Browsing by Author "Kumar, M.V.M."

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On predicting the frequent execution patterns in information systems
(2017) Kumar, M.V.M.; Thomas, L.; Annappa, B.
Process mining research discipline offers a spectrum of techniques for analysing event logs. Event logs represent the history of process execution. This information can be used for monitoring, analysing and improving the operational processes. The currently available methods in process mining emphasise on constructing the static process model. These models depict various dimensions of the process under analysis. But, models can only represent the past execution history and can't be used to guide and control the prospectus execution of the process. There is a need for the methods and techniques which guide the future execution of process in the light of recorded information. This paper introduces a technique for identifying and predicting the frequent control-flow execution patterns in information systems. The proposed Position Weight Matrix proven to be efficient during experimentation and validation studies. � 2017 IEEE.
An online decision support system for recommending an alternative path of execution
(2017) Thomas, L.; Kumar, M.V.M.; Annappa
Traditional process execution follow the path of execution drawn by the process analyst without observing the behaviour of resource and other real time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: yMiner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique for identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour yMiner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimized by 59% by decreasing their load. � 2017 IEEE.
Smartphone based emotion recognition and classification
(2017) Sneha, H.R.; Rafi, M.; Kumar, M.V.M.; Thomas, L.; Annappa, B.
This paper proposes a method that classifies the emotion status of a human being based on one's interactions with the smart phone. Due to one or the other practical limitations, the existing set of emotion recognition methods are difficult to use on daily basis (most of the known methods cause inconvenience to user since they require devices like wearable sensors, camera, or answering a questionnaire). The essence of this paper is to analyze the textual content of the message and user typing behavior to build a classifier that efficiently classifies the future instances. Each observation in the data set consists of 14 features. A machine learning technique called Naive Bayes classifier is applied to construct the classifier. Method proposed is capable of classifying emotions in one of the seven classes (anger, disgust, happy, sad, neutral, surprised, and fear). Experimental result has shown 72% accuracy in classification. � 2017 IEEE.