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New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

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computers, Materials & Continua 2022年第12期73卷 4595-4609页

作者： Deepak Sukheja Javaid Ahmad Shah G.Madhu K.Sandeep Kautish Fahad A.Alghamdi Ibrahim.S.Yahia El-Sayed M.El-Kenawy Ali Wagdy Mohamed Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia Department of Mathematics Dr.APJ Abdul Kalam University Indore(M.P)India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia LBEF Campus Kathmandu44600Nepal Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Laboratory of Nano-Smart Materials for Science and Technology(LNSMST) Department of PhysicsFaculty of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Nanoscience Laboratory for Environmental and Biomedical Applications(NLEBA) Semiconductor Lab.Department of PhysicsFaculty of EducationAin Shams UniversityRoxyCairo 11757Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Sciences Engineering The American University in CairoEgypt

Efficient decision-making remains an open challenge in the research community,and many researchers are working to improve accuracy through the use of various computational *** this case,the fuzzification and defuzzification processes can be very *** is an effective process to get a single number from the output of a fuzzy *** defuzzification as a center point of this research paper,to analyze and understand the effect of different types of vehicles according to their *** this paper,the multi-criteria decision-making(MCDM)process under uncertainty and defuzzification is discussed by using the center of the area(COA)or ***,to find the best solution,Hurwicz criteria are used on the defuzzified ***-making technique is proposed using Hurwicz criteria for triangular and trapezoidal fuzzy *** proposed technique considers all types of decision makers’perspectives such as optimistic,neutral,and pessimistic which is crucial in solving decisionmaking problems.A simple case study is used to demonstrate and discuss the Centroid Method and Hurwicz Criteria for measuring risk attitudes among *** significance of the proposed defuzzification method is demonstrated by comparing it to previous defuzzification procedures with its application.

关键词： Defuzzification decision-making fuzzy numbers Hurwicz multicriteria decision-making ranking order

MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors

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MotionNeXt: A Deep Learning Neural Network for Recognizing H...

International Symposium on Communications and Information Technologies (ISCIT)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Recognition of human activities using wearable sensors is essential for applications related to health and well-being. However, the complex data captured by inertial sensors poses challenges in accurately identifying actions. This research introduces MotionNeXt, a residual deep neural network incorporating aggregated transformation. MotionNeXt utilizes residual and multi-kernel blocks to extract spatial and temporal characteristics from raw IMU data. Subsequently, it employs global average pooling, a fully connected layer, and softmax for classification purposes. An attention layer enables the model to focus on specific segments of the input sequence while categorizing each time step. MotionNeXt is evaluated using a publicly available dataset of activities collected from wearable inertial measurement devices. It achieves state-of-the-art accuracy, boasting an F1-score of 99.37%, surpassing previous deep learning methods by 2- 5%. By integrating accelerometer, gyroscope, and magnetometer modalities, MotionNeXt mitigates the limitations of individual inputs, significantly improving accuracy across various models.

关键词： Deep learning Accuracy Measurement units Inertial sensors Magnetometers Motion segmentation Information and communication technology Gyroscopes Data mining Wearable sensors

Age of Information in Multi-Source Updating Systems: An M/G/1 Vacation Queueing Model

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Age of Information in Multi-Source Updating Systems: An M/G/...

IEEE International Conference on Communications (ICC)

作者： Muthukrishnan Senthil Kumar Aresh Dadlani Masoumeh Moradian Behrouz Maham Theodoros A. Tsiftsis Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore India Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Informatics and Telecommunications University of Thessaly Lamia Greece

Concurrent with the rise of real-time wireless systems enabled by the Internet of Things, the age of information (AoI) has been widely perceived as a crucial destination-centric performance metric to quantify the timeliness of data delivery. This paper deals with the analysis of information freshness in a multi-source M/G/1 queueing system with utilization of idle server time, referred to as server vacation, in an effective manner. In particular, the status update packets in our model are generated independently by a finite set of source nodes and according to a Poisson process, while their service time follows a general random variable. Using stochastic decomposition and the Laplace-Stieltjes transform, we derive the average AoI (AAoI) expression for the multi-source M/G/1 queueing model with generally-distributed vacation time in closed form. Our numerical simulations validate the accuracy of the derived AAoI expression and assess the impact of different parameters on the system performance.

