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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Gupta, Manish Kumar Dwivedi, Rajendra Kumar Sharma, Annu Farooq, Mansoor Britto Raj, S. Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application Gida Gorakhpur India RajaRajeshwari College of Engineering Department of Computer Science & Application Bengaluru India University of Kashmir Department of Management Studies Jammu & Kashmir India Rrase College of Engineering Department of Computer Science & Engineering Chennai601301 India

ISBN: (纸本)9798350302820

Blockchain (BC) is a cryptographic technology that enables the decentralized storage of an immutable record of transactions. As a result, many industries are interested in integrating BC into their IT systems. Although BC-based platforms are used in enterprise solutions, concerns regarding accessibility, privacy, performance, and scalability are still legitimate. Permissioned Blockchain (PBC) frameworks provide a solution to securely store private records. The main objective of this paper is to focuses on the performance and scalability of popular private BC platforms. To evaluate each platform, we varied the workloads and assessed performance metrics. Our comparative analysis provides valuable insights into the strengths and weaknesses of each platform, enabling enterprises to make informed decisions when choosing a private BC solution. © 2023 IEEE.

关键词： Blockchain

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Three-dimensional modeling of vascular tissue and its mechanical characteristics 4

Three-dimensional modeling of vascular tissue and its mechan...

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4th International Conference on Frontiers technology of information and computer, ICFTIC 2022

作者： Xu, Zewei Xu, Rui Luo, Xu Zhu, Jian Jiangsu Aerospace 706 Information Technology Co. Ltd Beijing Institute of Computer Technology and Application NanJing China

ISBN: (纸本)9798350321951

Vascular transplantation is the main method for treating cardiovascular diseases. Autologous or xenogenous blood vessels exist in limited source, immunological rejection and no growth ability. Artificial blood vessel construction based on three-dimensional printing and tissue engineering technology provides new ideas for solving the above problems. To reconstruct the three-dimensional model of CT data of vascular tissue with MC algorithm as the core, and to analyze the reconstruction model with finite element method. Firstly, the medical image of human vascular tissue was extracted effectively by Mimics software, and the three-dimensional reconstruction and optimization of vascular tissue were completed. Four kinds of artificial vascular tissue models with different wall thickness and external diameter were designed. Then the stress of four groups of vascular tissue was analyzed by finite element software, and the suitable artificial vascular model for implantation was obtained. © 2022 IEEE.

关键词： Finite element method

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Building Edge Detection technology from Remote Sensing Image Based on NSCT and Tensor Voting 8

Building Edge Detection Technology from Remote Sensing Image...

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2024 8th International Conference on Machine Vision and information technology, CMVIT 2024

作者： Wu, Yingbin Zhao, Peng Zhou, Mingquan Geng, Shengling Zhang, Dan School of Computer Science Qinghai Normal University Qinghai Xining China School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China

An edge detection technology based on the combination of non-downsampling contour wave transform (NSCT) and tensor voting is proposed, which aims to obtain more accurate and detailed edge information of buildings in remote sensing images. Firstly, NSCT is used for image decomposition to obtain the subband frequency information of different scales and angles. Then, position encoding is performed on these subband coefficients to obtain second-order symmetric tensors at the corresponding positions. Tensors of different scales and angles at the same position are weighted and summed to complete feature fusion. Finally, the edge features of the image are obtained based on tensor voting theory. The experimental results show that compared to common edge detection technologies, such as Canny, Fast Edge (Fast edge detection using structured forests) and HED (Holistically-Nested Edge Detection), our method can more accurately and intensively reflect the boundaries of buildings and the edge information of roofs, providing better support for the analysis of building types and architectural styles. Compared to the HED method, which is based on deep learning, our method improves PSNR and SSIM metrics by 0.98 and 0.03, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Edge detection

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Machine Learning-Based Cybersecurity Optimization in Internet of Things-Enabled Autonomous Vehicles 1

Machine Learning-Based Cybersecurity Optimization in Interne...

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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Musa, Usman Ibrahim Musa, Aminu Ibrahim Galadima, Yakubu Ibrahim Kantunsung, Ramadan Osman Gupta, Sakshi Dua, Sakshi Lovely Professional University School of Computer Application Punjab India Esgt Benin University Department of Information Technology Cotonou Benin Al-Qalam University Department of Computer Science Katsina Nigeria University College of Engineering Osmania University Telangana India Amity University Amity Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350348637

The swift proliferation of Internet of Things-Enabled Autonomous Vehicles, or IoT-AVs, caused serious apprehensions about potential security weaknesses. In response, we provide a unique solution that improves cybersecurity in IoT-AVs by applying machine learning (ML) approaches. Three critical areas are the focus of our research: intrusion detection optimization (IDOpt), network reliability and latency optimization (NetOpt), and user authentication optimization (AuthOpt). Through biometric approaches which include fingerprint recognition, we accomplish powerful user authentication with 99% accuracy by using convolutional neural networks (CNNs). For Network Reliability and Latency Optimization, the Random Forest Regressor algorithm minimizes downtime and latency issues, improving vehicle performance with 97% accuracy. Additionally, we use the K-Nearest Neighbors (KNN) technique in intrusion detection optimization to protect IoT-AVs against harmful assaults. We use this ML model and obtain a 99% accuracy rate, allowing us to quickly identify and stop intrusions. The entire security posture of IoT-AVs is strengthened greatly by this optimization. To validate the effectiveness of our proposed approach, we compare our results with existing models and algorithms. The outcomes demonstrate the superiority of our ML-based optimizations over the previous approaches. Our integrated methodology establishes new benchmarks for security enhancement in IoT-AVs. © 2024 IEEE.

