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Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

computer Systems Science & engineering 2023年第3期44卷 1939-1950页

作者： Dhanalakshmi R Sovan Samanta Arun Kumar Sivaraman Jeong Gon Lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran Department of Computer Science and Engineering KCG College of TechnologyChennai600097Tamil NaduIndia Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Instrumentation Engineering PDEAs College of EngineeringManjari412307PuneIndia Engineering and Research Services HCL TechnologiesChennai600119India

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a *** can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently *** fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible ***,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation *** bipolar valued fuzzy set(HBVFS)is a generalization of *** paper aims to introduce a general framework of multi-attribute group decision-making using social *** propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making *** the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of *** Type-2 decision making processs,there are two major aspects we ***,consistency tests and checking of consensus models are given for detecting that the judgments are logically ***,the framework demands(partial)decision-makers to review their ***,the coherence and consensus of several HBVFSs are established forfinal ranking of *** proposed framework is clarified by an example of software packages selection of a university.

关键词： Group decision-making aggregation operators hesitant bipolar-valued fuzzy set

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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Quasi-Bound States in the Continuum and Enhanced Light-Matter Interactions in GaN/InGaN Epitaxial Metasurfaces

Quasi-Bound States in the Continuum and Enhanced Light-Matte...

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2024 Laser Science, LS 2024

作者： Xu, Yuan Liu, Jiangnan Singh, Anshuman Tsai, Cheng-Chia Malek, Stephanie C. Shin, Walter Jin Wu, Jiahao Zhu, Freddie Liu, David Yu, Nanfang Mi, Zetian Soltani, Mohammad Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Raytheon BBN Technologies CambridgeMA02138 United States

We fabricated GaN metasurfaces doped with InGaN quantum dots by templated molecular beam epitaxy (MBE) that support tunable high Q-factor quasi-bound states in the continuum (q-BICs) and demonstrated efficient optical... 详细信息

关键词： Gallium nitride

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Leveraging Information-Centric Edge Computing in Private 5G Cellular Networks via the W3C Web of Things 22

Leveraging Information-Centric Edge Computing in Private 5G ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Romann, Jan Damer, Hugo Hakim Gias, T. M. Rayhan Bergmann, Olaf University of Applied Sciences Emden/Leer Faculty of Technology Department of Electrical Engineering and Computer Science Constantiaplatz 4 Emden26723 Germany

ISBN: (纸本)9798331508050

In this paper, we present an architecture for the deployment of an edge computing system based on Named Data Networking (NDN) as part of a private 5G network. Our proposed architecture integrates IP-based and non-IP-based components using the W3C Web of Things (WoT) standards, including a Thing Description Directory (TDD). Our results highlight the potential of both edge computing and WoT concepts for private 5G deployments but also reveal several standardization gaps that need to be closed to make effective use of the new technologies. © 2025 IEEE.

关键词： Internet of things

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Integrating Multiscale Mapper Features with Machine Learning Techniques to Improve Breast Cancer Classification Accuracy 11

Integrating Multiscale Mapper Features with Machine Learning...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Netshifhire, Rabelani Visaya, Maria Vivien University of Johannesburg Department of Electrical and Electronic Engineering Science Johannesburg South Africa University of Johannesburg Department of Mathematics and Applied Mathematics Johannesburg South Africa DSI-NRF CoE-MaSS Johannesburg South Africa

ISBN: (纸本)9798350367300

Breast cancer classification is critical for early detection and treatment planning. The complexity of breast cancer data poses feature engineering challenges for conventional machine learning (ML) methods, which often fail to capture these *** employ the Mapper algorithm, a topological data analysis (TDA) tool, for feature engineering. Two variants of Mapper algorithm are utilized, namely Singlescale Mapper (SM) and Multiscale Mapper (MM). SM is constrained by scale sensitivity, dimensionality limitations, and parameter tuning challenges. MM enhance SM by capturing topological features at multiple resolutions, offering a robust hierarchical view of *** the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, three feature sets namely conventional, SM-derived, and MM-derived features are evaluated across various selected ML models to assess their potential to enhance classification *** demonstrated that MM-derived features integrated with ML models improved breast cancer classification accuracy, outperforming both conventional and SM-derived features. This findings highlight the potential of TDA's MM as a feature engineering tool to improve breast cancer classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of the Parameter of the Single-Phase Flow Model in the Reservoir with the Power Law of Filtration 5

Identification of the Parameter of the Single-Phase Flow Mod...

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5th International Conference on Problems of Cybernetics and Informatics, PCI 2023

作者： Gamzaev, Khanlar Leonov, Sergey Huseynzade, Sevil Kerimova, Sevinc Azerbaijan State Oil and Industry University Department of General and Applied Mathematics Baku Azerbaijan Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9798350319064

The process of unsteady flow of a single-phase liquid in a cylindrical reservoir arising under the elastic mode of reservoir development is considered. To describe this process, a power law of filtration is proposed for a single-phase flow in a porous medium. Within the framework of the proposed model, the task of identifying one parameter of the model characterizing the simulated reservoir system is set. To construct a discrete analog of the inverse problem, the finite difference method is used using an explicit-implicit approximation in time. To solve the resulting system of nonlinear equations, a special representation is proposed that allows reducing the nonlinear system to two independent systems of linear equations. As a result, an explicit formula is obtained for determining the approximate value of the desired reservoir parameter. Based on the proposed method, numerical experiments were carried out for model problems. © 2023 IEEE.

关键词： Filtration

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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2023 International Conference on electrical engineering and Informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

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Deep Neural Networks for Estimating DOA of Multiple Radio Waves 7

Deep Neural Networks for Estimating DOA of Multiple Radio Wa...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Qian, Shen Cheng, Meng Faculty of Informatics Kanagawa University Department of Applied Systems and Mathematics Yokohama Japan Shanghai Normal University The College of Information Mechanical and Electrical Engineering Shanghai China

ISBN: (纸本)9798350384437

This letter proposes a deep neural network (DNN)-based direction-of-arrival (DOA) estimation approach. Novel DOA selection methods, threshold selection and peak selection, are utilized to select the DOA from the DNN outputs. Unlike conventional methods that require prior knowledge of the number of incoming waves, the proposed network architecture offers a more flexible approach to tracking and positioning without knowing the number of incoming waves in advance. Moreover, an amendment to reduce output near the peak point is proposed to reduce DOA estimation errors. The flexibility opens up new possibilities and has demonstrated high DOA estimation accuracy for multiple incident waves. © 2024 IEEE.

关键词： Direction of arrival

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