Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Bat-termite: A novel hybrid bio inspired routing protocol for mobile ad hoc networks(Inderscience Publishers, 2014) Manjappa, M.; Guddeti, R.M.R.In this paper, the authors have proposed a novel hybrid bio-inspired routing protocol for Mobile Ad Hoc Networks (MANETs), referred to as bat-termite, by combining the unique features of both social insect termites and mammals bats. The primary objective of the proposed work is to design an adaptive routing protocol for MANETs based on the hill building nature of the termites. The secondary objective of the proposed work is to improve the backup route maintenance of the proposed algorithm using the echo-location feature of the bats. The proposed bat-termite algorithm exhibits superior routing features such as quick route discovery, high robustness with efficient management of multiple routes and rapid route repair. The bat-termite algorithm is simulated in NS-2 and the simulation results are compared with the bio-inspired (termite and D-Termite) and non bio-inspired (AODV and AOMDV) routing protocols from the performance evaluation point of view. Copyright © 2014 Inderscience Enterprises Ltd.Item A novel sentiment analysis of social networks using supervised learning(Springer-Verlag Wien michaela.bolli@springer.at, 2014) Anjaria, M.; Guddeti, R.M.R.Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013. © 2014, Springer-Verlag Wien.Item Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework(3D Research Center 3drc@kw.ac.kr, 2014) Mukherjee, S.; Guddeti, R.M.R.Abstract: We propose a hybrid method for stereo disparity estimation by combining block and region-based stereo matching approaches. It generates dense depth maps from disparity measurements of only 18 % image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining segment boundaries using morphological filtering and connected components analysis; then determining boundaries’ disparities using sum of absolute differences (SAD) cost function. Complete disparity maps are reconstructed from boundaries’ disparities. We consider an application of our method for depth-based selective blurring of non-interest regions of stereo images, using Gaussian blur to de-focus users’ non-interest regions. Experiments on Middlebury dataset demonstrate that our method outperforms traditional disparity estimation approaches using SAD and normalized cross correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further, our method is highly parallelizable using CPU–GPU framework based on Java Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096 × 2,304). © 2014, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg.Item Communication and computation optimization of concurrent kernels using kernel coalesce on a GPU(John Wiley and Sons Ltd, 2015) Bayyapu, B.; Guddeti, R.M.R.; Raghavendra, P.S.General purpose computation on graphics processing unit (GPU) is rapidly entering into various scientific and engineering fields. Many applications are being ported onto GPUs for better performance. Various optimizations, frameworks, and tools are being developed for effective programming of GPU. As part of communication and computation optimizations for GPUs, this paper proposes and implements an optimization method called as kernel coalesce that further enhances GPU performance and also optimizes CPU to GPU communication time. With kernel coalesce methods, proposed in this paper, the kernel launch overheads are reduced by coalescing the concurrent kernels and data transfers are reduced incase of intermediate data generated and used among kernels. Computation optimization on a device (GPU) is performed by optimizing the number of blocks and threads launched by tuning it to the architecture. Block level kernel coalesce method resulted in prominent performance improvement on a device without the support for concurrent kernels. Thread level kernel coalesce method is better than block level kernel coalesce method when the design of a grid structure (i.e., number of blocks and threads) is not optimal to the device architecture that leads to underutilization of the device resources. Both the methods perform similar when the number of threads per block is approximately the same in different kernels, and the total number of threads across blocks fills the streaming multiprocessor (SM) capacity of the device. Thread multi-clock cycle coalesce method can be chosen if the programmer wants to coalesce more than two concurrent kernels that together or individually exceed the thread capacity of the device. If the kernels have light weight thread computations, multi clock cycle kernel coalesce method gives better performance than thread and block level kernel coalesce methods. If the kernels to be coalesced are a combination of compute intensive and memory intensive kernels, warp interleaving gives higher device occupancy and improves the performance. Multi clock cycle kernel coalesce method for micro-benchmark1 considered in this paper resulted in 10-40% and 80-92% improvement compared with separate kernel launch, without and with shared input and intermediate data among the kernels, respectively, on a Fermi architecture device, that is, GTX 470. A nearest neighbor (NN) kernel from Rodinia benchmark is coalesced to itself using thread level kernel coalesce method and warp interleaving giving 131.9% and 152.3% improvement compared with separate kernel launch and 39.5% and 36.8% improvement compared with block level kernel coalesce method, respectively. © 2014 John Wiley & Sons, Ltd.Item Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center(Institute of Electrical and Electronics Engineers, 2019) Sharma, N.K.; Guddeti, R.M.R.Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation. © 2019 IEEE.Item EmoWare: A context-aware framework for personalized video recommendation using affective video sequences(Institute of Electrical and Electronics Engineers Inc., 2019) Tripathi, A.; Ashwin, T.S.; Guddeti, R.M.R.With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to make optimal decisions in dynamic environments has been proven and very well conceptualized by reinforcement learning (RL). The learning characteristics of deep-bidirectional recurrent neural networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and propose EmoWare (emotion-aware), a personalized, emotionally intelligent video recommendation engine, employing a novel context-aware collaborative filtering approach, where the intensity of users' spontaneous non-verbal emotional response toward the recommended video is captured through interactions and facial expressions analysis for decision-making and video corpus evolution with real-time feedback streams. To account for users' multidimensional nature in the formulation of optimal policies, RL-scenarios are enrolled using on-policy (SARSA) and off-policy (Q-learning) temporal-difference learning techniques, which are used to train DBRNN to learn contextual patterns and to generate new video sequences for the recommendation. System evaluation for a month with real users shows that the EmoWare outperforms the state-of-the-art methods and models users' emotional preferences very well with stable convergence. © 2013 IEEE.Item Students’ affective content analysis in smart classroom environment using deep learning techniques(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Gupta, S.K.; Ashwin, T.S.; Guddeti, R.M.R.In the era of the smart classroom environment, students’ affective content analysis plays a vital role as it helps to foster the affective states that are beneficial to learning. Some techniques target to improve the learning rate using the students’ affective content analysis in the classroom. In this paper, a novel max margin face detection based method for students’ affective content analysis using their facial expressions is proposed. The affective content analysis includes analyzing four different moods of students’, namely: High Positive Affect, Low Positive Affect, High Negative Affect, and Low Negative Affect. Engagement scores have been calculated based upon the four moods of students as predicted by the proposed method. Further, the classroom engagement analysis is performed by considering the entire classroom as one group and the corresponding group engagement score. Expert feedback and analyzed affect content videos are used as feedback to the faculty member to improve the teaching strategy and hence improving the students’ learning rate. The proposed smart classroom system was tested for more than 100 students of four different Information Technology courses and the corresponding faculty members at National Institute of Technology Karnataka Surathkal, Mangalore, India. The experimental results demonstrate the train and test accuracy of 90.67% and 87.65%, respectively for mood classification. Furthermore, an analysis was performed over incidence, distribution and temporal dynamics of students’ affective states and promising results were obtained. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item UAV based cost-effective real-time abnormal event detection using edge computing(Springer, 2019) Shahzad Alam, M.S.; Natesha, B.V.; Ashwin, T.S.; Guddeti, R.M.R.Recent advancements in computer vision led to the development of a real-time surveillance system which ensures the safety and security of the people in public places. An aerial surveillance system will be advantageous in this scenario using a platform like Unmanned Aerial Vehicle (UAV) will be very reliable and can be considered as a cost-effective option for this task. To make the system fully autonomous, we require real-time abnormal event detection. But, this is computationally complex and time-consuming due to the heavy load on the UAV, which affords limited processing and payload capacity. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the large computation tasks to the cloud while keeping limited computation on-board UAV device using edge computing technique. Further, our proposed system will maintain the minimum communication between UAV and cloud. Thus it not only reduces the network traffic but also reduces the end-to-end delay. The proposed method is based on the state-of-the-art YOLO (You Only Look Once) technique for real-time object detection deployed on edge computing device using Intel neural compute stick Movidius VPU (Vision Processing Unit), and we applied abnormal event detection using motion influence map on the cloud. Experimental results demonstrate that the proposed system reduces the end-to-end delay. Further, Tiny YOLO is six times faster while processing the frames per second (fps) when compared to other state-of-the-art methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment(Institute of Electrical and Electronics Engineers, 2020) Domanal, S.G.; Guddeti, R.M.R.; Buyya, R.In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time. © 2008-2012 IEEE.Item Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks(Springer, 2020) Ashwin, A.; Guddeti, R.M.R.Predicting the students’ emotional and behavioral engagements using computer vision techniques is a challenging task. Though there are several state-of-the-art techniques for analyzing a student’s affective states in an e-learning environment (single person’s engagement detection in a single image frame), a very few works are available for analyzing the students’ affective states in a classroom environment (multiple people in a single image frame). Hence, in this paper, we propose a novel hybrid convolutional neural network (CNN) architecture for analyzing the students’ affective states in a classroom environment. This proposed architecture consists of two models, the first model (CNN-1) is designed to analyze the affective states of a single student in a single image frame and the second model (CNN-2) uses multiple students in a single image frame. Thus, our proposed hybrid architecture predicts the overall affective state of the entire class. The proposed architecture uses the students’ facial expressions, hand gestures and body postures for analyzing their affective states. Further, due to unavailability of standard datasets for the students’ affective state analysis, we created, annotated and tested on our dataset of over 8000 single face in a single image frame and 12000 multiple faces in a single image frame with three different affective states, namely: engaged, boredom and neutral. The experimental results demonstrate an accuracy of 86% and 70% for posed and spontaneous affective states of classroom data, respectively. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
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