A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks.

Variational Quantum Reinforcement Learning via Evolutionary Optimization

Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using the amplitude encoding; Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

Development and application of a hybrid implementation research framework to understand success in reducing under-5 mortality in Rwanda

Background: We describe the development and testing of a hybrid implementation research (IR) framework to understand the pathways, successes, and challenges in addressing amenable under-5 mortality (U5M) – deaths preventable through health system-delivered evidence-based interventions (EBIs) – in low- and middle-income countries (LMICs). Methods: We reviewed existing IR frameworks to develop a hybrid framework designed to better understand U5M reduction in LMICs from identification of leading causes of amenable U5M, to EBI choice, identification, and testing of strategies, work to achieve sustainability at scale, and key contextual factors. We then conducted a mixed-methods case study of Rwanda using the framework to explore its utility in understanding the steps the country took in EBI-related decision-making and implementation between 2000-2015, key contextual factors which hindered or facilitated success, and to extract actionable knowledge for other countries working to reduce U5M. Results: While relevant frameworks were identified, none individually covered the scope needed to understand Rwanda’s actions and success. Building on these frameworks, we combined and adapted relevant frameworks to capture exploration, planning, implementation, contextual factors in LMICs such as Rwanda, and outcomes beyond effectiveness and coverage. Utilizing our hybrid framework in Rwanda, we studied multiple EBIs and identified a common pathway and cross-cutting strategies and contextual factors that supported the country’s success in reducing U5M through the health system EBIs. Using these findings, we identified transferable lessons for other countries working to accelerate reduction in U5M. Conclusions: We found that a hybrid framework building on and adapting existing frameworks was successful in guiding data collection and interpretation of results, emerging new insights into how and why Rwanda achieved equitable introduction and implementation of health system EBIs that contributed to the decline in U5M, and generated lessons for countries working to drop U5M.

Construction of a Removable Partial Denture (RPD): Comparison between the Analog Procedure and the Selective Laser Melting Procedure

A partial removable denture is a device that allows the patient to recover from a partial edentulism. This case report describes the realization of a chrome–cobalt partial removable denture by using two different realization methods: (1) analogic framework and (2) hybrid framework. This allowed us to compare the stability, retention as well as clasp quality of both the products and to highlight their respective advantages, disadvantages, and limitations.