关键词： Adversarial machine learning

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Unlocking the Power of Natural Language Processing to Automate Knowledge Discovery 3

Unlocking the Power of Natural Language Processing to Automa...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, V. Kodmelwar, Manohar K. Adlin Jebakumari, S. Shekhawat, Jyoti Sakhare, Nitin N. Vijayalakshmi, V.J. Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350319125

natural language processing (NLP) is an increasing number of powerful tools for knowledge discovery, permitting companies to automate techniques like textual content analysis and sentiment analysis that used to require big manual effort. NLP helps the automation of tasks inclusive of information person intent, comprehending textual content, extracting records, producing summaries, and classifying documents. With NLP, those duties can be achieved in a fraction of the time with some distance extra accuracy and without the want for manual processing. By way of leveraging present technology, companies can liberate the energy of NLP to automate knowledge discovery and substantially improve their aggressive advantage. © 2023 IEEE.

关键词： automation information substantially summaries techniques

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images 10

An Integrated Image Classification Approach to Detect the Fl...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Siddique, Mohammed Ahmed, Tasneem Husain, Mohd Shahid Integral University Advanced Computing Research Lab Department of Computer Application Lucknow India Majan University College Faculty of Information Technology Muscat Oman CAS-Ibri University of Technology and Applied Sciences Department of Information Technology Muscat Oman

ISBN: (纸本)9789380544472

The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR) images which offer images in close to real-time. The Sentinel-1A satellite images from the European Space Agency provide C-band SAR images that have helped researchers achieve enhanced processes of flood detection and flood mapping in flood-prone areas. In regular cases, the Random Forest (RF) and K-Nearest Neighbor (KNN) supervised classification techniques provide higher accuracies than the unsupervised K-Means classification techniques. The integrated approach with the resultant accuracy provides long-reliable elucidation towards flood damages and assessment control. Thenceforth, the RF and KNN supervised classifications based outcomes were integrated with the outcomes of K-Means iterative clustering unsupervised classification producing an enhanced overall accuracy. The overall accuracy from the integrated approach for the city of Basti and Ayodhya is 97% and 93.8% which is higher than the accuracies derived from the supervised and unsupervised classifications. Further, these processed results when refined by time-series analysis over a number of years along with implementing change detection techniques would help in monitoring the flood situation and undertaking precautionary measures. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Synthetic aperture radar

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Inception and LightGBM-based Model for Poly-Cystic Ovary Syndrome Detection with CNN in Ultra-sound Images 4

Inception and LightGBM-based Model for Poly-Cystic Ovary Syn...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Puri, Shalini Soni, Akanksha Choudhary, Vishal Manipal University Department of Information Technology Rajasthan Jaipur India UIT-RGPV Department of Electronics Communication & Engineering Bhopal India Lovely Professional University Department of Computer Application Punjab India

ISBN: (纸本)9798350354218

Nowadays, a common hormonal condition that affects women of reproductive age and frequently results in infertility is Poly-Cystic ovary syndrome. Recognizing the Poly-Cystic ovary syndrome early is essential for symptom management and mitigating associated health risks. The traditional methods are used for diagnosis, encompassing high androgen levels, anovulation, and Poly-Cystic ovaries observed via ultra-sound. However, the challenging aspect of manually assessing Poly-Cystic ovaries observed via ultra-sound through follicle counting and thickness determination by medical professionals persists. In addition to addressing patient symptoms, clinicians must rely on a combination of biochemical and clinical indicators, lacking standardized diagnostic procedures. To alleviate these diagnostic challenges, this paper presents a hybrid Poly-Cystic Ovary Syndrome Detection model using a convolutional neural network comprising an ensemble inception model and a light gradient-boosting machine on ultra-sound Images. It significantly improves the detection of the Poly-Cystic ovaries observed and achieves an accuracy of 98.4%, surpassing InceptionV3's 96.3%. This study substantiates this performance gain through comprehensive quantitative effectiveness assessment metrics. This study proposes a promising model for accurate and efficient Poly-Cystic ovary syndrome diagnosis, aiding clinicians in providing timely and targeted care to affected individuals. © 2024 IEEE.

关键词： Deep neural networks

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Side-channel analysis attacks based on deep learning network

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Frontiers of computer Science 2022年第2期16卷 35-45页

作者： Yu OU Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

There has been a growing interest in the sidechannel analysis(SCA)field based on deep learning(DL)*** DL network or model has been developed to improve the efficiency of ***,few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate *** on the convolutional neural network and the autoencoder,this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three *** TAPDC model detects the periodicity of power trace,relating power to the intermediate values and mining the deeper features by the multi-layer convolutional *** implement the TAPDC model and compare it with two classical models in a fair *** evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption ***,Using the classifier layer,this model links power information to the probability of intermediate *** completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

关键词： side-channel analysis template attack machine learning deep learning

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