Every investor's dream is to maximize return with minimum risk. Since this is practically impossible, the target is to optimize the risk and return. Different asset classes perform differently at different points of time. The performance is affected by the business as well as other local and global macroeconomic parameters. Crude oil, real estate, gold etc. have given very high returns previously but have turned unattractive in recent times. Equity market has over a long term returned handsome benefits but is highly volatile and hence fraught with risks. The risk free investments like fixed, on the other hand, fall in the low-risk low-return category. The purpose of this study is to analyze the returns of various asset classes and correlate these with their risk characteristics in order to verify whether there is always a positive relation between risk and return across all asset classes and to find out the portfolio mix of the various asset classes corresponding to the desired return and risk.
Network security is a very important aspect for internet enabled systems. As the internet keeps developing the number of security attacks as well as their severity has shown a significant increase. The Intrusion Detection System (IDS) plays a very important role in discovering anomalies and attacks in the network. The aim of an intrusion detection system is to identify those entities that attempt to destabilize security controls that have been put in place. The field of machine learning is rapidly gaining more attention in the development of these intrusion detection systems. Machine learning techniques can be broadly classified into three broad categories: Supervised, Un-supervised and semi-supervised. The supervised learning method displays good classification accuracy for those attacks that are aready known to us. But this method requires a large amount of training data.The availability of labelled data is not only time consuming but also very expensive. The evolving field of semi-supervised learning offers a promising direction for supplementary research. Hence, in this paper we propose a semi-supervised approach for a pattern based IDS to improve performance and to reduce the false alarm rate. The experimentation is performed on KDD CUP99 dataset and we use the J48 Algorithm in order to implement the semi-supervised learning.
A Strong financial system is important for any nation to face the challenges of post globalization era. The major challenges that are faced by Indian banking industry are the role of financial instrumentation in different phases of the business cycle, the emerging compulsions of the new prudential norms and benchmarking the Indian financial system against international standards and best practices. These challenges can be met with efficient human resource management. Human resource management (HRM) practices are being increasingly considered as major contributory factors in financial performance of commercial banks. This research study aims at knowing the HRM practices in commercial banks operating in India and to correlate bank’s performance and HRM practices. The study also identifies the HR challenges faced by the banks and while concluding the research the study suggests improving managerial efficiency and excellence in commercial banks which can be achieved through HRM practices.
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