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Literature Survey: Decentralized Health Record Management System

Literature Survey: Decentralized Health Record Management Sy...

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AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Anshika Pandita Suchitra Morwadkar Padmaja Joshi Janvi Girase Nikita Gosavi Isha More Cummins College of Engineering for Women Pune India Centre for Development of Advanced Computing Mumbai India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

In today's fast-paced world of technology, we rely on digital interactions more than ever. But with this convenience comes a growing need for security and trust. As we navigate our daily lives online, issues such as data breaches, unauthorized access, and fraud are real concerns that can affect us all. It is vital to prioritize the safety of our digital experiences to build the trust we need to thrive in this digital age. These challenges are particularly acute in the management of personal health records (PHRs), where existing centralized systems do not offer reliable, easily traceable, and securely managed control over patient's medical data. This paper explores the revolutionary capabilities of blockchain technology in addressing the critical shortcomings of traditional PHR management systems. The unique characteristics of the blockchain are decentralization, security, immutability, transparency, and anonymity, which presents a groundbreaking solution to the issues plaguing current systems. By eliminating the risks associated with centralized data storage, blockchain provides a secure and transparent platform for managing health records, ensuring that they remain tamper-proof. The literature survey conducted in this paper examines various research studies that have proposed and explored Blockchain-based solutions for health record management. These studies highlight the significant advantages of blockchain, including enhanced data integrity, privacy, and patient control over health information. Comparative analysis underscores the need for a paradigm shift from centralized to decentralized systems that enhance both the security and efficiency of managing health records.

关键词： Surveys Data privacy Navigation Scalability Smart contracts Memory Medical services Blockchains Safety Reliability

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Enhancing Agricultural Practices with AgroDialogue: A Chatbot Framework

Enhancing Agricultural Practices with AgroDialogue: A Chatbo...

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IEEE Punecon

作者： Hruturaj Nikam Mahesh Bhargava Sibadatta Sasmal Pavan Dhote Lenali Singh Sunita Dhotre Kartikey Garg Tanya Bansal Mohit Raj Anurag Thakur MTG Centre for Development of Advanced Computing Pune Bharati Vidyapeeth College of Engineering Pune

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

The agriculture sector faces significant challenges due to the limited adoption of efficient practices by farmers, leading to reduced crop yields and quality. This research introduces a specialized chatbot tailored for agricultural inquiries, leveraging advanced technologies such as Large Language Models integrated with Knowledge Graphs and vector databases. The chatbot performs thorough query analysis and delivers personalized information, facilitated by a user-friendly interface that supports seamless interaction, effective query categorization, and clear responses. Additionally, it incorporates voice functionality to improve accessibility for users with learning disabilities or literacy challenges. Unlike existing solutions, this chatbot is uniquely designed for agriculture, offering free, portable, and reliable guidance on farming techniques and related topics. By empowering farmers to make informed decisions about crop selection, pest management, and irrigation methods, the system aims to promote sustainable farming practices and enhance financial outcomes. The research methodology includes data preparation, model selection, and the implementation of a knowledge graph memory base. The development of APIs and databases ensures effective communication and user engagement, with comprehensive testing validating the system’s functionality, performance, and accuracy in diverse scenarios. This paper highlights the potential of advanced technologies in creating specialized agricultural chatbots, providing a promising approach to disseminate agricultural knowledge and support stakeholders in the sector.

