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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Rezaei, Sepehr Shams, Mohammadreza Meybodi, Mohsen Alambardar University of Isfahan Faculty of Mathematics and Statistics Department of Applied Mathematics and Computer Science Isfahan Iran University of Isfahan Shahreza Campus Department of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

With the increasing use of mobile phones and messaging services, SMS spam has become a significant issue for users. In this paper, we propose a novel approach1 to tackle this problem by using Sine-Cosine Algorithm (SCA) and Complex Multi-Layer Perceptron (C-MLP). Specifically, we apply the SCA method to reduce the dimensionality of the feature space and CMLP to improve the performance of spam detection. Also, in this paper, we investigate the effectiveness of different classification algorithms, including Support Vector Machines, Random Forests, K-nearest neighbors, Naive Bayes, bagging, and voting approaches. Our experimental results show that the proposed approach achieves high accuracy and outperforms existing methods in terms of both accuracy and F-measure. The proposed approach can be helpful in designing effective SMS spam filters and improving the overall user experience.1All the code used in this research is publicly available on the first author's GitHub repository: https://***/seper-sw/*** © 2023 IEEE.

关键词： Support vector machines

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Coverage modeling of fault-tolerant system under copula and waiting repair policy

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Life Cycle Reliability and Safety engineering 2024年第1期13卷 1-14页

作者： Tyagi, Vaishali Ram, Mangey Arora, Ritu Department of Applied Science & amp Department of Mathematics Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Department of Mathematics and Statistics Gurukul Kangri Deemed to be University Kanya Gurukul Campus (Haridwar) Uttarakhand Haridwar India

This paper is related to the reliability of a three-unit repairable system having the concept of two types of repairs with waiting repair time and imperfect coverage. The concept of imperfect coverage for switching failed components is taken into account. If any unit fails, it is immediately repaired with the coverage probability c but, if the repair facility is not available instantly, then it has to wait for repairs and when the system waits for repairs, it is repaired by two types of repair facility. The Markov process is used to model the system mathematically to get the transient probabilities related to the system states. Laplace transform is utilized to assess the transient probabilities, which are additionally used to assess some reliability qualities such as availability, mean time to failure (MTTF), expected profit, and sensitivity. The failure time of the units is assumed to follow an exponential distribution whereas, the time to repair follows the general and Gumbel–Hougaard family of copula distribution. Numerical simulations have been taken to explore the availability, MTTF, profit, and sensitivity associated with the system. Results and conclusions are made for the system based on the graphical study. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Gumbel–Hougaard copula distribution Imperfect coverage Markov process Waiting time to repair

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

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computer Systems science & engineering 2023年第3期44卷 1869-1880页

作者： S.Siamala Devi K.Venkatachalam Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced *** diagnosis of many diseases will improve the patient *** cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation *** data generated through these technologies will demand the bandwidth,data rate,latency of the *** this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the *** nodes that are far away from edge node will forward the data to its neighbor cluster head using *** scheme will reduce the number of transmissions over the *** aggregated data are preprocessed at edge node to remove the noise for better *** node will reduce the overhead of cloud *** aggregated data are forward to cloud server for central storage and *** proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the *** cost for proposed system for 300 nodes is 0.34μ*** energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ *** optimization approaches and encryption method will ensure the data privacy.

关键词： Discrete grey wolf optimization data aggregation cloud computing IoT WSN smart healthcare elliptic curve elgamal energy optimization

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

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A Neuro-Fuzzy Approach to MOPP Open Time Prediction for Chemical Threats in Low-Intensity Conflicts

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International Journal of Fuzzy System Applications 2022年第1期11卷 1-17页

作者： Kumar, Vijay Tyagi, Lakshya Department of Applied Mathematics Amity Institute of Applied Sciences Amity University Noida India Department of Computer Science and Engineering ASET Amity University Noida India

CBRN (chemical, biological, radiological, and nuclear) weapons systems hold the potential to cause great damage to a fighting unit. Chemical warfare agents are cheaper and easier to produce and deploy. Mission-oriented protective posture (MOPP) equipment is used to isolate soldiers from the toxic environment in case of a chemical attack. Sustained wearing of MOPP overgarments can put soldiers at the risk of dehydration and heat stress, while reducing their capability to carry out labour-intensive tasks efficiently. A hierarchal adaptive neuro-fuzzy inference system (HANFIS) model is proposed to predict worst case MOPP open times for surfaces covered with chemical agent resistant coating (CARC). The model can provide decision support for commanders when determining MOPP open or unmasking time. From the HANFIS simulation results, it was concluded that the model can accurately predict MOPP open times according to data present in military field manuals in a manner that is computationally efficient and easy to interpret based on linguistic rules, and most importantly, easy to customize according to the needs of in-theatre commander based on intelligence on the enemy's arsenal of CW agents and weather conditions of their unit's location. This work adds to the body of evidence that soft computing techniques can be effectively applied to the design of decision support systems for defence sciences. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

关键词： Chemical warfare

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Optimal Lot Size and Backordered Quantity Under Carbon Tax

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International Journal of applied and Computational mathematics 2024年第4期10卷 1-16页

作者： Raiya, Geetanjali Mittal, Mandeep Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida 201313 India Department of Mathematics School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

Researchers and industry are working together to develop low carbon inventory models that comply with carbon pricing regulations while maintaining company profits. However, this task becomes more challenging when companies also face issues related to product quality imperfections. To address this issue, a new study has developed an economic order quantity (EOQ) model that takes into account carbon emission tax, defective rates, selling prices, imperfect items, scrap items and backordering. The objective of this mathematical model is reduce overall expenses, encompassing emissions-related costs. A numerical example is come up to adorn the model, based on previous data. Sensitivity analysis has also been performed to validate the model and understand system characteristics, identifying the factors that affect total emissions. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Carbon tax Defective items EOQ Optimization Shortages

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series Data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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Categorizing methods for integrating machine learning with executable specifications

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science China(Information sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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