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检索条件"机构=Centre For Development Of Advanced Computing"

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Delay Assessment of Parallel Multiplier Using Ultra-Scale FPGA Devices 3rd

Delay Assessment of Parallel Multiplier Using Ultra-Scale FP...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Awasthi, Shivangi Aditya, Chandrasekhar Kumar, Parveen Singh, Balwinder Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9789819730896

A chip designer mainly focuses on reducing area, power consumption, and delay when designing Integrated Circuit (IC). In many Digital Signal Processing (DSP) systems, multipliers play a vital role. If the delay of multipliers gets reduced, it will change the whole circuit delay, so researchers are going to reduce the area and delay of the circuit. This paper basically gives information about the comparison of all types of parallel multipliers such as an array, Baugh-Wooley, braun, and a tree multiplier that is the daddamultiplieronultra-scale Field Programmable Gate Array (FPGA) devices. Which gives analysis about the capability and performance of multipliers. This simulation work is done on the Xilinx using the ISM simulator, different ultrascale FPGA devices (Znyque, Kintex 7, Virtex 7, and Artix 7). In the last, a comparative table gives information about the different parameters like delay and area (Number of Look-up tables) used by the particular multiplier. The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Table lookup

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Detection of AI Manipulated Videos Using Modern Deep Learning Algorithms 8th

Detection of AI Manipulated Videos Using Modern Deep Learnin...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Gupta, Satendra Saini, Tapas Kumar, Anoop Centre for Development of Advanced Computing Telangana Hyderabad India

ISBN: (纸本)9789819721467

Video manipulation is the method to edit the video for various purposes. The deepfake is the manipulated media created using modern deep learning techniques which are the new AI-based methods for video manipulation, Deepfakes are generated for various good and bad purposes. The bad use of Deepfakes has been rising prominently. Researchers worldwide have been developing methods for Deepfake creation and Deepfake detection. The rate of Deepfake Creation has been rising owing to the opportunity for adversaries for financial fraud. This work is toward the detection of Deepfakes using various AI-based methods. The AI-based methods are majorly based on Deep learning models which are trained for Deepfake Detection through the form of AI Inferencing. Over the last decade, many AI models have been designed for various real-world computer vision problems starting from AlexNet followed by new popular models for instance VGGNet, ResNet, InceptionNet, and many more. EfficientNets and Transformers are the recent state of the art in Deep learning models. For the purpose of Deepfake Detection, we have used these newer architectures. We have trained the model for Deepfake Detection using the publicly available Dataset of DFDC Faceforensics++ and CelebDFV2. We have achieved comparable results of 84% accuracy on the popular Deepfake Detection challenge test set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Deepfake Image Forgery Detection for Suspicious Images 5th

Deepfake Image Forgery Detection for Suspicious Images

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5th International Conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Chanda, Kunal Ahmed, Washef Banik, Souvik Centre for Development of Advanced Computing Kolkata India

ISBN: (纸本)9789819725496

Deepfake technology has emerged as a potent tool for creating realistic yet fabricated images that pose significant challenges to the veracity of visual content. This paper proposes an advanced framework for the detection of deepfake image forgery, aiming to identify suspicious images with a high degree of accuracy. The approach integrates a multi-faceted analysis, incorporating digital forensics, deep learning techniques and image quality metrics. Using the power of deep learning networks—more specifically, XceptionNet and a GAN network that makes up EfficientNet—this paper presents a novel method for deepfake image forgery detection that can effectively discriminate between real and manipulated images for both low and high quality AI-synthesized images. Low-quality images are those which are generated through face swap and face reenactment while high quality images are those which are generated through face synthesis. We're grouping into classes that are real, fake and suspicious, suspicious for those which are false positives for the real and fake classes. Lastly, additional analysis is conducted on the fake and suspicious classes in order to draw appropriate conclusions. Our experimental findings show that the suggested method for deepfake image forgery detection performs better than current approaches. The proposed method achieves 95% of accuracy rate on a dataset consisting of both real and fake images. This implies that the technique might prove to be a useful resource for identifying deepfakes in practical settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image quality

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Artificial Intelligence (AI)-Deep Neural Network (DNN) based Classification model to find BI-RADS score from Mammogram Images 5

Artificial Intelligence (AI)-Deep Neural Network (DNN) based...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Sapkale, Snehal C. Yenishetti, Swapna Panat, Lakshmi Aiqt Centre for Development of Advanced Computing Pune India

