This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions (happy, sad, angry, fear, surprised, neutral etc..). Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations with existing system Emotion recognition using brain activity. In this paper by using an existing simulator I have achieved 97 percent accurate results and it is easy and simplest way than Emotion recognition using brain activity system. Purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper neural network has been used for better results. In the end of paper comparisons of existing Human Emotion Recognition System has been made with new one.

Main objective of this research work is to improve the checkpoint efficiency for integrated multilevel checkpointing algorithms and prevent checkpointing from becoming the bottleneck of cloud data centers. In order to find an efficient checkpoint interval, checkpointing overheads has also considered in this paper. Traditional checkpointing methods stores persistently snapshots of the present job state and use them for resuming the execution at a later time. The attention of this research is strategies for deciding when and whether a checkpoint should be taken and evaluating them in regard to minimizing the induced monetary costs. By varying rerun time of checkpoints performance comparisons are which will be used to evaluate optimal checkpoint interval.
The purposed fail-over strategy will work on application layer and provide highly availability for Platform as a Service (PaaS) feature of cloud computing.

This paper presents an methodology for providing high availability to the demands of cloud's clients. To succeed this objective, failover approaches for cloud computing using combined checkpointing procedures with load balancing algorithms are purposed in this paper. Purposed methodology assimilate checkpointing feature with load balancing algorithms and also make multilevel barrier to diminution checkpointing overheads. For execution of purposed failover approaches, a cloud simulation environment is established, which the ability to provide high availability to clients in case of disaster/recovery of service nodes. Also in this paper comparison of developed simulator is made with existing approaches. The purposed failover strategy will work on application layer and provide highly availability for Platform as a Service (PaaS) feature of cloud computing.

This paper presents a methodology for providing high availability to the demands of cloud's clients. To attain this objective, failover stratagems for cloud computing using integrated checkpointing algorithms are purposed in this paper. Purposed strategy integrate checkpointing feature with load balancing algorithms and also make multilevel checkpoint to decrease checkpointing overheads. For implementation of purposed failover strategies, a cloud simulation environment is developed, which has the ability to provide high availability to clients in case of failure/recovery of service nodes. \The primary objective of this research work is to improve the checkpoint efficiency and prevent checkpointing from becoming the bottleneck of cloud data centers. In order to find an efficient checkpoint interval, checkpointing overheads has also considered in this paper. By varying rerun time of checkpoints comparison tables are made which can be used to find optimal checkpoint interval.
The purposed failover strategy will work on application layer and provide highly availability for Platform as a Service (PaaS) feature of cloud computing.