Study of Network Traffic Analysis and Prediction

Network traffic analysis is the way toward chronicle, evaluating and examining system traffic with the end goal of execution, security as well as general system tasks and the executives. Analysis and prediction of network traffic has applications in wide far reaching set of zones and has recently pulled in noteworthy number of studies. Various types of trials are directed and condensed to distinguish different issues in existing PC arrange applications. System traffic examination and forecast is a proactive way to deal with guarantee secure, dependable and subjective system correspondence. Different systems are proposed and tested for analyzing system traffic including neural network based strategies to data mining methods. So also, different Linear and non-linear models are proposed for system traffic prediction. A few intriguing mixes of system examination and forecast strategies are actualized to achieve proficient and compelling outcomes [3]

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the  developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques

Recent progress in research on the pharmacological potential of mushrooms and prospects for their clinical application

International audienceFungi are considered one of the most diverse, ecologically significant, and economically important organisms on Earth. The edible and medicinal mushrooms have long been known by humans and were used by ancient civilizations not only as valuable food but also as medicines. Mushrooms are producers of high- and low-molecular-weight bioactive compounds (alkaloids, lectins, lipids, peptidoglycans, phenolics, polyketides, polysaccharides, proteins, polysaccharide-protein/peptides, ribosomal and non-ribosomal peptides, steroids, terpenoids, etc.) possessing more than 130 different therapeutic effects (analgesic, antibacterial, antifungal, anti-inflammatory, antioxidant, antiplatelet, antiviral, cytotoxic, hepatoprotective, hypocholesterolemic, hypoglycemic, hypotensive, immunomodulatory, immunosuppressive, mitogenic/regenerative, etc.). The early record of Materia Medica shows evidence of using mushrooms for treatment of different diseases. Mushrooms were widely used in the traditional medicine of many countries around the world and became great resources for modern clinical and pharmacological research. However, the medicinal and biotechnological potential of mushrooms has not been fully investigated. This review discusses recent advances in research on the pharmacological potential of mushrooms and perspectives for their clinical application

Effects of alirocumab on cardiovascular and metabolic outcomes after acute coronary syndrome in patients with or without diabetes: a prespecified analysis of the ODYSSEY OUTCOMES randomised controlled trial

Background After acute coronary syndrome, diabetes conveys an excess risk of ischaemic cardiovascular events. A reduction in mean LDL cholesterol to 1.4-1.8 mmol/L with ezetimibe or statins reduces cardiovascular events in patients with an acute coronary syndrome and diabetes. However, the efficacy and safety of further reduction in LDL cholesterol with an inhibitor of proprotein convertase subtilisin/kexin type 9 (PCSK9) after acute coronary syndrome is unknown. We aimed to explore this issue in a prespecified analysis of the ODYSSEY OUTCOMES trial of the PCSK9 inhibitor alirocumab, assessing its effects on cardiovascular outcomes by baseline glycaemic status, while also assessing its effects on glycaemic measures including risk of new-onset diabetes

Apolipoprotein B, Residual Cardiovascular Risk After Acute Coronary Syndrome, and Effects of Alirocumab.

Background: Apolipoprotein B (apoB) provides an integrated measure of atherogenic risk. Whether apoB levels and apoB lowering hold incremental predictive information on residual risk after acute coronary syndrome beyond that provided by low-density lipoprotein cholesterol is uncertain. Methods: The ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab) compared the proprotein convertase subtilisin/kexin type 9 inhibitor alirocumab with placebo in 18 924 patients with recent acute coronary syndrome and elevated atherogenic lipoproteins despite optimized statin therapy. Primary outcome was major adverse cardiovascular events (MACE; coronary heart disease death, nonfatal myocardial infarction, fatal/nonfatal ischemic stroke, hospitalization for unstable angina). Associations between baseline apoB or apoB at 4 months and MACE were assessed in adjusted Cox proportional hazards and propensity score–matched models. Results: Median follow-up was 2.8 years. In proportional hazards analysis in the placebo group, MACE incidence increased across increasing baseline apoB strata (3.2 [95% CI, 2.9–3.6], 4.0 [95% CI, 3.6–4.5], and 5.5 [95% CI, 5.0–6.1] events per 100 patient-years in strata 35–<50, and ≤35 mg/dL, respectively). Compared with propensity score–matched patients from the placebo group, treatment hazard ratios for alirocumab also decreased monotonically across achieved apoB strata. Achieved apoB was predictive of MACE after adjustment for achieved low-density lipoprotein cholesterol or non–high-density lipoprotein cholesterol but not vice versa. Conclusions: In patients with recent acute coronary syndrome and elevated atherogenic lipoproteins, MACE increased across baseline apoB strata. Alirocumab reduced MACE across all strata of baseline apoB, with larger absolute reductions in patients with higher baseline levels. Lower achieved apoB was associated with lower risk of MACE, even after accounting for achieved low-density lipoprotein cholesterol or non–high-density lipoprotein cholesterol, indicating that apoB provides incremental information. Achievement of apoB levels as low as ≤35 mg/dL may reduce lipoprotein-attributable residual risk after acute coronary syndrome. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01663402.gov; Unique identifier: NCT01663402.URL: https://www