Unsolicited emails, popularly referred to as spam, have remained one of the biggest threats to cybersecurity globally. More than half of the emails sent in 2021 were spam, resulting in huge financial losses. The tenacity and perpetual presence of the adversary, the spammer, has necessitated the need for improved efforts at filtering spam. This study, therefore, developed baseline models of random forest and extreme gradient boost (XGBoost) ensemble algorithms for the detection and classification of spam emails using the Enron1 dataset. The developed ensemble models were then optimized using the grid-search cross-validation technique to search the hyperparameter space for optimal hyperparameter values. The performance of the baseline (un-tuned) and the tuned models of both algorithms were evaluated and compared. The impact of hyperparameter tuning on both models was also examined. The findings of the experimental study revealed that the hyperparameter tuning improved the performance of both models when compared with the baseline models. The tuned RF and XGBoost models achieved an accuracy of 97.78% and 98.09%, a sensitivity of 98.44% and 98.84%, and an F1 score of 97.85% and 98.16%, respectively. The XGBoost model outperformed the random forest model. The developed XGBoost model is effective and efficient for spam email detection.

This study examined the developments in the field of Scheduling algorithms in the last 30 years (1992–2021) to help researchers gain new insight and uncover the emerging areas of growth for further research in this field. This study, therefore, carried out a bibliometric analysis of 12,644 peer-reviewed documents extracted from the Scopus database using the Bibliometrix R package for bibliometric analysis via the Biblioshiny web interface. The results of this study established the development status of the field of Scheduling Algorithms, the growth rate, and emerging thematic areas for further research, institutions, and country collaborations. It also identified the most impactful and leading authors, keywords, sources, and publications in this field. These findings can help both budding and established researchers to find new research focus and collaboration opportunities and make informed decisions as they research the field of scheduling algorithms and their applications.

From the development and sale of a product through its delivery to the end customer, the supply chain encompasses a network of suppliers, transporters, warehouses, distribution centers, shipping lines, and logistics service providers all working together. Lead times, bottlenecks, cash flow, data management, risk exposure, traceability, conformity, quality assurance, flaws, and language barriers are some of the difficulties that supply chain management faces. In this paper, deep learning techniques such as Long Short-Term Memory (LSTM) and One Dimensional Convolutional Neural Network (1D-CNN) were adopted and applied to classify supply chain pricing datasets of health medications. Then, Bayesian optimization using the tree parzen estimator and All K Nearest Neighbor (AllkNN) was used to establish the suitable model hyper-parameters of both LSTM and 1D-CNN to enhance the classification model. Repeated five-fold cross-validation is applied to the developed models to predict the accuracy of the models. The study showed that the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) outperforms other approaches employed in this study. The accuracy of the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) from one-fold to 10-fold, produced the highest range between 61.2836% and 63.3267%, among other models.