In the rapidly evolving landscape of e-commerce, online shopping has gradually gained widespread acceptance. Yet, physical retail channels continue to exert a significant influence on product sales. Within this framework, sales forecasting occupies a pivotal position in traditional commerce and is indispensable for guiding corporate strategy formulation. However, most existing forecasting models fail to fully harness the potential impact of multi-source external information on consumer purchasing behavior. To address these challenges, this study proposes a novel sales forecasting model that synergizes the Extreme Gradient Boosting (XGBoost) algorithm with multi-source data integration techniques. Not only have we comprehensively collected an extensive range of external data, but we have also integrated the embedding and aggregation techniques of Point of Interest (POI) data to enrich the set of external information features available to the model. Leveraging feature engineering techniques, these heterogeneous data are transformed into formats amenable to model training. Relying on the nonlinear modeling capabilities of XGBoost and its efficacy in handling large-scale datasets, we trained and optimized the model. Empirical results indicate that our proposed method, predicated on multi-source data integration, significantly outperforms traditional models based on a single data source, thereby enhancing prediction accuracy and providing more precise inventory management strategy support for businesses.
Objective: Because of the aging of parts and mechanical structure, the vibration of connecting rod is easy to produce abnormal state, while the abnormal vibration states are difficult to detect in a timely manner,thus resulting in shutdown and material breakage during the tobacco processing process, or uneven tobacco screening, thereby reducing the quality of tobacco products . In order to solve this problem, an automatic detection technology based on Dempster-Shafter (DS) theory is proposed in this paper, so as to deal with the abnormal vibration of connecting rod in time. Methods: The image data inside the tobacco sieve vibrating groove were collected, and the Kanade-Lucas-Tomasi (KLT) algorithm was used to track the continuous frames of the image and extract the connecting rod feature points at adjacent moments of the tobacco sieve vibrating groove. A series of microphones are constructed to obtain the location of noise source signals, and the noise source signals are denoised using wavelet coefficients. DS theory was used to fuse the image features and sound signals, calculate the vibration state conflict factor of connecting rod, judge the vibration state of connecting rod according to the conflict factor, and complete the automatic detection of abnormal vibration of connecting rod in the vibration groove of tobacco screen. Results: The experimental results show that the proposed method can accurately identify the fault position of the connecting rod and classify the abnormal vibration level, and the results of abnormal vibration amplitude of the connecting rod in the vibrating groove of tobacco screening are highly consistent with the actual results. Conclusion: The vibration anomaly automated detection technology designed in this article can be used for the analysis of the state of connecting rod mechanical components in the tobacco industry and the diagnosis of faults.
Breast cancer remains a major global health concern, and early diagnosis is crucial for improving patient outcomes. This study explores the performance of Fourier Convolutional Neural Networks (FCNNs) in comparison to traditional Convolutional Neural Networks (CNNs) like VGG16 for classifying breast cancer using ultrasound images. We evaluated the models on key performance metrics, including accuracy, precision, recall, and AUC-ROC, while also considering computational factors such as training time and memory consumption. Results show that VGG16 consistently delivers stable performance across different learning rates, while FCNN16 significantly outperforms VGG16 at a lower learning rate of 0.0001, achieving near-perfect classification metrics. These findings highlight the potential of FCNNs for breast cancer diagnosis, though further research is necessary to fully optimize this approach.
Histopathological image analysis remains at the forefront of computational pathology presenting numerous challenges and demanding tasks, primarily due to the complex nature of tissue structures and the extensive scale of whole slide images (WSIs). Deep learning models have been widely used in histopathology image analysis, especially convolutional neural network (CNN)-based models for classification. However, CNNs have certain limitations due to their small receptive field. Recent works employed adaptations of the classical transformer architecture to visual data [1] [2]. Models such as Vision Transformer (ViT) and Swin Transformer leverage the powerful multi-head self-attention mechanism and have demonstrated comparable or superior performance to state-of-the-art CNN-based classification models. Despite their successes, these models require huge amounts of training data to effectively learn representations as they lack the inherent inductive biases of CNNs. This work compares Vision Transformers with baseline CNN models using a breast cancer histopathological dataset. Further, we employ a novel knowledge-distillation approach to enhance the learning efficiency of vits, When trained with a limited amount of data, Unlike previous works, we aimed to minimize convolution operations when generating patch embeddings to preserve spatial information before reaching the transformer attention layers, we achieved an accuracy of 87.7% for the ViT-base trained as a student of ResNet50, which represents a 1.2% improvement in accuracy over the standalone ViT-base. [3]
Considering the uncertainty of stock data, this paper studies interval-valued fuzzy portfolio decision model with ESG and some realistic constraints. First, a screening process is employed to identify investable stocks based on their ESG ratings. Then, a multi-period portfolio decision model with short-selling constraint is established by maximizing terminal wealth and minimizing terminal risk. By using the weighted programming method the proposed portfolio model is transformed into a single-objective model to solve the optimal decision strategy. Finally, a numerical example of stock data from the Shenzhen Stock Exchange is given to illustrate the efficiency of the presented portfolio decision model.
