Intrusion Detection System using the Hybrid Model of Classification Algorithm and Rule-Based Algorithm

Intrusion detection system ID is necessary to secure the system from various intrusions. Analysis of the communication to categorize the data as useful or malicious data is crucial. The cyber security employed using intrusion detection systems should not also cause the extra time to perform the categorization. Nowadays machine learning techniques are used to make the identification of malicious data or an intrusion with the help of classification algorithms. The data set used for experimenting is KDD cup 99. The effect of individual classification algorithms can be improvised with the help of hybrid classification models. This model combines classification algorithms with rule-based algorithms. The blend of classification using machine and human intelligence adds an extra layer of security. An algorithm is validated using precision, recall, F-Measure, and Mean age Precision. The accuracy of the algorithm is 92.35 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely

Preoperative A1C and clinical outcomes in patients with diabetes undergoing major noncardiac surgical procedures

To evaluate the relationship between preoperative A1C and clinical outcomes in individuals with diabetes mellitus undergoing noncardiac surgery. Data were obtained from the National Surgical Quality Improvement Program database and the Research Patient Data Registry of the Brigham and Women's Hospital. Patients admitted to the hospital for ≥1 day after undergoing noncardiac surgery from 2005 to 2010 were included in the study. Of 1,775 patients with diabetes, 622 patients (35%) had an A1C value available within 3 months before surgery. After excluding same-day surgeries, patients with diabetes were divided into four groups (A1C ≤6.5% [N = 109]; >6.5-8% [N = 202]; >8-10% [N = 91]; >10% [N = 47]) and compared with age-, sex-, and BMI-matched nondiabetic control subjects (N = 888). Individuals with A1C values between 6.5 and 8% had a hospital length of stay (LOS) similar to the matched control group (P = 0.5). However, in individuals with A1C values ≤6.5 or >8%, the hospital LOS was significantly longer compared with the control group (P < 0.05). Multivariate regression analysis demonstrated that a higher A1C value was associated with increased hospital LOS after adjustments for age, sex, BMI, race, type of surgery, Charlson Comorbidity Index, smoking status, and glucose level on the day of surgery (P = 0.02). There were too few events to meaningfully evaluate for death, infections, or readmission rate. Our study suggests that chronic hyperglycemia (A1C >8%) is associated with poor surgical outcomes (longer hospital LOS). Providing a preoperative intervention to improve glycemic control in individuals with A1C values >8% may improve surgical outcomes, but prospective studies are needed

Journal of Computer Science &amp; Systems Biology- Open Access www.omicsonline.com Research Article JCSB/Vol.2 July-August 2009 Optimizing Number of Inputs to Classify Breast Cancer Using Artificial Neural Network

Copyright: © 2009 Garg B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The Objective of this research work is to prove significant role of each attribute to decide breast cancer type using Computer Aided Diagnosis. One of major challenges in medical domain is the extraction of intelligible knowledge from medical diagnostic data in minimum time and cost This research shows that out of these attributes stated, some attributes can be ignored to decide the type Breast Cancer as if the number of inputs are less then it reduces the time and cost in analyzing the breast cancer. In this paper, significant role of each attribute is proved by experiment in matlab