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Scoring rule and its application in intuitionistic fuzzy parameterized soft set-based decision-making problem

Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14209-14224页

作者： Paik, Biplab Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University W.B. Midnapore721 102 India

There is an importance of multi-criteria group decision-making (MCGDM) problems based on soft set theory in decision analysis. This article focuses on MCGDM problems based on intuitionistic fuzzy parameterized soft sets (IFPSS) structure. Our goal in decision-making is to select an object by verifying all the parameters. Fuzzy ranking is one of the essential critical tools used in the MCGDM process. The ranking system plays a vital role in the comparison of Intuitionistic fuzzy numbers (IFN) in MCGDM, like fuzzy numbers. However, most of the ranking methods on IFN mentioned in the literature have some limitations. By addressing the limitations of existing score functions and proposing a new and improved method for ranking IFNs, this article can make a significant contribution to the field of MCDM and decision-making under uncertainty. We have also introduced a new similarity measure of two intuitionistic fuzzy soft sets (IFSS). Using the concept of scoring rule, we have defined the reduced FP-soft set of an IFPSS. Additionally, we have calculated the adequate standard of a decision-maker in MCGDM problems under the IFPSS environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision theory

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Optimization of neural network calculations using integer arithmetic 5

Optimization of neural network calculations using integer ar...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Zakovorotnyi, Oleksandr Khulap, Andrii Department of Computer Engineering Programming National Technical University Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350384499

Most of the sensor devices in the Internet of Things systems are based on energy-efficient microcontrollers, the computing resources of which are limited, as well as the amount of available memory. Increasing the security of the use of such devices with the help of neural networks is an important and urgent problem. The article describes the possibility of using artificial neural networks in small microcontrollers with limited resources. The purpose of this work is to check the possibility of calculating neural networks based on integer arithmetic to reduce the time of calculating a neural network and eliminate data normalization operations, as well as to evaluate the feasibility of using such neural networks in the field of security of the Internet of Things in comparison with traditional methods, such as black lists and white lists. The following results were obtained: when switching to integer arithmetic, compared to floating point, the accuracy of the result calculations is within the permissible error of neural network training, that is, it has not changed. Execution time decreased by 30-96%, depending on the architecture of the microcontroller. The program size is reduced by 22-48%, also depending on the microcontroller architecture. Conclusions: the possibility and expediency of using neural networks optimized for microcontrollers with limited resources was proved. This will increase the security of Internet of Things systems, especially against device authentication threats and intrusion detection. Prospects for further research are determined. ©2024 IEEE.

关键词： microcontroller neural network optimization software implementation

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Towards LLM-Based System Migration in Language-Driven Engineering 8th

Towards LLM-Based System Migration in Language-Driven Engin...

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8th International Conference on Engineering of computer-Based Systems, ECBS 2023

作者： Busch, Daniel Bainczyk, Alexander Steffen, Bernhard Department of Computer Science Chair for Programming Systems TU Dortmund University Dortmund44227 Germany

ISBN: (纸本)9783031492518

In this paper we show how our approach of extending Language Driven Engineering (LDE) with natural language-based code generation supports system migration: The characteristic decomposition of LDE into tasks that are solved with dedicated domain-specific languages divides the migration tasks into portions adequate to apply LLM-based code generation. We illustrate this effect by migrating a low-code/no-code generator for point-and-click adventures from JavaScript to TypeScript in a way that maintains an important property: generated web applications can automatically be validated via automata learning and model analysis by design. In particular, this allows to easily test the correctness of migration by learning the difference automaton for the generated products of the source and the target system of the migration. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Verification

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ChatGPT in the Loop: A Natural Language Extension for Domain-Specific Modeling Languages 1st

ChatGPT in the Loop: A Natural Language Extension for Dom...

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1st International Conference on Bridging the Gap between AI and Reality, AISoLA 2023

作者： Busch, Daniel Nolte, Gerrit Bainczyk, Alexander Steffen, Bernhard Department of Computer Science Chair for Programming Systems TU Dortmund University Dortmund44227 Germany

ISBN: (纸本)9783031460012

This paper presents an approach to no-code development based on the interplay of formally defined (graphical) Domain-Specific Languages and informal, intuitive Natural Language which is enriched with contextual information to enable referencing of formally defined entities. The paper focuses on the use and automated integration of these enriched intuitive languages via ChatGPT-based code generation to exploit the best of both language paradigms for domain-specific application development. To compensate for the lack of control over the intuitive languages we apply automated system-level validation via automata learning and subsequent model checking. All this is illustrated using the development of point-and-click adventures as a minimal viable example. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automata Learning Language-driven Engineering Large Language Models Low-Code/No-Code Prompt Engineering Software Engineering Verification Web Application

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DETECTION computer NETWORK INTRUSION USING DEEP NEURAL NETWORKS 5

DETECTION COMPUTER NETWORK INTRUSION USING DEEP NEURAL NETWO...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Poltoratskyi, Vadym Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350384499

In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to develop a model for detecting intrusions into computer networks based on the Transformer model using tabular input data. In this work, the UNSW-NB15 dataset is used as the source data. This dataset contains information about normal network behaviour as well as during synthetic intrusions. Models for intrusion detection in computer networks based on machine learning methods were investigated: Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, Random Forest. A method of converting tabular data into images was developed, which made it possible to build intrusion detection models based on Vision Transformer and Vision Transformer for small-size datasets on modern Transformer architecture. The research results showed that developed model based on Vision Transformer and Vision Transformer for small-size datasets improves the quality of identification, and eliminates the need for a preprocessing step such as dimensionality reduction. ©2024 IEEE.

