Stock trend prediction has long been an important research direction in the financial field, and it is also an extremely challenging task. Currently, most studies focus on a single prediction model to find a better prediction scheme by comparing the effects of different algorithms.This paper proposes a stock trend prediction method based on a Blending ensemble learning approach, which combines 55 technical indicators such as Exponential Moving Averages (EMA) and Relative Strength Index (RSI). PCA dimensionality reduction is used to further simplify the data representation of the features after SOM dimensionality reduction. The method employs two high-performing machine learning models with distinct algorithmic characteristics as base learners and Logistic Regression as the meta-learner to construct an efficient ensemble prediction framework. Using Apple Inc.'s stock (AAPL) as the research subject, the study utilises the confusion matrix as the core performance evaluation metric. Experimental results demonstrate that optimised through hyperparameter tuning. Experimental results indicate that the Blending ensemble learning model, optimized through hyperparameter tuning, outperforms the single prediction models in terms of accuracy.

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As cyber threats grow increasingly sophisticated, traditional Intrusion Detection System (IDS) struggle to maintain robustness when facing unknown zero-day attacks. This paper proposes an adaptive IDS framework based on lightweight model repair, which rapidly restores detection performance using a small set of synthetically generated zero-day samples. The architecture combines an autoencoder with a classifier head, jointly trained on the NSL-KDD dataset. This framework was evaluated against four canonical zero-day attack variants via controlled feature manipulation. To address the performance degradation, multiple minimal-scope repair strategies are tested. Repair strategies target three scopes: the decoder's final layer, the classifier head, or both components. The choice of repair scope is informed by empirical performance under different adaptation scenarios. According to the experiment results, fine-tuning only the classifier head can restore the recall to over 98% with strong generalization ability. Unified pooled repair and leave-one-out evaluations further verify the robustness and adaptability of the method across diverse attack scenarios.

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Deep neural networks (DNNs) are playing an important role in various areas including computer vision (CV) and natural language processing. This paper comprehensively analyzes model optimization techniques for deploying deep neural networks on resource-constrained embedded systems. We evaluate three core paradigms—pruning, quantization, and dynamic inference—focusing on their efficacy in balancing computational efficiency, memory footprint, and accuracy retention. For each technique, we conduct dedicated experiments spanning representative architectures including ResNet, VGG, Inception, and MobileNet variants to evaluate accuracy-FLOPS trade-offs. We also discuss and compare the practical deployment metrics for optimization techniques. Finally, we emphasize promising future directions.

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With the rapid progression of high technology, new-style fraud has emerged in the past decade, among which credit card fraud has been exceptionally grave. Hence, the credit card fraud detection is of vital importance in preventing financial loss, establishing consumer protection and maintaining trust in digital transactions. Previous studies tend to implement dimensionality reduction methods like Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to preprocess the data. Additionally, various models—including Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest—were independently employed to assess their individual performance in prior studies. The results of each model were then systematically compared against one another to identify the top-performing algorithm. There are, however, primarily two research gaps. Few research have used dimensionality elevation procedures, despite the fact that dimensionality reduction approaches are frequently used in preprocessing processes. Furthermore, the stacking approach has been proven to be successful in other domains, such as virus diagnosis, but it has been utilized infrequently in credit card fraud detection. Therefore, in this study, the dimensionality elevation method PolynomialFeatures is utilized to compare with the dimensionality reduction method t-SNE and a stacking model is implemented to explore whether it will have a higher accuracy compared to a single model. This study chooses Logistic Regression as the stacking model due to its simple linear form, making it less prone to overfitting compared to complex meta-models like Random Forests or Deep Neural Networks.

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