Lung and colon cancers are the most common causes of death. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only possibility to improve the chances of survival and reduce cancer mortality. Using artificial intelligence in the histopathological diagnosis of colon and lung cancer can provide significant help to specialists in identifying cases of colon and lung cancers with less effort, time and cost. The objective of this study is to set up a computer-aided diagnostic system that can accurately classify five types of colon and lung tissues (two classes for colon cancer and three classes for lung cancer) by analyzing their histopathological images. Using machine learning, features engineering and image processing techniques, the five models XGBoost, SVM, RF, LDA and MLP were used to perform the classification of histopathological images of lung and colon cancers that were acquired from the LC25000 dataset. The main advantage of using machine learning models is that they allow for better interpretability of the classification model since they are based on feature engineering; however, deep learning models are black box networks whose working is very difficult to understand due to the complex network design. The acquired experimental results show that machine learning models give satisfactory results and are very precise in identifying classes of lung and colon cancer subtypes. The XGBoost model gave the best performance with an accuracy of 99% and a F1-score of 98.8%. The implementation and the development of this model will help healthcare specialists identify types of colon and lung cancers. The code will be available upon request.

This study shows that there is no systematic review of research progress in literature throughout the field of sustainable construction waste management by 3R (reduction, reuse, and recycling) A lifecycle approach, The need for processes, strategies, rating systems and policies for robust and efficient waste management is widely recognized. The paper aims to evaluate. A review of sustainable construction waste management in Malaysia to maximize the 3R and reduce the disposal of construction waste by implementing a sustainable strategy throughout project lifecycle. Managing landfill shortages and long-term negative environmental economic and social effects of sustainable waste disposal are now becoming crucial for the sustainability of public health and natural ecosystems. To make adjustments, causes and factors responsible for sustainable construction waste management and progress in moving towards sustainability, it is therefore important to define the existing waste management system and causative factors adopted by industries. It allows a major shift in waste management of Malaysia by improvising current technology for waste management in a much more sustainable way. Furthermore, this ongoing research would develop sustainable construction waste procedures to sustain environmental, economic and social development for a Malaysian construction project.