关键词： Irrigation Databases Large language models Knowledge graphs Chatbots Reliability engineering Vectors Stakeholders Farming Testing

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India's V2X Future: C-V2X vs. DSRC - A Comparative Study

India's V2X Future: C-V2X vs. DSRC - A Comparative Study

引用

International Conference on Engineering and Emerging Technologies (ICEET)

作者： Sunnam Venkata Srikanth Santosh Sam Koshy R Harin M Prasad C Murali Krishna Embedded Systems & IoT Centre for Development of Advanced Computing (C-DAC) Hyderabad India

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

The next generation of automotive vehicles will unlikely succeed without connectivity. Automakers and infrastructure developers worldwide currently use two distinct V2X wireless communication technologies i.e. Dedicated Short-Range Communications (DSRC) and Cellular Vehicle-to-Everything (C-V2X). Vehicles use these low-latency communication technologies to communicate with each other (V2V), with road users (V2P), and with infrastructure or mobile networks (V2I). These technologies have enhanced the safety, reliability, and efficiency of the transportation system, while also improving mobility. This paper discusses the performance and evaluation of these two technologies for vehicular communication. Various communication parameters such as range, PER, IPG, and latency were evaluated under different conditions both in the lab and field for both technologies. This study contributes to a better comprehension of these technologies and provides valuable insights for India's future implementations of connected vehicle technology.

关键词： Connected vehicles Transportation Road safety Safety Automobiles Reliability Dedicated short range communication Vehicle-to-everything Standards Next generation networking

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Preliminary Implementation of Toeplitz Hashing on Processor, Co-Processor and SoC

Preliminary Implementation of Toeplitz Hashing on Processor,...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Guru Satya Dattatreya Pandeeti Janhvi Dixit Asif Hussain Dibya Prakash Behera Umang Dubey Anindita Banerjee Samrit Kumar Maity Manish Modani Centre for Development of Advanced Computing Pune Maharashtra India NVIDIA Pune Maharashtra India

Toeplitz Hashing is a class of functions in the 2-universal hash function family, that is widely adopted in quantum secure applications, particularly for the privacy amplification in quantum key distribution to generate secret keys, and as a strong randomness extractor for the quantum random number generators. In this work, we have investigated the preliminary implementation of Toepltiz hashing on processors, co-processors, and system-on-chip (SoC). We have implemented the fast-Fourier transform (FFT)-based Toepltiz hashing on Intel Xeon Cascade-lake 8268 processor and NVIDIA Tesla V100 GPU compute hardware. We have also implemented the binary multiplication-based Toeplitz hashing on the AMD Xilinx ZCU102 FPGA evaluation kit. Further, we present an example of heterogenous computing, by combining an Intel Core i7 CPU with the VEGA ET1031-based Aries v2.0 development board. Finally, we report, for the first time, an implementation of convolution of two matrices on an indigenously designed RISC-V VEGA SoC. Further, we have compared the implementation results following two approaches, i.e. normal matrix multiplication and the number theoretic transform (NTT) method. We report that there is a tenfold reduction in the computational time using the NTT method over the conventional method.

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A SIMD Dynamic Fixed Point Processing Engine for DNN Accelerators

A SIMD Dynamic Fixed Point Processing Engine for DNN Acceler...

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IEEE International Symposium on Quality Electronic Design

作者： Gopal Raut Pranose J Edavoor David Selvakumar Ritambhara Thakur Secure Hardware and VLSI Design Group Centre for Development of Advanced Computing Bangalore India

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

This research introduces a novel Processing Engine (PE) for convolution layers in Deep Neural Network (DNN) accelerators. The PE integrates multi-precision dynamic fraction fixed-point Multiply-Accumulate (MAC) and Activation Function (AF) units (8/16 bits with shared resources), functioning as a Single Instruction, Multiple Data (SIMD) engine. This innovative design enables dynamic precision switching between 8-bit and 16-bit computations, supporting multi-precision (N=8 or 16) operations with dynamic fraction fixed-point data. Pareto analysis optimizes resources and determines the ideal number of parallel Cordic stages required for multi-precision dynamic fraction fixed-point AF in the SIMD Engine. The proposed approach demonstrates minimal accuracy loss, less than 1% for LeNet with MNIST, 1% for AlexNet with CIFAR-10, and 2% for VGG16 with CIFAR-10, compared to reference float32-based implementations. Experimental results show a significant reduction in hardware resources for MAC unit and AF, 51.83% and 43.50%, respectively, indicating substantial improvements over a separate MAC and AF unit design for 8 and 16 bits in a SIMD engine-based setup. This multi-precision design, sharing resources for 4 $\times$ 8 and 1 $\times$ 16-bit processing, optimizes resource utilization, boosts versatility and throughput, enabling efficient computations across diverse DNN applications, models, and layers using a unique SIMD-enabled PE with integrated MAC and AF units for dynamic fraction fixedpoint operations.

