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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology 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 Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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HDFR: A Hydrologic Data and Modeling System with On-Demand Access to environmental Sensing Data for Decision Making 17

HDFR: A Hydrologic Data and Modeling System with On-Demand A...

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17th International Conference on Ubiquitous information Management and Communication, IMCOM 2023

作者： Luna, Daniel Hernandez, Felipe Liang, Yao Liang, Xu University of Pittsburgh Department of Civil and Environmental Engineering PittsburghPA United States Indiana University-Purdue University Indianapolis Department of Computer and Information Science IndianapolisIN United States

ISBN: (纸本)9781665453486

This paper introduces the Hydrologic Disaster Forecasting and Response (HDFR), an online data and modeling integration software system that facilitates the machine-to-machine access to and the management of environmental sensing data from space and ground products. Available data sources include in-situ measurements from weather and hydrographic stations;remote sensing products from Doppler precipitation radars in the United States, Earth-monitoring satellites that measure precipitation, soil moisture, and snow cover;and numerical weather prediction model outputs from the U.S. National Weather Service. Additionally, the HDFR system provides a suite of hydrologic modeling tools;including data fusion, storm severity assessment, and hydrologic model preprocessing for the Distributed Hydrology Soil Vegetation Model (DHSVM);that are seamlessly incorporated with the diverse suite of data products. Two example workflows demonstrate how this unified framework could help bridge the gap between the online and on-demand accessing of growing wealth of Earth-observing data and hydrologic prediction for scientific and engineering applications. © 2023 IEEE.

关键词： Remote sensing

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Nafion-Based Chipless RFID Humidity Sensor for Smart Tag Applications 1

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21st National Conference on Sensors and Microsystems, AISEM 2022

作者： Marchi, Giada Mulloni, Viviana Donelli, Massimo Lorenzelli, Leandro Sensors and Devices Fondazione Bruno Kessler Trento Italy Department of Civil Environmental and Mechanical Engineering University of Trento Trento Italy Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9783031257063

ISBN: (纸本)9783031257056

A humidity sensor based on frequency-domain microwave chipless technology is studied and experimentally characterized in this paper. The chiplessmicrowave sensor is composed of amicrowave resonator coupled with a 50 Ω microstrip transmission line, covered with a 180 μm-thick Nafion layer, which acts as sensitive material. The response of the sensor at 25 °C and for a relative humidity (RH) variation in the range of 0-40% has been experimentally assessed. The sensor shows good sensitivity both in terms of frequency and intensity variations and good linearity. The sensor follows the RH variations in a fully reversible way and the response time is estimated in a few seconds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Humidity sensors

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SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs 29

SemLog: A Semantics-based Approach for Anomaly Detection in ...

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

作者： Tan, Xiaoman Han, Ningning Lu, Siyang Chen, Wu Wang, Dongdong College of Computer and Information Science College of Software Southwest University Chongqing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Environmental and Construction Engineering University of Central Florida Department of Civil OrlandoFL United States

ISBN: (纸本)9798350330717

Syslog-based anomaly detection is crucial for protecting the systems from malicious attacks or malfunctions. System logs are semi-structured text messages printed by logging statements to record the system's run-time status, involving rich semantic information. However, the existing BERT-based log anomaly detection method is based on the log key sequence, does not consider the semantics of the log data, and discards the variable part, resulting in a high rate of missed detection. In this paper, we propose SemLog, a self-supervised framework for log anomaly detection based on BERT. By incorporating log semantics and variables and employing multi-feature fusion, we mitigate the independent assumption issue in the Masked Language Modeling model. The experimental results on three benchmarks show that SemLog achieves high performance compared with the state-of-the-art approaches for anomaly detection. © 2023 IEEE.

关键词： BERT log anomaly detection log semantics log variables multi-feature fusion

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Application of Industrialised Building System in the Field of Construction toward Sustainability: Future Routes and a Theoretical Mapping

Application of Industrialised Building System in the Field o...

