Cloud computing, also known as on-demand computing, provides different kinds of services for the users. As the name suggests, its increasing demand makes it prone to various intruders affecting the privacy and integrity of the data stored in the cloud. To cope with this situation, intrusion detection systems (IDS) are implemented in the cloud. An effective IDS constitutes of less time-consuming algorithm with less space complexity and higher accuracy. To do so, the number of features are reduced while maintaining minimal loss of information. In this paper, the authors have proposed a model by which the features are selected on the basis of mutual information gain among correlated features. To achieve this, they first group the features according to the correlativity. Then from each group, the features with the highest mutual information gain in their respective groups are selected. This led them to a reduced feature set which provides quick learning and thus produces a better IDS that would secure the data in the cloud.

Speech recognition in multilingual scenario is not trivial in the case when multiple languages are used in one conversation. Language must be identified before we process speech recognition as such tools are language-dependent. We present a language identification system (or AI tool) to distinguish top-seven world languages namely Chinese, Spanish, English, Hindi, Arabic, Bangla and Portuguese [G. F. Simons and C. D. Fennig (eds.), Ethnologue: Laguage of the Americas and the Pacific, Twentieth Edn. (SIL Internatinal, 2017)]. The system uses linear predictive coefficients-based feature, i.e. the line spectral pair–grade ratio (LSP–GR) feature, and ensemble learning for classification. Experiments were performed on more than 200[Formula: see text]h of real-world YouTube data and the highest possible accuracy of 96.95% was received. The results can be compared with other machine learning classifiers.

Speech recognition has established a strong bond with various technological boons for the day to day life of the rustics across the continents. Such advances have not yet propagated to the grassroot level of India, one of the reasons for it being the multilingual nature of our country. We are habituated in using multiple languages while talking, which makes the task of speech recognition challenging thereby making Language Identification an important task. The technique of automatically identifying language from spoken phrases is termed as Automatic Language Identification. The problem of multilingual speech further elevates for South Indian languages which at times become very difficult to distinguish with negligible prior knowledge. In this paper, an Automatic Language Identification System is proposed to distinguish the 4 Dravidian languages which are also known as South Indian languages due to their pre dominant use in the South Indian subcontinent. Dataset size ranged up to the size of 12224 clips and a highest accuracy of 96.46% was obtained by using a newly proposed Line Spectral Pair-Grade (LSP-G) feature along with FURIA based classification technique.