关键词： Convolution Electron accelerators Artificial neural networks Switches Dynamic scheduling Throughput Hardware

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A Data Flow Scheduler for Multi-precision SIMD Processing Engine Systolic Array CNN Accelerators

A Data Flow Scheduler for Multi-precision SIMD Processing En...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Ritambhara Thakur Pranose J Edavoor Gopal Raut David Selvakumar Secured Hardware and VLSI Design Group Centre for Development of Advanced Computing Bangalore India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Conventional inference data-flow techniques for DNN accelerators often involve layer-wise mapping, resulting in frequent off-chip memory accesses. To address this issue, an efficient scheduling mechanism is proposed for a systolic array architecture with a multi-precision fixed-point Processing Engine (PE) featuring SIMD capabilities. By restructuring input feature maps and kernel data, the approach optimizes data reuse for both on-chip and off-chip memory accesses. The Channel-First scheduling mechanism significantly reduces DMA (memory read) overheads, enhancing efficiency. Analysis shows memory read overhead reductions of 3×, 6×, and 62× for input feature maps of LeNet-5, AlexNet, and VGG16 models, respectively. For kernel weights, reductions of 10×, 214×, and 371× are achieved. Software emulation of SIMD-enabled PEs across different precisions (8-bit, 16-bit) shows minimal accuracy loss: less than 1% and 2% for LeNet on MNIST and AlexNet, VGG16 on CIFAR-10 respectively, in comparison with Float32 precision.

关键词： Analytical models Single instruction multiple data Processor scheduling Emulation Dynamic scheduling Systolic arrays Software

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Video-Based COVID-19 Monitoring System 1

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Shetty, Devesh Zaidi, Fayeq Parkhetiya, Asfaq Gangwar, Abhishekh Patil, Deepali Shree L. R. Tiwari College of Engineering Mumbai India Department of Biometrics Centre for Development of Advanced Computing Pune India Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9789819994892

ISBN: (纸本)9789819994885

This study introduces an innovative solution to address the ongoing COVID-19 pandemic challenges. With the importance of preventive measures like mask-wearing and social distancing in public places, our project aims to support these guidelines by using advanced machine learning and computer vision. We employ CCTV cameras and convolutional neural networks for precise mask adherence detection. Our goal is to develop a highly accurate and efficient system that can easily integrate into existing infrastructure, contributing to public health and safety during these unprecedented times…. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision

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Feature Fusion for Malware Traffic Detection Using Image Visualization and Transfer Learning 10

Feature Fusion for Malware Traffic Detection Using Image Vis...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kumar, Sanjeev Sapru, Yudhishthira Purushottama Rao, K. Centre For Development of Advanced Computing Cyber Security Technology Division Mohali India CERT-In Indian Computer Emergency Response Team Delhi India Nit Department of Computer Applications Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350353068

