Faculty Publications
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Item Learner centered design approach for E-learning using 3D virtual tutors(IEEE Computer Society help@computer.org, 2013) Mukherjee, S.; Singhal, H.; Jha, P.; Kokane, A.; Rastogi, P.; Mittal, R.; Guddeti, G.Most of the existing E-learning system designs have focused on the development of feature-rich, but usable systems with little effort in motivating students to develop interest in the teaching-learning process. This paper discusses the learner centered design approach for web-based tutoring to motivate young learners using 3D virtual tutors in a requirement-based, flexible pedagogical model. Students can choose course(s) and the study-mode. In the guided mode, the student is mentored by a human tutor, whereas a student in un-guided mode is tutored by 3D avatar. The student has access to study materials, educational videos and applets that are provided by the tutors, the student also has access to forums for doubt clearing and online assignments to be submitted for tutors' evaluation. Tutors can track students' progress using online quiz and reports modules. Further, tutors have access to teaching aids like online chat system and whiteboard-based teaching in a virtual classroom environment. © 2013 IEEE.Item Effective E-learning using 3D Virtual Tutors and WebRTC Based Multimedia Chat(Institute of Electrical and Electronics Engineers Inc., 2014) Kokane, A.; Singhal, H.; Mukherjee, S.; Guddeti, G.R.M.This paper discuss the novel implementation of Learner Centered Design Approach of E-learning System using 3D Virtual Tutors [1] and further enhances this work in facilitating young learners to interact with human tutors using WebRTC Based Multimedia Chat system. The present work adds three main features such as: Firstly, addition of live video lecture session by which students can interact with the tutor just like video call of Skype system. Secondly, the presentation of 3D virtual tutors' narrations of articles in the text form of live transcriptions of avatars' speech. Thirdly, introduction of timed quiz by which a real-life objective examination can be mimicked and thereby evaluating the performance of students. Lastly, we present a comparative evaluation of the original system and its improved version with respect to responses received from a large pool of young learners using the system. © 2014 IEEE.Item Conceptualization and Design of Remotely-Accessible Hardware Interface (RAHI) Laboratory(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Potdar, S.M.; Gupta, V.; Umesh, P.; Gangadharan, K.V.With the rising popularity of e-learning through means like Massive Open Online Courses (MOOCs), remote-triggered and virtual laboratories, new and innovative technologies for enhancing the learning experience are in demand. E-learning resources for electronics hardware are generally simulation-based, as getting access to high-end hardware is difficult for students due to cost and availability. In this paper, a novel method to create a remotely-accessible, low-cost, modular, and scalable hardware learning platform is proposed and demonstrated through a prototype. Users can interact with the system through a web interface anytime-anywhere and verify results on actual hardware through real-time visual and textual feedback and learn at their pace. The prototype demonstrates a web application hosted on a Linux-PC server interacting with a Raspberry Pi. Student activities are logged in a database for future reference and correction by instructors. The software stack used for the system is free and open-source. The prototype system was launched on a pilot run, gaining positive feedback from students and teachers. Hence, such a system can undergo comprehensive implementation in educational institutions and for the delivery of MOOCs with minimal investment for both laboratory setups and learners. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor(Elsevier Ltd, 2017) M.a, M.A.; Jaidhar, C.D.The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%. © 2017 Elsevier LtdItem Affective database for e-learning and classroom environments using Indian students’ faces, hand gestures and body postures(Elsevier B.V., 2020) Ashwin, T.S.; Guddeti, R.M.R.Automatic recognition of the students’ affective states is a challenging task. These affective states are recognized using their facial expressions, hand gestures, and body postures. An intelligent tutoring system and smart classroom environment can be made more personalized using students’ affective state analysis, and it is performed using machine or deep learning techniques. Effective recognition of affective states is mainly dependent on the quality of the database used. But, there exist very few standard databases for the students’ affective state recognition and its analysis that works for both e-learning and classroom environments. In this paper, we propose a new affective database for both the e-learning and classroom environments using the students’ facial expressions, hand gestures, and body postures. The database consists of both posed (acted) and spontaneous (natural) expressions with single and multi-person in a single image frame with more than 4000 manually annotated image frames with object localization. The classification was done manually using the gold standard study for both Ekman's basic emotions and learning-centered emotions, including neutral. The annotators reliably agree when discriminating against the recognized affective states with Cohen's ? = 0.48. The created database is more robust as it considers various image variants such as occlusion, background clutter, pose, illumination, cultural & regional background, intra-class variations, cropped images, multipoint view, and deformations. Further, we analyzed the classification accuracy of our database using a few state-of-the-art machine and deep learning techniques. Experimental results demonstrate that the convolutional neural network based architecture achieved an accuracy of 83% and 76% for detection and classification, respectively. © 2020 Elsevier B.V.Item Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework(Springer Science and Business Media B.V. editorial@springerplus.com, 2020) Ashwin, T.S.; Guddeti, R.M.R.Effective teaching strategies improve the students’ learning rate within academic learning time. Inquiry-based instruction is one of the effective teaching strategies used in the classrooms. But these teaching strategies are not adapted in other learning environments like intelligent tutoring systems, including auto tutors. In this paper, we propose an automatic inquiry-based instruction teaching strategy, i.e., inquiry intervention using students’ affective states. The proposed model contains two modules: the first module consists of the proposed framework for predicting the unobtrusive multi-modal students’ affective states (teacher-centric