## 1 Introduction
### Challenges  
Traditional methods of fiber identification involve manual inspection, which can be time-consuming and subjective [10]. The main challenges using traditional methods to determine natural fibre characteristics are the low precision level. For instance, during the determination of weights, direct weight-assignment techniques based on the expert's comprehension of the importance of criteria are acquired. Then multi-criteria analysis models are used to figure out the weights [11]. The methods used to determine the weights can be separated into four categories: direct and indirect, holistic and decomposed, statistical and algebraic, and compensatory and non-compensatory. In direct methods, the decision-maker evaluates two criteria using a ratio scale. In indirect techniques, weights are determined based on preferences. In algebraic approaches, a basic system of equations is used to determine the \(n\) weight based on an \(n-1\) set of judgements, while regression analysis is used during weight selection [12]. Decomposed processes compare one pair of criteria, while holistic approaches allow for the expression of preferences and evaluate the alternatives overall by considering both the criteria and the alternatives. It is evident that the methods used to determine criteria weights are not classified uniquely and are instead done on a variety of grounds [13]. However, the ability of natural fibers to absorb materials or their hygroscopic properties is a significant feature to be considered [14]. Throughout their existence, composite materials are frequently exposed to a variety of atmospheric conditions, including unsteady hygroscopic conditions. Nonetheless, considerable hygroscopic behavior of the fibres results in high-moisture absorption in damp surroundings under humid conditions. As a result, both the fibers' structure and the composites they are integrated into undergo changes that impact their physical and mechanical properties [15]. Therefore, to properly regulate these novel composite materials, it is important to comprehend these mechanisms of moisture absorption, as well as the manner in which water affects the ultimate properties of these fibres and their composites [16]. To more effectively encourage the use of these fiber-based composites as structural materials for various applications in the future, it is crucial to understand and anticipate their sorption behavior [17]. The dynamic vapor sorption (DVS) technique is used to determine the uptake of water as a function of relative humidity, temperature, and exposure time. It is one of the commonly used methods for the determination of hygroscopic properties of fibers [18]. However, it is difficult to pinpoint the exact moisture content at a specific relative humidity with the use of DVS. It also requires a limited number of samples that may not represent the entire fiber; achieving equilibrium at each humidity level can be time-consuming. Henceforth, the use of machine-learning (ML) techniques can provide improved efficiency and accuracy of the fiber properties from the experimental data to evaluate the complex relationship between the fibers' features and hygroscopic behavior.