## 1 Introduction
Doping conductive materials, including carbon-based substances (e.g., carbon black [25], graphene [26], and CNTs [27]), conductive polymers (such as poly(3,4-dioxthiophene ethylene glycol) [28] and polypyrrole [29]), nitride materials (MXene [30]), or metallic materials (like silver nanoparticles [31] and nickel powders [25]), within elastomeric substrates is an effective method to enhance the sensitivity of flexible sensing materials. Doping carbonaceous nanoparticles with a high specific surface area significantly improves the electrical conductivity of composites and facilitates the establishment of conductive networks at reduced percolation thresholds [32]. Co-doping various carbonaceous nanoparticles can produce a synergistic effect, where the interaction between different materials enhances the overall performance, thereby further lowering the percolation threshold [33]. [PERSON] et al. [34] introduced a novel technique for fabricating a flexible piezoresistive pressure sensor utilising a porous polydimethylsiloxane sponge as the substrate, incorporating reduced graphene oxide and multi-walled CNTs as fillers to create a high-performance sensor featuring a dual conducting network. The sensor features a pressure detection range of 0-200 kPa, a sensitivity of 1.62 kPa\({}^{-1}\), a reaction time of 61 ms, and can endure 22,000 loading/unloading cycles at 0-15% compressive strain. [PERSON] [35] indicated that Fe\({}_{3}\)O\({}_{4}\) nanoparticles exhibit significant chemical stability and biocompatibility, thereby preserving the stability of the conducting network within the polymer matrix. Graphene- Fe\({}_{3}\)O\({}_{4}\) nanosheets were synthesised via the hydrothermal technique by [PERSON] et al. [36]. The G-F nanosheets incorporated into the polydimethylsiloxane (PDMS) matrix significantly reduced the percolation threshold, resulting in an approximately 10 orders of magnitude enhancement in the electrical conductivity of the composite compared to that of pure PDMS. The enhanced interfacial interactions between the G-F nanosheets and the matrix also contributed to the composites demonstrating notable hysteresis. In conclusion, Fe\({}_{3}\)O\({}_{4}\), CNT, and MLG nanoparticles can improve composite sensing materials' hysteresis and sensitivity characteristics. Furthermore, there is a lack of pertinent research on the concurrent improvement of hysteresis and sensitivity in sensing materials across an extensive pressure range, rendering the investigation presented in this paper valuable. Owing to the synergistic interactions among fillers and the intricacies of the reaction mechanism, ascertaining the appropriate quantity of these materials to incorporate remains challenging once the requisite type of filler is identified. Estimating the preparation conditions without mathematical models is a labour-intensive and expensive endeavour due to the interplay of numerous elements. Machine learning can forecast the mechanical, thermal, optical, electrical, or other characteristics of fabricated materials depending on the composition of the flexible composite and the manufacturing process [37]. [PERSON] et al. [38] utilised RSM to predict the elastic modulus and modulus of rupture of polyvinyl alcohol/acrylonitrile/nanoclay nanocomposites. The created model had an R\({}^{2}\) value approaching 1 for the experimental data, and the residual normal probability plot conformed to a straight line, signifying a high level of accuracy in the model. [PERSON] et al. [39] forecasted the compressive and flexural strengths of carbon nanotube (CNT)-reinforced cementitious nanocomposites via an RF model. The model accounted for 98.2% of the variability in the training data regarding compressive strength and achieved a prediction accuracy of 86.9%, demonstrating a commendable performance. The model accounted for 92.7% of the variability in the training data regarding flexural strength and achieved a prediction accuracy of 78.2%. [PERSON] et al.