attentive and in-attentive states) using the facial expressions, hand gestures and body postures. The second module consists of the proposed automated inquiry-based instruction teaching strategy to compare the learning outcomes with and without inquiry intervention using affective state transitions for both an individual and a group of students. The proposed system is tested on four different learning environments, namely: e-learning, flipped classroom, classroom and webinar environments. Unobtrusive recognition of students’ affective states is performed using deep learning architectures. After student-independent tenfold cross-validation, we obtained the students’ affective state classification accuracy of 77% and object localization accuracy of 81% using students’ faces, hand gestures and body postures. The overall experimental results demonstrate that there is a positive correlation with r= 0.74 between students’ affective states and their performance. Proposed inquiry intervention improved the students’ performance as there is a decrease of 65%, 43%, 43%, and 53% in overall in-attentive affective state instances using the inquiry interventions in e-learning, flipped classroom, classroom and webinar environments, respectively. © 2020, Springer Nature B.V.Item An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting(Springer Science and Business Media Deutschland GmbH, 2021) Shetty, R.P.; Sathyabhama, A.; Pai, P.S.Accurate wind speed forecasting (WSF) has become increasingly important to overcome the adverse effects of stochastic nature of the wind on wind power generation. This paper proposes a multi-step hybrid online WSF model by combining online sequential extreme learning machine (OSELM), optimized variational mode decomposition (OVMD) and cuckoo search optimization algorithm (CSO). OVMD decomposes the wind speed series into subseries, and CSO selects the input features for each subseries. Multi-step forecasting for each subseries is performed using OSELM model optimized by CSO. Finally, the forecasting results are obtained by the aggregate calculations. The proposed model has been examined by using 10-min average wind speed data collected in monsoon and winter seasons from a supervisory control and data acquisition system of a 1.5 MW wind turbine situated in central dry zone of Karnataka, India. The results reveal that the model proposed captures the nonlinear characteristics of the wind speed in a better manner in comparison with the batch learning approach, giving accurate wind speed forecasts. This can help wind farms to estimate the wind power in a location efficiently. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item Factors influencing E-learning adoption in India: Learners' perspective(Springer, 2021) Vanitha, P.S.; Alathur, S.In the era of electronic-learning 3.0, existing dimensions related to technologies and learner are not adequately explored while discussing e-learning adoption. In the current study, technology and learner dimensions are converged to overcome this insufficiency in analysing e-learning adoption. Earlier studies have reported less about e-learning adoption in higher education through the users' lens. System parameters and learner attributes were derived from theories of information systems and literature on learning theories. To validate the research model, 704 responses were collected through a questionnaire survey from India, where e-learning is gearing up. The present article utilised Partial Least Square Structural Equation Modeling (PLS-SEM), which describes the relationship between constructs in the research model. The study identifies technology and learner dimension factors that influence e-learning adoption in developing countries like India. The study also put forward implications and policy recommendations from the findings. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Semantic segmentation of low magnification effusion cytology images: A semi-supervised approach(Elsevier Ltd, 2022) Aboobacker, S.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.Cytopathologists examine microscopic images obtained at various magnifications to identify malignancy in effusions. They locate the malignant cell clusters at a low magnification and then zoom in to investigate cell-level features at a high magnification. This study predicts the malignancy at low magnification levels such as 4X and 10X in effusion cytology images to reduce scanning time. However, the most challenging problem is annotating the low magnification images, particularly the 4X images. This paper extends two semi-supervised learning (SSL) models, MixMatch and FixMatch, for semantic segmentation. The original FixMatch and MixMatch algorithms are designed for classification tasks. While performing image augmentation, the generated pseudo labels are spatially altered. We introduce reverse augmentation to compensate for the effect of the spatial alterations. The extended models are trained using labelled 10X and unlabelled 4X images. The average F-score of benign and malignant pixels on the predictions of 4X images is improved approximately by 9% for both Extended MixMatch and Extended FixMatch respectively compared with the baseline model. In the Extended MixMatch, 62% sub-regions of low magnification images are eliminated from scanning at a higher magnification, thereby saving scanning time. © 2022 Elsevier LtdItem An Optimized Question Classification Framework Using Dual-Channel Capsule Generative Adversarial Network and Atomic Orbital Search Algorithm(Institute of Electrical and Electronics Engineers Inc., 2023) Revanesh, M.; Rudra, B.; Guddeti, R.M.R.The advancement in education has emphasized the need to evaluate the quality of the examination questions and the cognitive levels of students. Many educational institutions now acknowledge Bloom's taxonomy-based students' cognitive levels evaluating subject-related learning. Therefore, in this paper, a novel optimized Examination Question Classification framework, referred to as QC-DcCapsGAN-AOSA, is proposed by combining the Dual-channel Capsule generative Adversarial Network (DcCapsGAN) with Atomic Orbital Search Algorithm (AOSA) for preprocessing a real-time online dataset of university examination questions, thus identify the key features from the raw data using Term Frequency Inverse Document Frequency (TF-IDF) and finally classifying the examination questions. Atomic Orbital Search Algorithm is used to fine-tune the parameters' weights of the DcCapsGAN, and then uses these weights to categorize questions as Knowledge Level, Comprehension Level, Application Level, Analysis Level, Synthesis Level, and Evaluation Level. Experimental results demonstrate the superiority of the proposed method (QC-DcCapsGAN-AOSA) when compared to the state-of-the-art methods such as QC-LSTM-CNN and QC-BiGRU-CNN with an accuracy improvement of 23.65% and 29.04%, respectively. © 2013 IEEE.