Malware threats have become increasingly sophisticated and more complex, making detecting and preventing them difficult. Traditional malware detection methods, such as signature-based detection, are no longer effective against new malware variants. Deep learning, especially convolution neural networks, has evolved significantly as end-to-end malware detection without much requirement of domain expert analysis for feature engineering. This research paper proposes a new image visualization-based malware detection and classification using transfer learning and machine learning classifiers. At first, the network traffic dumps (PCAPs) are transformed into grayscale images. After that, pre-trained CNN models, including VGG16, VGG19, EfficientNetB0, EfficientNetB1, and Xception, are customized to extract the textural features via transfer learning by adding a fully connected layer of different dimensions. Feature fusion is performed by stacking the extracted feature maps from different pre-trained models to form a single feature map. Finally, the fused feature maps are fed to train three machine learning models to obtain the best classifier. The hyperparameter optimization of all classifiers is performed using grid-search and randomized search algorithms. This study performs intensive experiments using 913 public image datasets, and the proposed method obtained 94.71% accuracy, which is an excellent performance even using a smaller dataset. The experiment results reveal that the proposed method is well-suited to deal with live malware traffic in real-world industrial IoT environments. © 2024 IEEE.

关键词： Malware

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FaceReZero Transformer for Unbiased Occlusion Invariant Deep Face Recognition with Train-Time Augmentations

FaceReZero Transformer for Unbiased Occlusion Invariant Deep...

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International Conference on Image Information Processing

作者： Ronak Shah Mrugank Purohit Suraj Gadhe Kapil Mehrotra Swati Mehta Centre for Development of Advanced Computing (C-DAC) Pune India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The deep learning techniques for face recognition have achieved remarkable performance. However, the accuracy degrades when confronted with heavy occlusions like masks, goggles, scarves, and other obstructions. A further challenge is to recognize the low-quality images coming from the surveillance. This poses significant challenges for existing face recognition systems. To address these challenges, 1) we propose a set of 34 face data augmentation techniques tailored for real-world scenarios, designed to improve the accuracy of face recognition models. We do not recreate the training data after applying augmentations. Instead, we apply the augmentations directly to a mini-batch of 128 facial images during the training step, which adds only a mere 100 milliseconds overhead, regardless of the number of GPUs. It significantly enhances model accuracy across various mask face recognition challenge datasets while maintaining the accuracy on public benchmark datasets that do not include masks. Furthermore, 2) We adopted the ReZero is All You Need for face recognition and called it FaceReZero. It is a transformer-based model that combines overlapping patches, skip connections, an additional trainable parameter and a LayerNorm before Multi-head attention and MLP block in each layer. The FaceReZero demonstrated improved accuracy over Vision Transformer, Face Transformer and ReZero. In comprehensive evaluation, we assess the model trained with the proposed data augmentation and FaceReZero architecture on different test sets. Our approach achieved significantly improved accuracy on the MR-All, Children, and Multi-Racial test sets while demonstrating performance closely aligned with the Masks test set.

关键词： Training Image recognition Face recognition Surveillance Training data Information processing Transformers

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Generalising Whole Page Handwritten Text Recognition With Parallel Scheduled Sampling

Generalising Whole Page Handwritten Text Recognition With Pa...

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International Conference on Image Information Processing

作者： H N Vivek Kapil Mehrotra Ronak Shah Swati Mehta Centre for Development of Advanced Computing (C-DAC) Pune India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The Encoder-Decoder architecture is often used for the seq-to-seq conversion problems in natural language processing. The Encoder is used for feature extraction and the Decoder to generate the sequence. This paper addresses a similar Encoder-Decoder architecture for the image-to-text conversion for offline Handwritten Text (whole paragraphs as input without prior segmentation). The resnet-based encoder and transformer-based decoder inspire the model. It was observed that the auto-regressive nature of transformers coupled with teacher forcing, produced good results in training but failed badly while inferencing on unseen data. So, we propose Parallel Scheduled Sampling and teacher forcing to generalize the model for better inferencing. This makes the model more generalized as it replaces the ground truth labels used in teacher forcing with the model-predicted labels with a random probability. Thus, making the model less dependent on teacher forcing as the training proceeds. We have extended the training dataset by augmenting 6000 lines & 12000 paragraphs to cover handwritten data variations. The evaluation was done on the standard RWTH Aachen data splits(Test & Val) for IAM dataset.

关键词： Training Handwriting recognition Image segmentation Text recognition Information processing Predictive models Transformers

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