Browsing by Author "Raghu, G."

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    An E-Learning System with Multifacial Emotion Recognition Using Supervised Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2016) Ashwin, T.S.; Jose, J.; Raghu, G.; Guddeti, G.R.
    E-Learning systems based on Affective computingare popularly used for emotional/behavioral analysis of the users. Emotions expressed by the user is depicted by detecting the facialexpression of the user and accordingly the teaching strategies willbe changed. The present eLearning systems mainly focus on thesingle user face detection. Hence, in this paper, we proposemultiuser face detection based eLearning system using supportvector machine based supervised machine learning technique. Experimental results demonstrate that the proposed systemprovides the accuracy of 89% to 100% w.r.t different datasets(LFW, FDDB, and YFD). Further, to improve the speed ofemotional feature processing, we used GPU along with the CPUand thereby achieve a speedup factor of 2. © 2015 IEEE.
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    An E-Learning System with Multifacial Emotion Recognition Using Supervised Machine Learning
    (2016) Ashwin, T.S.; Jose, J.; Raghu, G.; Ram Mohana Reddy, Guddeti
    E-Learning systems based on Affective computingare popularly used for emotional/behavioral analysis of the users. Emotions expressed by the user is depicted by detecting the facialexpression of the user and accordingly the teaching strategies willbe changed. The present eLearning systems mainly focus on thesingle user face detection. Hence, in this paper, we proposemultiuser face detection based eLearning system using supportvector machine based supervised machine learning technique. Experimental results demonstrate that the proposed systemprovides the accuracy of 89% to 100% w.r.t different datasets(LFW, FDDB, and YFD). Further, to improve the speed ofemotional feature processing, we used GPU along with the CPUand thereby achieve a speedup factor of 2. � 2015 IEEE.
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    Memory-based load balancing algorithm in structured peer-to-peer system
    (2018) Raghu, G.; Sharma, N.K.; Domanal, S.G.; Ram Mohana Reddy, Guddeti
    There are several load balancing techniques which are popular used in Structured Peer-to-Peer (SPTP) systems to distribute the load among the systems. Most of the protocols are concentrating on load sharing in SPTP Systems that lead to the performance degeneration in terms of processing delay and processing time due to the lack of resources utilization. The proposed work is related to the sender-initiated load balancing algorithms which are based on the memory. Further to check the performance of the proposed load balancing algorithm, the experimental results carried out in the real-time environment with different type of network topologies in distributed environment. The proposed work performed better over existing load balancing algorithm such as Earliest Completion Load Balancing (ECLB) and First Come First Serve (FCFS) in terms of processing delay and execution time. � Springer Nature Singapore Pte Ltd. 2018.
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    Memory-based load balancing algorithm in structured peer-to-peer system
    (Springer Verlag service@springer.de, 2018) Raghu, G.; Sharma, N.K.; Domanal, S.G.; Guddeti, G.
    There are several load balancing techniques which are popular used in Structured Peer-to-Peer (SPTP) systems to distribute the load among the systems. Most of the protocols are concentrating on load sharing in SPTP Systems that lead to the performance degeneration in terms of processing delay and processing time due to the lack of resources utilization. The proposed work is related to the sender-initiated load balancing algorithms which are based on the memory. Further to check the performance of the proposed load balancing algorithm, the experimental results carried out in the real-time environment with different type of network topologies in distributed environment. The proposed work performed better over existing load balancing algorithm such as Earliest Completion Load Balancing (ECLB) and First Come First Serve (FCFS) in terms of processing delay and execution time. © Springer Nature Singapore Pte Ltd. 2018.