The dramatically development of internet, Security of network traffic is becoming a major issue of computer network system. Attacks on the network are increasing day-by-day. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent intrusion detection. In this paper, We propose a hybrid fuzzy rough with Naive bayes classifier, Support Vector Machine and K-nearest n...

We have previously described a hybrid keyword search that combines metadata search with a traditional keyword search over unstructured context data. This hybrid search paradigm provides the inquirer additional options to narrow the search with some semantic aspect from the XML metadata query. But in earlier work, we experienced the scalability limitations of a single-machine implementation. In ...

In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in many domains, present machine-human (hybrid) algorithms that screen items efficiently and estimate the gain over human-only or machine-only screening in terms of performance and cost.

Machine translation (MT) from English to Portuguese has not typically received much attention in existing research. In this paper, we focus on MT from English to Portuguese for the specific domain of information technology (IT), building a small in-domain parallel corpus to address the lack of IT-specific and publicly-available parallel corpora and then adapted an existing hybrid MT system to t...

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). This paper presents two hybrid approaches for modeling IDS. Decision trees (DT) and support vector machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT–SVM) and an ensemble approach combining the base ...

This paper presents two hybrid architectures combining rulebased and statistical machine translation (RBMT and SMT) technology. In the first case, several existing MT engines are combined in a multiengine setup, and a decoder for SMT is used to select and combine the best expressions proposed by different engines. The other architecture uses lexical entries found using a combination of SMT tech...

Machine learning methods used for decision support must achieve (a) high accuracy of decisions they recommend, and (b) deep understanding of decisions, so decision makers could trust them. Methods for learning implicit, non-symbolic knowledge provide better predictive accuracy. Methods for learning explicit, symbolic knowledge produce more comprehensible models. Hybrid machine learning models c...

A third generation plasma immersion ion implanter dedicated to biomedical materials and research has been designed and constructed. The distinct improvement over first and second generation multipurpose plasma immersion ion implantation equipment is that hybrid and combination techniques utilizing metal and gas plasmas, sputter deposition, and ion beam enhanced deposition can be effectively con...

Current machine algorithms for analysis of unstructured data typically show low accuracies due to the need for human-like intelligence. Conversely, though humans are much better than machine algorithms on analyzing unstructured data, they are unpredictable, slower and can be erroneous or even malicious as computing agents. Therefore, a hybrid platform that can intelligently orchestrate machine ...

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior...