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8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Sanjana K Sowmya, V. Gopalakrishnan, E.A. Soman, K.P. Amrita School of Engineering Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham CoimbatoreTamilNadu India

ISBN: (纸本)9789811557873

Atrial fibrillation is a life-threatening cardiac disease which requires a long and tedious process of detection. So, the detection of atrial fibrillation has gained great importance. One of the most reliable ways to detect cardiac disease is through analysis of ECG signal. In this paper, we show that the performance of a deep residual skip convolution neural network-based approach for automatic detection of atrial fibrillation can be improved by hyperparameter tuning. For the present work, atrial fibrillation dataset from the 2017 PhysioNet/CinC Challenge is used. The proposed method obtained an overall accuracy of 96.08% and weighted average F1 score of 0.96, a recall of 0.96 and a precision of 0.96. The main advantage of the present work is the improved accuracy achieved using a lighter model which is trained for a lesser number of epochs. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A fusion based approach for uneven illumination correction in retinal images using Gaussian Filtering method

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Procedia Computer Science 2025年 260卷 991-999页

作者： Suchithra M Soman K P Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore-641112 India Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore-641112 India

Enhancing the poor-quality retinal images is one of the inevitable pre-processing steps in retinal image analysis for the accurate diagnosis and treatment of different eye-related issues such as diabetic retinopathy, macular degeneration, glaucoma, etc. This paper presents a systematic approach to address this challenge by proportionally adjusting the red, green, and blue channels to ensure consistent luminance correction, enhancement, and color preservation. The core of our approach involves generating a luminance scaling matrix using a Gaussian low-pass filter applied to the red channel. This matrix is then used to correct the luminance proportion in each channel, resulting in new luminance-enhanced channels. We then applied Contrast Limited Adaptive Histogram Equalization(CLAHE) for enhancing the structural properties of the luminance corrected retina image. Our approach is effectively validated on two publically available datasets Diaretdb0 and Diaretdb1. We have tested the effectiveness of our proposed method using different evaluation metrics such as entropy, standard deviation, Natural Image Quality Evaluator(NIQE) and Perception-based Image Quality Evaluator(PIQE) measures and also confirms that our approach significantly improves the quality of retinal images, making them more suitable for accurate analysis and diagnosis.

关键词： Uneven illumination Gaussian Filtering image enhancement CLAHE

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INFLUENCE of ADDITIONAL SPECTRAL BANDS for EPIPHYTE SEGMENTATION on DRU-NET

INFLUENCE of ADDITIONAL SPECTRAL BANDS for EPIPHYTE SEGMENTA...

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2022 Annual Conference, ASPRS 2022

作者： Menon, Anjana K. Sajith Variyar, V.V. Sivanpillai, Ramesh Sowmya, V. Brown, Gregory K. Soman, K.P. Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore India Wyoming GIS Center University of Wyoming LaramieWY82072 United States Department of Botany University of Wyoming LaramieWY82072 United States

Dense Residual U-Net (DRU-Net) is a neural network used for image segmentation. It is based on the U-Net architecture and isa combination of modified ResNet as the encoder and modified DenseNet as the decoder blocks. DRU-Net captures both the local and contextual information. Previous studies on DRU-Net have not tested the influence of the spectral resolution of the images. In an earlier study, the DRU-Net was trained with grayscale images for epiphyte segmentation. The network trained and tested with grayscale images underperformed while varying the illumination and occupancy of the target in the frame. In this study, the same network was trained and tested with RGB images for assessing the increase in overall learning. The performance of the network in segmenting epiphytes under conditions such as good/poor illumination and high/low target occupancy was analyzed. Dice and Jaccard scores were used as evaluation metrics. The DRU-Net model trained with RGB images had an improvement of 20% over the grayscale model in both average Dice and average Jaccard scores of the target class. Based on the higher Dice and Jaccard scores, adding additional spectral information improves DRU-Net learning. The increased computation time required for training DRU-Net with RGB images will result in better output. This model could be further used for identifying multiple epiphytes in images with poor illumination and different occupancy conditions. © 2022 A. K. Menon et al.