ISBN: (纸本)9798350376685

Breast cancer is the leading cancer among women globally. It is reported that with every four minutes, an Indian woman is diagnosed with breast cancer. To describe cancer, concept of staging is used. The cancer's stage tells us how far it has grown into nearby tissues. In breast cancer, there are total five stages from 0 (zero) to 4. If breast cancer is detected early between the stage 0-2, then its five-year survival rate exceeds 90% [1]. Survival becomes more difficult in higher stages of cancer. The low survival rates of breast cancer in India is due to delay in detection. Breast imaging tests - such as mammograms, magnetic resonance imaging (MRI) and ultrasounds, help doctors assess breast tissue for the early detection of breast cancer. The results of these tests are scored as BI-RADS (Breast Imaging Reporting and Data System) categories. These categories ranges from category 0 to category 6 (high likelihood of cancer). Radiologists determine the BI-RADS category by applying specific criteria and assessing the imaging tests. There is a need of an efficient and reliable tool that would help in automatic reading of mammogram characteristics and also assist radiologists in mammographic image interpretation. This paper describes a deep neural network (DNN)-based classification model that would efficiently detect mammogram characteristics and categorise it into a relevant BI-RADS category with an accuracy of 71.78%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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DRBD and XCAT Integration: Optimizing High Availability Performance 2

DRBD and XCAT Integration: Optimizing High Availability Perf...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Chaudhary, Divya Singh, Chitranjan Pawar, Pravin Wandhekar, Sanjay HPC- Tech Centre for Development of Advanced Computing Pune India

ISBN: (纸本)9798350360523

This paper delves into the significance of the Distributed Replicated Block Device (DRBD) within Linux clusters, emphasizing its pivotal role in providing essential features like High Availability (HA), fault tolerance, and high-performance computing. Implemented through userspace management applications, shell scripts, and a kernel driver, DRBD facilitates efficient load-sharing among nodes. Paired with Pacemaker and Corosync, it establishes a robust clustered environment with seamless failover, effectively reducing the risk of single points of failure. The study focuses on the DRBD setup with xCAT, concentrating on testing failover times and highlighting xCAT's contribution to automating cluster management tasks. This paper also explores the collaboration between DRBD, Pacemaker, and Corosync, emphasizing xCAT's role in delivering High Availability. Furthermore, the study aims to assess the migration of resources within the DRBD setup, providing valuable insights into the system's responsiveness during resource transfers. © 2024 IEEE.

关键词： Fault tolerance

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A Blockchain-Based Security Assessment Framework 26

A Blockchain-Based Security Assessment Framework

引用

26th International Conference on advanced Communications Technology, ICACT 2024

作者： Satyanarayana, N. Centre for Development of Advanced Computing ESecurity Department Hyderabad India

ISBN: (纸本)9791188428120

Using Blockchain Technology for Security Assessment results in effective monitoring capabilities especially when data analytics components are inbuilt in such a system. At present days, we can see the availability of many Security Information and Event Management (SIEM) tools that follow a client-server model for capturing data from different resources and performing data analysis on the server side. However, such tools serve the purpose of a single institute and depend on the trust level in a multi-institute or multi-center-project kind of environment where they can be used. Another limitation could be that if the server is attacked, the whole exercise would be futile. The lack of trust and concerns about data integrity in such an environment makes performing root cause analysis of security risks difficult. Blockchain technology ensures a tamper-proof, time-stamped, and decentralized storage repository that helps in maintaining data integrity even in complex and untrusted multi-institute or multi-center-project environments while assuring data provenance. This article presents a unified and comprehensive security assessment framework that produces a compliance report along with threat perception level by monitoring and assessing resources across multi-institute or multi-center-project in different geographical locations while supporting data privacy by leveraging Blockchain Technology capabilities. © 2024 Global IT Research Institute - GIRI.

关键词： Blockchain

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Holistic Cyber Threat Hunting Using Network Traffic Intrusion Detection Analysis for Ransomware Attacks

Holistic Cyber Threat Hunting Using Network Traffic Intrusio...

引用

International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Sangher, Kanti Singh Noor, Arti Sharma, V.K. School of IT Centre for Development of Advanced Computing Noida201307 India

