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2024 IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Ali, Nasir Tiwari, Abhishek Neiwal, Rahul Kumar Centre for Development of Advanced Computing Embedded Systems Division Noida India Ministry of Electronics and Information Technology Electronics Division New Delhi India

ISBN: (纸本)9798350376876

This paper explores the innovative application of Quantum Convolutional Neural Networks (QCNN) in the classification of the MNIST fashion dataset. Quantum computing presents novel opportunities for enhancing machine learning algorithms, particularly in handling complex datasets. The MNIST fashion dataset is a large collection of Zalando's article images, serves as an ideal candidate for demonstrating the efficacy of QCNN. This paper dives into the implementation of QCNN, compares QCNN's performance with traditional neural networks, and further evaluates QCNN's potential in revolutionizing image classification tasks in the realm of Quantum Machine Learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Embankment Breach Simulation and Inundation Mapping Leveraging High-Performance computing for Enhanced Flood Risk Prediction and Assessment

Embankment Breach Simulation and Inundation Mapping Leveragi...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Dutta, Upasana Singh, Yogesh Kumar Prabhu, T.S. Murugesh Yendargaye, Girishchandra Kale, Rohini Khare, Manoj Kumar Kumar, Binay Panchang, Rajani HPC-ESEG Group Centre for Development of Advanced Computing IP Panchvati Pashan Pune India Department of Environment Science SPPU Pune India

Embankment breaches represent significant hazards to communities and infrastructure, precipitating catastrophic flood occurrences. The precise prediction of floods and understanding the scope of inundation stemming from embankment failures are imperative for effective disaster preparedness and response. This research delves into a case study on simulating embankment breaches to evaluate the extent of flooding. Leveraging advanced hydrodynamic models validated through high-performance computing (HPC) systems, and integrating real-time data assimilation, we aim to improve accuracy in flood forecasting. The study endeavours to bolster flood risk management by furnishing detailed inundation maps and insights into embankment breach dynamics, thereby facilitating enhanced preparedness and response strategies. Our findings reveal that simulations conducted on multicore processors offer superior performance compared to single-core setups, yielding enhanced result accuracy and providing administrators with increased lead time. It unlocks high-resolution simulations for intricate basin details, explores a wider range of flood scenarios quickly, and allows for efficient ensemble modelling to assess model uncertainty. Through HPC utilization, we can harness high-resolution digital elevation models (DEMs) for 2D-hydrodynamic modelling, enabling rapid assessment of water spread resulting from embankment breaches within a mere 20-minute timeframe, a significant improvement from the previous 3-4 hours duration. © Author(s) 2024.

关键词： Risk management

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Malicious program detection using distinct textural features and fine-tuned InceptionV3 model 5

Malicious program detection using distinct textural features...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Kumar, Sanjeev Sapru, Yudhishthira Chahal, Navdeep Singh Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India Indian Computer Emergency Response Team CERT-In Delhi India

ISBN: (纸本)9798350376753

Various prevalent malware detection methods employ static signature-based detection, dynamic analysis, and other techniques to identify malware. However, these traditional malware detection techniques have become less effective due to sophisticated evasion techniques employed by malware authors. In addition, malware analysis is a highly compute-intensive process. This research introduces a new malware detection architecture that deploys different textural features and transfer learning. In this approach, the entire binary executable program is first converted into a grayscale image before extracting the deep textural features by taking the whole image as input to the convolution neural network (CNN) model. Subsequently, a pre-trained InceptionV3 model is used to extract the textural characteristics of the visualized image. The dimension of feature maps is reduced by customizing the InceptionV3 model and adding fully connected (FC) dense layers. This study performs the feature correlation analysis to select only highly distinct textural features. Finally, the selected feature map trains a softmax classifier to predict unknown malware families. This study conducts the experiments using two well-known public benchmarked datasets-MalImg and Microsoft BIG. The proposed model reported a classification accuracy of 98.57 % using the MalIng data set and an accuracy of 97.78 % for the Microsoft data set. The proposed model is superior to similar approaches in the literature. The minimal computation cost of feature extraction makes it well-suited for malware threat detection in industrial environments. Index Terms-Cybersecurity, Deep learning, Image visualization, Malware detection, Transfer learning © 2024 IEEE.

