Intrusion detection using decision tree classifier with feature reduction technique

The number of internet users and network services is increasing rapidly in the recent decade gradually. A Large volume of data is produced and transmitted over the network. Number of security threats to the network has also been increased. Although there are many machine learning approaches and methods are used in intrusion detection systems to detect the attacks, but generally they are not efficient for large datasets and real time detection. Machine learning classifiers using all features of datasets minimized the accuracy of detection for classifier. A reduced feature selection technique that selects the most relevant features to detect the attack with ML approach has been used to obtain higher accuracy. In this paper, we used recursive feature elimination technique and selected more relevant features with machine learning approaches for big data to meet the challenge of detecting the attack. We applied this technique and classifier to NSL KDD dataset. Results showed that selecting all features for detection can maximize the complexity in the context of large data and performance of classifier can be increased by feature selection best in terms of efficiency and accuracy

Self-reported health and smoking status, and body mass index: a case-control comparison based on GEN SCRIP (GENetics of SChizophRenia In Pakistan) data

Introduction Individuals with schizophrenia are at a high risk of physical health comorbidities and premature mortality. Cardiovascular and metabolic causes are an important contributor. There are gaps in monitoring, documenting and managing these physical health comorbidities. Because of their condition, patients themselves may not be aware of these comorbidities and may not be able to follow a lifestyle that prevents and manages the complications. In many low-income and middle-income countries including Pakistan, the bulk of the burden of care for those struggling with schizophrenia falls on the families.Objectives To determine the rate of self-reported physical health disorders and risk factors, like body mass index (BMI) and smoking, associated with cardiovascular and metabolic disorders in cases of schizophrenia compared with a group of mentally healthy controls.Design A case-controlled, cross-sectional multicentre study of patients with schizophrenia in Pakistan.Settings Multiple data collection sites across the country for patients, that is, public and private psychiatric OPDs (out patient departments), specialised psychiatric care facilities, and psychiatric wards of teaching and district level hospitals. Healthy controls were enrolled from the community.Participants We report a total of 6838 participants’ data with (N 3411 (49.9%)) cases of schizophrenia compared with a group of healthy controls (N 3427 (50.1%)).Results BMI (OR 0.98 (CI 0.97 to 0.99), p=0.0025), and the rate of smoking is higher in patients with schizophrenia than in controls. Problems with vision (OR 0.13 (0.08 to 0.2), joint pain (OR 0.18 (0.07 to 0.44)) and high cholesterol (OR 0.13 (0.05 to 0.35)) have higher reported prevalence in controls. The cases describe more physical health disorders in the category ‘other’ (OR 4.65 (3.01 to 7.18)). This captures residual disorders not listed in the questionnaire.Conclusions Participants with schizophrenia in comparison with controls report more disorders. The access in the ‘other’ category may be a reflection of undiagnosed disorders

Rationale, design, and baseline characteristics in Evaluation of LIXisenatide in Acute Coronary Syndrome, a long-term cardiovascular end point trial of lixisenatide versus placebo

BACKGROUND: Cardiovascular (CV) disease is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Furthermore, patients with T2DM and acute coronary syndrome (ACS) have a particularly high risk of CV events. The glucagon-like peptide 1 receptor agonist, lixisenatide, improves glycemia, but its effects on CV events have not been thoroughly evaluated. METHODS: ELIXA (www.clinicaltrials.gov no. NCT01147250) is a randomized, double-blind, placebo-controlled, parallel-group, multicenter study of lixisenatide in patients with T2DM and a recent ACS event. The primary aim is to evaluate the effects of lixisenatide on CV morbidity and mortality in a population at high CV risk. The primary efficacy end point is a composite of time to CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. Data are systematically collected for safety outcomes, including hypoglycemia, pancreatitis, and malignancy. RESULTS: Enrollment began in July 2010 and ended in August 2013; 6,068 patients from 49 countries were randomized. Of these, 69% are men and 75% are white; at baseline, the mean ± SD age was 60.3 ± 9.7 years, body mass index was 30.2 ± 5.7 kg/m(2), and duration of T2DM was 9.3 ± 8.2 years. The qualifying ACS was a myocardial infarction in 83% and unstable angina in 17%. The study will continue until the positive adjudication of the protocol-specified number of primary CV events. CONCLUSION: ELIXA will be the first trial to report the safety and efficacy of a glucagon-like peptide 1 receptor agonist in people with T2DM and high CV event risk