Debabrata Datta

One of the major causes of death for women is breast cancer. A substantial number of women diagnosed with breast cancer die due to inaccuracies in diagnosis and delays in treatment. Cancer prediction must be accurate in order to improve treatment quality and patient survival rates. This study evaluates logistic regression (LR), decision tree algorithm (DTA), and adaptive boosting (AdaBoost) (AB ensemble learning algorithm) in conjunction with rough set theory (RST) to enhance breast cancer classification using the Wisconsin diagnosis breast cancer dataset (WDBC). By employing rough set approximations, including the upper and lower bounds of features, this study introduces a novel rough AdaBoost (Rough AB) algorithm to improve classification accuracy. Various performance indices are compared across algorithms. The proposed Rough AB algorithm demonstrated superior performance, particularly in prediction accuracy for both benign and malignant cases. It incorporates roughness to determine the starting node of the decision stump, offering a significant improvement in ensemble learning techniques for medical diagnostics. It gives practical implications for clinical decision-making, potentially enabling more reliable and timely breast cancer diagnoses, which can significantly impact patient outcomes. The proposed method leverages rough set approximations to refine feature selection and improve prediction accuracy. Also, it positions RST as an explainable artificial intelligence (XAI) technique, highlighting its interpretability, ethical transparency, and potential integration with deep learning for clinical deployment.

Among all the materials, diamond turning of heterogeneous materials like copper beryllium (CuBe) poses serious machining challenges as the heterogeneity in the workpiece affects the quality of generated surface. Therefore, the present study is aimed to understand the effect of tool–workpiece interactions on the surface characteristics of heterogeneous CuBe workpiece material. Experiments and molecular dynamics simulation (MDS) were carried out to analyze the various surface and subsurface interactions during cutting. Results from the experiments on both the materials for whole cutting length show that the average roughness values on CuBe-machined surface are found to be ∼48% higher than those of copper (Cu). Scanning electron microscopy (SEM) results show that while deterministic lay pattern is obtained in the case of Cu, the CuBe-machined surface possesses near-random lay pattern, which is also reflected by the fast Fourier transform (FFT) spectrum of surface roughness profiles. Experimental and MDS results reveal that the hard precipitate suffers cracks which propagate vertically as well as radially and as the tool travels from Cu-rich phase to Be-rich phase, ductile to brittle transition in cutting mechanism is observed. Furthermore, it is observed that diamond-turned Cu and CuBe surfaces are contaminated by the oxides of C and Cu. MDS results verify the mechanisms involved in the surface and subsurface interactions during diamond turning.