关键词： Deep learning

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The Indian healthcare system turns to digital health: eSanjeevaniOPD as a national telemedicine service

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Journal of Information Technology Teaching Cases 2023年第1期13卷 67-76页

作者： Naithani, Charu Sood, Sanjay P Agrahari, Amit Information Technology & Systems Indian Institute of Management Lucknow India Health Informatics Division Centre for Development of Advanced Computing Mohali India

Healthcare infrastructure in the emerging economies is largely concentrated in their urban areas, and the burgeoning rural population is usually deprived of quality medical care. Telemedicine systems are expected to bridge this gap. This case study documents telemedicine’s significance and allows students to examine India’s national patient-to-doctor telemedicine service, eSanjeevaniOPD. The portal provides free and contactless consultation by a government doctor using video calls. COVID-19 hastened the adoption of eSanjeevaniOPD in the country, but its post-covid future would largely depend on feature innovation, architecture development, and digital strategies. With a total of three million teleconsultations in one year, eSanjeevaniOPD is one of the world’s largest digital healthcare delivery systems. This case provides insight into Indian health infrastructure, summarizes the journey of eSanjeevaniOPD, and raises questions on the digital transformation of the Indian healthcare delivery system. © Association for Information Technology Trust 2021.

关键词： COVID-19 pandemic Developing Economy Digital Health Digital Tranformation Healthcare Delivery System Indian Public Sector Teaching Case Telemedicine

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Towards Quantum-inspired Evolutionary Algorithms for Denoising PCG Signals 7

Towards Quantum-inspired Evolutionary Algorithms for Denoisi...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity University Amity School of Engineering and Technology Noida India Amity University Amity Innovation and Design Centre Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9798350368673

This document aims to reaffirm the superiority of quantum-inspired evolutionary algorithms(QiEA) over conventional algorithms. The study proposes a novel QiEA with sorted mutation and greedy local restart (SMGLR) algorithm for finding the optimal coefficients of a filter using a multi-objective optimization function. PCG signals sourced from the Washington Hearts Sounds and Murmurs database and the Michigan Heart Sounds database were filtered using this enhanced filter. Comparative analysis was conducted against the proposed algorithm and established techniques such as Wavelet-based denoising and Variable Mode decomposition. Results: The results revealed that the filter designed using QiEA-SMGLR exhibited superior performance in terms of improved Signal-to-Noise Ratio (SNR) in both comparisons. A comprehensive statistical analysis yielded a correlation coefficient of close to 0.99 indicating strong signal integrity. The QiEA-SMGLR algorithm outperforms Wavelet-based and VMD methods by improving the output SNR by 20% to 100%. This variance can be attributed to the intensity of noise introduced. The result reaffirms the hypothesis that the QiEA-SMGLR algorithm is superior in improving signal clarity and precision over traditionally popular techniques. The improvement in the analysis of Phonocardiogram (PCG) signals could help improve patient diagnostics, amidst the fight against global cardiac problems. © 2024 IEEE.

关键词： Quantum computers

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A Novel Implementation of Marksheet Parser Using PaddleOCR

arXiv

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arXiv 2024年

作者： Bagaria, Sankalp Irene, S. Harikrishnan Elakia, V.M. Centre for Development of Advanced Computing Chennai India

When an applicant files an online application, there is usually a requirement to fill the marks in the online form and also upload the marksheet in the portal for the verification. A system was built for reading the uploaded marksheet using OCR and automatically filling the rows/ columns in the online form. Though there are partial solutions to this problem – implemented using PyTesseract – the accuracy is low. Hence, the PaddleOCR was used to build the marksheet parser. Several pre-processing and post-processing steps were also performed. The system was tested and evaluated for seven states. Further work is being done and the system is being evaluated for more states and boards of India. © 2024, CC BY.

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Improving Design Accuracy of a Finite Impulse Response Fractional Order Digital Differentiator Filter Using Quantum-inspired Evolutionary Algorithm 14

Improving Design Accuracy of a Finite Impulse Response Fract...

