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Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency. [ABSTRACT FROM AUTHOR]

Android set-top boxes, often known as STBs, have emerged as a popular option for streaming and viewing digital content. Testing Android STBs, however, can be extremely difficult due to the complicated hardware and software configurations they require. In addition to being time-consuming and error-prone, manual testing can be exceedingly costly. Because of this, automation testing has emerged as a possible solution to the problems that have been identified. This article presents an overview of the current state of the art in automation testing for Android STBs from a technical perspective. This article highlights the difficulties typically connected with testing Android STBs, the advantages of automation testing, and the various tools and methods utilised for automation testing. Integrating AI and machine learning approaches are explored to demonstrate the potential for increasing efficiency and adaptability in Android STB automation testing. The findings contribute to the evolution of testing techniques for Android STBs by providing insights into tool selection, performance optimisation, and future approaches for intelligent automation. In addition, the article provides an overview of recent research conducted in this area and an analysis of the current and future directions that automation testing for Android STBs will take. [ABSTRACT FROM AUTHOR]