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Mobile-based learning provides new experience to the learners to learn anything from anywhere and anytime by using their portable or mobile device. Vast educational contents and also different media formats can be supported by the mobile devices. Access speed of those materials has also improved a lot. With this advancement, providing required content or materials in the desired format to the learner is essential to the learning management system. Also, it is very important to guide the learner based on their interest in learning. With this outset, the proposed mobile learning system helps the learners to access different courses under different levels and different specializations. The course contents are in different formats called learning objects (LO). In order to provide personalized learning experience to the learner, the system finds the learner's preferences and selects the desired learning objects. It also recommends some specializations with level to the learners to achieve higher grades.

The automatic speech recognition (ASR) is an active field of research. The performance of the ASR can be degraded due to various features like environmental noise, channel distortion and speech rate variability. The speech rate variability is one of the important features that affect the accuracy of the speech recognition system (SRS). In this research work, the speech signal is categorized as slow, normal and fast speech using features like the sound intensity level, time duration and root mean square. This paper addresses the enhancement of the performance of a SRS by applying time normalization to the speech signal. The comparison of the proposed Model and baseline syllable based SRS is done.

Big Data available in almost all departments of every organization spread throughout the globe in a huge volume and category. The issues such as data heterogeneity and advanced processing capabilities are provided solution with the proposed system. Data heterogeneity is tack-led by using the automatic schema mapping in the proposed work which is knowledge based solution. Inventive processing is achieved using ontology extraction and semantic inference in the proposed work. The solution is evaluated in terms of its performance and effective-ness with the publish/subscribe paradigm. The state of art analysis of huge volume of variety of data and sensory information is the real complexity. The Advanced Message Queuing Protocol is used in the proposed work for the state of art substance explanation of flooding IoT data to have dynamic mingling. The proposed work gives way to produce huge amount of data that can affect the working of the smart city systems that uses IoT data. To point out the reliability and summarization of the data, information model is used in the proposed work. The data size and average exchanged message time are the measures used to examine the working of the framework. A detailed assessment of the various sensors is carried out to inspect the storage data volume and computational cost for the substance explanation of the framework.

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