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2012 16th International Conference on System Theory, Control and Computing, ICSTCC 2012

作者： Şerbencu, A.E. Şerbencu, A. Control Systems and Electrical Engineering Departament Automatic Computer Science Electrical and Electronic Engineering Faculty Dunarea de Jos University of Galati Romania

ISBN: (纸本)9786068348483

Differential Ant Stigmergy Algorithm (DASA) is a recent meta-heuristic method which represents an adaptation of Ant Colony Optimization (ACO) to continuous optimization problems. Other adaptations of ACO to continuous optimization exist but DASA seems to be very efficient for the class of highdimension real-parameters optimization problems, becoming a competitor to more classical methods such as Particle Swarm Optimization (PSO). The PSO is a meta-heuristic which is also inspired from insects' life as ACO. Even both methods use a population of entities, the memory of PSO is larger as those of DASA. DASA uses just one current solution around which the population search and the difference that generated last improvement. PSO stores the previous best position and velocity of every particle. This paper attempts to examine if an elitist version of DASA has at least the same effectiveness (finding the true global solution) as PSO, but with significantly better efficiency (less function evaluation). The performance comparison of DASA and PSO is implemented using a set of six test functions well known for their difficulty. © 2012 Dunarea de Jos University.

关键词： Ant colony optimization

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Verifying and analyzing adaptive logic through UML state models

Verifying and analyzing adaptive logic through UML state mod...

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1st International Conference on Software Testing, Verification and Validation, ICST 2008

作者： Ramirez, Andres J. Cheng, Betty H. C. Software Engineering and Network Systems Laboratory Department of Computer Science and Engineering Michigan State University East Lansing MI 48824 United States

ISBN: (纸本)076953127X

It is becoming increasingly important to be able to adapt an application's behavior at run time in response to changing requirements and environmental conditions. Adaptive programs are typically difficult to specify, design, and verify. A variety of conditions may trigger an adaptation, each of which may involve different types of adaptation mechanisms. In many cases, adaptive systems are concurrent, thus further exacerbating the complexity. Furthermore, it is important that adaptations do not put the system into an inconsistent state during or after adaptation. This paper presents an iterative approach to modeling and analyzing UML behavioral design models of adaptive systems, where the UML state diagrams are automatically translated into Promela code for analysis with the Spin model checker. The adaptive models are analyzed for adherence to both system invariants and properties that should hold during adaptation. We demonstrate this approach on applications for the mobile computing domain where we verify the design models against formally-specified properties. © 2008 IEEE.

关键词： Adaptive systems

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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Joint Pilot Design and Beamforming Optimization in Massive MIMO Surveillance systems

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China Communications 2022年第4期19卷 83-97页

作者： Caihong Kai Xiangru Zhang Xinyue Hu Wei Huang School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of TechnologyHefei 230601China School of Electronics and Information Engineering Anhui UniversityHefei 230601China

This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output(MIMO)frequency-division duplexing(FDD) wireless legitimate surveillance system. With the proposed scheme,the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total power budget at the training and transmission phases,while guaranteeing the information from suspicious source can be successfully decode, joint pilot design,power allocation and beamforming strategy are formulated as optimization problems for the two objective functions: MSE and effective eavesdropping rate. A closed-form expression of the optimal pilot with the limited length can be obtained via the channel correlation. The optimal power problem at the training phase can be solved by a simple bisection method. Then,based on the obtained imperfect estimated channel,the jamming beamforming at monitor optimization algorithm is proposed by utilizing the convex Semidefinite Programming approach to maximize the effective eavesdropping rate. Numerical results show that the proposed joint pilot design, power allocation and beamforming optimization scheme can improve the surveillance performance of the legitimate monitor as compared to the existing passive eavesdropping and jamming-assisted eavesdropping.

关键词： proactive eavesdropping massive MIMO channel estimation pilot contamination beamforming

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ProDV - A case study in delivering visual analytics

ProDV - A case study in delivering visual analytics

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1st IEEE Conference on Visual Analytics science and Technology, VAST 10

作者： Overby, Derek Keyser, John Wall, Jim Department of Computer Science and Engineering Texas A and M University United States Department of Industrial and Systems Engineering Texas A and M University United States

ISBN: (纸本)9781424494866

We present a custom visual analytics system developed in conjunction with the test and evaluation community of the US Army. We designed and implemented a visual programming environment for configuring a variety of interactive visual analysis capabilities. Our abstraction of the visualization process is based on insights gained from interviews conducted with expert users. We show that this model allowed analysts to implement multiple visual analysis capabilities for network performance, anomalous sensor activity, and engagement results. Long-term interaction with expert users led to development of several custom visual analysis techniques. We have conducted training sessions with expert users, and are working to evaluate the success of our work based on performance metrics captured in a semi-automated fashion during these training sessions. We have also integrated collaborative analysis features such as annotations and shared content. ©2010 IEEE.

关键词： Graphical user interfaces

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Improved infrared thermography based image construction for biomedical applications using Markov Chain Monte Carlo method

Improved infrared thermography based image construction for ...

