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9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350383249

This paper presents a novel secure high-level synthesis (HLS) methodology for designing an optimized and secure reusable convolutional hardware accelerator against intellectual property (IP) piracy using retinal signature. These reusable hardware accelerator designs used in system-on-chips (SoCs) of computing systems are susceptible to the hardware threat of piracy. Therefore, the proposed approach firstly performs compiler driven high level transformation in order to optimize the design latency, followed by embedding the retinal biometric signature of IP vendor in the form of encoded hardware security constraints into structurally transformed scheduled design during register allocation module of HLS. These embedded security constraints thereby enable the robust detection against pirated design versions. The proposed approach achieves the following: i) optimized and secure design for convolutional hardware accelerator ii) robust detection against piracy at zero design cost overhead iii) significantly lower probability of coincidence (Px) indicating stronger digital evidence and higher tamper tolerance (TT) than recent approaches. © 2023 IEEE.

关键词： Convolution

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A Novel Function Fusion Approach for Serverless Cold Start

A Novel Function Fusion Approach for Serverless Cold Start

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Tirkey, Kanchan Kumari, Anisha Mohanty, Sagarika Sahoo, Prof. Bibhudatta National Institute of Technology Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350325904

Serverless computing is a new cloud technology that is popular due to its fast reaction time and scale-to-zero features, where users are only charged for the actual quantity of resource utilization and the code can work closer to the users to minimize the delay. The code that can run independently on a different container serves as the processing element in serverless computing. A container needs to be set up and prepared each time a request for function implementation is made. This is referred to as a "cold start,"and it takes some time before the actual execution *** cold start issue substantially impacts the overall response time of a workflow that consists of functions because it can happen in any of the functions. One method for reducing a workflow's cold start delay is function fusion. When two functions are combined to form one function, the cold start of the second function is removed. However, if parallel functions are fused, the workflow reaction time can be increased because the parallel functions run sequentially even though the second function's cold start is eliminated. This study provides a method for using function fusion and taking into account a parallel run to reduce the cold start latency of a workflow. We first identify the three latencies that have an impact on response time, then we show a workflow response time model that takes the latency into account. Finally, we effectively locate a fusion solution that can improve response time on a cold start. © 2023 IEEE.

关键词： Containers

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Dynamic Residual Illuminating Network for Low-Light Image Enhancement 9

Dynamic Residual Illuminating Network for Low-Light Image En...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Zhong, Xueyang Wang, Haihui Chen, Yanpu Wuhan Institute of Technology School of Computer Science & Engineering Wuhan China

ISBN: (纸本)9798350383706

A low-light image is an image that is taken under insufficient lighting conditions or captured by an underexposed camera. Low-light images commonly exhibit characteristics such as reduced contrast, diminished brightness, pronounced noise, and color distortion, which make them difficult to see and process. Low-light Image Enhancement is a task that involves improving the qualities of these images and is dominated by deep learning approaches nowadays. However, most deep learning methods utilize standard convolution with fixed kernels in their CNN architectures, hindering model's capability to process and recover more information. Besides, noise remains challenging in recovering low-light images. To mitigate these issues, we propose a model based on the Deep Lightening Network (DLN), we modify the Dynamic Region-Aware Convolution (DRConv) and serve them as the encoder and decoder to process more intrinsic features, we modify Residual in Residual Dense Block (RRDB) to a lighter one to reduce noise and alleviate computational burden. Experiments demonstrate the advantages of our proposed model in quantitative evaluation and visual quality. © 2023 IEEE.

关键词： Image enhancement

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Improving YOLOv7's Remote Sensing Image Object Detection

Improving YOLOv7's Remote Sensing Image Object Detection

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2023 China Automation Congress, CAC 2023

作者： Huang, Xi Wang, Haihui School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350303759

In order to address the challenge of poor detection accuracy concerning both dense and smaller targets within remote sensing images set against complex backgrounds, we propose an algorithm for target detection within remote sensing images utilizing an enhanced YOLOv7 framework. Initially, the ELAN module within the backbone segment is replaced with the cascade fusion Network, aiming to more effectively leverage the parameters within the backbone network. This augmentation enhances the model's aptitude for multi-scale feature representation. Subsequently, the EMA attention mechanism is introduced and integrated into the prediction location, serving to encode global contextual information and recalibrate channel weights within the features. This adjustment notably amplifies the feature representation capability. Experiments were conducted on the DIOR and NWPU VHR-10 datasets, in comparison with the YOLOv7 algorithm, the mAP exhibited improvements of 2.6% and 2.2% on these respective datasets. Moreover, detection accuracies were enhanced compared to several existing mainstream algorithms. The outcomes underscore the improved algorithm's effectiveness in substantially augmenting the detection accuracy of remote sensing image targets within complex backgrounds and diminishing instances of leakage and false detections in remote sensing targets. © 2023 IEEE.

关键词： Remote sensing

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Nature-inspired whale optimization technique for efficient information exchange in vehicular networks 12

Nature-inspired whale optimization technique for efficient i...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Jaypee University of Information Technology Department of Computer Science and Engineering Solan India

ISBN: (纸本)9781665462617

Vehicle Ad-Hoc Networks (VANETs) offer roadside connectivity to facilitate user information exchange while driving. It connects other vehicles and hierarchical infrastructure components for applications including real-time traffic and vehicle control. Concord information sharing is needed for application answers because of the increase in vehicle and information density. In this paper, a secure information exchange model has been proposed using nature-inspired whale optimisation algorithm as a way to provide faster data transmission speeds and security. The proposed model makes use of a social behavior model of humpback whales to explore and exploit the search space for finding the optimal solution. Successful information selection and traffic information retrieval depend on the prior and present judgements. This keeps extra data from clogging up the traffic and vehicle management application while the driver is using it. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-Based Optimal Cluster Routing in Wireless Sensor Networks(WSNs)

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China Communications 2022年第6期19卷 219-247页

作者： A.Balamurugan Sengathir Janakiraman M.Deva Priya A.Christy Jeba Malar Department of Computer Science&Engineering KPR Institute of Engineering and TechnologyCoimbatoreTamilnaduIndia Department of Information Technology CVR College of EngineeringHyderabadTelanganaIndia Department of Computer Science&Engineering Sri Eshwar College of EngineeringCoimbatoreTamilnaduIndia Department of Information Technology Sri Krishna College of TechnologyCoimbatoreTamilnaduIndia

Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless *** this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival ***,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical *** meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are *** this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data *** robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink *** specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of *** high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality *** simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.

关键词： Marine Predators Optimization Algorithm(MPOA) Particle Swarm Optimization(PSO) Optimal Cluster-based Routing Cluster Head(CH)selection Wireless Sensor Networks(WSNs)

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A Novel AI-based chatbot Application for Personalized Medical Diagnosis and review using Large Language Models

A Novel AI-based chatbot Application for Personalized Medica...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Akilesh, S. Sheik Abdullah, A. Abinaya, R. Dhanushkodi, S. Sekar, Rajeev School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305708

This research showcases a groundbreaking application in the medical industry by leveraging the cutting-edge OpenAI service's GPT-3.5 model, powered by large language models (LLMs). Through the implementation of a Java-based Android app, it introduces a medical chatbot that redefines the way individuals interact with healthcare systems. By capitalizing on the advanced capabilities of LLMs, the app enables users to effortlessly input their symptoms and receive accurate insights, personalized diagnoses, and actionable medical advice. The utilization of natural language processing and AI algorithms ensures a seamless and intuitive user experience. The significance of this innovation lies in the revolutionary impact of LLMs on the entire medical industry. The integration of LLMs empowers the app to comprehend complex medical queries, interpret contextual information, and deliver reliable and up-to-date medical knowledge to users. This transformative technology facilitates improved access to healthcare information, enhances decision-making processes, and ultimately contributes to the optimization of healthcare services. © 2023 IEEE.

