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2023 International Conference on computer, Internet of Things and Smart City, CIOTSC 2023

作者： Liu, Geng Luo, Ruihan Li, Shunmin Wu, Yanxia Zhao, Junhua China State Shipbuilding Corporation Limited System Engineering Research Institute Beijing China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350306569

The development of modern marine technology promotes frequent marine activities and marine traffic, and at the same time brings maritime security issues that cannot be ignored. Traditional optical and radio detection technologies are usually affected by many factors, and the detection effect is poor in bad weather and nighttime environments. In order to improve maritime safety requirements, infrared camera detection is regarded as an effective supplementary means. Considering that the current infrared detection capability is relatively weak, an orthogonal experimental design method is proposed. Through simulation, it is obtained that the detection distance is 120km of A at level 1, the spatial angle is 30-60° of B at level 2, the camera angle of view is the elevation angle of C at level 1, and the scanning period is D at level 3. The combination of 5s is the combination with the best recognition efficiency among the combinations of each factor level, that is, A1B2C1D3. First, briefly introduce the working principle of offshore infrared camera detection and the steps of orthogonal test design, then determine the level of factors that affect the detection ability, and design a factor level table. On this basis, the orthogonal test design was carried out, and 27 sets of test results were obtained. Finally, through the analysis and verification of the test results, the primary and secondary order of the factors affecting the detection ability were obtained, and then the optimal detection scheme was obtained. © 2023 IEEE.

关键词： Infrared detectors

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Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

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Journal of computer science & Technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLEM: A CASE STUDY IN FLORIDA, USA

TESTING FACILITY LOCATION WITH CONSTRAINED QUEUE TIME PROBLE...

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2024 Winter Simulation Conference, WSC 2024

作者： Monteiro, Almir Antonio Schneider, Rafael de Almeida Guimarães, Vanessa González, Pedro Henrique System Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil COPPEAD Graduate School of Business Federal University of Rio de Janeiro Brazil

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model’s output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability. © 2024 IEEE.

关键词： Monte Carlo methods

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den 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 Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Thamaraimanalan, T. Venkatesan, C. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hkbk College of Engineering Department of Electronics and Communication Engineering Bangalore India Knowledge Institute of Technology Department of Computer Science and Business System Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350337426

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection. © 2023 IEEE.

关键词： Feature extraction

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Experimental design method for air defense and anti-missile weapons based on uniform design

Experimental design method for air defense and anti-missile ...

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2023 International Conference on computer, Internet of Things and Smart City, CIOTSC 2023

作者： Li, Shunmin Luo, Ruihan Zeng, Peng Zhang, Yidan Wang, Zhili China State Shipbuilding Corporation Limited System Engineering Research Institute Beijing China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350306569

Due to the rapid development of modern military technology, aircraft have become one of the main threats to opponents in modern warfare during military operations. Air missile defense has also become a focus. This article demonstrates an effective performance optimization method. The study of this experimental design method is of great significance to the development of air defense and antimissile weapons and equipment systems. At present, experimental research on air defense and anti-missile weapons and equipment systems at home and abroad rarely adopts uniform design methods. This paper uses the uniform design method to optimize the testing of air defense and antimissile weapons and equipment systems. In the parameter space including 6 factors and 30 levels, the uniform design method was used to determine the effective combination of factor levels and the factors that have the greatest impact on system performance. Experiments have proven that this method can improve the interception accuracy of incoming aircraft. The proposed method is proven to be an effective interception accuracy optimization method and is applicable to other combat systems © 2023 IEEE.

关键词： Air to air missiles

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Artificial intelligence-based smart galliformes farm management system

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Journal of Umm Al-Qura University for engineering and Architecture 2025年第1期16卷 64-77页

作者： Himel, Galib Muhammad Shahriar Islam, Md. Masudul Kader, Md. Nasimul Rahman, Mustafizur School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Department of Computing and Information System Daffodil International Academy Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computing and Information System Daffodil International University Dhaka Bangladesh

This article introduces an Artificial Intelligent-driven system for Galliformes Farm Management, consulting, and disease control. Comprising both a web-based mobile app and a website, the system integrates physical electronic devices to regulate intelligent automation. The application employs Artificial Neural Network and computer vision for the identification of breeds and the recognition of diseases in galliform birds, utilizing TensorFlow for real-time detection. It notifies local farmers about bird flu infections with an integrated infection map using Google's map Application Programming Interface. The system also controls farm temperature and humidity through microcontrollers and sensors, offering consulting features such as cost calculation, food chart generation, and drug suggestions. © The Author(s) 2025.

关键词： Artificial intelligence Breed identification Disease classification Image classification Poultry farm automation Smart farm management

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Experimented Multi-Objective Teaching-Learning-Based Optimization for Medical Data Analysis

Experimented Multi-Objective Teaching-Learning-Based Optimiz...

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7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Darmawahyuni, Annisa Nurmaini, Siti Tutuko, Bambang Naufal Rachmatullah, Muhammad Marcelino, Jordan Isdwanta, Rendy Firdaus, Firdaus Iriani Sapitri, Ade Islami, Anggun Faculty of Engineering Universitas Sriwijaya Indonesia Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya South Sumatera Palembang Indonesia

ISBN: (纸本)9798350365351

Feature selection (FS) analyzes the most important features to enhance classification accuracy during preprocessing. The objective of FS techniques is to reduce the number of input variables and computational load to achieve optimal classification performance. However, finding the best features is challenging due to high dimensionality, with many features but limited data samples. Metaheuristic optimization algorithms (MOAs) are crucial in creating the best subset of features through exploration and exploitation phases. This study experiments with Teaching-Learning-based Optimization (TLO) as MOA from several diverse binary medical datasets. TLO draws attention to its excellence in improving performance with minimal features. TLO is a fairly straightforward algorithm without algorithm-specific parameters, with rapid convergence and implementation yet effectiveness. In this study, we have also conducted four classifiers to obtain the best model for minimizing the features and maximizing the performance of the model, i.e., decision tree (DT), random forest (RF), k-Nearest Neighbour (KNN), and support vector machine (SVM) with the default parameter of each classifier. As the result, TLO shows its excellence in achieving minimal features, but still outstanding performance with 100% accuracy, sensitivity, specificity, and precision. TLO is successfully applied to varying diverse medical datasets with varying number of features. © 2024 IEEE.

关键词： Contrastive Learning

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Avionics system Architectures for Software-Defined Spacecraft 1

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11th EAI International Conference on MOBILe Wireless MiddleWARE, Operating systems and Applications, MOBILWARE 2022

作者： He, Xiongwen Li, Jionghui Bai, Fan Jia, Xiaoyu Huang, Xiaofeng Xu, Mingwei Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

Developing upon open system architecture, Software-Defined Spacecraft, as a new generation of spacecraft, can support payload plug-and-play, application software loading as needed, and system function reconfiguration on demand. Therefore, the Software-Defined Spacecraft is able to solve the design and application limitations of traditional specific-designed spacecraft, making it become a significant developing trend of future spacecraft. Regarding to the software defined spacecraft bus, this paper analyzes the requirements for avionics system on the aspects of space field development, technology development and corresponding applications. Then, combining with the development status, the avionics system architecture of software defined spacecraft is proposed, along with the related key technologies. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Application programs

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