TCN-QV: an attention-based deep learning method for long sequence time-series forecasting of gold prices.

PloS one
Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely ...

Exploring the drivers of digital transformation in Chinese port and shipping enterprises: A machine learning approach.

PloS one
With the transition to a global green low-carbon economy, the urgency for digital transformation in the port and shipping industry has become increasingly prominent in making enterprises more efficient and sustainable. This study focuses on how Chine...

A dual-path convolutional neural network combined with an attention-based bidirectional long short-term memory network for stock price prediction.

PloS one
The complexities of stock price data, characterized by its nonlinearity, non-stationarity, and intricate spatiotemporal patterns, make accurate prediction a substantial challenge. To address this, we propose the DCA-BiLSTM model, which combines dual-...

AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments.

PloS one
Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during cr...

Comparing automated valuation models for real estate assessment in the Santiago Metropolitan Region: A study on machine learning algorithms and hedonic pricing with spatial adjustments.

PloS one
This study compares the precision and interpretability of two automated valuation models for evaluating the real estate market in the Santiago Metropolitan Region of Chile: machine learning algorithms, specifically LightGBM, and hedonic prices with s...

Forecasting stock prices using long short-term memory involving attention approach: An application of stock exchange industry.

PloS one
The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed to forecasting the Stock Market and controlling the situation to ensure economic stability over the ...

The analysis of marketing performance in E-commerce live broadcast platform based on big data and deep learning.

Scientific reports
This study aims to conduct a comprehensive and in-depth analysis of the marketing performance of e-commerce live broadcast platforms based on big data management technology and deep learning. Firstly, by synthesizing large-scale datasets and surveys,...