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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

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computers, Materials & Continua 2023年第2期74卷 2625-2640页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Shahab SBand Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathilticsSlovak University of Technology in BratislavaSlovakia

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the *** are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server ***,the attackers have designed even better mechanisms to break these security *** cryptography is the best data protection algorithm that exchanges data between authentic *** this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure ***,the XOR function will be applied to plain text,and then salt technique will be ***,a reversing mechanism will be implemented on that data to provide more data *** decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be *** the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

关键词： Cryptography symmetric algorithm encryption decryption cipher text cloud security asymmetric algorithm

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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Blockchain-based cross-domain authorization system for user-centric resource sharing

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Blockchain(Research and Applications) 2023年第2期4卷 67-81页

作者： Yuki Ezawa Shohei Kakei Yoshiaki Shiraishi Masami Mohri Masakatu Morii Department of Electrical and Electronic Engineering Kobe UniversityKobeHyogo657-8501Japan Department of Computer Science Nagoya Institute of TechnologyNagoyaAichi466-8555Japan Faculty of Informatics Kindai UniversityHigashi-OsakaOsaka577-8502Japan

User-centric data sharing is essential to encourage citizens'active participation in the digital *** key to smart cities,a form of the digital economy,is the promotion of public use of citizen ***,it is not easy to utilize data without citizens’*** this study,we took a technological approach to these *** access(UMA)is a well-known framework for delegating resource access rights to others on the *** UMA,authorization mechanisms are designed to be centralized so that resource owners can centrally manage access rights for various resources stored in different ***,the lack of transparency in the authorization mechanism is a barrier to its implementation in large-scale systems such as smart *** this study,we developed a blockchain-based cross-domain authorization architecture that enables a resource-sharing ecosystem in which organizations that wish to utilize data can freely trade with each *** proposed architecture solves the transparency problem that conventional authorization systems have had by designing the authorization mechanism on blockchain *** implemented the proposed architecture as smart contracts and evaluated its processing *** resultant time required for delegating access rights and accessing resources was less than 500 ***,we found that the fluctuation in the processing time overhead was *** on these results,we concluded that performance degradation with the proposed architecture is minor.

关键词： Access control Authorization Blockchain Hyperledger fabric User-managed access

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Accidental Face Recognition and Detection Using Machine Learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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TTT-SEG: A Multi-structure Segmentation on Cardiac MRI using Vision Testing-time Training Network

TTT-SEG: A Multi-structure Segmentation on Cardiac MRI using...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Su, Vanessa Yuan, Xiaohan Pan, Shaoyan Hu, Mingzhe Chen, Xiaoqian Tian, Sibo Yang, Xiaofeng Department of Computer Science Emory University AtlantaGA United States Department of Radiation Oncology School of Medicine Emory University AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781510685987

We propose TTT-SEG, a Test-Time Training Segmentation model, for automatic cardiac organ segmentation on MRIs. Manual contouring in MR imaging is time-consuming and labor-intensive, making automatic segmentation crucial for efficient therapy workflows. Despite advancements in Convolutional Neural Networks (CNNs) and Vision Transformer-based Networks achieving state-of-the-art accuracy, their performance is hindered by the lack of large-scale MRI datasets, leading to poor generalization on new patients. TTT-SEG addresses this issue by integrating a CNN with a novel learning-to-learn strategy. Our model features a dual-loop mechanism: a Reconstruction Learning Loop (RLL) for learning self-supervised features and an Adaptive Learning Loop (ALL) for segmentation features. Unlike the traditional networks, the RLL learns even during testing, improving segmentation accuracy for specific samples. Evaluating TTT-SEG on the Automated Cardiac Diagnosis Challenge (ACDC) dataset shows superior performance in segmenting the left and right ventricles and myocardium, outperforming methods like Vnet, nnUnet, SwinUNETR, and nnFormer. These results demonstrate TTT-SEG's potential to enhance cardiac organ segmentation and improve cardiac-related diagnosis and therapy workflows. © 2025 SPIE.

关键词： Electrocardiography

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DUCK: A Drone-Urban Cyber-Defense Framework Based on Pareto-Optimal Deontic Logic Agents 37

DUCK: A Drone-Urban Cyber-Defense Framework Based on Pareto-...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Deb, Tonmoay Dix, Jürgen Jeong, Mingi Molinaro, Cristian Pugliese, Andrea Li, Alberto Quattrini Santos, Eugene Subrahmanian, V.S. Yang, Shanchieh Zhang, Youzhi Department of Computer Science Northwestern University EvanstonIL United States Department of Computer Science Dartmouth College HanoverNH United States Department of Informatics Modeling Electronics and System Engineering University of Calabria Rende Italy Department of Informatics Technical University of Clausthal Clausthal Germany Department of Computer Engineering Rochester Institute of Technology RochesterNY United States CAIR Hong Kong Institute of Science and Innovation Hong Kong

ISBN: (纸本)9781577358800

Drone based terrorist attacks are increasing daily. It is not expected to be long before drones are used to carry out terror attacks in urban areas. We have developed the DUCK multiagent testbed that security agencies can use to simulate drone-based attacks by diverse actors and develop a combination of surveillance camera, drone, and cyber defenses against them. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Terrorism

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