In contemporary email communication, the ever- expanding volume of digital correspondence has ushered in an era where big data plays a pivotal role in addressing the challenge of distinguishing between legitimate (ham) and unsolicited (spam) emails. The primary objective of this paper is the meticulous identification and establishment of criteria for the discrimination between ham and spam emails. To achieve this, the study harnesses data from three distinct datasets, aiming to identify common attributes shared across all emails, irrespective of their classification, while concurrently devising methodologies for precise spam detection. Central to this endeavor is the evaluation of the effectiveness of feature selection techniques, specifically Chi-Square and Pearson Correlation, in elevating the accuracy of email classification. The investigation extends to assessing how the combination of these feature selection techniques with the broader machine learning framework can be optimized. This optimization entails the application of diverse preprocessing techniques to the datasets, all designed to amplify the precision of email classification. Furthermore, this research scrutinizes the performance evaluation metrics employed in the assessment of email classifiers. By conducting comprehensive experiments, the study identifies optimal classifiers based on rigorous evaluation metrics. This contributes valuable insights to the toolkit of techniques for proficient email classification within the realm of big data analysis.

The automated diagnosis of breast cancer through Whole Slide Images (WSI) is a critical endeavour to combat the threat it poses to women’s health. However, traditional deep learning algorithms strongly rely on Independent and Identically Distributed (I.I.D) and then encounter challenges related to multi-instance bias when analyzing multiple tissue sections from the same patient, limiting their generalization capability. To address this, this study introduces Multi-Instance Bias Suppression (MIBS), a novel approach leveraging adversarial training to mitigate patient-specific overfitting. MIBS employs an instance-level discriminator to guide feature generation, disentangling instance-specific cues from broader diagnostic patterns. Through competitive adversarial training, MIBS enhances feature generalization, effectively addressing overfitting and boosting cross-patient accuracy. Validated on the BreakHis dataset, MIBS effectively tackles multi-instance bias-induced overfitting. By bridging the gap between cutting-edge deep learning techniques and the challenges posed by large-scale medical image data, MIBS advances the accuracy and applicability of breast cancer diagnosis. Our approach addresses the multi-instance bias challenge and integrates seamlessly with big data, propelling medical image analysis to new heights of efficiency and precision.