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14th International Conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9798350344837

In this paper, a quantum-inspired evolutionary algorithm (QiEA) is applied to determine the optimal coefficients of a finite impulse response fractional-order digital differentiator filter. QiEA brings the power of quantum-inspired methods to the principles of natural evolution, which uses operators to gradually improve solutions over time. The design accuracy in terms of absolute magnitude error (AME) and phase error achieved in QiEA was compared with the AME achieved by the genetic algorithm (GA) and cuckoo search algorithm (CSA). In addition, we investigated the impact of increasing population sizes on these design parameters of the fractional order (FOD) digital differentiator filter. The simulation results demonstrate conclusively that the FOD digital differentiator's design accuracy using QiEA beats both CSA and GA in terms of accuracy, error function reduction, and identifying optimal filter coefficients. © 2024 IEEE.

关键词： Genetic algorithms

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Use of AI to Augment Multilingual Content in Cyberspace for development – An Indian Case Study

Use of AI to Augment Multilingual Content in Cyberspace for ...

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IEEE Global Humanitarian Technology Conference (GHTC)

作者： Vijayalakshmi B Kondapur Aneesh Deshpande Nishkarsh Krishan Centre for Development of Advanced Computing Hyderabad India

ISBN: (数字)9798350377347

ISBN: (纸本)9798350377354

India, the most populous country in the world, is endowed with a rich and diverse linguistic culture and heritage. Meeting the aspirations and information needs of such a diverse population is a herculean task. Given the importance of information access to fuel development, the barriers of divides need to be bridged, for which technology is a transformational tool. This paper discusses the experiences of using Artificial Intelligence (AI) enabled tools to augment content authoring, digitization, translation, review, discovery and delivery of pro-development information in Indian languages through the collaborative knowledge sharing platform of Vikaspedia.

关键词： Reviews Cyberspace Collaboration Fuels Artificial intelligence

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Optimization Strategies to Accelerate BLAS Operations with ARM SVE

Optimization Strategies to Accelerate BLAS Operations with A...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Aniket P. Garade Sushil Pratap Singh Juliya James H V Deepika Haribabu P S A Kumar S D Sudarsan Centre for Development of Advanced Computing Bengaluru India

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

Optimized mathematical libraries designed for specific hardware platforms are critical for achieving maximum performance in scientific and engineering applications. These libraries play a key role in accelerating computations and improving code efficiency. The Scalable Vector Extension (SVE) for the ARM architecture is a recent development that enhances vectorization capabilities, with wide vectors, leading to significant performance improvements. This paper explores vector optimizations for Basic Linear Algebra Subprograms (BLAS) routines, targeting both single and double precision data. It details the strategies for vectorizing BLAS operations using SVE. The approach is implemented with OpenBLAS, and experimental results reveal notable performance gains, demonstrating the efficacy of SVE in accelerating computational tasks on ARM platforms.

关键词： Program processors High performance computing Computer architecture Performance gain Pareto optimization Libraries Vectors Hardware Computational efficiency Optimization

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Comparative Analysis of GCC and LLVM for Performance Optimization on Aarch64

Comparative Analysis of GCC and LLVM for Performance Optimiz...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Mriganka Bezbaruah Samruddhi Dhakulkar Prachi Pandey Haribabu P S A Kumar S D Sudarsan Centre for Development of Advanced Computing Bengaluru India

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

The emergence of ARM-based architectures in high-performance computing (HPC) has necessitated a closer examination of compiler behaviors and optimizations. This paper investigates the performance and optimization capabilities of GCC and LLVM compilers for Aarch64 processors on HPC workloads. We focus on ARM-based HPC processors used in systems like Grace Hopper and Fujitsu A64FX, emphasizing on their implementations of the Scalable Vector Extension (SVE) and other architectural features that enhance performance. Through comprehensive benchmarking, including vectorization analysis with the Test Suite for Vectorizing Compilers (TSVC), we evaluate the performance of the compilers based on execution times, applied optimizations, and code portability across these systems. By comparing recent versions of GCC (v14) and LLVM (v18) with their previous versions, we highlight performance improvements and identify optimization gaps. Our analysis of assembly code offers insights into vectorization, register allocation, and SIMD instruction generation, providing valuable recommendations for future compiler enhancements. This study aims to give insights for the development of more efficient compilers, ultimately advancing the performance of Aarch64-based HPC systems.

关键词： Program processors Codes Single instruction multiple data Memory management Benchmark testing Vectors Registers Resource management Optimization Assembly

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