关键词： Image segmentation

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Significance of Atrous Spatial Pyramid Pooling (ASPP) in Deeplabv3+ for Water Body Segmentation

Significance of Atrous Spatial Pyramid Pooling (ASPP) in Dee...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Gosula Sunandini Ramesh Sivanpillai V Sowmya V. V Sajith Variyar Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Wyoming GIS Center University of Wyoming Laramie WY USA

Water body segmentation is a difficult task because of complex shapes, edges, sizes, and the surroundings. DeeplabV3+ has proven to perform well on segmentation tasks with multi-scale features. Atrous Spatial Pyramid Pooling (ASPP) module present in Deeplab models helps to increase the field of view for extracting multi-scale features. In this work, we analyze how the ASPP extracts multi-scale features in DeeplabV3+ by comparing the performance of Deeplabv3+ model with ASPP and without ASPP for water segmentation task. The dataset used for this study is a public dataset that contains RGB satellite images and corresponding masks from sentine1-2 A/B satellite. The evaluation metrics used for this study are IoU score, Dice score, Recall and Precision. The results obtained from the study shows that DeeplabV3+ model with ASPP extract the water bodies with different sizes. It also captures the boundaries accurately compared to the model without ASPP module.

关键词： Measurement Analytical models Image segmentation Satellites Shape Signal processing Feature extraction

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Performance Improvement of Water Body Segmentation by DeeplabV3+Using Two Dimensional Variational Mode Decomposition

Performance Improvement of Water Body Segmentation by Deepla...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Bichu George Variyar.V.V Sajith V Sowmya. Ramesh Sivanpillai Center for Computational Engineering and Networking(CEN)Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Wyoming GIS Center University of Wyoming Laramie WY USA

Monitoring changes in surface water bodies and other earth surface features uses remote sensing data. The DeeplabV3+ network is an encoder decoder based deep neural network that is widely used to segment images with good precision. To improve the predictions made by the DeeplabV3+ model, a novel technique based on the two-dimensional variational mode decomposition (2D-VMD) is proposed in the present work. Sentinel 2A/B images dataset from Kaggle is used for this study. The images and their corresponding annotations are also available. The masks were obtained using the index known as Normalized Water Difference Index (NDWI). From 2841 images, we cropped 100 x100 subsets resulting in 1,50,204 images. The proposed method is found to be effective in improving the predictions made by DeeplabV3+ model. With respect to the images considered for the study, the average F1 score increased from 0.33 to 0.47. The average Jaccard score increased from 0.21 to 0.34.

关键词： Earth Deep learning Image segmentation Neural networks Predictive models Signal processing Sensors

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Artificial Intelligence Framework for COVID Protocol Detection

Artificial Intelligence Framework for COVID Protocol Detecti...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd International Conference on

作者： Kabilan N Sajith Variyar V V Adithan P Sowmya V P S Sai Ganesh Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781665499453

Artificial intelligence is now penetrating into all the domains. Any domain can incorporate artificial intelligence to automate their process. In the outbreak of COVID pandemic, artificial intelligence has been very useful in many ways. artificial intelligence helps in automating process where it’s not always possible for people to do and to reduce the wastage of human resource. Here we proposed a frame work to automate the detection of covid protocol violation in public places. Our work detecting people with & without masks and detects social distancing with a single model. The best performing model from the standard convolution neural network architectures namely VGG16 and MobileNetV2 are used in the present work, from the experiments it’s found that MobileNetV2 outperformed VGG16. The developed system can easily be integrated/implemented on various embedded devices with limited computational capacity by using the MobileNetV2 architecture. Compared to other previous works, our work outstands by having good accuracy and compatible to use in real life application because of its requirement of less computational complexity.

关键词： COVID-19 Protocols Pandemics Convolution computational modeling Neural networks Human factors

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Technical and Economic Sustainability of Alternative Livelihood Strategies at the Grassroots: A Case Study in Byse, Karnataka, India 8th

Technical and Economic Sustainability of Alternative Livelih...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Cornet, Souresh Viswanathan, P.K. Winkler, Jake Praveen, Neeraja Sengupta, Angan Sneha, S. Geetha, P. Amrita School for Sustainable Futures Amrita Vishwa Vidyapeetham Amritapuri India Amrita School of Business Amrita Vishwa Vidyapeetham Amritapuri India School of Management University at Buffalo BuffaloNY United States Amrita School of Business Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819766741

In the context of climate change and agriculture distress, Indian rural areas increasingly need to develop non-agriculture-related livelihood opportunities. This is to ensure year-long stability, irrespective of seasons and natural calamities. However, the rural context poses many challenges in terms of socio-cultural complexity and resource constraints. This study aimed to better understand how to develop alternative livelihood opportunities at the grassroots that could be sustainable in the long term. It explores the reality of rural areas through the case study of a community in the state of Karnataka, India. A multidisciplinary, collaborative, and participatory approach is undertaken to thoroughly understand the community and identify the key technical and business requirements for developing a new income-generating activity in that specific community. Guidelines for developing this activity are provided. Findings illustrate the synergies between the technical and business aspects when developing livelihood opportunities, and outline the importance of a multidisciplinary and bottom-up approach to capture the complexity of rural communities when conducting grassroots projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sustainable agriculture

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Finding Network Motifs: A comparative study between ILP and Symmetric Rank-One NMF

Finding Network Motifs: A comparative study between ILP and ...