关键词： Logistic regression

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An Improved Numerical Solution of Modified Regularized Long Wave Equation by Quartic Trigonometric B-Spline

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International Journal of Applied and Computational Mathematics 2025年第2期11卷 1-17页

作者： Kırlı, Emre Mersin, Mehmet Ali Department of Mathematics Aksaray University Aksaray 68000 Türkiye Computer Programming Aksaray University Aksaray 68000 Türkiye

This study presents the application of a numerical method specifically designed to solve the Modified Regularized Long Wave equation, a crucial model in the analysis of non-linear wave dynamics. The proposed method employs a Quartic Trigonometric B-spline approach for spatial discretization, which ensures smooth and accurate interpolation across the spatial domain, while temporal integration is conducted using the well-established fourth-order Runge–Kutta (RK4) scheme, known for its stability and precision. To evaluate the performance of the method, it is applied to three test problems: the propagation of a single solitary wave, the interaction between two and three solitary waves. The invariant quantities are computed for all test cases. To ensure the stability of the method, a stability analysis is performed through the computation of eigenvalues. The results demonstrate that the proposed method achieves a high degree of accuracy in preserving the invariant properties and produces minimal error, highlighting its efficiency and reliability. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Modified regularized long wave Quartic trigonometric B-spline Runge Kutta Solitary wave

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Model Based Development Methodology in Compliance with ISO 26262 for Safe Automotive ECU

Model Based Development Methodology in Compliance with ISO 2...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Hafiz, Muhammad Ikram University of Kassel Department of Computer Architecture and System Programming Kassel Germany

ISBN: (纸本)9798350327816

This work presents a practical approach of designing a functionally safe ECU for automotive application by implementing the Model Based Development (MBD) methodology. Functional safety (FuSa) in automotive can be achieved by strictly following the processes of electrical and electronic systems development as given in the ISO 26262. The scope of FuSa encompasses the risks emerging from systematic failures and random hardware failures. One approach to mitigate these failures is the Model Based Development (MBD) on which the ISO 26262 provides guidance. This approach enables to model an ECU and accelerates an IP creation by automatic RTL code generation for rapid prototyping on an FPGA level. In this work the MBD is implemented in accordance to the ISO 26262 to design and test a hardware model of an embedded ECU. The embedded Design under Test (DUT) is simulated and its output verified in Matlab/Simulink. Upon successful results, the RTL codes of the DUT are generated for a target FPGA. The RTL codes are simulated and the functionality of the DUT is verified at the FPGA level. © 2023 IEEE.

关键词： FPGA Functional Safety HDL code generation ISO 26262 Model Based Development systematic failures

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A fault-tolerant scheduling strategy through proactive and clustering techniques for scientific workflows in cloud computing

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Soft Computing 2025年第2期29卷 755-781页

作者： Farhood, Suha Mubdir Khorsand, Reihaneh Hussein, Nashwan Jasim Ramezanpour, Mohammadreza Branch Islamic Azad University Isfahan Iran Department of Programming Collage of Information Technology University of Babylon Hillah Iraq Department of Computer Engineering Mobarakeh Branch Islamic Azad University Mobarakeh Isfahan Iran

Cloud computing offers solutions for various scientific and business applications. Large-scale scientific applications, which are organized as scientific workflows, are carried out using cloud computing. However, the higher failure rates in cloud computing can be attributed to the numerous servers and components dealing with intense workloads. This study presents a fault-tolerant scheduling approach using proactive and clustering methods for scientific workflows in cloud computing. Initially, the task clustering issue is addressed by consolidating multiple short-duration tasks into a single job to improve the runtime performance of workflow executions. Subsequently, an automated workflow scheduling strategy is outlined with four key stages: monitoring, analysis, planning, and execution. During monitoring, clustered jobs and the capacities of available cloud resources are observed. In the analysis phase, the accuracy of failure prediction is enhanced by employing the Group Method of Data Handling (GMDH) neural network prior to any faults or failures. The planning stage introduces a novel hybrid multi-objective algorithm, MOPSO-aSA, based on MOPSO and adaptive simulated annealing (SA), to streamline workflow scheduling in error-prone execution environments. Moreover, the reliability of application execution is maintained through re-clustering and migration techniques following any faults or failures. Finally, based on the experimental findings, it is evident that the proposed strategy surpasses other methods in terms of makespan, total cost, energy consumption, and failure rate. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Simulated annealing

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Spam detection with fasttext based features

Spam detection with fasttext based features

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Karadeniz, Talha Tokdemir, Gül Maraş, H. Hakan Department of Software Engineering Çankaya University Ankara Turkey Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Programming Çankaya University Ankara Turkey

ISBN: (纸本)9798350379433

Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative;mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification. © 2024 IEEE.

关键词： Support vector machines

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Time variant multi-objective linear fractional interval-valued transportation problem

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Applied Mathematics(A Journal of Chinese Universities) 2022年第1期37卷 111-130页

作者： Dharmadas Mardanya Sankar Kumar Roy Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar UniversityMidnapore 721102West BengalIndia

This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time-variant multi-objective linear fractional transportation problem is formulated here. We take into account the parameters as cost, supply and demand are interval valued that involved in the proposed model, so we treat the model as a multi-objective linear fractional interval transportation problem. To solve the formulated model, we first convert it into a deterministic form using a new transformation technique and then apply fuzzy programming to solve it. The applicability of our proposed method is shown by considering two numerical examples. At last, conclusions and future research directions regarding our study is included.

关键词： fractional transportation problem multi-objective optimization interval number time variant parameter fuzzy programming Pareto optimal solution

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