The development of effective techniques has been an urgent demand in the field of the cyber security community. Machine learning for cyber security has become an issue of great importance recently due to the effectiveness of machine learning in cyber security issues. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analysing the information for possible security problems.  Our main goal is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.

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Background: Acute intestinal obstruction is one of common abdominal emergency and is associated with significant morbidity and mortality, especially if it progresses to bowel ischemia. The aims of this study was to analyse various modes of presentation of acute intestinal obstruction in both children and adult age group, etiopathogenesis, various therapeutic modalities of treatment and to accomplish operative management and anticipate the post operative complications.Methods: 50 consecutive patients of all age groups presenting with acute intestinal obstruction were admitted were taken randomly and managed between October 2013 to September 2015.Out of these 50 cases, 36 were male, 14 were female, patients with subacute intestinal obstruction and patients with paralytic ileus were excluded in this study. Plain X-ray erect abdomen was done in all cases. Ultrasonography was done only in those patients whose X-ray finding was inconclusive. CT as a modality also included for work up.Results: Mean age distribution was 35.4years and the standard deviation measured 24.57. Incidence in male (36) was more as compared to female population (14). Pain abdomen was found in 45(90%) patients, vomiting in 35(70%) patients, distension abdomen in 34 (68%) patients and constipation in 30 (60%) patients were noted as many patients had coincidence of symptoms. Commonest cause of acute intestinal obstruction noted was postoperative adhesions.Conclusions: Postoperative adhesions were the commonest cause of obstruction. Earlier the presentation, the better the outcome was found.

Clustering <span>is a significant idea for extending the scalability and enhancing the energy in the mobile ad-hoc network (MANET). In addition, the clustering concept is used to diminishes the cost of communication. The re-clustering procedure makes expensive, and frequent re-clustering procedure makes extra routing overhead and extra energy utilization. To solve these issues, received signal strength indication (RSSI) based clustering and aggregating data (RCAD) using Q-learning in MANET is proposed. In this approach, we build the clusters by node RSSI. The fuzzy logic system (FLS) is used to select the cluster head (CH) by the node mobility and node utilization energy. Q-learning-based data-aggregation for improving mobile node routing efficiency in MANET. Here, we can find an optimum next-hop node utilizing their Q-values established on the rewards (RD). Since the RD rule is used to decide the best solution for the Q-learning technique. This RD is computed by present bandwidth (PB), present energy (PE), present packet delivery (PDD), and hop count (HC) parameter for selecting the data aggregator from sender to receiver. The experimental outcomes illustrate that the RCAD approach increases 155 CH round and raises 24% cluster lifetime in the MANET.</span>

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