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Intelligent Technologies (CONIT), International Conference on

作者： Aadharsh Aadhithya A Sachin Kumar S Vinith Rejathalal Sujadevi VG Prabaharan Poornachandran Soman K P Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Centre for Internet Studies and Artificial Intelligence Amrita Vishwa Vidhyapeetham Amritapuri Kerala India

This paper compares the performance of two popular methods, an integer linear programming (ILP) based method and a non-negative matrix factorization (NMF) based approximation, for identifying network motifs. Our analysis demonstrates that the ILP-based method is highly accurate but computationally expensive, whereas the NMF-based method is faster and more scalable but less accurate. The choice of method should depend on the specific research question and the size and complexity of the network being analyzed. We also highlight the need for further research to develop more efficient and accurate methods for identifying network motifs, especially for very large and complex networks. Understanding the significance of network motifs and their functions can lead to new therapeutic strategies and improved diagnostics for a wide range of *** addition, we provide an easy-to-use Matlab code implementation for researchers to get started with analyzing their own network motifs. Our code is based on both the ILP-based method, and can be customized to suit the specific needs of the user. This implementation can help facilitate the wider adoption of network motif analysis and enable researchers to gain a deeper understanding of complex systems.

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PokkahScan - An Intelligent Drone for detection of Pokkah Boeng Disease in Sugarcane Using Transfer Learning and eXplainable AI 15

PokkahScan - An Intelligent Drone for detection of Pokkah Bo...

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15th International Conference on Computing Communication and networking Technologies, ICCCNT 2024

作者： Ravikumar, Rahul Sujith Kumaar, M.V. Krishna, Shirish Jayasurkanth, M. Yasshwanth, V.R. Saravanan, Sanjay Kishore Thangavel, Senthil Kumar Thangeswari, S. Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Center for Computational Engineering & Networking Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Aerospace Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Tamilnadu Agricultural University Department of Plant Pathology Coimbatore India Amrita School of Physical Sciences Department of Mathematics Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning becomes apparent as it allows for leveraging pre-existing knowledge from large datasets to improve the performance of models trained on smaller, domain-specific datasets. IoT has also played a significant role in our automation process. Transfer learning leveraging MobilenetV2 is particularly beneficial for this problem of detecting Pokkah Boeng disease in sugarcane crops, as it enables the adaptation of pretrained models, such as MobileNetV2, to accurately identify Pokkah Boeng disease even with limited set of data. LIME is employed to provide transparent explanations for the model's predictions, enhancing its interpretability and facilitating informed decision-making in agricultural management. By combining transfer learning with xAI and aerial drone technology, having add-ons of IoT and facilitating a comprehensive comparative study, PokkahScan offers a comprehensive solution for timely disease detection and crop management in sugarcane farming. © 2024 IEEE.

关键词： Agricultural Analytics Crop Management Disease Detection Early Detection IoT MobilenetV2 Pokkah Boeng Disease Sugarcane Crop Transfer Learning xAI

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Refined Zero Band Filtering for Estimation of Instants of Significant Excitation from Emotive Speech 18

Refined Zero Band Filtering for Estimation of Instants of Si...

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18th IEEE India Council International Conference, INDICON 2021

作者： Ashwini, R. Govind, D. Center for Computational Engineering and Networking Tamilnadu Amritanagar Coimbatore 641112 India Greenfields Department of Computer Science Engineering Andhra Pradesh Vaddeswaram Guntur 522501 India

ISBN: (纸本)9781665441759

The objective of the present work is to improve the estimation of instants of significant excitation from speech signals with emotions. In particular, the work focusses on the refinement to the zero band filtering (ZBF) based instants estimation for emotive speech and singing voices. The choice of speech data with emotions is due to the nature of the data with rapid variations in the prosodic parameters which in turn poses challenges in the accurate estimation of instants. The ZBF approach, being the reliable method for instants estimation from the speech data with rapid prosodic variations, suffers from degraded performance due to the occurrence of spurious zero crossings in zero band filtered signal (ZBFS). The refinement is introduced by suppressing the spurious zero crossings by the low pass filtering of conventional ZBF segments with cutoff frequencies which are adaptively estimated as average pitch values obtained from each of the segments. The effectiveness of the refined ZBF method is confirmed by relative drop in the false estimation rates and miss rates of the hypothesized instants of significant excitation for emotive speech. © 2021 IEEE.

关键词： Cutoff frequency

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