A business process re-engineering value in improving the business process is undoubted. Nevertheless, it is incredibly complex, time-consuming and costly. This study aims to review available literature in the use of machine learning for business process re-engineering. The review investigates available literature in business process re-engineering frameworks, methodologies, tools, techniques, and machine-learning applications in automating business process re-engineering. The study covers 200+ research papers published between 2015 and 2020 in reputable scientific publication platforms: Scopus, Emerald, Science Direct, IEEE, and British Library. The results indicate that business process re-engineering is a well-established field with scientifically solid frameworks, methodologies, tools, and techniques, which support decision making by generating and analysing relevant data. The study indicates a wealth of data generated, analysed and utilised throughout business process re-engineering projects, thus making it a potential greenfield for innovative machine-learning applications aiming to reduce implementation costs and manage complexity by exploiting the data’s hiding patterns. This suggests that there were attempts towards applying machine learning in business process management and improvement in general. They address process discovery, process behaviour prediction, process improvement, and process optimisation. The review suggests that expanding the applications to business process re-engineering is promising. The study proposed a machine-learning model for automating business process re-engineering, inspired by the Lean Six Sigma principles of eliminating waste and variance in the business process.

Trust is one of the most critical factors that determine willingness to use e-government services. Despite its significance, most previous studies investigated the factors that lead to trusting such services in theoretical aspects without examining the technical solutions. Therefore, more effort is needed to preserve privacy in the current debate on trust within integrated e-government services. Specifically, this study aims to develop a model that examines instruments extracted from privacy by design principles that could protect personal information from misuse by the e-government employee, influencing the trust to use e-government services. This study was conducted with 420 respondents from Oman who were familiar with using e-government services. The results show that different factors influencing service trust, including the need for privacy lifecycle protection, privacy controls, impact assessments, and personal information monitors. The findings reveal that the impeding factors of trust are organizational barriers and lack of support. Finally, this study assists e-government initiatives and decision-makers to increase the use of services by facilitating privacy preservation instruments in the design of e-government services.

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.

Histopathological image analysis remains the cornerstone of cancer diagnosis; however, manual assessment is challenged by stain variability, differences in imaging magnification, and complex morphological patterns. The proposed multi-pretrained deep learning fusion (MPDLF) approach combines two widely used CNN architectures: ResNet50, which captures deeper semantic representations, and VGG16, which extracts fine-grained details. This work differs from previous fusion studies by providing a controlled evaluation of early, intermediate, and late fusion for integrating two pretrained CNN backbones (ResNet50 and VGG16) under single-modality histopathology constraints. To isolate the fusion effect, identical training settings are used across three public H&E datasets. Early fusion achieved the best test performance for the two primary tasks reported here: breast cancer binary classification (accuracy = 0.9070, 95% CI: 0.8742–0.9404; AUC = 0.9707, 95% CI: 0.9541–0.9844) and renal clear cell carcinoma (RCCC) five-class grading (accuracy = 0.8792, 95% CI: 0.8529–0.9041; AUC (OvR, macro) = 0.9895, 95% CI: 0.9859–0.9927). Future work will extend these experiments to additional magnification levels (100×, 200×, and 400×) for breast cancer histopathology images and explore advanced hybrid fusion strategies across different histopathology datasets.