Developing Hopfield Neural Network For Color Image Recognition

Hopfield Neural Network (HNN) is an iterative auto-associative network which consists of a single layer of fully connected processing elements and converges to the nearest match vector. This network alters the input patterns through successive iterations until a learned vector evolves at the output. Then the output will no longer change with successive iterations. HNN faces real problems when it deals with images of more than two colors, noisy convergence, limited capacity, and slow learning and converging according to the number of vectors and their sizes. These problems were studied and tested the proposed solutions to obtain the optimum performance of HNN and set a starting for future research

Comparison of Alexandrite and Diode Lasers for Hair Removal in Dark and Medium Skin: Which is Better?

Introduction: To improve laser hair removal (LHR) for dark skin, the fluence rate reaching the hair follicle in LHR is important. This paper presents the results of a comparative study examining the function of wavelength on dark skin types using 755 nm alexandrite and 810 nm diode lasers.Methods: The structure of the skin was created using a realistic skin model by the Advanced Systems Analysis Program.Result: In this study, the alexandrite laser (755 nm) and diode laser (810 nm) beam–skin tissue interactions were simulated. The simulation results for both lasers differed. The transmission ratio of the diode laser to the dark skin dermis was approximately 4% more than that of the alexandrite laser for the same skin type. For the diode laser at skin depth z = 0.67 mm, the average transmission ratios of both samples were 36% and 27.5%, but those for the alexandrite laser at the same skin depth were 32% and 25%.Conclusion: Both lasers were suitable in LHR for dark skin types, but the diode laser was better than the alexandrite laser because the former could penetrate deeper into the dermis layer