Browsing by Author "Guddeti, G.R."

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A hybrid community detection based on evolutionary algorithms in social networks
(Institute of Electrical and Electronics Engineers Inc., 2016) Jami, V.; Guddeti, G.R.
In social network analysis, community detection is an optimization problem of finding out partitions of maximum modularity density from a network. It is a NP-hard problem which can be done using evolutionary algorithms such as Particle Swarm Optimization, Cat Swarm Optimization, Genetic Algorithm and Genetic Algorithm with Simulated Annealing. In this work, we proposed an algorithm based on Genetic Algorithm with Simulated annealing for not being trapped into local optimal solution which is giving more better results. The main motto of our work is to get better communities with low computation cost. We tested our proposed algorithm on three standard datasets such as Zachary's Karate Club Dataset, American College Football and Dolphin Social Network Dataset. Experimental results demonstrate that our proposed algorithm outperforms state of the art approaches. Â© 2016 IEEE.
A laboratory investigation on a single row of suspended porous pipes was conducted in a two-dimensional regular wave flume to study their hydraulic performance. The wave energy losses at the structure were computed and the effects of depth of submergence, incident wave steepness, water depth, pipe diameter, percentage of perforations, size of perforations and relative wave height on loss coefficient were studied. It was found that as incident wave steepness increases, loss coefficient K 1 increases. Water depth has insignificant effect on K 1. It is also observed that as percentage of perforations increases, K 1 increases. For the range of variables studied, as the relative wave height increases, K 1 decreases.
(Energy dissipation at single row of suspended perforated pipe breakwaters) Rao, S.; Rao, N.B.S.; Shirlal, K.G.; Guddeti, G.R.
2003
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. Â© 2015 IEEE.
Analysis of sigmoidal utility function based handover in heterogeneous wireless networks
(Institute of Electrical and Electronics Engineers Inc., 2016) Chandavarkar, B.R.; Guddeti, G.R.
Sigmoidal utility function is one of the approaches evolved in network selection during handover decisions in heterogeneous wireless networks. Many variations of the sigmoidal utility function exist in literature, with their own merits and demerits. The objective of this paper is to compare and analyse the performances of some of the popularly used sigmoidal utility functions using MATLAB. Further, common issues pertaining to these functions are addressed. Â© 2015 IEEE.
Detection and analysis model for grammatical facial expressions in sign language
(Institute of Electrical and Electronics Engineers Inc., 2016) Bhuvan, M.S.; Rao, D.V.; Jain, S.; Ashwin, T.S.; Guddeti, G.R.; Kulgod, S.P.
The proposed research explores a relatively new area of expression detection through facial points in a sign language to enhance the computer interaction with the deaf and hard of hearing. The research mainly focuses on facial points collected from Kinect as basis for expression detection as opposed to numerous gesture based studies on sign language. This helps in deploying the applications in smart phones as it is feasible to capture facial point easily rather than hand gestures. Exhaustive experimentation is carried out with ten different machine learning algorithms for detecting nine different types of expression modeled as different binary classification problem for each expression. This is done for user dependent model and user independent model scenarios. The optimal classifier for each expression is found to outperform the current state-of-the-art techniques and has ROC area greater than 0.95 for each expression. It is found that user independent model's performance is comparable to user dependent model, hence is suggested as it is easy and efficient to deploy in practical applications. Finally, the importance of each facial point in detecting each type of expression has been mined, which can be instrumental for future research and for various application using facial points as basis for decision making. Â© 2016 IEEE.
Enhanced Framework for IoT Applications on Python Based Cloud Simulator (PCS)
(Institute of Electrical and Electronics Engineers Inc., 2016) Jaiswal, A.; Domanal, S.; Guddeti, G.R.
As innovation develops, more human-made devices are able to communicate with each other by means of Internet. This enables the Internet of Things (IoT) era to emerge. The amount of information generated by IoT applications can overpower computer infrastructures which are not prepared for such a huge data hence they need more CPU cycles. Distributed computing offers a solution at infrastructure level that eases such problems by offering highly scalable computing platforms. This necessitates arranging the framework on demand to meet invariant changes which applications require, in a pay-per-use mode. Current methodologies empowering IoT applications are area specific or concentrate just on communication between devices, therefore they can not be effectively deployed to different domains. To address this issue, in this paper, we present a data centric framework for advancement of IoT applications executed in python based cloud simulator. The framework handles association with information sources, information filtering and use of cloud resources including provisioning, load balancing, and planning thus enabling developers to concentrate on the application logic and encouraging the advancement of loT applications. Â© 2015 IEEE.
Entropy-difference based stereo error detection
(Institute of Electrical and Electronics Engineers Inc., 2016) Mukherjee, S.; Cheng, I.; Guddeti, G.R.; Basu, A.
Stereo depth estimation is error-prone; hence, effective error detection methods are desirable. Most such existing methods depend on characteristics of the stereo matching cost curve, making them unduly dependent on functional details of the matching algorithm. As a remedy, we propose a novel error detection approach based solely on the input image and its depth map. Our assumption is that, entropy of any point on an image will be significantly higher than the entropy of its corresponding point on the image's depth map. In this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo depth estimates and a binary classification method to identify incorrect depths. Experiments on the Middlebury dataset show the effectiveness of our method. Our proposed stereo confidence measure outperforms 17 existing measures in all aspects except occlusion detection. Established metrics such as precision, accuracy, recall, and area-under-curve are used to demonstrate the effectiveness of our method. Â© 2016 IEEE.
Load Balancing in Cloud Environment Using a Novel Hybrid Scheduling Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2016) Domanal, S.; Guddeti, G.R.
