Integration of Grab Methods with Artificial Neural Networks for Enhanced Decision-Making Systems

This study explores the architecture, development, and practical applications of artificial neural networks (ANNs), with advances in Gray Relational Analysis (GRA) techniques. ANNs are computational models designed to model biological neural systems, consisting of layers of interconnected neurons—i.e., input, hidden, and output layers—connected by synaptic weights. Operating through a connectionist approach, these networks effectively mimic the four basic functions of biological neurons: receiving input, integrating information, processing data, and generating output. Traditional ANNs, although powerful, face limitations in embedded systems due to their reliance on high-precision digital information transfer and the resulting resource demands. This has prompted the development of more efficient alternatives, such as spiking neural networks (SNNs), which use event-driven spiking signals to reduce power consumption and memory usage. The field has advanced further by incorporating theoretical models for quantum neural computing and the use of genetic algorithms employing various crossovers and mutation techniques. The versatility of ANNs is evident in a variety of applications. In healthcare, they aid in pattern recognition associated with conditions such as breast cancer and diabetes. For water resource management, ANN models predict relationships between rainfall and water levels. In financial sectors, they analyze complex economic conditions and assess credit risk for small business loans. Industrial applications include modelling complex systems in manufacturing plants, although widespread adoption in this field is limited. Complementing neural network advances, GRA methods have emerged to address multi-criteria decision-making challenges. Notable advances include the GRAS techniques have been developed to correct matrices with negative values, while fuzzy GRA methods now include interval-valued triangular fuzzy numbers and probabilistically uncertain linguistic word sets. Recent advances include innovations such as score values and normalized Hamming distances within single-valued neutrosophic fuzzy stein summary, these computational approaches offer powerful ways to solve complex, multidimensional problems, with recent studies highlighting improved performance and promising prospects for future application.