Background: Supporting women to initiate and continue breastfeeding is a global challenge. A range of breastfeeding interventions employing electronic technologies (e-technologies) are being developed, which offer different delivery modes and features over the internet; however, the impact of internet-based e-technologies on breastfeeding outcomes remains unclear. Objective: This study aimed to identify the characteristics of current internet-based breastfeeding interventions employing e-technologies and investigate the effects of internet-based e-technologies on breastfeeding outcomes. Methods: A systematic search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in the following databases: Scopus, Web of Science, the Cochrane Database of Systematic Reviews, ScienceDirect, Google Scholar, the Association for Computing Machinery, SpringerLink, and Institute of Electrical and Electronics Engineers Xplore. Results: This systematic review included 16 studies published between 2007 and 2018, with 4018 women in 8 countries. The characteristics of the interventions were grouped based on (1) mode of delivery (web-based, mobile phone apps, and computer kiosk), (2) purpose of the interventions (education and support), and (3) key strategies (monitoring and breastfeeding tracking, personalization, online discussion forum, web-based consultation, and breastfeeding station locators). Combining educational activities with web-based personalized support through discussion forums appeared to be the most effective way to improve breastfeeding outcomes and long-term exclusive breastfeeding rates. Monitoring and breastfeeding trackers appeared to be the least effective ways. Conclusions: This study demonstrated a variety of internet-based e-technologies that professionals can use to promote, educate, and support breastfeeding women. Future internet-based breastfeeding interventions employing e-technologies might consider improving interaction with mothers and personalizing the content of the proposed interventions.

The widespread use of Internet of Things (IoT) devices across critical infrastructure sectors has created extensive and heterogeneous attack surfaces that conventional rule-based security frameworks are structurally unable to address. This paper proposes and evaluates a machine learning-based cybersecurity risk assessment framework for IoT networks using Convolutional Neural Networks (CNNs), Random Forest, Support Vector Machines (SVM), and Autoencoders. Models were trained and validated on a harmonized pool of two publicly available IoT network datasets, TON_IoT and IoTID20, using stratified 5-fold cross-validation within the training partition and an independent held-out test set reserved for the final risk assessment. Empirical results demonstrate that the CNN model achieved the highest vulnerability detection performance across all evaluated metrics: 96.7% accuracy, 95.8% precision, 96.1% recall, and a 95.9% F1-score, outperforming Random Forest (95.1%), SVM (92.3%), and Autoencoder (89.5%). The proposed four-stage risk assessment pipeline, which comprises Data input module, Anomaly detection module, Risk scoring module, and Alert generation module, identified a total of 177 network anomalies within the held-out test set. Notably, 80.2% of these detections (n = 142) fell into the medium- or high-severity categories under a heuristic prioritization scheme, indicating that most CNN-flagged events in the evaluated data warrant analyst attention. This framework provides a proactive defense mechanism, which offers critical implications for deploying adaptive security protocols in heterogeneous IoT ecosystems.