The smart city vision has driven the rapid development and advancement of interconnected
technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this …

Time series clustering is the act of grouping time series data without recourse to a label.
Algorithms that cluster time series can be classified into two groups: those that employ a time …

The direct measurement of external loads acting on structures remains a challenge in many
engineering applications. In this context, mechanical load identification has received …

The detailed reservoir characterization was examined for the Central Indus Basin (CIB),
Pakistan, across Qadirpur Field Eocene rock units. Various petrophysical parameters were …

This study constructs a distributed generation (DG) multi-objective hierarchical optimal
planning model. A solution method is proposed based on an improved beluga whale …

This paper proposes a novel clustering and dynamic recognition–based auto-reservoir
neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park …

With the development of the personalized and diversified service production paradigm,
production lines need to be updated in a timely manner to meet market demand. In this …

It remains challenging in characterizing uncertain and imprecise information when clustering
fuzzy number data. To solve such a problem, this paper investigates a new credal-based …

Providing the right products, at the right place and time, according to their customer's
preferences, is a problem-seeking solution, especially for companies operating in the retail …

Ensemble clustering refers to learning a robust and accurate consensus result from a
collection of base clustering results. Despite extensive research on this topic, it remains …