This research explores the application of Long Short-Term Memory (LSTM) networks for stock market analysis and prediction, focusing on four major technology stocks: Apple Inc. (AAPL), Google LLC (GOOG), Microsoft Corporation (MSFT), and Amazon.com Inc. (AMZN). Historical stock price data from Yahoo Finance spanning from January 1, 2012, to the present is utilized. The study aims to develop and evaluate an LSTM-based prediction model for forecasting future stock prices. The LSTM model consists of two LSTM layers with 128 and 64 units, respectively, followed by two dense layers. The model is trained using the Adam optimizer and mean squared error (MSE) loss function. Evaluation of the model is done using the root mean squared error (RMSE) metric. The results demonstrate the potential of LSTM models in capturing complex patterns in stock price movements and making reasonably accurate predictions.

This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future potential.

In the era of industrial transformation and intelligent manufacturing, ensuring the surface quality of metal materials is crucial for maintaining component performance and system reliability. Although deep learning-based visual inspection has shown promise in automating defect detection, existing methods heavily rely on large-scale labeled data, which are often scarce and costly to obtain in industrial settings. Furthermore, the high inter-class similarity and variability of metal surface textures pose additional challenges to robust defect classification under low-data regimes. To address these issues, we propose an attention-guided few-shot learning framework specifically tailored for metal surface defect classification. Our method comprises two key components. First, we design a Dual-Branch Attention Module to enhance feature extraction by explicitly modeling both channel-wise dependencies and spatial saliency. This module leverages lightweight convolutional operations to highlight discriminative regions and mitigate feature degradation due to limited training data. Second, we introduce a Cross-Set Guided Attention mechanism to improve semantic alignment between support and query samples. By employing scaled dot-product attention, the model dynamically adjusts feature representations based on cross-sample correlations, thereby enabling fine-grained discrimination of visually similar defect types. Extensive experiments conducted on benchmark metal defect datasets demonstrate that our framework significantly outperforms existing few-shot learning baselines in both classification accuracy and generalization capability. The proposed method provides a practical and efficient solution for real-time industrial quality inspection in data-scarce scenarios.

STRENGTHS NOVEL CONTRIBUTION: The introduction of OpenAD as the first real-world benchmark for open-world 3D object detection is a significant advancement in autonomous driving research. The dataset’s inclusion of 206 object categories and 2,000 div...