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31st Annual International Conference of the IEEE engineering in Medicine and Biology Society: engineering the Future of Biomedicine, EMBC 2009

作者： Umadevi, V. Suresh, S. Raghavan, S.V. Network Systems Laboratory Department of Computer Science and Engineering IIT Madras Chennai India Department of Mechanical Engineering IIT Madras Chennai India

ISBN: (纸本)9781424432967

Breast Thermography is one of the scanning techniques used for breast cancer detection. Looking at breast thermal image it is difficult to interpret parameters of tumor such as depth, size and location which are useful for diagnosis and treatment of breast cancer. In our previous work (ITBIC) we proposed a framework for estimation of tumor size using clever algorithms and the radiative heat transfer model. In this paper, we expand it to incorporate the more realistic Pennes bio-heat transfer model and Markov Chain Monte Carlo (MCMC) method, and analyze it's performance in terms of computational speed, accuracy, robustness against noisy inputs, ability to make use of prior information and ability to estimate multiple parameters simultaneously. We discuss the influence of various parameters used in its implementation. We apply this method on clinical data and extract reliable results for the first time using breast thermography. ©2009 IEEE.

关键词： Thermography (imaging)

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Quantifying the Influence of Websites Based on Online Collective Attention Flow

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Journal of computer science & Technology 2015年第6期30卷 1175-1187页

作者：李勇张江孟小峰王常青 School of Information Renmin University of China Beijing 100872 China College of Computer Science and Engineering Northwest Normal University Lanzhou 730070 China School of Systems Science Beijing Normal University Beijing 100875 China DNSLAB China Internet Network Information Center Beijing 100190 China

The availability of network big data, such as those from online users＇ surfing records, communication records, and e-commerce records, makes it possible for us to probe into and quantify the regular patterns of users＇ long-range and complex interactions between websites. If we see the Web as a virtual living organism, according to the metabolic theory, the websites must absorb ＂energy＂ to grow, reproduce, and develop. We are interested in the following two questions： 1） where does the ＂energy＂ come from？ 2） will the websites generate macro influence on the whole Web based on the ＂energy＂？ Our data consist of more than 30 000 online users＇ surfing log data from China Internet Network Information Center. We would consider the influence as metabolism and users＇ attention flow as the energy for the websites. We study how collective attention distributes and flows among different websites by the empirical attention flow network. Different from traditional studies which focused on information flow, we study users＇ attention flow, which is not only a ＂reversed＂ way to study Web structure and transmission mode, but also the first step to understand the underlying dynamics of the World Wide Web. We find that the macro influence of websites scales sub-linearly against the collective attention flow dwelling time, which is not consistent with the heuristics that the more users＇ dwelling time is, the greater influence a website will have. Further analysis finds a supper-linear scaling relationship between the influence of websites and the attention flow intensity. This is a websites version of Kleiber＇s law. We further notice that the development cycle of the websites can be split into three phases： the uncertain growth phase, the partially accelerating growth phase, and the fully accelerating growth phase. We also find that compared with the widespread hyperlinks analysis models, the attention flow network is an effective theoretical tool to estimate and rank websites.

关键词： attention flow network allometric scaling law influence of website online collective behavior

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On variable selection of a nonlinear non-parametric system with a limited data set: A stepwise algorithm 19

On variable selection of a nonlinear non-parametric system w...

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Bai, Er-Wei Li, Kang Zhao, Wenxiao Dept. of Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Academy of Mathematics and Systems Science China

ISBN: (纸本)9783902823625

This paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm. © IFAC.

关键词： Parameter estimation

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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

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computer systems science & engineering 2023年第4期45卷 753-767页

作者： Mesfer Al Duhayyim Areej A.Malibari Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare *** advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging *** cancer(OC)has commonly occurred in head and neck *** identification of OC enables to improve survival rate and reduce mortality ***,the design of CAD model for OC detection and classification becomes ***,this study introduces a novel computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)*** proposed CADOC-SFOFC model determines the existence of OC on the medical *** accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)***,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification ***,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced *** experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

关键词： Oral cancer computer aided diagnosis deep learning fusion model seagull optimization classification

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Image-Based Determination of the Growth or Shrinkage of Wounds at the Dermal Layer

Image-Based Determination of the Growth or Shrinkage of Woun...

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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Barkana, Berra Z. Barkana, Duha A. Faezipour, Miad Masuk High School MonroeCT06468 United States University of Bridgeport Departments of Computer Science and Engineering and Biomedical Engineering BridgeportCT06604 United States

ISBN: (纸本)9781728176246

Acute and chronic wounds affect people of all ages around the world. Monitoring the size of open wounds has been proven to be effective for the diagnosis and prognosis of several medical complication such as ulcers, chronic wounds, and a variety of infections. This work presents an image-based approach to calculate the percentage of growth or shrinkage of wounds at the dermal layer. Image segmentation is applied to determine the region of interest in the wound image. Changes in the wound size (area) are computed to quantify the healing percentage. © 2020 IEEE.

关键词： Shrinkage

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