Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models. This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines. The paper is an integrated analysis of two types of prediction algorithms: AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks to forecast user behavior and demand for financial services in the future from transactional history. The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast. association rule mining – in particular the Apriori algorithm – is used to find latent patterns and relationships between user transactions and financial products. This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation. The hybrid approach, which combines both approaches, proves to increase user interaction and recommendation accuracy by 20% compared to the previous systems, according to experiments. The paper emphasises the possibilities of using big data in the construction of scalable, individualized financial recommendation systems.

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In intelligent warehousing and transportation processes, the centralization of material units significantly enhances storage and handling efficiency. Among these, the centralized unitization of material pallets is in high demand and widely applied in practical operations. In multi-SKU scenarios, achieving efficient palletizing—particularly online mixed palletizing—poses a major challenge in logistics operations. This process aims to save manpower while ensuring operational efficiency. To address this issue, this paper presents a combined heuristic algorithm that integrates an anthropomorphic heuristic algorithm with a greedy algorithm incorporating local perturbations. The proposed approach accounts for constraints such as mass, volume, center of gravity, non-overlapping placement, and stability. Experimental results demonstrate that this algorithm effectively resolves the palletizing challenges for multi-SKU goods, significantly reducing space waste.

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Urbanization is a significant driver of land use change, particularly in rapidly growing metropolitan areas. This research investigates Greenfield City land use change in the 20-year period (2000-2020) using GIS and satellite data. The mapping shows where the greatest land-use changes occurred, ranging from increased residential and commercial developments to the loss of agricultural fields and the omission of green space. The work applies multi-temporal analyses of Landsat satellite images taken in 2000, 2010 and 2020 to estimate land cover change and its effects on urban planning and sustainability. They indicate that there’s a clear rise in housing and business developments, but also a steep decline in farming and greenspace. These transformations affect the environment, with habitat loss, biodiversity destruction and encroachment on natural resources. The paper wraps up by focusing on the issues of sustainability in urban planning and how better land use planning is required to reduce the negative environmental effects of urban sprawl.

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In intelligent scenarios, large language models (LLMs) are used to create characters that interact with users, providing guidance and relevant information. The higher the degree of anthropomorphism of these roles, the better the emotional experience they provide, which is beneficial for user interaction and enhances user experience. Therefore, evaluating the character-creation capabilities of LLMs is essential. This study used a questionnaire and used another LLM (ChatGPT-4o) to assess the impact of emoji usage and language style on the anthropomorphism and emotional expression of content generated by LLMs. The results indicate that when using emojis, the characters exhibit higher levels of anthropomorphism and emotional expression. Additionally, informal language styles contribute to enhancing both anthropomorphism and emotional expression.

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