In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on analyzing financial historical data based on statistical techniques, which is known as a type of time series analysis with limited achievements. Recently, deep learning techniques, specifically recurrent neural network (RNN), has been designed to work with sequence prediction. In this paper, a long short-term memory (LSTM) network, which is a special kind of RNN, is proposed to predict stock movement based on historical data. In order to construct an efficient portfolio, multiple portfolio optimization techniques, including equal-weighted modeling (EQ), simulation modeling Monte Carlo simulation (MCS), and optimization modeling mean variant optimization (MVO), are used to improve the portfolio performance. The results showed that our proposed LSTM prediction model works efficiently by obtaining high accuracy from stock prediction. The constructed portfolios based on the LSTM prediction model outperformed other constructed portfolios-based prediction models such as linear regression and support vector machine. In addition, optimization techniques showed a significant improvement in the return and Sharpe ratio of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Standard and Poor 500 (S&P 500) index in both active returns and Sharpe ratios.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer’s disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Key agreements that use only password authentication are convenient in communication networks, but these key agreement schemes often fail to resist possible attacks, and therefore provide poor security compared with some other authentication schemes. To increase security, many authentication and key agreement schemes use smartcard authentication in addition to passwords. Thus, two-factor authentication and key agreement schemes using smartcards and passwords are widely adopted in many applications. Vaidya et al. recently presented a two-factor authentication and key agreement scheme for wireless sensor networks (WSNs). Kim et al. observed that the Vaidya et al. scheme fails to resist gateway node bypassing and user impersonation attacks, and then proposed an improved scheme for WSNs. This study analyzes the weaknesses of the two-factor authentication and key agreement scheme of Kim et al., which include vulnerability to impersonation attacks, lost smartcard attacks and man-in-the-middle attacks, violation of session key security, and failure to protect user privacy. An efficient and secure authentication and key agreement scheme for WSNs based on the scheme of Kim et al. is then proposed. The proposed scheme not only solves the weaknesses of previous approaches, but also increases security requirements while maintaining low computational cost.

This study proposes an innovative solution for credit card fraud detection, utilizing a stacking ensemble of machine learning classifiers enhanced with sophisticated data resampling techniques. The model demonstrates exceptional performance, achieving an F1-score of 0.92, precision of 0.95, recall of 0.88, an AUPRC of 0.96, and a perfect ROC-AUC of 1.00. These results significantly outperform standalone models like XGBoost and Decision Tree, showcasing the strength of the proposed approach. The study addresses two critical challenges, class imbalance, and overfitting, by employing K-means SMOTEENN to balance minority class representation while reducing synthetic data noise, thus lowering the risk of overfitting. Additionally, the stacking ensemble’s integration of diverse classifiers produces a generalized decision boundary, enhancing model robustness in real-world scenarios. This ensures a precise fraud detection mechanism without compromising sensitivity. Furthermore, the proposed model incorporates Explainable AI (XAI) techniques to enhance interpretability and trust. By leveraging Local Interpretable Model-Agnostic Explanations (LIME), the study identifies key features driving model predictions, illustrating how both base learners and the meta-learner contribute to the final decision. This transparency not only bolsters stakeholder confidence but also provides actionable insights for financial institutions. Overall, the proposed approach marks a notable step forward in protecting financial transactions from fraudulent activities.