Purpose -This paper seeks to develop a model for risk management of knowledge loss in a project-based organization in Iran. Design/methodology/approach -This study uses a multi-stage research approach. In the first stage, existing practices are examined to develop a model for risk management of knowledge loss. In the second stage, the model is evaluated by testing it in a case study. The methods integrated as the foundations of the Integrated KM and RM model are: the PMBOK risk management (RM) approach, the Fraunhofer IPK knowledge management (KM) model, and the TVA knowledge risk assessment framework. Findings -The analytical approach includes a six-step integrated model that manages the risk of critical knowledge in the case study. The results show that, after a year of implementing the model, the job positions facing knowledge loss were reduced by 88 percent.Research limitations/implications -The integrated KM and RM model can be used to assist the planning, establishment and evaluation of knowledge loss in projects. This helps to ensure that key issues regarding knowledge loss are covered during the planning and implementation phases of project management. Originality/value -This study provides an integrated perspective of KM in project-based organizations. It offers valuable guidelines that can help decision makers consider key issues during a risk assessment of knowledge factors in project management. Outputs of this model can prepare an extensive assessment report about the risk of knowledge loss in a project-based organization with suggestions for preservation plans to mitigate its effects.

Purpose -The main aim of this paper is to study the effects of organizational culture on environmental responsiveness capability (ERC), both directly and through the mediation of knowledge management (KM) in selected Iranian Industrial Research Organizations (IIRO). Furthermore, the effects of four types of organizational culture on ERC and KM in the target population are compared. Design/methodology/approach -Relationships between the ERC, KM and organizational culture are considered using survey data through the structural equation modelling approach. Five-point Likert questionnaire has been used as a tool for measuring variables. The authors sample includes 276 members of 13 selected target organizations whose names are not mentioned due to prior agreement. Findings -Results show that organizational culture has a positive and significant relationship with ERC, both directly and indirectly through the mediation of KM. Additionally, compared with other types of organizational cultures, innovativeness culture has the highest correlation with ERC, both directly and through KM as a mediating variable. Furthermore, cooperativeness culture has a direct significant relationship with ERC, whereas consistency and effectiveness cultures indirectly have significant and positive relationships with ERC through KM. Therefore, results of this research provide appropriate evidence that ERC can be affected directly by innovativeness culture and KM. Originality/value -The advantage of this paper compared to other related research is to study on ERC based on cultural and knowledge-related variables. Hence, it can extend the literature of ERC, and it can be useful for the managers who are dealing with industrial research company.

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Intrusion Detection Systems have considerable importance in preventing security threats and protecting computer networks against attackers. So far, various classification approaches using data mining and machine learning techniques have been proposed to the problem of intrusion detection. However, using single classifier systems for intrusion detection suffers from some limitations including lower detection rate for low-frequent attacks, detection instability, and complexity in training process. Ensemble classifier systems combine several individual classifiers and obtain a classifier with higher performance. In this paper, we propose a new ensemble classifier using Radial Basis Function (RBF) neural networks and fuzzy clustering in order to increase detection accuracy and stability, reduce false positives, and provide higher detection rate for low-frequent attacks. We also use a hybrid combination method to aggregate the individual predictions of the base classifiers, which helps to increase detection accuracy. The experimental results on NSL-KDD data set demonstrate that our proposed system has a higher detection accuracy compared to other wellknown classification systems. It also performs more effectively for detection of low-frequent attacks. Furthermore, the proposed ensemble method offers better performance compared to popular ensemble methods.