Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

The impact of financial risks extends beyond the normal operation and survival of industrial and commercial enterprises and financial institutions. They also have the potential to impede the stable development of a country's and even the world's financial economy. This is clearly demonstrated by the severe consequences of the frequent financial crises that have occurred in recent years. It thus follows that the prevention of financial risks has become one of the core tasks of the operation and management of industrial and commercial enterprises and financial institutions. Portfolio optimization techniques are employed in the construction of a variety of asset allocation models, with reinforcement learning algorithms used to dynamically adjust investment strategies with the objective of maximizing returns and minimizing risks. The preliminary construction of an asset allocation model is achieved through the utilization of a genetic algorithm. Genetic algorithms emulate the processes of natural selection and genetic variation to identify the optimal portfolio of assets that will yield the greatest returns at a pre-established level of risk. Subsequently, the Deep Q-Learning algorithm is introduced to facilitate dynamic adjustments and optimization of the asset allocation, based on the initial construction. Deep Q-learning employs deep neural networks to forecast the prospective returns of disparate investment strategies, thereby optimizing the decision-making process through continuous learning and updating. The combination of genetic algorithms and deep Q-learning enables the system to identify the optimal investment strategy under a diverse range of initial conditions and to adapt it in real-time to respond to market fluctuations and uncertainties. The experimental analysis demonstrates that the proposed method exhibits an exemplary capacity for risk control and a robust ability to generate stable income growth across diverse market environments. In simulation experiments, portfolios constructed using this method demonstrate lower volatility and higher average returns than those generated through traditional methods.

Paralyzed people must maintain proper posture to prevent respiratory problems, pressure sores, and muscular contractures. A paralyzed person may slip out of their wheelchair several times as a result of poor sitting position. To overcome these challenges, a novel Emperor Penguin optimized sensor Infused wheelChair (EPIC) framework has been proposed to constantly monitor the real-time posture of the paralyzed person’s health state. The proposed model aims to monitor wheelchair posture and health in real-time, which can automatically detect posture issues, providing timely alerts and feedback to the user via a mobile application. The proposed framework utilizes the Emperor Penguin Optimizer (EPO) algorithm for feature selection to improve the accuracy of posture detection. A Deep Maxout Network (DMN) analyzes the features to predict the posture of the wheelchair-bound patient. The proposed EPIC framework has been assessed using a Python simulator. The effectiveness of the proposed EPIC framework has been determined using evaluation metrics such as precision, specificity, accuracy, and sensitivity. The proposed EPIC technique advances the overall accuracy by 10.1%, 7.73%, and 2.84% better than posture recognition, Graphic User Interface (GUI), and Independent Component Analysis-Kantorovich Distance (ICA-KD) respectively.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

Financial time series exhibit high volatility and non-linearity, making analysis particularly challenging. Traditional statistical methods like ARIMA and GARCH struggle with non-linear data. At the same time, despite capturing complex price dynamics, machine learning and deep learning approaches often face the risk of overfitting. Additionally, the limited parameters of one-dimensional financial time series signals constrain feature representation. To tackle these challenges, we propose an efficient multimodal financial time series prediction network utilizing self-supervised learning based on the custom-designed SPPMFN network for stock trend forecasting. Our approach introduces a novel signal transformation strategy to extract richer multi-scale feature representations from financial time series signals. Specifically, we convert one-dimensional stock price time series into two-dimensional image sequences across varying time intervals using Gramian Angular Fields. These transformed data modalities are then simultaneously processed by the SPPMFN, enabling the extraction of features from multiple dimensions. Furthermore, we introduce a self-supervised learning framework that enhances the model’s ability to capture intrinsic relationships within the data, allowing it to detect underlying patterns while effectively mitigating overfitting. Experimental results on the CSI300E, CSI100E, and S&P500 datasets demonstrate the effectiveness of our method, showing superior performance in accurately predicting high-yield stocks and significantly outperforming industry benchmarks. Specifically, our model improves accuracy by over 3% and achieves a cumulative return of 21.62% on the CSI300E dataset. On the S&P500 dataset, accuracy increased by over 1.4% and cumulative return reached 18.66%. These findings highlight the robustness and practical applicability of our method across diverse financial markets.

The increasing complexity of financial markets and the growing need for personalisation present significant challenges to the traditional investment advisory model. To address this issue, this paper puts forth a proposal for an investment advisor robot 2.0 system based on deep neural networks. This system is designed to offer bespoke investment recommendations aligned with the risk appetite, financial objectives, and market dynamics of individual investors. The proposed method employs a long short-term memory network (LSTM) to generate personalised investment strategies in real time through the analysis of historical market data, investor behaviour data and real-time financial information. The innovation lies in the introduction of a multi-objective optimisation mechanism, which automatically adjusts the allocation of the portfolio through deep learning algorithms, optimises the balance between risk and return, and significantly improves the accuracy and practicality of investment recommendations. The experimental results demonstrate that, in comparison to the conventional investment advisory model, the system exhibits enhanced resilience in responding to market volatility across a range of simulated scenarios. It is also evident that the system is capable of formulating more forward-thinking investment strategies, while simultaneously demonstrating heightened stability and yield.