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Skin cancer is a perilous kind of cancer caused by damaged DNA and it leads to death. This damaged DNA causes uncontrolled proliferation of cells. Even though, the image analysis of lesions is highly difficult due to light reflections from skin surface, fluctuations at color lighting, variety of lesions' forms and sizes in skin cancer. Because of these issues, automatic recognition of skin cancer accurateness is decreased. Therefore, a Graph Convolutional Neural Network (GCNN) by ResNet 152 Transfer Learning Architype optimized with Chameleon Swarm Optimization Algorithm (GCNN-ResNet 152 TL-CSOA) is proposed at this manuscript for enhancing skin cancer detection with classification in medical image processing. Initially, the input images are taken from International Skin Imaging Collaboration (ISIC) of dermoscopic skin cancer imagery data set. Afterward, the input images are pre-processed utilizing trilateral filter method for removing noise. The pre-processed output is supplied to the process of feature extraction. Here, image features, like morphologic, gray scale statistic and Haralick texture features are extracted by Gray-Level Co-Occurrence Matrix window adaptive approach (GLCM-WAA) technique. After that, the GCNN-ResNet 152 TL classifies the skin cancer images into Actinic Keratosis, Basal Cell Carcinoma, Malignant Melanoma and Squamous Cell Carcinoma. Additionally, GCNN-ResNet 152 TL weight parameters is tuned by Chameleon Swarm Optimization Algorithm (CSOA). The simulation process is executed at Python tool. From simulation, the proposed approach attains 23.34%, 12.03%, 21.42% improved accuracy and 18.23%, 21.23%, 14.56% higher sensitivity compared with existing approaches. [ABSTRACT FROM AUTHOR]

Test metrics succeed in analyzing the current level of maturity in testing and give a projection on the way to proceed with testing activities by allowing us to set goals and predict future trends. The objective of test metrics is to capture the planned and actual quantities: the effort, time and resources required to complete all the phases of development of the software project. Test case prioritization is an effective and practical technique in regression testing. It schedules test cases in order of precedence that increases their ability to meet some performance goals, such as code coverage, rate of fault detection. In this paper, we present a new metrics, based on varying requirement priorities, test case priorities, test case execution time, and fault severities. The case study illustrates that the rate of "units-of-test-case-priority-satisfied- per-unit-test-case-time" can be increased, and with improvement on testing quality and customer satisfaction. To assess the practicality of our approach, we apply it to a realistic example from the industrial projects. Also we summarize a test process measurement project of TECHZONE™ (Software development Concern with Testing Department) test teams, and analyze the effectiveness of set of metrics for cost, time, and quality to measure the quality of test process based on the results of the proposed metrics. [ABSTRACT FROM AUTHOR]