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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms 3

Investigation of Task Scheduling in Cloud Computing by using...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Ravi, Jayavadivel Rajkumar, N. Viji, C. Loganathan, D. Sushma, K.S.N. Stalin, M. Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Information Science & Engineering Cambridge Institute of Technology Karnataka Bangalore India School of Computer Science and Engineering REVA University Karnataka Bangalore India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures. © 2023 Elsevier B.V.. All rights reserved.

关键词： Cloud computing

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Potential of Deep Learning for Image Processing 11

Potential of Deep Learning for Image Processing

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Gill, Rupali Logeshwaran, J. Aggarwal, Priyanshi Srivastava, Durgesh Verma, Aditya Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Sri Eshwar College of Engineering Department of Ece Coimbatore641202 India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350350357

Deep learning has emerged as an effective tool for fixing a diffusion of image processing responsibilities such as object detection, category, segmentation, and reconstruction. This technique involves using synthetic neural networks to learn features and styles immediately from a big data set, rather than relying on predefined rules or handmade capabilities. It has resulted in enormous enhancements in performance for photo processing duties, surpassing traditional strategies in accuracy and performance. One key gain of deep getting to know for image processing is its capacity to deal with complicated and excessive-dimensional records, including photos, with many parameters. It permits the creation of extra complicated and accurate models that can deal with numerous records and produce better outcomes. Additionally, deep studying fashions can continuously enhance and adapt as they are uncovered to greater statistics, making them greater sturdy and flexible in processing picks. © 2024 IEEE.

关键词： Image enhancement

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Fault-tolerant Hamiltonian cycle strategy for fast node fault diagnosis based on PMC in data center networks

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Mathematical Biosciences and engineering 2024年第2期21卷 2121-2136页

作者： Zhao, Zhipeng Hu, Zhenyu Zhao, Zhiyu Du, xiaoyu Chen, Tianfei Sun, Lijun Minister of Education Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China College of Engineering Economics Henan Finance University Henan Zhengzhou450001 China Luoyang Railway Station of China Railway Zhengzhou Bureau Group Co. LTD Henan Luoyang450001 China College of Computer and Information Engineering Henan University Henan Kaifeng475000 China

System-level fault diagnosis model, namely, the PMC model, detects fault nodes only through the mutual testing of nodes in the system without physical equipment. In order to achieve server nodes fault diagnosis in large-scale data center networks (DCNs), the traditional algorithm based on the PMC model cannot meet the characteristics of high diagnosability, high accuracy and high efficiency due to its inability to ensure that the test nodes are fault-free. This paper first proposed a fault-tolerant Hamiltonian cycle fault diagnosis (FHFD) algorithm, which tests nodes in the order of the Hamiltonian cycle to ensure that the test nodes are faultless. In order to improve testing efficiency, a hierarchical diagnosis mechanism was further proposed, which recursively divides high scale structures into a large number of low scale structures based on the recursive structure characteristics of DCNs. Additionally, we proved that 2(n−2)nk−1 and (n−2)tn,k/tn,1 faulty nodes could be detected for BCuben,k and DCelln,k within a limited time for the proposed diagnosis strategy. Simulation experiments have also shown that our proposed strategy has improved the diagnosability and test efficiency dramatically. © 2024 the Author(s), licensee AIMS Press.

关键词： Failure analysis

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Research progress and prospect of mine fire intelligent monitoring and early warning technology in recent 20 years

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Meitan Kexue Jishu/Coal science and technology (Peking) 2024年第1期52卷 154-177页

作者： Deng, Jun Li, xin Wang, Kai Wang, Weifeng Yan, Jun Tang, Zongqing Kang, Furu Ren, Shuaijing College of Safety Science and Engineering Xi’an University of Science and Technology Xi’an710054 China Shaanxi Provincial Key Laboratory of Coal Fire Disaster Prevention and Control Xi’an University of Science and Technology Xi’an710054 China Xinjiang Mining Safety Service and Security Center Urumqi830063 China

In order to strengthen the construction of mine fire intelligent monitoring and early warning system, the research idea of mine fire intelligent monitoring and early warning technology, and summarizes the research progress of mine fire intelligent monitoring and early warning technology is put forward. The research progress from three aspects of mine fire intelligent perception technology and equipment, prediction technology and modeling, and intelligent early warning system and platform is summarized. First, the internal and external cause fire information monitoring technology and equipment are summarized, the recognition process based on image-video recognition is summarized, and the content of multi-source information fusion and its application in the fire monitoring process are described. Secondly, the prediction techniques and models for mine fires are introduced, including machine learning algorithms such as support vector machine, artificial neural network, and random forest algorithms. Then, the mine fire early warning system and platform are elaborated. On the basis of summarizing the graded early warning technology for spontaneous coal combustion and exogenous fires in mines, the progress of the mine fire early warning system platform in terms of perception layer, network layer, service fusion layer and application layer is introduced. The connotation of each layer of the early warning system and the application framework are summarized, and the intelligent monitoring and early warning system for fires in mines is constructed. Finally, the future development trend of mine fire intelligent monitoring and early warning technology in China is outlooked including following aspects. In terms of intelligent perception technology of mine fire information, it is proposed to strengthen the research and development of sensing technology and equipment. In terms of intelligent prediction technology for mine fires, it is proposed to strengthen the research on the loca

