Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

To deeply process the health data of the elderly, this paper designs a deep mining method for elderly health data based on an improved association clustering approach. Initially, health data samples from the elderly are collected. The Apriori algorithm is enhanced with interest constraints, connectivity operations are employed to generate candidate itemsets, and those that do not meet the requirements are eliminated. Associated feature quantities are then extracted from the health data. Subsequently, a fuzzy K-means algorithm with weight attributes is incorporated as the core method, and a balance coefficient is calculated using the principle of balanced contribution. Finally, the improved fuzzy K-means algorithm is utilised to complete data classification, detect abnormal data points, and achieve deep mining of the health data. The results indicate that the proposed method has a false alarm rate of less than 3.21% and a false negative rate of less than 1.81%, demonstrating a superior mining effect compared to the comparison method.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

This study presents an enhanced temporal fusion framework for pharmaceutical demand forecasting, designed to handle both baseline consumption and promotion-driven sales fluctuations.The approach integrates heterogeneous data sources -such as therapeutic seasonality and catchment health indicators -into a multivariate feature space, and incorporates a knowledge-guided attention mechanism within a temporal fusion transformer to decouple regular and promotional demand.Systematic factor analysis further quantifies the influence of product, store, and temporal variables.Using a dataset of 1.2 million retail transactions, the proposed model reduces forecasting error by 23.6% over traditional methods.Ablation studies confirm the importance of future promotion signals (accuracy loss of 15.4%, p < 0.001), while analysis reveals that seasonal effects vary significantly by drug type.Store-level factors such as deprivation index and competition proximity also significantly affect promotional effectiveness.The framework offers three key contributions: adaptive feature engineering for retail contexts, integration of domain knowledge into temporal modelling, and empirical identification of demand drivers.Practical deployment yielded 31% fewer stock-outs and 27% lower inventory costs, demonstrating its value for resilient and data-driven pharmaceutical supply chain optimisation.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

This study presents an enhanced temporal fusion framework for pharmaceutical demand forecasting, designed to handle both baseline consumption and promotion-driven sales fluctuations. The approach integrates heterogeneous data sources - such as therapeutic seasonality and catchment health indicators - into a multivariate feature space, and incorporates a knowledge-guided attention mechanism within a temporal fusion transformer to decouple regular and promotional demand. Systematic factor analysis further quantifies the influence of product, store, and temporal variables. Using a dataset of 1.2 million retail transactions, the proposed model reduces forecasting error by 23.6% over traditional methods. Ablation studies confirm the importance of future promotion signals (accuracy loss of 15.4%, p < 0.001), while analysis reveals that seasonal effects vary significantly by drug type. Store-level factors such as deprivation index and competition proximity also significantly affect promotional effectiveness. The framework offers three key contributions: adaptive feature engineering for retail contexts, integration of domain knowledge into temporal modelling, and empirical identification of demand drivers. Practical deployment yielded 31% fewer stock-outs and 27% lower inventory costs, demonstrating its value for resilient and data-driven pharmaceutical supply chain optimisation.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.