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An Efficient Digital Short Signature Scheme using CCM for HC-IoT Environments

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An Efficient Digital Short Signature Scheme using CCM for HC...

2022 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2022

作者： Meshram, Chandrashekhar Obaidat, Mohammad S. Imoize, Agbotiname Lucky Bahkali, Ismail Meshram, Akshaykumar Hsiao, Kuei-Fang Jayawanti Haksar Government Post Graduation College Department of Post Graduation Studies and Research in Mathematics M.P. Betul460001 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States The University of Jordan KASIT Amman Jordan University of Science and Technology Beijing Beijing China University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum44801 Germany King Abdulaziz University Department of Information Science Jeddah21589 Saudi Arabia Yeshwantrao Chavan College of Engineering Department of Applied Mathematics M.S. Nagpur India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

With billions of life-improving applications proliferating the global space, the Human-Centered Internet of Things (HC-IoT) grips excellent promise. The Internet will comprise more than 50 billion linked things, comprising mobile devices, GPS devices, sensors, actuators, and all the smart things that can be imagined. The HC-IoT comprises many smart gadgets to distribute identified information via online platforms. The goal is to encrypt messages between the sender and receiver during data exchange with the right instruction to challenge any human malicious attack. Different signature-based schemes have been proposed to ensure secure communication of critical user information. Smart gadgets claim lightweight characteristics, posing remarkable strengths and safety. The core challenge with signature-based techniques is the incurred computational cost owing to the enormous resources required for signature and verification procedures. The current paper projects an efficient Digital Short Signature (DSS) Scheme that uses Conformal Chaotic Maps (CCM) for protected communication in HC-IoT-based smart devices. The security of the projected scheme is thoroughly connected to a CCM in the random oracle model (ROM). The proposed DSS-CCM scheme offers more security assurance than the existing signature systems. The presented scheme would find practical applications in smart cards and emerging wireless security systems. © 2022 IEEE.

关键词： Internet of things

TPOPT: EFFICIENT TRAINABLE TEMPLATE OPTIMIZATION ON LOW-DIMENSIONAL MANIFOLDS

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arXiv 2023年

作者： Yan, Jingkai Wang, Shiyu Wei, Xinyu Rain Wang, Jimmy Márka, Zsuzsanna Márka, Szabolcs Wright, John Department of Electrical Engineering Columbia University United States Department of Computer Science Columbia University United States Department of Physics Columbia University United States Data Science Institute Columbia University United States Columbia Astrophysics Laboratory Columbia University United States Department of Applied Physics and Applied Mathematics Columbia University United States Department of Mathematics Columbia University United States

In scientific and engineering scenarios, a recurring task is the detection of low-dimensional families of signals or patterns. A classic family of approaches, exemplified by template matching, aims to cover the search space with a dense template bank. While simple and highly interpretable, it suffers from poor computational efficiency due to unfavorable scaling in the signal space dimensionality. In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability. We provide a theoretical analysis of the convergence of Riemannian gradient descent for TpopT, and prove that it has a superior dimension scaling to covering. We also propose a practical TpopT framework for nonparametric signal sets, which incorporates techniques of embedding and kernel interpolation, and is further configurable into a trainable network architecture by unrolled optimization. The proposed trainable TpopT exhibits significantly improved efficiency-accuracy tradeoffs for gravitational wave detection, where matched filtering is currently a method of choice. We further illustrate the general applicability of this approach with experiments on handwritten digit data. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

HomOpt: A Homotopy-Based Hyperparameter Optimization Method

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arXiv 2023年

作者： Abraham, Sophia J. Maduranga, Kehelwala D.G. Kinnison, Jeffery Carmichael, Zachariah Hauenstein, Jonathan D. Scheirer, Walter J. Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mathematics Tennessee Tech University CookevilleTN38505 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States

Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality data available at scale. However, a third critical component is the fine-tuning of hyperparameters, which plays a pivotal role in achieving optimal model performance. Despite its significance, hyperparameter optimization (HPO) remains a challenging task for several reasons. Many HPO techniques rely on naive search methods or assume that the loss function is smooth and continuous, which may not always be the case. Traditional methods, like grid search and Bayesian optimization, often struggle to quickly adapt and efficiently search the loss landscape. Grid search is computationally expensive, while Bayesian optimization can be slow to prime. Since the search space for HPO is frequently high-dimensional and non-convex, it is often challenging to efficiently find a global minimum. Moreover, optimal hyperparameters can be sensitive to the specific dataset or task, further complicating the search process. To address these issues, we propose a new hyperparameter optimization method, HomOpt, using a data-driven approach based on a generalized additive model (GAM) surrogate combined with homotopy optimization. This strategy augments established optimization methodologies to boost the performance and effectiveness of any given method with faster convergence to the optimum on continuous, discrete, and categorical domain spaces. We compare the effectiveness of HomOpt applied to multiple optimization techniques (e.g., Random Search, TPE, Bayes, and SMAC) showing improved objective performance on many standardized machine learning benchmarks and challenging open-set recognition tasks. © 2023, CC BY.

关键词： Machine learning

Age-Gain-Dependent Random Access for Event-Driven Periodic Updating

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arXiv 2024年

作者： Zhu, Yuqing Zhu, Yiwen Gong, Aoyu Lin, Yan Lo, Yuan-Hsuan Zhang, Yijin The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland The Department of Applied Mathematics National Pingtung University Pingtung90003 Taiwan

This paper considers utilizing the knowledge of age gains to reduce the average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easily implementable decentralized manner, so that the contention can be limited to devices with age gains as high as possible. For the basic case that each device utilizes its knowledge of age gain of only itself, we provide an analytical modeling by a multi-layer discrete-time Markov chains (DTMCs), where an external DTMC manages the jumps between the beginnings of frames and an internal DTMC manages the evolution during an arbitrary frame, for obtaining optimal access parameters offline. For the enhanced case that each device utilizes its knowledge of age gains of all the devices, we require each device to adjust its access parameters for maximizing the estimated network expected AoI reduction per slot, through maintaining a posteriori joint probability distribution of local age and age gain of an arbitrary device in a Bayesian manner. Numerical results validate our study and demonstrate the advantage of the proposed schemes over other schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

A BERNOULLI-BARYCENTRIC RATIONAL MATRIX COLLOCATION METHOD WITH PRECONDITIONING FOR A CLASS OF EVOLUTIONARY PDES∗

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arXiv 2024年

作者： Luo, Wei-Hua Gu, Xian-Ming Carpentieri, Bruno Guo, Jun School of Mathematics and Physics Hunan University of Arts and Science Hunan Changde415000 China School of Mathematics Southwestern University of Finance and Economics Sichuan Chengdu611130 China Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 P.O. Box 407 Groningen9700 AK Netherlands Faculty of Engineering Free University of Bozen-Bolzano Bolzano39100 Italy College of Applied Mathematics Chengdu University of Information Technology Sichuan Chengdu610225 China

We propose a Bernoulli-barycentric rational matrix collocation method for two-dimensional evolutionary partial differential equations (PDEs) with variable coefficients that combines Bernoulli polynomials with barycentric rational interpolations in time and space, respectively. The theoretical accuracy O { (2π)−N + hdxx−1 + hydy−1} of our numerical scheme is proven, where N is the number of basis functions in time, hx and hy are the grid sizes in the x, y-directions, respectively, and 0 ≤ dx ≤ bh−xa , 0 ≤ dy ≤ dh−yc . For the efficient solution of the relevant linear system arising from the discretizations, we introduce a class of dimension expanded preconditioners that take the advantage of structural properties of the coefficient matrices, and we present a theoretical analysis of eigenvalue distributions of the preconditioned matrices. The effectiveness of our proposed method and preconditioners are studied for solving some real-world examples represented by the heat conduction equation, the advection-diffusion equation, the wave equation and telegraph *** Codes 65M70, 65Y05, 65D25 Copyright © 2024, The Authors. All rights reserved.

关键词： Linear systems

Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion