Scalp and hair diseases, affecting millions worldwide, pose significant challenges in terms of accurate diagnosis and effective treatment. Traditionally reliant on expert evaluation, these conditions can often be misdiagnosed due to their complex and overlapping symptoms. In recent times, especially in the world of information technology, convolutional neural networks (CNNs) have become more prominent than ever thanks to their ability to analyze and process image data for classification and recognition tasks. CNNs learn to recognize patterns from images through convolutional layers to detect characteristic features in images and have revolutionized the field of image recognition, offering promising applications in medical diagnostics. Despite their potential, few studies have thoroughly explored the capabilities of multiple CNN architectures in the context of dermatology. This study aims to bridge this gap by evaluating the effectiveness of several CNN models—VGG16, VGG19, Inception-V3, ResNet50, and ResNet152—in detecting scalp and hair diseases. The findings indicate that VGG16 and VGG19 consistently outperform other models in accuracy across all disease categories, demonstrating their robustness and reliability for this application. By providing a comparative analysis of these architectures with a user interface (UI), we seek to advance automated diagnostic methods, ultimately enhancing clinical decision-making and patient care.
This article studies the multi-venue basketball event scheduling plan based on a simulated annealing algorithm, aiming to improve the stability and fairness of event scheduling. By reviewing the application advantages and disadvantages of traditional intelligent algorithms in basketball event scheduling, this paper introduces the principle of simulated annealing algorithm and its application in basketball event scheduling. A basketball tournament scheduling plan based on a simulated annealing algorithm was designed and implemented for multiple competition venues, and its effectiveness was verified through experiments. The experimental results show that compared with traditional intelligent algorithms, the plan exhibits better performance in terms of stability and fairness.
This article introduces a recognition learning system based on a poetry database and text pattern function.The system aims to help students better understand and remember poetry and improve the efficiency of Chinese language learning.The system's core components include a poetry database, text pattern recognition, and personalized learning modules.The poetry database contains a wealth of poetry works, providing students with rich learning resources.The text pattern recognition module can automatically recognize patterns in poetry, such as rhyme and contrast, to help students understand the structural characteristics of poetry.The personalized learning module provides suggestions and exercises based on students' learning progress and abilities to achieve precise teaching.
Research on advertising campaigns is intriguing due to their recent emergence and rapid expansion.These campaigns encounter various challenges, such as determining the most effective advertising strategies to achieve their objectives.This study aims to present a practical model for advertising campaigns, outlining the necessary steps for companies to follow.The research methodology involved a qualitative analysis of 180 advertising-related articles, with 80 articles selected as a sample through library studies.Data collection included semi-structured interviews with 10 advertising experts and the distribution of an open questionnaire to identify key indicators.The findings led to developing a framework comprising 9 categories, 27 themes, and 54 indicators for advertising campaigns.Indicators such as storyboards, client briefs, campaign scripting, and slogan writing are identified as novel elements within campaign models, offering valuable insights for advertising organizations.
