The purpose of this study is to show the disagreement between the clinical and pathological diagnosis. The hospital records of 150 patients with clinical diagnosis of acute appendicitis have been studied to find out the histopathological criterion , and 104 cases confirmed by histopathology study. Eighty four patients (56%) were male and 66 were female (44%). The majority of them were in 15-24 ages group. The most accurate diagnosis was in male group (81%) and in female it was 54.5%. The most acuurate diagnosis was in 25 - 34 years ages group and the lowest was in 35-54 years ages group. Highly competent, 2 surgeons make positive diagnosis of acute appendicitis and removed appendix with acute inflammation, that the accuracy of diagnosis was about 79% . The lowest accuracy of diagnosis was about 47%. I conclude that disagreement between surgeons and pathologists was happend in 26.6% of cases.

With the increasing spread of attacks on computer networks, the use of intrusion detection systems is inevitable. The purpose of an intrusion detection system is to monitor abnormal activities and to distinguish between normal and abnormal behaviors (intrusion) in a host system or in a network. One of the main problems of intrusion detection systems is the high volume of alarms, which practically eliminates the possibility of dealing with them. An intrusion detection system is effective that can detect a wide range of attacks while reducing the amount of false alarms. In this paper, a new feature-based intrusion detection approach is proposed in which the AdaBoost algorithm combines with the Harris Hawks optimization algorithm and optimized parameters. Studies show that the proposed method detects malicious samples in computer networks with an average accuracy in the CICIDS2017 dataset is 99.86% and for the NSL_KDD dataset is 99.88%; comparing the findings with similar works also indicates that the proposed method is more accurate than them in distinguishing invasive samples from normal.