We propose a hybrid scheduling algorithm for load balancing in a distributed environment by combining the methodology of Divide-And-Conquer and Throttled algorithms referred to as DCBT. Our algorithm plays an important role in distributing the incoming load in an efficient manner so that it maximizes resource utilization in a cloud environment. Further, load balancer plays an important role in cloud environment by assigning incoming tasks to Virtual Machines (VM) intelligently. The main aim of the proposed DCBT is to reduce the total execution time of the tasks and thereby maximizing the resource utilization. Further, the proposed DCBT algorithm is analyzed using Cloud Sim simulator and also in customized distributed environment using python. Experimental results demonstrate that the proposed algorithm gives better efficiency in both Cloud Sim and customized environments. The proposed DCBT utilizes the Virtual Machines more efficiently while reducing the execution time of the tasks allocated to Request Handlers (RH) by 9.972% in comparison to the Modified Throttled algorithm. Â© 2015 IEEE.
Mobile Ad Hoc networks: Bio-inspired quality of service aware routing protocols
(CRC Press, 2016) Guddeti, G.R.; Manjappa, M.
In recent years, a lot of work has been done in an effort to incorporate Swarm Intelligence (SI) techniques in building an adaptive routing protocol for Mobile Ad Hoc Networks (MANETs). Since centralized approach for routing in MANETs generally lacks in scalability and fault-tolerance, SI techniques provide a natural solution through a distributed approach for the adaptive routing for MANETs. In SI techniques, the captivating features of insects or mammals are correlated with the real world problems to find solutions. Recently, several applications of bio-inspired and nature-inspired algorithms in telecommunications and computer networks have achieved remarkable success. The main aims/objectives of this book, "Mobile Ad Hoc Networks: Bio-Inspired Quality of Service Aware Routing Protocols", are twofold; firstly it clearly distinguishes between principles of traditional routing protocols and SI based routing protocols, while explaining in detail the analogy between MANETs and SI principles. Secondly, it presents the readers with important Quality of Service (QoS) parameters and explains how SI based routing protocols achieves QoS demands of the applications. This book also gives quantitative and qualitative analysis of some of the SI based routing protocols for MANETs. Â© 2017 by Taylor & Francis Group, LLC. All rights reserved.
Simplified and improved multiple attributes alternate ranking method for vertical handover decision in heterogeneous wireless networks
(Elsevier, 2016) Chandavarkar, B.R.; Guddeti, G.R.
Multiple Attribute Decision Making (MADM) is one of the best candidate network selection methods used for Vertical Handover Decision (VHD) in heterogeneous wireless networks (4G). Selection of the network in MADM is predominantly decided by two steps, i.e., attribute normalization and weight calculation. This dependency in MADM results in an unreliable network selection for handover, and in a rank reversal (abnormality) problem during the removal and insertion of the network in the network selection list. Hence, this paper proposes a Simplified and Improved Multiple Attributes Alternate Ranking method referred to as SI-MAAR to eliminate the attribute normalization and weight calculation methods, thereby solving the rank reversal problem. Further, the MATLAB simulation results demonstrate that the proposed SI-MAAR method outperforms MADM methods such as TOPSIS, SAW, MEW and GRA with respect to the network selection reliability and rank reversal problems. © 2015 Elsevier B.V. All rights reserved.
Striking the Balance between Novelty and Accuracy in Location-Based Recommendation System
(Institute of Electrical and Electronics Engineers Inc., 2019) Agrawal, V.; Sahu, S.; Oommen, S.; Guddeti, G.R.
With widespread popularity of Location-Based Social Networks (LSBNs), the recommendation problem in this domain has led to significant amount of research regarding its practical applications. Despite extensive studies on recommendation systems based on parameters such as GPS trajectories, user-item ratings and check-in data, few methodologies take novelty of recommendations as a significant parameter. In this paper, we attempt to provide an improved approach to recommend points of interest (POIs) to users using a graph based approach which is in accordance with their personal interests and preferences. The proposed algorithm provides users with a personalized ranked list of venues based on their past check-in data and social relationships, which is novel yet accurate at the same time. It takes into account the existing challenges and is based on two key components: User Preferences and Social Relationships that are inferred from their past check-in history and Entropy of Venues which determines the novelty of recommendations provided. In short, it returns a ranked list of 'k' venues which are most likely to suit the personal taste of the user. Experimental results demonstrate that the proposed methodology outperforms the baseline methods in terms of novelty and accuracy. Â© 2019 IEEE.
Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues
(Institute of Electrical and Electronics Engineers Inc., 2019) Ashwin, T.S.; Guddeti, G.R.
Pervasive intelligent learning environments can be made more personalized by adapting the teaching strategies according to the students' emotional and behavioral engagements. The students' engagement analysis helps to foster those emotions and behavioral patterns that are beneficial to learning, thus improving the effectiveness of the teaching-learning process. Unobtrusive student engagement analysis is performed using the students' non-verbal cues such as facial expressions, hand gestures, and body postures. Though there exist several techniques for classifying the engagement of a single student present in a single image frame, there are limited works on the students' engagement analysis in a classroom environment. In this paper, we propose a convolutional neural network architecture for unobtrusive students' engagement analysis using non-verbal cues. The proposed architecture is trained and tested on faces, hand gestures and body postures in the wild of more than 350 students present in a classroom environment, with each test image containing multiple students in a single image frame. The data annotation is performed using the gold standard study, and the annotators reliably agree with Cohen's ? = 0.43. We obtained 71% accuracy for the students' engagement level classification. Further, a pre-test/post-test analysis was performed, and it was observed that there is a positive correlation between the students' engagement and their test performance. © 2013 IEEE.