1. Faculty Publications
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Item Preface(2019) Wang J.; Ram Mohana Reddy, Guddeti; Kamakshi Prasad V.; Sivakumar Reddy V.[No abstract available]Item Multimodal group activity state detection for classroom response system using convolutional neural networks(2019) Sebastian A.G.; Singh S.; Manikanta P.B.T.; Ashwin T.S.; Ram Mohana Reddy, GuddetiHuman–Computer Interaction is a crucial and emerging field in computer science. This is because computers are replacing humans in many jobs to provide services. This has resulted in the computer being needed to interact with the human in the same way as the human does with another. When humans talk to each other, they gain feedback based on how the other person responds non-verbally. Since computers are now interacting with humans, they need to be able to detect these facial cues and accordingly adjust their services based on this feedback. Our proposed method aims at building a Multimodal Group Activity State Detection for Classroom Response System which tries to recognize the learning behavior of a classroom for providing effective feedback and inputs to the teacher. The key challenges dealt here are to detect and analyze as many students as possible for a non-biased evaluation of the mood of the students and classify them into three activity states defined: Active, passive, and inactive. © Springer Nature Singapore Pte Ltd. 2019Item A novel real-time face detection system using modified affine transformation and Haar cascades(2019) Sharma R.; Ashwin T.S.; Ram Mohana Reddy, GuddetiHuman Face Detection is an important problem in the area of Computer Vision. Several approaches are used to detect the face for a given frame of an image but most of them fail to detect the faces which are tilted, occluded, or with different illuminations. In this paper, we propose a novel real-time face detection system which detects the faces that are tilted, occluded, or with different illuminations, any difficult pose. The proposed system is a desktop application with a user interface that not only collects the images from web camera but also detects the faces in the image using a Haar-cascaded classifier consisting of Modified Census Transform features. The problem with cascaded classifier is that it does not detect the tilted or occluded faces with different illuminations. Hence to overcome this problem, we proposed a system using Modified Affine Transformation with Viola Jones. Experimental results demonstrate that proposed face detection system outperforms Viola–Jones method by 6% (99.7% accuracy for the proposed system when compare to 93.5% for Voila Jones) with respect to three different datasets namely FDDB, YALE and “Google top 25 ‘tilted face’” image datasets. © Springer Nature Singapore Pte Ltd. 2019Item A novel hybrid algorithm for overlapping community detection in social network using community forest model and nash equilibrium(2019) Sarswat A.; Ram Mohana Reddy, GuddetiOverlapping community detection in social networks is known to be a challenging and complex NP-hard problem. A large number of heuristic approaches based on optimization functions like modularity and modularity density are available for community detection. However, these approaches do not always give an optimum solution, and none of these approaches are able to clearly provide a stable overlapping community structure. Hence, in this paper, we propose a novel hybrid algorithm to detect the overlapping communities based on the community forest model and Nash equilibrium. In this work, overlapping community has been detected using backbone degree and expansion of the community forest model, and then a Nash equilibrium is found to get a stable state of overlapping community arrangement. We tested the proposed hybrid algorithm on standard datasets like Zachary’s karate club, football, etc. Our experimental results demonstrate that the proposed approach outperforms the current state-of-the-art methods in terms of quality, stability, and less computation time. © Springer Nature Singapore Pte Ltd. 2019Item Role of intensity of emotions for effective personalized video recommendation: A reinforcement learning approach(2018) Tripathi A.; Manasa D.G.; Rakshitha K.; Ashwin T.S.; Ram Mohana Reddy, GuddetiDevelopment of artificially intelligent agents in video recommendation systems over past decade has been an active research area. In this paper, we have presented a novel hybrid approach (combining collaborative as well as content-based filtering) to create an agent which targets the intensity of emotional content present in a video for recommendation. Since cognitive preferences of a user in real world are always in a dynamic state, tracking user behavior in real time as well as the general cognitive preferences of the users toward different emotions is a key parameter for recommendation. The proposed system monitors the user interactions with the recommended video from its user interface and web camera to learn the criterion of decision-making in real time through reinforcement learning. To evaluate the proposed system, we have created our own UI, collected videos from YouTube, and applied Q-learning to train our system to effectively adapt user preferences. © Springer Nature Singapore Pte Ltd. 2018Item Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues(2019) Ashwin, T.S.; Ram Mohana Reddy, GuddetiPervasive 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.Item UAV based cost-effective real-time abnormal event detection using edge computing(2019) Alam, M.S.; Natesha, B.V.; Ashwin, T.S.; Ram Mohana Reddy, GuddetiRecent 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 Students affective content analysis in smart classroom environment using deep learning techniques(2019) Gupta, S.K.; Ashwin, T.S.; Ram Mohana Reddy, GuddetiIn 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 Simplified and improved multiple attributes alternate ranking method for vertical handover decision in heterogeneous wireless networks(2016) Chandavarkar, B.R.; Ram Mohana Reddy, GuddetiMultiple 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.Item Simplified and Improved Analytical Hierarchy Process Aid for Selecting Candidate Network in an Overlay Heterogeneous Networks(2015) Chandavarkar, B.R.; Ram Mohana Reddy, GuddetiAnalytical hierarchy process (AHP) is one of the pairwise comparison, attributes weight calculation approach of multiple attribute decision making aid to select the candidate network for seamless handoff in an overlay heterogeneous network. The main challenging issue in AHP is manually computing the reciprocal matrix results in an inconsistency indicated by the consistency ratio >0.1. This paper proposes a simplified and improved AHP (SI-AHP), which accepts the perceived one-dimensional linguistic values of the attributes from the decision maker. Further, SI-AHP is used to automatically compute the reciprocal matrix for the attribute weights calculation with the minimum involvement of the decision maker resulting in reduced computational time and improved consistency. The consistency ratio of SI-AHP is further improved by deriving the reciprocal matrix of pairwise comparison of any one of the attribute to others. Using the MATLAB simulations, the proposed SI-AHP is evaluated for the consistency ratio of voice and download traffic and also for 78,125 different combinations of one-dimensional linguistic values of the attributes. SI-AHP s weight calculated for the decision attributes is used in the multiple attribute decision making approach for selecting the candidate network in an overlay heterogeneous network. 2015, Springer Science+Business Media New York.