Pawsome Detect: Harnessing Pattern Recognition and Data Mining for Canine Age and Breed Detection

Abstract

PAWSOMEDETECT leverages pattern recognition to revolutionize canine age and breed detection. Determining a dog's age and breed from photos has become vital due to rising popularity of dogs as pets and the demand for personalized, data-driven pet care. Traditional methods rely on manual inspection and judgment, prone to errors. Furthermore, mating compatibility among breeds is a consideration for dog owners. This aids users in identifying compatible breeds for mating, increasing successful mating chances. To achieve accurate age and breed, deep learning models are trained on a vast collection of annotated dog photos. These models use Convolutional Neural Networks (CNNs) and transfer learning to improve performance and generalize knowledge, revealing age and breed patterns. Researchers, breeders, and owners can benefit from this data. Following ISO-25010 guidelines, the study confirms the system's accuracy in age and breed determination, as well as its ability to detect user locations for successful mating. It meets response time and resource utilization requirements, ensuring timely operations. In conclusion, "PAWSOMEDETECT" a groundbreaking pattern recognition-based method for accurate age and breed identification in dogs. It provides data-driven solutions for pet care, mating compatibility, and more, benefiting users and dogs. The study's continued enhancements promise even greater accuracy and functionality.