Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A Novel Islanding Detection Method Based on Transfer Learning Technique Using VGG16 Network(Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Gaonkar, D.N.The escalating need for energy in the recent times is unprecedented, which is driving the penetration of renewable energy sources in distribution system in a big way. The growing number of renewable sources in a system has made the control, operation and protection of the system very complex. Among others, one of the key issues in seamless interconnection of renewable energy sources to a system is islanding. This paper proposes a new and efficient islanding detection method that employs transfer learning based technique. The results show that the proposed method can successfully classify islanding events with a good accuracy. © 2019 IEEE.Item Power Quality Event Classification Using Transfer Learning on Images(Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Santhosh, J.; Sreckala, S.P.K.; Gangwani, S.; Gaonkar, D.N.Given the ever-increasing complexity of the electrical grid system, power quality events have been surging in frequency with each passing day. Due to their potential to cause massive losses for a wide variety of customers, it is crucial that such events are detected and classified immediately for appropriate response. in this paper, a novel approach has been developed wherein Transfer Learning techniques have been employed to classify power quality events using image classification. More specifically, the VGG16 model has been utilized to classify five distinct power quality issues by using scalograms as input images. 489 scalograms were generated via feature extraction using wavelet transforms. The VGG16 model has then been trained and tested using the same. Thereafter, the model performance has been evaluated, and the results have been discussed. © 2019 IEEE.Item Isolated Kannada Character Recognition using Densely Connected Convolutional Network(Institute of Electrical and Electronics Engineers Inc., 2022) Sandhya, S.; Geetha, V.Handwritten Character Recognition and Identification are one of the most interesting problem statements in the present digitized world because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. Recognition of handwritten characters is challenging due to various reasons like high variance in the writing styles across the globe, poor quality of the handwritten text compared to the printed text and the size of the handwritten text. Kannada language has a history of over 1000 years. Kannada vowels and consonants are curvy and symmetric in nature and hence recognition in an offline system becomes difficult. Hence, recognition of Handwritten Kannada characters effectively serves as the main objective of this work. This work proposes a DenseNet121 based Character Recognition model that effectively recognizes the Handwritten Kannada characters. Transfer Learning is used to improve the overall performance of the model. The proposed model achieved a training accuracy of 96.7% and test accuracy of 96.28%, hence proving the effectiveness of the model. © 2022 IEEE.Item Identifying Similar Questions in the Medical Domain Using a Fine-tuned Siamese-BERT Model(Institute of Electrical and Electronics Engineers Inc., 2022) Merchant, A.; Shenoy, N.; Bharali, A.; Anand Kumar, A.M.A large number of people search about their health related problems on the web. However, the number of sites with qualified and verified people answering their queries is quite low in comparison to the number of questions being put up. The rate of queries being searched on such sites has further increased due to the COVID-19 pandemic. The main reason people find it difficult to find solutions to their queries is due to ineffective identification of semantically similar questions in the medical domain. For most cases, answers to the queries people ask would be present, the only caveat being the question may be present in a different form than the one asked by the particular user. In this research, we propose a Siamese-based BERT model to detect similar questions using a fine-tuning approach. The network is fine-tuned with medical question-answer pairs and then with question-question pairs to get a better question similarity prediction. © 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.Item Classification of Micro-Moment-Based Anomalous Power Consumption Using Transfer Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Nayak, R.; Jaidhar, C.D.The identification of unusual power usage in buildings is crucial for improving energy efficiency. Using an electrical consumption monitoring system can help with energy conservation by identifying unusual energy consumption patterns. This paper suggests a micro-moment-based methodology for detecting abnormal power use. This study makes use of a benchmark dataset called SimDataset, which is used in most of the micro-moment classification-related works. On the images