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2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Alawag, Aawag Mohsen Alaloul, Wesam Salah Liew, M.S. Al-Ashmori, Ammar Alqadami, Ahmed Tawifk Ammad, Syed Universiti Teknologi Petronas Civil and Environmental Engineering Perak Malaysia Universiti Teknologi Petronas Computer and Information Science Perak Malaysia

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Industrialized Building System (IBS) is a construction system in which components are factory-made in a measured atmosphere (on or off-site), placed, conveyed, and collected on site with minimal additional site activities, according to the Construction Industry Development Board Malaysia (CIDB). The application of IBS in construction projects could significantly improve site performance in terms of protection, quality, charge efficiency, production, sustainability and waste reduction. In the construction industry, sustainability has become increasingly essential. Meanwhile, due to the extremely difficult constraints that stakeholders encounter in adopting new technology and a scarcity of experienced labour, the degree of IBS implementation in Malaysia remains low. According to several studies, typical IBS is gradually being pushed to the background. However, no systematic study applying bibliometric examination of IBS studies to more investigate relevant study in this field was uncovered. This study offers an outline of the IBS current studies gathered from "Web of science"database. To visualise the material included in the study scope, VOSviewer was combined with bibliometric analytic techniques. As a conclusion, six clusters were discovered that focused on these primary theme segments: IBS approach, prefabrication, construction, innovation, sustainability, and management. Based on the current gap, this paper makes recommendations for future IBS research. Assisting the IBS research community with research and development. © 2022 IEEE.

关键词： Sustainable development

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Embedded Metacognitive learning to Enhance the educational Socioscientific Decision-Making of science Students'

Embedded Metacognitive learning to Enhance the educational S...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Velos, Severina P. Baird, Jeem Clyde C. Velos, Arnulf Jairo P. Velos, Mary Angelie P. Oyebode, Oluwadare Joshua Sunway University Dept of Computing and Information Systems Malaysia The Federal Polytechnic Dept of Computer Science Ado Ekiti Nigeria Cebu Technological University Cebu Philippines Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria

ISBN: (纸本)9798350358155

The current research set out to find out how different kinds of collaborative training affected students' ability to make socioscientific decisions and their metacognitive abilities in a science classroom setting. Both presenting socioscientific issues and proposing and evaluating solutions to such difficulties are part of socioscientific decision making. We looked at two different collaborative training techniques that were built using the IMPROVE method and had different embedded metacognitive instructions. Three hundred and thirty-five seniors from three different high schools participated: one in a traditional collaborative learning context (COLED), another in which metacognitive questions were embedded (COLED+EMB), and a control group that did not receive any kind of intervention. On both aspects of socioscientific decision making, the results show that students in the two training circumstances fared better than students in the control group. Students in the COLED+EMB condition were not more successful than those in the COLED condition, nevertheless. All conditions showed an improvement in students' learning outcomes on the regulatory component of metacognition over time. The posttest results showed that students in the COLED+EMB condition performed the best on average, but these findings were not statistically significant. The discussion centres on the potential consequences of incorporating metacognitive instruction into scientific courses. © 2024 IEEE.

关键词： Contrastive Learning

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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computer Systems science & engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt 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 Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Curl Constraint-Preserving Reconstruction and the Guidance it Gives for Mimetic Scheme Design

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Communications on Applied Mathematics and Computation 2023年第1期5卷 235-294页

作者： Dinshaw S.Balsara Roger Käppeli Walter Boscheri Michael Dumbser Physics and ACMS Departments University of Notre DameNotre DameINUSA Seminar for Applied Mathematics(SAM) Department of MathematicsETH ZürichCH-8092 ZurichSwitzerland Department of Mathematics and Computer Science University of FerraraFerraraItaly Laboratory of Applied Mathematics Department of CivilEnvironmental and Mechanical EngineeringUniversity of TrentoTrentoItaly