关键词： GPT 3.5 Model Large Language Models Medical Chatbot Natural Language Processing OpenAI Service

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Machine Learning Techniques for the Prediction of Non-Communicable Diseases

Machine Learning Techniques for the Prediction of Non-Commun...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Lavanya, J.M. Sheela Subbulakshmi, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350334357

Due to the abundance of available data, it is necessary to find intriguing patterns and information. There are numerous tools available now for removing interesting information from raw data. Using machine learning to analyze data is a crucial part of using data effectively. Machine learning methods are utilized for finding patterns in massive datasets and aid decision-making. Having reliable tools for predicting diseases in their early stages is crucial. Machine learning (ML) methods are quite good at predicting diseases. Disease prediction should use machine learning to develop medical data sets. Data sets from medical records have been used in developing disease prediction systems based on machine learning techniques. This paper examines the performance of classification methodologies like Decision Tree, Logistic Regression, Naive Bayes, k-NN, Linear Regression, SVM, Random Forest, K-Means, Gradient Boosting algorithms, LightGBM, CatBoost, XGBoost, and GBM, Dimensionality Reduction Algorithms on different datasets of other diseases such as Kidney, and Ovarian Cancer. The experimental system for evaluating the performance of various algorithms utilizing disease data sets is acquired. Different classification algorithms are used to examine the values of other metrics, like successful precision, F-Measure, the time taken, recall, and classified instances. © 2023 IEEE.

关键词： Decision trees

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Robust Smart Face Recognition System Based on Integration of Local Binary Pattern (LBP), CNN and MTCNN for Attendance Registration

Robust Smart Face Recognition System Based on Integration of...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sriman, B. Wise, D.C. Joy Winnie Silviya, S.H. Annie Kumar, D. Saravaanaa Sujan, K. Venkat Sai Bruhathi, S. Rajalakshmi Institute of Technology Computer Science and Engineering TamilNadu Chennai India

ISBN: (纸本)9798350334357

Face recognition is one of the most effective image-processing applications and is essential in the technological era. The identification of the facial image is a current problem for authentication purposes, particularly in the case of student attendance. The design of this system aims to digitally replace the outdated method of collecting attendance with handwritten records. The methods now used to take attendance are complicated and time-consuming. Hence, this method is suggested as a solution to all of these issues. The suggested method uses the integrated benefits of Local Binary Pattern(LBP), CNN, and MTCNN. Attendance reports will be created and maintained in excel format following face recognition. The created system is less expensive to install and requires less work. © 2023 IEEE.

关键词： Local binary pattern

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A privacy-preserved blockchain for vehicles in smart societies 23

A privacy-preserved blockchain for vehicles in smart societi...

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6th International Conference on Advances in Robotics, AIR 2023

作者： Dewangan, Narendra Kumar Chandrakar, Preeti Computer Science & Engineering National Institute of Technology Raipur60104578 India

ISBN: (纸本)9781450399807

Data collection from the mobile vehicle is the most vulnerable task. In an open network of vehicles, device identities must be protected. Our work in this paper describes a proof-of-authentication-based blockchain to collect and store data from vehicular devices with privacy preservation in the blockchain. We are using Ed25519 for the identity generation for devices and vehicles, so we can protect the real identity of the vehicles by generating hash-based wallets. Providing privacy for the vehicle's real identity is required due to the immutable property of the blockchain. Since blockchain stores each transaction, if any personal information or private information is committed to the ledger, then the data provider can be in a vulnerable situation. To provide protection from this kind of situation, the data privacy of the vehicle and owner is required. We are dividing the areas of the smart city into different data collection nodes named CRA (Certification and Registration Authority). This CRA acts as the node for the proposed blockchain. The novelty of our work is that device and vehicle identities must be preserved while collecting data from mobile vehicles in the smart city scenario. The CRA is selected only to be a miner in the proposed system because the vehicles do not have large storage and computation power. The blockchain is implemented using a custom Python-based blockchain. Our proposed work involves collecting data from the devices embedded in the vehicles and storing it in the python-based, lightweight, consensus-enabled blockchain. Our proposed work improves the secure connection between devices and CRA. The proposed system is fast, secure, and privacy-preserving. © 2023 ACM.

关键词： Blockchain

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