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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 IEEE.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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Authenblue: A New Authentication Protocol for the Industrial Internet of Things

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computers, Materials & Continua 2021年第4期67卷 1103-1119页

作者： Rachid Zagrouba Asayel AlAbdullatif Kholood AlAjaji Norah Al-Serhani Fahd Alhaidari Abdullah Almuhaideb Atta-ur-Rahman Department of Computer Information System College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of *** the IoT evolution led to quality of life enhancement,many of its devices are *** lack of robust key management systems,efficient identity authentication,low fault tolerance,and many other issues lead to IoT devices being easily targeted by *** this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network(CPANs)in an Industrial IoT(IIoT)*** study proposed Authenblue protocol as a new Blockchainbased authentication *** enhance the authentication process and make it more secure,Authenblue modified the way of generating IIoT identifiers and the shared secret keys used by the IIoT devices to raise the efficiency of the authentication *** enhance the authentication protocol that other models rely on by enhancing the approach used to generate the User Identifier(UI).The UI values changed from being static values,sensors MAC addresses,to be generated values in the inception *** approach makes the process of renewing the sensor keys more secure by renewing their UI values instead of changing the secret *** this study,Authenblue has been simulated in the Network Simulator 3(NS3).Simulation results show an improved performance compared to the related work.

关键词： Authentication industrial internet of things security Authenblue blockchain NS3

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Output Linearization of Single-Input Single-Output Fuzzy system to Improve Accuracy and Performance

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computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth Science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method 8

A Comparative Analysis on Three Duplication Elements in Copy...

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8th IEEE International Conference on Software Engineering and computer systems, ICSECS 2023

作者： Nor Azaimi, Nur Izzati Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Department of Computer System and Networking Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. © 2023 IEEE.

关键词： Copy-Move forgery DEFACTO duplication regions PatchMatch

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Analysis and Forecasting of Blockchain-based Cryptocurrencies and Performance Evaluation of TBATS, NNAR and ARIMA 4

Analysis and Forecasting of Blockchain-based Cryptocurrencie...

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4th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2022

作者： Sadia, Iqra Mahmood, Atif Binti Mat Kiah, Laiha Azzuhri, Saaidal Razalli University of Malaya Department of Computer System & Technology Kuala Lumpur Malaysia University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

The rapid growth of cryptocurrencies has gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. Therefore the intelligent management and under-standing the characteristics of cryptocurrencies are becoming more interesting. The price of cryptocurrencies are characterized by linear and nonlinear trend, seasonality and high volatility, which increases the risk factors for investors. This study ex-periments with three different time series forecasting methods, specifically considered for Cryptocurrencies price such as Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Monero (XMR) and Cardano (XRP), and devises a procedure to evaluate their performance. Time series data are collected and examined using descriptive statistics. In next step, the White Neural Network is used for Non-Linearity and Dickey-Fuller for nonstationary and correlation among different settings of datasets. Based on these analyses, we evaluate efficient financial forecasting models such as Autoregressive Integrated Moving Average (ARIMA), Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) and Neural Network Autoregressive (NNAR) with reference to different parameters configuration of these models. The performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criterion and models are ranked by statistical mean and standard deviation of MAPE values. The NNAR model gives minimum MAPE of 2.823 while the minimum convergence time of 4.9835s is observed with TBATS and hence, these are ranked at top amongst other models respectively. These results underpin that neural network-based models perform equally well on both types of nonlinear and linear financial data and, thus, have the potential to improve the impact of financial transaction and cryptocurrencies price bringing more innovation in the decision making process. © 2022 IEEE.

