A study on the use of quarter point crack tip element in CCT and DEN specimen

Aerospace Engineerin

Fatigue Crack Growth Predictions of Surface Cracks under Constant-Amplitude and Variable-Amplitude Loading

Mechanical Maritime and Materials Engineerin

Growth of surface cracks under fatigue loading: A literature survey

Aerospace Engineerin

Crack opening stress measurements of surface cracks in 7075-T6 Al alloy plate specimens through electron fractography

Aerospace Engineerin

Sub-center commerce corridor

Vision: A vibrant sub-center that supports the potential for all people to develop adequate housing and business opportunities, regardless of their income levels. Mission: Transform the main street of Ayiga into a Corridor that will stimulate community development by providing a new framework of infrastructure, public services, transportation, open space that is of new housing and business development.Department of Culture, Delegation of the Flemish Government in South Africa, Embassy of Belgiumhttps://africanperspectivesconference.wordpress.com

Classification of adeno carcinoma, high squamous intraephithelial lesion, and squamous cell carcinoma in Pap smear images based on extreme learning machine

Cervical cancer is a malignant tumour that attacks the female genital area originating from epithelial metaplasia in the squamous protocol junction area. One method of diagnosis of cervical cancer is to do a Pap smear examination by taking a cervical cell smear from the woman�s cervix and observing its cell development. However, examination of cervical cancer from Pap smear results usually takes a long time. This is because medical practitioners still rely on visual observations in the analysis of the results of Pap smear so that the results are subjective. Therefore, we need a programme that can help the classification process in establishing a diagnosis of cervical cancer with high accuracy results. In this study, a cervical cancer classification program was developed using a combination of the Grey Level Co-occurrence Matrix (GLCM) and Extreme Learning Machine (ELM) methods. There are three classes of cervical cell images classified, namely adenocarcinoma, High Squamous Intraepithelial Lesion (HSIL) and Squamous Cell Carcinoma (SCC). From the results of the training program obtained an accuracy 100 and from the testing program obtained an accuracy of 80. © 2020 Informa UK Limited, trading as Taylor & Francis Group