ISBN: (纸本)9789819950904

In recent times, cybercriminals have penetrated diverse areas or sectors of the human business enterprise to initiate ransomware attacks against information technology infrastructure. They demand for money called ransom from organizations and individuals to save valuable data. There are varieties of ransomware attacks floating worldwide using intelligent algorithms and with the usage of different setup vulnerabilities. In our research work, we are exploring the latest trends in terms of sector-wise infiltration, captured the most popular among available and also the distribution of the number of attacks using the location information available at the country level. To achieve the correlation between the sectors and locations along with the parametric analysis, we have utilized artificial intelligence techniques. Accuracy of the prediction of attack based on the sector level analysis we have implemented Random Forest and XGBoost algorithm. This research work focuses primarily on two aspects, first is to explore the different aspects of ransomware attacks using intelligent machine learning algorithms. The method used insights to severity of spread of ransomware attacks, second research outcome is to forensically evidence finding of the attack traces using traffic analysis. The challenge is to learn from the previous weaknesses available in the infrastructure and at the same time to prepare the organization and countries' own prevention methods based on the lessons learnt, our exploratory analysis using the latest set of data implementing with AI will give a positive dimension in this area. Also, the proactive approach for managing the data safely is based on the finding of digital forensic analysis of infected ransomware traffic. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Implementation of advanced Metering Infrastructure in the Indian Context with an Indigenous Approach 10

Implementation of Advanced Metering Infrastructure in the In...

引用

10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Neethu, K.V. Jiju, K. Indhu, T.R. Gopi, Arun Joseph, Aby Davidson, C. John, Sherin Mahesh, T. Centre for Development of Advanced Computing Power Electronics Group Thiruvananthapuram India Centre for Development of Advanced Computing Software Training & Development Centre Thiruvananthapuram India Kerala State Electricity Board Ltd Thiruvananthapuram India

ISBN: (纸本)9798331532895

The implementation of indigenously developed advanced Metering Infrastructure (AMI) solutions for electricity utilities in the Indian context is explored in this work. The pilot deployment was carried out with the support of Kerala State Electricity Board Limited (KSEBL), and the project was funded by the National Mission on Power Electronics Technology (NaMPET) under the Ministry of Electronics and Information Technology (MeitY), Government of India. Implementing such a system came with its own set of challenges, from identifying suitable sites for meter deployment and gaining customer acceptance for smart meter technology to handling the complexities of heterogeneous communication interfaces. The project also required managing data from diverse meters in a structured format, finalizing detailed specifications, and ensuring seamless integration with existing billing systems through secure APIs. Additionally, implementing robust cybersecurity measures was critical to protect the integrity of the Meter Data Acquisition System (MDAS). Insights into these challenges and the solutions developed to address them are presented, providing valuable learnings for future AMI implementations. © 2024 IEEE.

关键词： advanced metering infrastructures

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RTM image conditioning using deep learning 84

RTM image conditioning using deep learning

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84th EAGE Annual Conference and Exhibition

作者： Kumar, A. Rastogi, R. Srivastava, A. Mahajan, B. Shearwater GeoServices Centre for Development of Advanced Computing

ISBN: (纸本)9781713884156

Reverse time migration (RTM) is a widely used method in seismic imaging, particularly in situations where other migration methods may not be effective, such as in complex geologies. However, traditional RTM images are affected by a strong low wavenumber noise, which tends to appear mainly at shallow depths and above strong reflectors and can mask migrated structures, making it difficult to interpret the resulting image. To reduce this noise, Laplacian filtering is often used, which adjusts the filtering parameters to preserve the characteristics of useful better signals while still effectively removing low-wavenumber noise. In this work, an alternative approach is proposed, which utilizes deep learning. Here, a residual neural network is trained on a dataset of input-output pairs to replicate the functionality of a modified Laplacian filter. This approach results in outcomes that are similar or potentially better than the conventional method. © 2023 84th EAGE Annual Conference and Exhibition. All rights reserved.

关键词： Deep learning

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Hardware Counter-based Performance Analysis of ANUGA Flood Simulator 2023

Hardware Counter-based Performance Analysis of ANUGA Flood S...

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15th International Conference on Contemporary computing, IC3 2023

作者： Jadhav, Harshada A Agrawal, Nisha Joshi, Sandeep K Shenoi, V Venkatesh Centre for Development of Advanced Computing Pune India

ISBN: (纸本)9798400700224

ANUGA is a Python-based finite volume solver on the unstructured grid for shallow water model in two dimensions for flood modelling. This paper is an account of the hardware counter-based performance study to understand the impact of the memory hierarchy (due to the relative sizes of cache) in two different generations of Intel architecture on the performance of this application. A quantitative analysis of the memory access in the compute-intensive portion of the solver (the time evolution of the quantities associated with the fluid flow on the domain) is attempted by instrumenting with the PAPI library. The hit rates at different levels of cache/memory for various decomposition of the region of interest (computational domain) across processes bring out the impact of optimal sub-domain sizes. The size of the working set ensures temporal locality for iterative loop computations handled by the individual processes, leading to better parallel performance on distributed memory systems. Further, this study showcases the possibility of achieving better performance with larger data sets through suitable decomposition such that the working set fits into various levels of the cache hierarchy. © 2023 ACM.

关键词： Floods

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