created from the dataset labeled with two classes and five classes, binary and multi-class classifications have both been used. Transfer learning is used by employing pre-trained CNN models, namely DenseNet121, ResNet50V2, and Xception model. The results depicted that the DenseNet121 model has outperformed all other models by giving the best accuracy of 99% and F1-score of 0.984. © 2023 IEEE.Item Transfer Learning Based Model for Colon Cancer Prediction Using VGG16(Institute of Electrical and Electronics Engineers Inc., 2023) Koppad, S.; Annappa, B.; Acharjee, A.Colon cancer, or a colorectal cancer, is a malignant neoplasm that originates in the colon. It is one of the most prevalent forms of cancer globally, with significant impacts on morbidity and mortality rates. The essential task is to detect it and detect it at an initial phase for curing the patient precisely. The artificial intelligence plays important roles in the colon cancer prediction. The authors proposed various models on colon cancer prediction using ML and DL. The existing approaches are unable to achieve good accuracy for the colon cancer prediction. This research work suggests a transfer learning based framework for the colon cancer prediction. This framework is planned on the basis of VGG16 and CNN in colon cancer prediction. The proposed framework is implemented in python and results is analysed concerning accuracy, precision, recall. © 2023 IEEE.Item Seismic Image Retrieval and Classification with Novel Slice Shuffling Data Augmentation(Institute of Electrical and Electronics Engineers Inc., 2024) Gowhar, S.G.; Agrawal, S.; Reddy, M.S.; Anand Kumar, M.This paper introduces a framework for automating the analysis of seismic images, which is essential for geological studies. Leveraging the intersection of Computer Vision and Geology, it addresses the scarcity of automated solutions in this domain. Developed within the Reflection Connection challenge, the framework facilitates query-based retrieval of seismic images and identifying structural features. Techniques such as fine-tuning pre-trained architectures and employing one-shot classification methods were explored. Additionally, a novel data augmentation method, Slice Shuffling Augmentation for Geological features that enhanced the model performance was developed. © 2024 IEEE.Item Semantic Segmentation of Remotely Sensed Images using Multisource Data: An Experimental Analysis(Institute of Electrical and Electronics Engineers Inc., 2024) Putty, A.; Annappa, B.; Prajwal, R.; Pariserum Perumal, S.P.Remotely sensed data obtained from diverse sensors provide rich information for a wide range of applications in remote sensing, such as land use and land cover mapping. Due to the availability of a large amount of data, advanced deep-learning techniques have been incorporated into this domain. However, these techniques require a significant amount of annotated data, which can be challenging to obtain for land-use and land-cover mapping. Multisource data fusion has become crucial in remotely sensed image analysis to overcome this challenge, providing significant benefits across various applications. This paper analyzes the fusion of multisource data tailored for land-use and land-cover mapping. The analysis showcases that incorporating the novel knowledge transfer approach from multisource data has helped to achieve a 1-6% improvement in mIoU for the Kaggle Aerial Image dataset. © 2024 IEEE.Item An Ensemble Deep Learning Approach for Emotion Monitoring System in Online Examinations(Institute of Electrical and Electronics Engineers Inc., 2024) Bhardwaj, S.; Ramu, S.; Guddeti, R.M.R.Around the world, a large number of students experience difficult life situations that have an effect on their emotional and mental health and, ultimately, their academic performance in examinations (exams in short). Emotions have an effect on a student's motivation, focus, and memory, and finally how they perform in exams. An Emotion Monitoring System could be really helpful for understanding how students are feeling during exams and how it affects their overall performance. The contribution of this paper involves in designing a novel facial emotion tracking system which can be used for analyzing facial expressions in real-time thus providing timely emotional support during exams. In this work, we utilized five pretrained deep learning models, namely: DenseNet-121, MobileNetV2, EfficientNet-B0, Inception-V3 and Xception - to classify emotions on processed Emoset dataset. Further, we developed an ensemble model by fusing aforementioned two top-performing deep learning models, thus harnessing the strengths of both models. From the results it can be inferred that ensemble model outperforms the individual pretrained models giving an accuracy of 98.67%. The superior performance of the ensemble models makes it an ideal choice for implementing emotion recognition in real-time applications like Emotion Monitoring in exams. © 2024 IEEE.