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Failure management and cost-aware traffic engineering are two important tasks done in Network Operation Centers (NOC). These are performed by expert technicians who must carefully analyze the network state and the flow of incoming alarms to decide how, where and when to take actions on the network. While based on implicit guiding principles, these network actions are very hard to automate with explicit rules due to the high complexity of the system; hence NOC action is essentially a manual process today. To automate part of that process, in this paper we introduce an Action Recommendation Engine (ARE) that can learn implicit NOC action rules with supervised machine learning from historical data. As a result, ARE can recommend suitable action(s) to remedy network faults and engineer the traffic to minimize costs, all while maximizing the users’ Quality of Experience. To quantify the effectiveness of different NOC action scenarios, we introduce the QoE-OPEX metric which balances between users’ quality of Experience and ISP’s operational costs. After proper model training on 56,000 data points with 66 features, we demonstrate that ARE can effectively reproduce implicit action-taking logic of NOC technicians, thus moving us one step closer to reliable autonomous networks and fully-automated NOCs.

Although the autonomous detection of network status and localization of network faults can be a valuable tool for network and service operators, very few works have investigated this subject. As a result in today’s networks, fault detection and localization remains a mostly manual process. In this article, we propose a machine learning (ML) method that can automatically detect the status of a network and localize faults. Our method uses the decision tree, gradient boosting (GB), and extreme GB ML algorithms to detect the network status as normal, congestion, and network fault. In comparison, existing related work can at best classify the network status as faulty or nonfaulty. Experimental results show that our method yields accuracies of up to 99% on a dataset collected through an emulated network.

Background Acute ankle sprain is a very common injury which comprises 80% of all ankle injuries. Acute ankle sprain affects almost exclusively the lateral ligamentous complex including ATFL followed by CFL, while PTFL is rarely of clinical significance. Objective To study the clinical trials in a Meta-analytical form, in order to compare surgical versus conservative treatments for the management of acute injuries of the lateral ligament complex of the ankle. Materials and Methods We performed this systematic review and meta-analysis in accordance to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analyses Of Observational Studies in Epidemiology (MOOSE) statements. PRISMA and MOOSE are reporting checklists for Authors, Editors, and Reviewers of Meta-analyses of interventional and observational studies. Results In the present study, we searched Medline via PubMed, SCOPUS, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL) from their inception till April 2019. The search retrieved 702 unique records. We then retained 48 potentially eligible records for full-texts screening. Finally, 14 studies (Total No. of patients=1896) were included in the present systematic review and meta-analysis Conclusion: Starting a physiotherapy programme as early as possible is essential to control pain and swelling, to improve and maintain the range of motion, and to minimize the risk of stiffness and muscle wasting, which are the two decisive factors which delay patients from returning to their normal activities. Regardless of severity, surgery for acute ankle sprain is not recommended anymore.

The Internet is constantly growing in size and becoming more complex. The field of networking is thus continuously progressing to cope with this monumental growth of network traffic. While approaches such as Software Defined Networking (SDN) can provide a centralized control mechanism for network traffic measurement, control, and prediction, still the amount of data received by the SDN controller is huge. To process that data, it has recently been suggested to use Machine Learning (ML). In this paper, we review existing proposal for using ML in an SDN context for traffic measurement (specifically, classification) and traffic prediction. We will especially focus on approaches that use Deep learning (DL) in traffic prediction, which seems to have been mostly untapped by existing surveys. Furthermore, we discuss remaining challenges and suggest future research directions.

Cloud Gaming systems are among the most challenging networked-applications, since they deal with streaming high-quality and bulky video in real-time to players’ devices. While all industry solutions today are centralized, in this paper we introduce an AI-assisted hybrid networking architecture that, in addition to the central cloud servers, also uses some players’ computing resources as additional points of service. We describe the problem, its mathematical formulation, and potential solution strategy.

Network latency is an important metric for many networked systems. For small-scale systems, explicit measurements are carried out to collect $N \times (N-1)$ latency values to cover any pairs of nodes in the network. But this is not practical for large-scale systems due to the significant traffic and processing overhead needed for actual end-to-end latency measurements. Therefore, instead of actual measurements, researchers have proposed to estimate the round-trip times (RTT) to predict the latencies between all nodes within a network based on a small set of actual RTT measurements. However, such methods not only assume that the network is symmetric, which is not necessarily the case in reality, but also require time to converge. In this work, we present a novel method of network latency estimation using Artificial Intelligence (AI), specifically machine learning, which not only does not require any explicit measurements, but is also drastically faster than existing methods. Our method is trained using the well-known iConnect-Ubisoft dataset of actual RTT measurements, and uses the IP address as the primary input. Performance evaluations using two different datasets show that 73.6% and 59.3% of the measurements, respectively for each dataset, are within 20% estimation error.

The aortic arch and its branches form during the third week of embryogenesis. The most common human pattern has the innominate artery, the left common carotid artery and the left subclavian artery all as separate branches. Large imaging studies have shown 70% of people have a normal branching pattern with 20% having a common origin of the innominate artery and left common carotid artery, but these studies were performed without reference to the anatomy of the aortic valve (bicuspid versus tricuspid).Bicuspid aortic valve (BAV) is the commonest congenital cardiac malformation and as the arch branching patterns are developmental in origin we decided to see if the frequency of arch variants in BAV and tricuspid aortic valve (TAV) patients differed, as this has not previously been looked at. We examined Computerised Tomographic aortograms and echocardiograms of BAV and TAV patients to assess the aortic arch branching pattern and any possible association with the valve morphology. 28 BAV and 57 TAV patients were assessed. For BAV the branching patterns were: 86% normal (24/28) and 14% abnormal (4/28), and for TAV: 70% normal (40/57) and 30% abnormal (17/57). Although this is a small study our TAV group demonstrated comparable normal/abnormal arch variants as the published literature, but the BAV group appears to have fewer arch variants. This is the first study in the literature to look at the arch branching variants when consideration of the aortic valve morphology (BAV versus TAV) is taken into account.