id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-5650	meijing, Song ; Saad, Nor Hasliza Md. 	A study of deep learning-based algorithms for supply chain logistics demand forecasting	2025	15	.pdf	application/pdf	7117	267	41	Outlier removal in supply chain logistics demand data based on local outlier factor, the specific steps are as follows: (1) Define the distance of k . Among where: the input layer is responsible for receiving supply chain logistics demand data; the BiLSTM layer is used to capture the long-term dependencies in the input data; the AM layer allows the prediction model to focus on the important information in the input data to improve the accuracy of the model prediction; the full connectivity layer integrates the information from the attention layer and maps it to the prediction feature space for further processing and extraction of the key features to prepare for the final prediction results; the output layer is responsible for transforming the processing results of the full connectivity layer into the final prediction information, i.e. the predicted value of supply chain logistics demand.	cache/easat-5650.pdf	txt/easat-5650.txt