AIMC Topic: Commerce

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LSR-YOLO: A lightweight and fast model for retail products detection.

PloS one
Advanced computer vision techniques, particularly deep learning-based object detection, are enhancing the accuracy and efficiency of product identification in retail settings, driving the integration of intelligent systems within urban environments a...

Combining wood traits as a promising timber origin verification and its application in the Brazilian trade chain.

The Science of the total environment
Tracing the geographic origin of wood remains a major challenge in the fight against illegal timber in tropical countries. Methods using anatomical, chemical, isotopic and DNA markers have been successfully tested, but methods are either expensive, u...

Cause-and-effect relationships in a nonlinear model of Bitcoin's energy use and price volatility effect.

PloS one
The environmental impact of Bitcoin (BTC) has been a source of concern due to its substantial energy consumption, which is a result of its proof-of-work mining algorithm and transaction processes. The global usage levels of Bitcoin are comparable to ...

Exploring the impact of differences between AI streamers and human streamers on consumer purchase intention in live e-commerce: A grounded theory approach.

PloS one
With the rapid development of live e-commerce, AI streamers have gradually emerged as a new industry trend. However, significant differences exist between AI streamers and human streamers in terms of interaction styles, emotional expression, and user...

Predicting stock returns using machine learning combined with data envelopment analysis and automatic feature engineering: A case study on the Vietnamese stock market.

PloS one
In financial markets, predicting stock returns is an essential task for investors. This paper is one of the first studies using business efficiency scores calculated from data envelopment analysis to predict stock returns. In the meantime, this is al...

Dual-model approach for concurrent forecasting of electricity prices and loads in smart grids: Comparison of sparse encoder NAR and GA-optimized LSTM.

PloS one
Accurate forecasting of electricity prices and loads is challenging in smart grids due to the strong interdependence between load and price. To address this, we propose two deep recurrent neural network models that forecast both load and price concur...

An improved artificial gorilla troops optimizer for BP neural network-based housing price prediction.

PloS one
In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their...

Machine learning approaches for predicting the link of the global trade network of liquefied natural gas.

PloS one
With the rising geopolitical tensions, predicting future trade partners has become a critical topic for the global community. Liquefied natural gas (LNG), recognized as the cleanest burning hydrocarbon, plays a significant role in the transition to a...

Baltic dry index forecast using financial market data: Machine learning methods and SHAP explanations.

PloS one
The Baltic Dry Index (BDI) is a critical benchmark for assessing freight rates and chartering activity in the global shipping market. This study forecasts the BDI using diverse financial data, including commodities, currencies, stock markets, and vol...

Resource trading strategies with risk selection in collaborative training market.

PloS one
The rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying ec...