Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Blockchain based Data Access Control using Smart Contracts(Institute of Electrical and Electronics Engineers Inc., 2019) Kiran, A.; Dharanikota, S.; Annappa, A.The keystone of information security has been access control. Very often, User data is misused and users are oblivious to the use of their data by unauthorized parties. Current strategies to provide storage for confidential data and subsequent authentication involve relying on a trusted third party for the same, which could be victims of Denial of Service (DoS) attacks or technical failures. This paper examines a strategy where the underlying framework for providing Access Control is the blockchain, hence decentralizing the mechanism of providing access control. Further in this paper, we demonstrate and model the User Data access on the Ethereum framework. Personal Information of the user by a website or an application is retrieved on a need-to-know basis from the off-blockchain, as determined by the user, the true owner of the data. Personal data is highly protected and the different permissions to different websites or applications are determined by the Smart Contract. © 2019 IEEE.Item A Novel Artificial Intelligence-Based Lung Nodule Segmentation and Classification System on CT Scans(Springer Science and Business Media Deutschland GmbH, 2022) Dodia, S.; Annappa, A.; Mahesh, M.A.Major innovations in deep neural networks have helped optimize the functionality of tasks such as detection, classification, segmentation, etc., in medical imaging. Although Computer-Aided Diagnosis (CAD) systems created using classic deep architectures have significantly improved performance, the pipeline operation remains unclear. In this work, in comparison to the state-of-the-art deep learning architectures, we developed a novel pipeline for performing lung nodule detection and classification, resulting in fewer parameters, better analysis, and improved performance. Histogram equalization, an image enhancement technique, is used as an initial preprocessing step to improve the contrast of the lung CT scans. A novel Elagha initialization-based Fuzzy C-Means clustering (EFCM) is introduced in this work to perform nodule segmentation from the preprocessed CT scan. Following this, Convolutional Neural Network (CNN) is used for feature extraction to perform nodule classification instead of customary classification. Another set of features considered in this work is Bag-of-Visual-Words (BoVW). These features are encoded representations of the detected nodule images. This work also examines a blend of intermediate features extracted from CNN and BoVW characteristics, which resulted in higher performance than individual feature representation. A Support Vector Machine (SVM) is used to distinguish detected nodules into benign and malignant nodules. Achieved results clearly show improvement in the nodule detection and classification task performance compared to the state-of-the-art architectures. The model is evaluated on the popular publicly available LUNA16 dataset and verified by an expert pulmonologist. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A Novel Bi-level Lung Cancer Classification System on CT Scans(Springer Science and Business Media Deutschland GmbH, 2022) Dodia, S.; Annappa, A.; Mahesh, M.A.Purpose: Lung cancer is a life-threatening disease that affects both men and women. Accurate identification of lung cancer has been a challenging task for decades. The aim of this work is to perform a bi-level classification of lung cancer nodules. In Level-1, candidates are classified into nodules and non-nodules, and in Level-2, the detected nodules are further classified into benign and malignant. Methods: A new preprocessing method, named, Boosted Bilateral Histogram Equalization (BBHE) is applied to the input scans prior to feeding the input to the neural networks. A novel Cauchy Black Widow Optimization-based Convolutional Neural Network (CBWO-CNN) is introduced for Level-1 classification. The weight updation in the CBWO-CNN is performed using Cauchy mutation, and the error rate is minimized, which in turn improved the accuracy with less computation time. A novel hybrid Convolutional Neural Network (CNN) model with shared parameters is introduced for performing Level-2 classification. The second model proposed in this work is a fusion of Squeeze-and-Excitation Network (SE-Net) and Xception, abbreviated as “SE-Xception†. The weight parameters are shared for the SE-Xception model trained from CBWO-CNN, i.e., a knowledge transfer approach is adapted. Results: The recognition accuracy obtained from CBWO-CNN for Level-1 classification is 96.37% with a reduced False Positive Rate (FPR) of 0.033. SE-Xception model achieved a sensitivity of 96.14%, an accuracy of 94.75%, and a specificity of 92.83%, respectively, for Level-2 classification. Conclusion: The proposed method’s performance is better than existing deep learning architectures and outperformed individual SE-Net and Xception with fewer parameters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Learning Engagement Assessment in MOOC Scenario(Institute of Electrical and Electronics Engineers Inc., 2022) Rashmi Adyapady, R.; Annappa, A.Engagement recognition is essential for monitoring online learning for efficient learning outcomes. By monitoring the student's engagement, the teacher will acquire timely feedback, diminish the dropout rates, and overcome educational problems. A novel Facial Engagement Analysis-Network (FEA-Net) is proposed for learning engagement assessment in Massive Open Online Courses (MOOC) scenarios. In a MOOC setting, the combination of spatio-temporal and OpenFace features fed into FEA-Net proved effective for classifying engagement levels. The proposed FEA-Net built using Depthwise Separable Convolution layer helped improve the system's performance by reducing the model complexity. The experiment results showed an improvement of 1.01% in terms of accuracy on the Dataset for Affective States in E-learning Environments (DAiSEE) dataset. © 2022 IEEE.Item FedPruNet: Federated Learning Using Pruning Neural Network(Institute of Electrical and Electronics Engineers Inc., 2022) Gowtham, L.; Annappa, A.; Sachin, D.N.Federated Learning (FL) is a distributed form of training the machine learning and deep learning models on the data spread over heterogeneous edge devices. The global model at the server learns by aggregating local models sent by the edge devices, maintaining data privacy, and lowering communication costs by just communicating model updates. The edge devices on which the model gets trained usually will have limitations towards power resource, storage, computations to train the model. This paper address the computation overhead issue on the edge devices by presenting a new method named FedPruNet, which trains the model in edge devices using the neural network model pruning method. The proposed method successfully reduced the computation overhead on edge devices by pruning the model. Experimental results show that for the fixed number of communication rounds, the model parameters are pruned up to 41.35% and 65% on MNIST and CIFAR-10 datasets, respectively, without compromising the accuracy compared to training FL edge devices without pruning. © 2022 IEEE.Item Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model(Elsevier B.V., 2022) Ambesange, S.; Annappa, A.; Koolagudi, S.G.Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated Learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy. © 2023 The Authors. Published by Elsevier B.V.