关键词： Errors

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Enhancing Breeding Control: The IoT-Driven Automated system for Budgie Farming 11

Enhancing Breeding Control: The IoT-Driven Automated System ...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nooyimsai, Saranpong Thaloy, Jarinya Jansengrat, Parinya Janratchakool, Weena Krohkaew, Jaturapith Crisnapati, Padma Nyoman Thwe, Yamin Rajamangala University of Technology Srivijaya Department of Information Technology Nakhon Si Thammarat Thailand Rajamagala University of Technology Department of Big Data Management and Analytics Thanyaburi Pathum Thani Thailand Rajamagala University of Technology Department of Computer Science Thanyaburi Pathum Thani Thailand Institute of Technology and Business Stikom Bali Department of Information System Bali Indonesia

ISBN: (纸本)9798350305968

In Thailand's Pak Phanang Nok Hong Yok Farm, where budgies are raised, it emphasizes the value of proper care and surroundings. The primary problem this research addresses is the need for efficient and automated management of budgie farms, encompassing aspects such as feeding, watering, and temperature control and overcoming language barriers for Thailand's budgie farmers. An innovative Internet of Things (IoT) solution is suggested, departing from conventional approaches to handle challenges connected to manual care and environmental management for farmers. This system provides solutions for these challenges while enhancing monitoring and facilitating automated control. Through this IoT-based solution, the research intends to increase productivity, save time, and decrease bird mortality while enabling users to manage their farms remotely. Comprehensive methodologies involving breeding, environmental considerations, and hardware and software design are outlined, resulting in significant advancement in budgie farming practices and showcasing IoT's potential in agriculture. © 2023 IEEE.

关键词： Birds

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ROS Supported Heterogeneous Multiple Robots Registration and Communication with User Instructions 2

ROS Supported Heterogeneous Multiple Robots Registration and...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Rajapaksha, U.U. Samantha Jayawardena, Chandimal MacDonald, Bruce A. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System and Engineering Malabe Sri Lanka University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9781665407410

Different types of heterogeneous multiple service robots are working in the same environment to help humans in many ways in a smart house. These service robots have different capabilities based on the different control and communication systems. The complexity of the programming of the robots is now reduced by using middleware like Robot Operating system (ROS). However, the communication and control of the heterogeneous service robots using very high-level instruction is still tricky because of differences in ROS topics. If a user has issued a high-level instruction to all multiple heterogeneous robots in the same environment, then all robots must complete the given task without considering the software differences in each robot. This research has developed a web-based interface where users can input high-level instruction to all multiple heterogeneous robots running in the same environment. We have used three levels of instructions. Level 01 moves robot forward for the given speed for some distance without obstacles. Level 02 is to move the robot to a specific location without obstacles. Level 03 is to navigate the robot to a goal with some obstacles in the environment. Initially, all robots need to register their software specifications and hardware specification Format (URDF) in the robot registration engine with the help of the ontology. Then all service robots act according to the instruction given by the interpreter. The proposed system was evaluated using a simulated environment with a gazebo using "Turtlebot"and "Tiago"robots. Time complexity analysis of all algorithms was completed using the Big O notation. © 2022 IEEE.

关键词： Ontology

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Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer 26

Identification of Molecular Biomarkers and Key Pathways amon...

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26th International Conference on computer and information technology, ICCIT 2023

作者： Yasmin, Mst. Farjana Faruk Hosen, Md. Basar, Md. Abul Alam Shadhin, Khairul Trisha, Arifa Ferdoushi Shahin Uddin, Muhammad Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh Department of Computing and Information System Dhaka Ashulia Bangladesh Department of Computer Science and Engineering Kanchon Purbachal American City1460 Bangladesh Department of Software Engineering Dhaka Ashulia Bangladesh

ISBN: (纸本)9798350359015

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network's top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested. © 2023 IEEE.

关键词： COPD Differential expressed genes Drug compounds enrichment analysis Hub gene IPF Lung cancer Protein-protein network

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease 5

Identification of Candidate Drugs and Molecular Pathways for...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Hosen, Md. Faruk Yasmin, Mst. Farjana Basar, Md. Abul Trisha, Arifa Ferdoushi Shadhin, Khairul Alam Mollah, Md. Sarwar Uddin, Muhammad Shahin Department of Computing and Information System Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Purbachal American CityKanchon 1460 Bangladesh Department of Software Engineering Ashulia Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798350394290

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs. © 2023 IEEE.

关键词： Proteins

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