Several important PDE systems,like magnetohydrodynamics and computational electrodynamics,are known to support involutions where the divergence of a vector field evolves in divergence-free or divergence constraint-preserving ***,new classes of PDE systems have emerged for hyperelasticity,compressible multiphase flows,so-called firstorder reductions of the Einstein field equations,or a novel first-order hyperbolic reformulation of Schrödinger’s equation,to name a few,where the involution in the PDE supports curl-free or curl constraint-preserving evolution of a vector *** study the problem of curl constraint-preserving reconstruction as it pertains to the design of mimetic finite volume(FV)WENO-like schemes for PDEs that support a curl-preserving involution.(Some insights into discontinuous Galerkin(DG)schemes are also drawn,though that is not the prime focus of this paper.)This is done for two-and three-dimensional structured mesh problems where we deliver closed form expressions for the *** importance of multidimensional Riemann solvers in facilitating the design of such schemes is also *** two dimensions,a von Neumann analysis of structure-preserving WENOlike schemes that mimetically satisfy the curl constraints,is also *** shows the tremendous value of higher order WENO-like schemes in minimizing dissipation and dispersion for this class of *** results are also presented to show that the edge-centered curl-preserving(ECCP)schemes meet their design *** paper is the first paper that invents non-linearly hybridized curl-preserving reconstruction and integrates it with higher order Godunov *** its very design,this paper is,therefore,intended to be forward-looking and to set the stage for future work on curl involution-constrained PDEs.

关键词： PDEs Numerical schemes Mimetic

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1,2-Norm and CUR Decomposition based Sparse Online Active Learning for Data Streams with Streaming Features

1,2-Norm and CUR Decomposition based Sparse Online Active Le...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Zhong He, Yi Wu, Di Zuo, Liudong Li, Keren Zhang, Wenbin Deng, Zhiqiang Southern Illinois University School of Computing United States William & Mary School of Data Science United States Southwest University College of Computer and Information Science United States California State University Dominguez Hills Computer Science Department United States University of Alabama at Birmingham Department of Mathematics United States Florida International University School of Computing and Information Sciences United States Louisiana State University Department of Civil and Environmental Engineering United States

ISBN: (纸本)9798350362480

Aiming at learning from a sequence of data instances over time, online learning has attracted increasing attention in the big data era. As two important variants, sparse online learning has been extensively explored by facilitating sparse constraints for online models such as truncated gradient, 1-norm regularization, 1-ball projection, and regularized dual averaging;while online active learning aims to build an online prediction model with a limited number of labeled instances, deploying the so called query strategies to select informative instances over time. However, most existing studies consider sparse online learning or online active learning with fixed feature spaces, whereby in real practice the features may be dynamically evolved over time. To the end, we propose a novel unified one-pass online learning framework named OASF for simultaneously online active learning and sparse online learning tailored for data streams described by open feature spaces, where new features can emerge constantly, and old features may be vanished over various time spans. Specifically, we technically develop an effective online CUR matrix decomposition based on the 1,2 mixed norm constraint for simultaneously selecting important up-to-date samples in a sliding window and facilitating stable and meaningful features in open feature spaces over time. If the loss function is simultaneously Lipschitz and convex, a sub-linear regret bound of our proposed algorithm is guaranteed with. Extensive experiments that are conducted with multiple streaming datasets have demonstrated the effectiveness of the proposed OASF compared with state-of-the-art online active learning and sparse online learning methods. © 2024 IEEE.

关键词： Active learning

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Ensemble Distillation for Out-of-distribution Detection 29

Ensemble Distillation for Out-of-distribution Detection

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

作者： Wang, Dongdong Xu, Jingyao Lu, Siyang Wei, Xiang Wang, Liqiang University of Central Florida Department of Civil Environmental and Construction Engineering OrlandoFL United States Beijing Jiaotong University School of Computer and Information Technolog Beijing China Beijing Jiaotong University School of Software Engineering Beijing China University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350330717

Out-of-distribution detection is critical to a reliable application of deep neural networks. To reduce model uncertainty, we propose a simple yet effective approach of ensemble knowledge distillation. We blend ensemble model and knowledge distillation to improve model generalization on indomain recognition, thereby yielding accurate and robust out-of-distribution detection. The former effectively expands data recognition feature space, while the latter further regularizes the model through knowledge distillation, enhancing in-domain feature recognition. This effective integration successfully yields lower model uncertainty on in-domain feature recognition and improves anomaly detection in a more scalable manner. We justify our approach through extensive experiments on various benchmarks, demonstrating its significant improvement in out- of-distribution detection. We validate our approach with a variety of up-to-date DNNs, like Vision Transformer. © 2023 IEEE.

关键词： Data Augmentation Ensemble Learning Knowledge Distillation Out-of-distribution Detection

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