Role of Yoga as Adjunctive Therapy for Migraines: A Narrative Review of the Literature.

Migraines are chronic, painful, and one of the most prevalent disabling primary headache disorders, mainly treated with pharmacological methods. Patients suffering from migraine suffer from a significantly reduced quality of life. The use of non-pharmacological methods to reduce the stress and anxiety associated with long-term chronic conditions can help improve quality of life, reduce disease burden, and subsequently alleviate the economic burden on the patient. This review aims to discuss the use of yoga in patients with migraine headaches as a non-pharmacological method. We discuss the most recently published literature discussing the use of yoga as an add-on therapy for patients with migraines in order to reduce the severity of their symptoms, anxiety, and stress. Despite the presence of limitations and the need for further studies, the current data suggest that yoga can be beneficial in helping patients suffering from migraine headaches by reducing their frequency, duration, and pain. Yoga has also demonstrated improvement in the headache impact severity migraine disability assessment test.

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems.

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.