关键词： Fires

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Redefining Diagnosis: A Novel Approach to Detect Brain Hemorrhages using Modified Learning Principles with Artificial Intelligence Association 1

Redefining Diagnosis: A Novel Approach to Detect Brain Hemor...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Ramakrishna G, Naga Venkata Babu, J. Jagan Shankari, R. Dilipkumar, E. Ravi, C.N. Vanaja, T. University of Technology and Applied Sciences-Al Musannah Faculty of Engineering Oman R.M.D Engineering College Department of Ece Tamil Nadu Chennai India Anna University Department of Ece Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Tamil Nadu Chennai India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Numerous studies have concentrated on developing computer-aided diagnostic methods. They work on the principle of fast and accurate diagnosis by processing and analysis of pictures of various human body parts. In order to determine if a brain haemorrhage is present in Computed Topography (CT) images of the brain, this research follows the aforementioned technique. In addition, the kind of haemorrhage is determined. An AI-assisted prediction scheme, AI-based Brain Haemorrhages Detection (AIBHD), is presented in this paper. In order to determine how well the suggested model works, it is cross-validated with regard to the conventional learning scheme, Random Forest Classifier (RFC). Image preprocessing, segmentation, feature extraction, and classification are some of the steps that make up the developed system. The trials that were carried out yielded really encouraging outcomes. A brain haemorrhage is a form of stroke in which blood clots form in the brain's surrounding tissues as a result of an arterial burst. This method classifies brain haemorrhages using computed tomography (CT) scans. The suggested approach, AIBHD, is used for picture segmentation after preprocessing and morphological procedures. The classification model takes this extracted picture as input and outputs details on the area and percentage of haemorrhage. The results of this study improve the likelihood of identifying haemorrhage in a picture and categorize it accordingly. On average, the system's accuracy in haemorrhage classification is determined to be 97.34%. © 2024 IEEE.

关键词： Artificial intelligence

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An Investigation of Explicit Indicators for Identifying Cyberstalking Incidents Towards Sexism using Keyword-Assisted Topic Model

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Journal of The Institution of Engineers (India): Series B 2024年 1-12页

作者： Muthusami, Rathinasamy Saritha, Kandhasamy Sarwe, Deepak Umrao Megala, Arumugam Dora Pravina, C.T. Department of Computer Applications Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Mathematics P. A. College of Engineering and Technology Tamil Nadu Pollachi India Department of Mathematics University of Mumbai Maharashtra Mumbai India Department of Mathematics Nandha Engineering College Tamil Nadu Erode India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai India

This research explores the use of explicit indicators to identify cyberstalking incidents, a growing concern in the digital age. As the web and computer technology have grown, cyberstalking is now a major problem that affects an estimated 1.3 million people a year in the US alone. This research evaluates the comparative legitimacy of several predictors, such as those pertaining to an individual’s background, sociodemographic characteristics, risk, protective domains, and sexism, that are linked to allegations of cyberstalking and victimisation. The results draw attention to the distinctive elements that contribute to adult individuals from different nations engaging in cyberstalking regarding sexism. The outcomes of the investigation can help guide the improvement of initiatives to prevent and cope with cyberstalking, giving organisations and governments’ significant data to develop preventative plans and effectively deploy resources. In this research, keyword-assisted topic model (KATM), a machine learning-based system, has been developed to detect cyberstalking incidents. By combining keywords and attributes, KATM is able to identify potential instances of cyberstalking in online communication. The established automated topic coherence metrics, such as normalised pointwise mutual information (NPMI), UCI-coherence, and UMass-coherence across the SemEval-2023 Task 10 dataset, are used to evaluate the topics obtained from the established topic model KATM and a few other topic models as benchmark, including latent Dirichlet allocation (LDA) and embedded topic model (ETM). The evaluation outcomes reveal that KATM consistently surpasses its counterparts in terms of topic coherence, excelling in all three metrics. © The Institution of Engineers (India) 2024.

关键词： Adversarial machine learning

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass 1

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-Dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease Dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 496-509页

作者： Ruhul Amin Khalil Nasir Saeed Mohammad Inayatullah Babar Tariqullah Jan Sadia Din IEEE Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar 25120Pakistan the Department of Electrical Engineering Northern Border UniversityArar 73222Saudi Arabia College of Science and Engineering Hamad Bin Khalifa UniversityDoha 34110Qatar

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater ***,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic *** communication technologies are already used for communication in the underwater environment;however,lacking localization *** and magnetic induction communication achieves higher data rates for short *** the contrary,acoustic waves provide a low data rate for long-range underwater *** proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final ***,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

关键词： Bayesian multidimensional scaling(BMDS) hybrid Cramer-Rao lower bound(H-CRLB) internet of underwater things(IoUT) signals of opportunity(SOA)approach

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