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Research on medical data storage and sharing model based on blockchain

High-Confidence Computing 2023年第3期3卷 61-67页

作者： Jian Zhao Wenqian Qiang Zisong Zhao Tianbo An Zhejun Kuang Dawei Xu Lijuan Shi College of Network Security Changchun UniversityChangchun 130022China College of Computer Science and Technology Changchun UniversityChangchun 130022China College of Electronic Information Engineering Changchun UniversityChangchun 130022China

With the process of medical informatization,medical diagnosis results are recorded and shared in the form of electronic data in the ***,the security of medical data storage cannot be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that cannot be *** solve the above problems,a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is *** on the idea of blockchain decentralization,the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions;A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data;Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy;A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is *** security verification,compared with the traditional medical big data storage and sharing mode,the model has better security and sharing.

关键词： Blockchain Encryption algorithm Medical data Secure message processing systems storage models Shared-resource systems

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

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computer Systems science & Engineering 2023年第2期44卷 1679-1689页

作者： Abdullah Alshehri Nayeem Khan Ali Alowayr Mohammed Yahya Alghamdi Information Technology Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Computer Science Department Faculty of Science and Arts at BuljurashiAl Baha UniversityAl Baha65799Saudi Arabia

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior *** framework models the user behavior as sequences of events representing the user activities at such a *** represented sequences are thenﬁtted into a recurrent neural network model to extract features that draw distinctive behavior for individual ***,the model can recognize frequencies of regular behavior to proﬁle the user manner in the *** subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular *** importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the *** detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including ***,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reﬂect a normal network workﬂ*** contrast,the irregular patterns can trigger an alert for a potential *** framework has been fully described where the evaluation metrics have also been *** experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM *** paper has been concluded with pro-viding the potential directions for future improvements.

关键词： Cybersecurity deep learning machine learning user behavior analytics

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Dynamic Spectrum Management and Spectrum Sharing Techniques in 5G Networks: A Survey

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Journal of Mobile Multimedia 2024年第5期20卷 997-1038页

作者： Mondal, Sonali Dutta, Manash Pratim Chakraborty, Swarnendu Kumar Department of Computer Science & Engineering National Institute of Technology Arunachal Pradesh India Department of Computer Science & Information Technology Cotton University Assam India

The enormous developments of gaming devices as well as mobile apps have increased the demand of bandwidth. Development of wireless applications has been affected because of the insufficient spectrum resources in the 3G and 4G network. This spectrum scarcity is the main limitation of 3G and 4G networks, which leads to the revolution of the 5G technology. Spectrum scarcity is a great challenge in wireless communication. There are two significant research areas that need to be explored such as, searching spectrum resource and maximum spectrum utilization. To address spectrum insufficiency, consistent research is needed to find out spectrum band and share the spectrum resource. In this work, complete Spectrum management framework is discussed as well as spectrum sharing techniques are classified. We have conducted a thorough survey on various spectrum sharing schemes. The literature survey has been classified based on some significant sharing techniques depending on spectrum access method, network architecture, spectrum allocation behavior etc. We have also summarized contribution as well as limitation of recent spectrum sharing approaches. To enhance 5G technology, related spectrum sharing surveys are studied and future research directions are also discussed here. © 2024 River Publishers.

关键词： 5G cognitive radio spectrum management framework spectrum sensing spectrum sharing

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Semantic segmentation of ultra-high resolution remote sensing images based on fully convolutional neural networks 4

Semantic segmentation of ultra-high resolution remote sensin...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhang, Hua Jiang, Zhengang Xu, Jun Yao, Xuekun School of Computer Science and Technology Changchun University of Science and Technology Changchun China Changchun Institute of Technology Jilin Key Laboratory of Changbai History and Culture and VR Technology Reconstruction Changchun China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China

ISBN: (纸本)9798350326444

Semantic segmentation has been widely used in various fields of remote sensing;however, ultra-high resolution remote sensing images, due to their extremely high resolution, exhibit more spatial details and cannot be directly segmented using manual segmentation or traditional semantic segmentation methods. We applied fully convolutional deep learning-based neural networks to the semantic segmentation of ultra-high-resolution remote sensing images and at the same time designed an efficient image segmentation strategy to reduce the difficulty of training while ensuring the integrity of the dataset. We conducted experiments on the ultra-high resolution remote sensing dataset Potsdam and demonstrated that the semantic segmentation method based on fully convolutional neural networks can achieve 93.78% MIoU and 97.39% PA on the training set. © 2023 IEEE.

关键词： Semantic Segmentation

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Synthetic data generation by diffusion models

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National science Review 2024年第8期11卷 19-21页

作者： Jun Zhu Department of Computer Science and Technology BNRist Center Tsinghua-Bosch Joint ML CenterTsinghua University Pazhou Laboratory (Huangpu)

Sufficient and high-quality data are a prerequisite for building complex machine learning systems, especially those with a large number of parameters (e.g., ChatGPT). However, it is typically challenging or even impossible to acquire a sufficient amount of real data to train such systems. For example, auto-driving systems may need to learn from various accidental events in order to be reliable in driving, while collecting such real data is difficult or ethically infeasible.

