The application of artificial intelligence (AI) continues to expand across various industries, especially in enhancing user experience and optimizing business processes. Through deep learning and machine learning algorithms, companies are able to analyze user behavior data and provide personalized recommendations, which effectively improve customer satisfaction and loyalty. This data-driven approach enables businesses to stand out in a highly competitive market. This paper explores the key role of machine learning-based personalized recommendation systems in improving user experience and highlights the importance of behavioral data-driven UI design for business success. Analyzing the practices of industry leaders such as Netflix and Amazon reveals how data-driven decisions can be leveraged to deliver tailored content and product recommendations that enhance user engagement and satisfaction. Research shows that successful recommendation systems not only rely on advanced technology applications, but also need to deeply understand user needs to optimize user interface design and promote effective user interaction. As technology continues to advance, personalized recommendation systems will become more intelligent, and companies should actively explore these innovative ways to increase user engagement and brand loyalty to achieve sustainable business growth.

Sentiment analysis is an important tool for revealing insights and shaping our understanding of market movements from financial articles, news, and social media. Despite their impressive abilities in financial natural language processing (NLP), large language models (LLMs) still have difficulties in accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. This article introduces a simple and effective instruction-tuning method to solve these problems. We have made significant progress in financial sentiment analysis by converting small amounts of supervised financial sentiment analysis data into command data and using this approach to fine-tune a generic LLM. In experiments, our approach outperforms state-of-the-art supervised sentiment analysis models and widely used LLMs such as ChatGPT and LLaMAs, especially when numerical and contextual understanding is critical.

This paper presents an in-depth learning process for Activities of Daily Living (ADL) and self-care planning in Adult Day Health Centers. The framework integrates multi-modal sensor data fusion with deep learning architectures to provide continuous monitoring and automated evaluation of older people’s legal status. The system uses a hierarchical system combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, enhanced by monitoring systems for physical-spatial learning. Many data streams from wearable devices, environmental sensors, and medical monitoring devices are becoming pre-processed and de-processed. The framework incorporates a dynamic care plan adaptation strategy utilizing reinforcement learning techniques for intervention optimization. Experimental validation conducted across three Adult Day Health Centers with 150 participants over six months demonstrated superior performance compared to traditional assessment methods. The system achieved 92.8% accuracy in ADL recognition tasks, with a 35% reduction in assessment time and a 62% decrease in false alarm rates. Clinical validation through 25 detailed case studies revealed early detection of health deterioration, averaging 3.2 days ahead of conventional methods. The proposed framework significantly enhances the efficiency and accuracy of elderly care delivery while reducing healthcare provider workload by 40%. This research contributes to advancing intelligent healthcare by creating a solution for ADL measurement and self-correction.

The building and construction industry is a significant contributor to global greenhouse gas emissions, with 37% of total emissions attributed to this sector. While efforts have primarily targeted operational carbon emissions, which stem from building operations like heating and cooling, the urgent need to address embodied carbon—associated with the materials used and their life cycle—has gained attention. This paper explores the critical role of material selection and innovative practices in reducing embedded carbon in the built environment. It highlights collaborative models and international cooperation essential for decarbonizing building materials to achieve net-zero emissions by mid-century. The findings underscore that embodied carbon currently represents a growing proportion of a building’s overall carbon footprint, necessitating proactive measures in the design and construction phases. By integrating life cycle assessments and prioritizing sustainable material choices, stakeholders can significantly diminish carbon emissions and align with global climate goals. Through case studies and best practices, this research advocates for a comprehensive approach to carbon reduction that encompasses both operational and embodied emissions in the built environment.

This paper presents a new Intelligent Anti-Money Laundering Transaction Pattern Recognition System based on Graph Neural Networks (GNNs). The proposed method addresses the limitations of anti-money laundering (AML) by using graphical representation and deep learning techniques. We introduce general methods for creating financial markets that are integrated according to different shapes, including structural and physical. A custom GNN architecture is developed, featuring heterogeneous graph convolution, listening mechanisms, and physical models to capture the exchange patterns. The analysis of the world’s big data shows that the best performance of our method achieved a 35.2% Money Laundering Detection Rate (MLDR) in the top 1% of the flag business, do the best way.