Background and Objective:Therapeutic Plasma Exchange (TPE) is performed effectively and safely in adult patients, but the use of TPE is limited in paediatric patients due to lack of universally accepted indications and technical challenges like establishment of adequate vascular access, low blood volume, increased incidence of adverse events during procedure and poor co-operation of patients during procedure. We present our experience of TPE in paediatric patients to assess the effectiveness and safety of TPE in paediatric patients.Materials and Methods:A total 122 TPE procedures were performed in 40 paediatric patients between 3 to 15 years of age group with Guillain Barre Syndrome (GBS). TPE procedures were performed on alternate days depending on the clinical condition of the patient. Patient's total blood volume was calculated as per Nadler's formula and processed through central double lumen catheter. 1-1.5 plasma volume was exchanged with normal saline and fresh frozen plasma.Results:A total of 122 TPE procedures (with an average of three procedures per patient) were performed on 40 paediatric patients. More than three TPE procedures were performed in 29 patients, of which 27 patients showed improvement from grade-0 and grade-I to grade-III. One did not show any response and succumbed to the disease. Complications were observed in 14 patients which were well managed. Inadequate vascular access was most common complication observed in 11 patients.Conclusion:TPE in paediatric patients has been increasing and has been shown to be effective as first line or adjunctive therapy in selected diseases. It is safe procedure when volume shifts, calcium supplementation and venous access are taken care.

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All the features in a dataset are not crucial in consideration some are either redundant or irrelevant. An effective dimensionality reduction technique is feature selection. It is needed for clustering of web document. The primary relevance of a safe network is Intrusion detection system. The is false alarm report of intrusion to the network as well as intrusion detection accuracy that happens due to the huge size of network data are problems of these security systems. This paper comes up with a new reliable hybrid method for an anomaly network-based IDS using Hybrid Feature selection as well as AdaBoost algorithms. They are used to achieve a high detection rate (DR) with low false positive rate (FPR). Hybrid Feature selection algorithm is used for feature selection. AdaBoost are used not only to evaluate but also to categories the features. The simulation result on NSL-KDD dataset makes sure that this reliable hybrid method has a compelling divergence from other IDS. The accuracy as well as detection rate of this method has been improved in comparison with legendary methods. Keywords: Hybrid Feature Selection, AdaBoost , Anomaly IDS, Network IDS, Dataset. I. INTRODUCTIONThis with the fast growth of information technology as well as network, people can grab more data from the network. The data characteristics dimension is becoming more and more. The classification accuracy of the intrusion detection is improved by the comprehensive information of the data. The security of network activities highly considered in the computer networks due to increase in Internet attacks. all abnormal patterns as well as should be identified by an IDS. It uses monitoring, detecting as well as responding to unauthorized activities within the system. The data is categorized into two main classes such as network-based as well as host-based. All packets in the network are checked by network-based IDS. This detection system can monitor traffic only on a specific part of the network. Host-based IDS is set up on either a local machine or system to collect information about machine host activities. In misuse detection method, the detection system tries to identify the patterns similar to patterns which are present in the database. It also finds out the known intrusions. In such a scenario, new attacks cannot be detected in the network because of no patterns exist in a database [3]. As a result, this method has high-accuracy rate as well as low false alarm rate. The decisions are made based on network normal behavior or features in case of anomaly detection method. As shown in Fig. 1, this new approach made up of the different main component such as Different attacks selection and definition, Hybrid computer network topology design, Anomaly based IDS technique selection, appropriate algorithm selection and definition to improve anomaly network-based IDS behavior, Appropriate dataset selection.