Acta neurologica Scandinavica, ISSN 0065-1427 suppl. 50, vol. 48, 1972
66 s. : tab. ; 24 cm
80 s. : tab., grafy ; 23 cm
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
- MeSH
- Bayesova věta MeSH
- big data MeSH
- časná diagnóza MeSH
- internet věcí * MeSH
- Publikační typ
- časopisecké články MeSH
Acta neurologica scandinavica ; Supplement Vol. 85. 137
54 s. : obr., tab., přeruš.bibliogr.
- Klíčová slova
- neuropatologie - výzkumy - sborníky prací, mozek - plasticita - stárnutí - sborníky prací,
- Konspekt
- Patologie. Klinická medicína
- NLK Obory
- neurologie
- MeSH
- acetylcholin diagnostické užití MeSH
- aerosoly MeSH
- bronchiální astma diagnóza etiologie MeSH
- bronchoprovokační testy MeSH
- lidé MeSH
- nebulizátory a vaporizátory MeSH
- voda MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- srovnávací studie MeSH
Acta neurologica Scandinavica, ISSN 0065-1427 suppl. 101, vol. 70, 1984
217 s. : tab., grafy ; 24 cm
- MeSH
- posuzování pracovní neschopnosti MeSH
- roztroušená skleróza klasifikace MeSH
- Publikační typ
- kongresy MeSH
- oslavné články MeSH
- Konspekt
- Patologie. Klinická medicína
- NLK Obory
- neurologie
- O autorovi
- Fog, Torben, 1912- Autorita
Acta neurologica Scandinavica, ISSN 0065-1427 suppl. 63, vol. 55, 1977
283 s. : tab., grafy ; 24 cm
- MeSH
- histokompatibilita MeSH
- histokompatibilní antigeny MeSH
- imunogenetika MeSH
- roztroušená skleróza MeSH
- Publikační typ
- kongresy MeSH
COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.
- MeSH
- algoritmy MeSH
- COVID-19 * MeSH
- internet věcí * MeSH
- lidé MeSH
- poskytování zdravotní péče MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Alterations in brain functioning, especially in regions associated with cognition, can result from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and are predicted to result in various psychiatric diseases. Recent studies have shown that SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) can directly or indirectly affect the central nervous system (CNS). Therefore, diseases associated with sequelae of COVID-19, or 'long COVID', also include serious long-term mental and cognitive changes, including the condition recently termed 'brain fog'. Hypoxia in the microenvironment of select brain areas may benefit the reproductive capacity of the virus. It is possible that in areas of cerebral hypoxia, neuronal cell energy metabolism may become compromised after integration of the viral genome, resulting in mitochondrial dysfunction. Because of their need for constant high metabolism, cerebral tissues require an immediate and constant supply of oxygen. In hypoxic conditions, neurons with the highest oxygen demand become dysfunctional. The resulting cognitive impairment benefits viral spread, as infected individuals exhibit behaviors that reduce protection against infection. The effects of compromised mitochondrial function may also be an evolutionary advantage for SARS-CoV-2 in terms of host interaction. A high viral load in patients with COVID-19 that involves the CNS results in the compromise of neurons with high-level energy metabolism. Therefore, we propose that selective neuronal mitochondrial targeting in SARS-CoV-2 infection affects cognitive processes to induce 'brain fog' and results in behavioral changes that favor viral propagation. Cognitive changes associated with COVID-19 will have increasing significance for patient diagnosis, prognosis, and long-term care.
- MeSH
- COVID-19 komplikace metabolismus patofyziologie psychologie přenos MeSH
- energetický metabolismus MeSH
- kognitivní dysfunkce metabolismus patofyziologie psychologie MeSH
- lidé MeSH
- mikrobiální viabilita MeSH
- mitochondrie metabolismus MeSH
- mozková hypoxie metabolismus patofyziologie psychologie MeSH
- neurony metabolismus MeSH
- replikace viru MeSH
- SARS-CoV-2 fyziologie MeSH
- virová nálož MeSH
- zdravé chování * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- úvodníky MeSH
