Purpose Enterprise risk management (ERM) is a risk management approach that calls for integrating all the organization-wide risks and takes a portfolio view point of managing organizational risks. The purpose of this paper is to investigate the factor that influence a firm’s decision to adopt ERM. Design/methodology/approach The authors employ a particular technique of survival data analysis, the Cox proportional hazards model, to investigate the factors that lead towards the decision of initiating an ERM programme. The authors constructed a unique sample of French firms derived from the information in 315 corporate news announcements for the hiring of a chief risk officer and information retrieved from publicly available annual reports to identify firms that initiated an ERM programme, over the period from year 1999 to 2008. Findings The results suggest that besides the growing international and local regulatory pressure, factors that are internal to the organizations like the expected probability of financial distress and its explicit and implicit costs, poor earnings performance and the existence of growth opportunities play vital role in motivating firms to adopt ERM. It was also found that corporate governance practices such as the independence of the board may also lead towards an initiation of the ERM. Originality/value This study makes theoretical and methodological contribution the ERM literature by employing a novel methodology and presenting empirical evidence based on data form French firms.

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Software Defined Networking (SDN) as an innovative network paradigm that separates the management and control planes from the data plane of forwarding devices by implementing both the management and control planes at a logically centralized entity, called controller. Therefore, it ensures simple network management and control. However, due to several reasons (e.g., deployment cost, fear of downtime) organizations are very reluctant to adopt SDN in practice. Therefore, a viable solution is to replace the legacy devices by SDN devices incrementally. This results in a new network architecture called hybrid SDN. In hybrid SDN, both SDN and legacy devices operate in such a way to achieve the maximum benefit of SDN. The legacy devices are running a traditional protocol and SDN devices are operating using Open-flow protocols. Network policies play an essential role to secure the entire network from several types of attacks like unauthorized access and port/protocol control. In a hybrid SDN, policy implementation is a tedious task that requires extreme care and attention due to the hybrid nature of network traffic. Network policies may be implemented at various positions in hybrid SDN, e.g., near the destination or source node, and at the egress or ingress ports of a router. Each of these schemes has some trade-offs. For example, if policies are implemented near the source nodes then each packet generated from the source must pass through the filter and, thus, requires more processing power, time, resources, etc. Similarly, if policies are installed near the destination nodes, then a lot of unwanted traffic generated causing network congestion. This is an NP-hard problem. To address these challenges, we propose a systematic design approach to implement network policies optimally by using decision tree and K-partite graph. By traversing all the policies, we built up the decision tree that identifies which source nodes can communicate with which destination. Then, we traverse the decision tree and constructs K-partite graph to find possible places (interfaces of the routers) where ACL policies are to be implemented based on the different criteria (i.e., the minimum number of ACL rules and the minimum number of transmissions for unwanted traffic). The edge weight represents the cost per criteria. Then, we traverse the K-partite graph to find the optimal place for ACL rules implementation according to the given criteria. The simulation results indicate that the proposed technique outperforms existing approaches in terms of computation time, traffic optimization and successful packet delivery, etc. The results also indicate that the proposed method improves network performance and efficiency by decreasing network congestion and providing ease of policy implementation.

Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

Purpose This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection. Design/methodology/approach Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam). Findings Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%. Originality/value This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.