关键词： generation diffusion models data synthetic

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Audio verification in forensic investigation using light deep neural network

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International Journal of Information technology (Singapore) 2024年第5期16卷 2813-2821页

作者： AL-Shakarchy, Noor D. Abdullah, Zahraa Najm Alameen, Zainab M. Harjan, Zahraa A. Department Of Computer Science Faculty of Computer Science and Information Technology Kerbala University Kerbala Iraq Kerbala University Kerbala Iraq Faculty of Computer Science and Information Technology Kerbala University Kerbala Iraq

Recently people have difficulties distinguishing real speech from computer-generated speech so that the synthetic voice is getting closer to a natural-sounding voice, due to the advancements in deep learning and voice-generation techniques. This high level of naturalness allows for synthesizing a person target voice and reproduce someone's voice with high accuracy. Audio forensics is care with improving the speech intelligibility, enhancement, and analysis in order to prove the authentication of recorded audio files it is authentic and no manipulations were done to them. This work proposes the Audio verification model in Forensic Investigation using one Dimensional Convolutional Neural networks with new light architecture. The proposed model build a natural voice characteristics pattern by analysis the information available in an audio file and extract salient features in order to build a feature maps, and then tracked these feature maps over time is the same model to make the decision about whether the audio is Synthetic or not. The proposed model performed in two main stages: the pre-processing stage then verification predictor stage, each stage contained several steps performed different functions. The experiments were conducted on a last version benchmark dataset called the Fake-or-Real Dataset from the APTLY lab and ASVspoof 2019 dataset. The proposed model achieve successful results, so that the accuracy realizes 99.89% (± 1.78%), and the value of the loss function is 0.007 on the Fake-or-Real Dataset, and achieved a 0.006% Equal Error Rate (EER) on ASVSpoof 2019 dataset. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Audio Batch normalization Dropout One dimensional convolutional neural network (1DCNN) Pooling Real voice Synthetic voice

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Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

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computer Modeling in Engineering & sciences 2024年第9期140卷 2397-2424页

作者： Hongchi Liu Xing Deng Haijian Shao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89119USA

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi

关键词： Remote sensing image image dehazing deep learning feature fusion

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VideoDreamer: Customized Multi-Subject Text-to-Video Generation With Disen-Mix Finetuning on Language-Video Foundation Models

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IEEE Transactions on Multimedia 2025年 27卷 2875-2885页

作者： Chen, Hong Wang, Xin Zeng, Guanning Zhang, Yipeng Zhou, Yuwei Han, Feilin Wu, Yaofei Zhu, Wenwu Tsinghua University Department of Computer Science and Technology Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China Beijing Film Academy Department of Film and TV Technology Beijing100088 China Beijing University of Technology Beijing100124 China

Customized text-to-video generation aims to generate text-guided videos with user-given subjects, which has gained increasing attention. However, existing works are primarily limited to single-subject oriented text-to-video generation, leaving the more challenging problem of customized multi-subject generation unexplored. In this paper, we fill this gap and propose a novel VideoDreamer framework, which can generate temporally consistent text-guided videos that faithfully preserve the visual features of the given multiple subjects. Specifically, VideoDreamer adopts the pretrained Stable Diffusion with temporal modules as its base video generator, taking the power of the text-to-image model to generate diversified content. The video generator is further customized for multi-subjects, which leverages the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, to tackle the attribute binding problem of multi-subject generation. Additionally, we present a disentangled motion customization strategy to finetune the temporal modules so that we can generate videos with both customized subjects and motions. To evaluate the performance of customized multi-subject text-to-video generation, we introduce the MultiStudioBench benchmark. Extensive experiments demonstrate the remarkable ability of VideoDreamer to generate videos with new content such as new events and backgrounds, tailored to the customized multiple subjects. © 1999-2012 IEEE.

关键词： Text to video Dogs Codes Visualization Generators Text to image Diffusion models Hands Training Noise measurement

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Classification of plant diseases in images using dense-inception architecture with attention modules

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Multimedia Tools and Applications 2024年 1-26页

作者： Khan, Sultan Daud Basalamah, Saleh Naseer, Atif Department of Computer Science National University of Technology Islamabad Pakistan Department of Computer Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Umm Al-Qura University Makkah Saudi Arabia

Agriculture plays a vital role in providing food to a growing world population. However, plant diseases and pests result in 50% reductions in crop yields, which exacerbates poverty and threatens a sustainable food system. Currently, plant disease detection mainly relies on manual visual inspection and analysis of the field, which is a time-consuming and unreliable method. Modern technology, such as smart agriculture, can leverage machine learning, computer vision, and remote sensing technologies to overcome the limitations of traditional methods. Several machine learning algorithms have been used for plant disease classification and detection. However, these models suffer from limitations such as dependence on complex hand-crafted features for classification, and limited adaptability to new data. Deep learning models, on the other hand, have the ability to automatically learn hierarchical representations from raw data, making them inherently more informative and adaptable to changing data patterns. While existing deep learning approaches for plant disease detection have achieved some degree of success, these models still face challenges in achieving superior performance due to similar visual appearances of different plant diseases, intra-class differences, and variation in scale and resolution. To address these challenges, we proposed a framework that consists of hybrid network and attention module. Our hybrid module combines the benefits of DenseNet and InceptionNet to capture multi-scale features, while the attention module addresses the challenges of inter-class similarity and intra-class variability by isolating the most discriminative feature and disregarding extraneous and insignificant information. We performed experiments on publicly available challenging dataset. From Experimental results, we demonstrate the effectiveness of the proposed framework in achieving high accuracy in plant disease detection. © The Author(s), under exclusive licence to Springer Sci

关键词： Agriculture

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