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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Abeer Abdullah Shujaaddeen Fadl Mutaher Ba-Alwi Ammar T. Zahary Ghaleb Al-Gaphari Abdulkader M. Al-Badani Ayman Alsabry Computer Science Faculty of Computer and Information Technology Sana'a University Sana'a Yemen International University of Technology Twintech Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana'a University Sana'a Yemen Department of Computers Faculty of Science and Engineering Aljazeera University Ibb Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

This paper developed a new model called Single Rule Random Forest (SrRF) that enhances the performance of the Fandom Forest technique(RF)and reduces its rules, then compared the performance of this model with a set of ML models as follows (DT, RF, GPT) of the datasets provided by the Tax Authority of Yemen which consists of 1083 records using two values for k in K-Fold Cross Validation). The results showed that by using the value k=10, the GPT Classifier gave the highest result of 100 % but this result will cause overfitting, then the SRI-RF Classifier gave the best result with 99.89%, while RF gave the worst result. By using the value k=5, the GPT Classifier also gave the highest result of 100 % but this result also caused overfitting, then the SrRF Classifier gave the best result, while RF gave the worst result. From the above, the researchers also note that training the data using K-fold cross-validation with the value K=10 gave better results. On the other side, our proposed model SRIRF with the value of k=10 gave the best result.

关键词： Training Finance Forestry Internet of Things Classification tree analysis

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Machine learning based fraudulent detection system for financial transactions

Machine learning based fraudulent detection system for finan...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Alam, Wahaj Ali, Raja Hashim Ali, Nisar Imad, Muhammad ul Abideen, Zain Shah, Muhammad Huzaifa Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14476 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain

ISBN: (纸本)9798331504465

Maintaining the integrity of financial systems and preventing people and organizations from suffering financial losses depend heavily on the ability to spot fraudulent financial transactions. Traditional rule-based fraud detection methods have trouble identifying intricate patterns and developing fraud tactics. Machine learning approaches have become powerful fraud detection tools in recent years, utilizing the strength of data-driven models to spot fraudulent actions. The Existing rule-based fraud detection has difficulty in identifying complex patterns and evolving fraud tactics. This study thoroughly investigates the use of machine learning techniques, including decision trees and random forests, for financial transaction fraud detection, while also exploring feature engineering approaches to extract essential data from transaction records such as temporal, spatial, and relational features. In this study, real-world financial transaction datasets are used to conduct experimental evaluations, comparing the performance of various machine learning models based on accuracy, precision, recall, and F1-score. The results indicate that certain algorithms outperform others, demonstrating promising and favorable outcomes for CatBoost algorithm. This significance of our study lies in showcasing the effectiveness of machine learning techniques, compared to traditional rule-based methods, for detecting fraud in financial transactions, leading to more promising outcomes and contributing to the integrity and security of financial systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Radial Basis Function Networks 7

Deep Radial Basis Function Networks

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7th International Conference on Contemporary Information technology and Mathematics, ICCITM 2021

作者： Alqezweeni, Mohie M. Gorbachenko, Vladimir I. Stenkin, Dmitry A. Penza State University Faculty of Computer Engineering Department of Computer Technology Penza Russia

ISBN: (纸本)9781665420327

The use of radial basis functions networks as physics-informed neural networks for solving direct and inverse boundary value problems is demonstrated. On the Levenberg-Marquardt basis optimization method, algorithms have been developed for solving partial differential equations. Comparison of the gradient descent method and the Levenberg-Marquardt method for solving the Poisson equation is given. To solve a direct boundary value problem describing processes in a piecewise homogeneous environment, an algorithm is proposed based on solving individual problems for each region with different properties of the environment associated with the conjugation conditions. It removes restrictions on the radial basis functions used. To solve the coefficient inverse problem of recovering the properties of the piecewise inhomogeneous medium, an algorithm based on parametric optimization is proposed. An algorithm uses two networks of radial basis functions. The first network approximates the solution to the direct problem. And another network approximates a function which describes the properties of the environment. Network learning is performed using an algorithm developed by the authors based on the Levenberg-Marquardt method. Expressions are obtained for the analytical calculation of the Jacobi matrix elements in the Levenberg-Marquardt method and the residual gradient vector elements. The application of the developed algorithms is demonstrated by the example of model direct boundary value problems and inverse coefficient boundary value problems for piecewise homogeneous media. © 2021 IEEE.

关键词： Radial basis function networks

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StakePage: Analysis of Stakeholders of an Information System Using Page Rank Algorithm 1

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Hassan, Tanveer Mohammad, Chaudhary Wali Sadiq, Mohd. Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

The requirements elicitation process is the sub-process of requirements engineering in which stakeholders are involved to identify the requirements of an information system. Various methods have been developed to identify and analyze the stakeholders like goal-oriented requirements elicitation process, traditional methods, etc. Based on our review, we found that little attention is given to the analysis of the stakeholders using different measures of social networks before the starting of the requirements elicitation process. Therefore, to address this issue, this paper presents a StackPage method for the analysis of the stakeholders using page rank algorithm of social network measures. The applicability of the StackPage method is discussed with the help of the stakeholders of an institute examination system. Finally, the results are compared with two state-of-the-art methods, i.e., StakeRare and StakeSoNet methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Requirements engineering

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Design and Implementation of Blockchain-Based Smart Contracts for Enhancing Ancillary Services Management in Electricity Markets 10

Design and Implementation of Blockchain-Based Smart Contract...

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10th IEEE International Conference on Power and Energy, PECon 2024

作者： bt Junaidi, Nazreen Abdullah, Md Pauzi Shaaban, Mohamed Hashim, Nurul Izzati bt Abdullah, Johari B. Fazari, Atiq Firdaus bin Department of Electrical and Electronics Engineering University of Malaysia Sarawak Sarawak Kota Samarahan94300 Malaysia Faculty of Electrical Engineering University Teknologi Malaysia Johor Bharu Johor81310 UTM Malaysia Department of Electrical Engineering National Chung Cheng University Minxiong Chiayi621301 Taiwan Faculty of Computer Science and Information Technology University of Malaysia Sarawak Kota Sarawak Samarahan94300 Malaysia

ISBN: (纸本)9798350361971

This paper presents a possible solution to the challenges of managing ancillary services on power systems, focusing on the development and execution of robust smart contracts on the Ethereum blockchain. These contracts automate essential procedures like bid submission, Market Clearing Price (MCP) calculation, and settlement, reducing manual involvement and improving operational efficiency. They ensure transparency and accountability through security mechanisms like role-based access restriction and event monitoring. The contracts were created using Solidity programming language and implemented on the Ethereum Sepolia test network. Unit tests using Foundry confirmed the functionality of each contract component, indicating their accuracy and effectiveness. The study highlights the novel features of the smart contract architecture and its potential impact on ancillary service administration. ©2024 IEEE.

关键词： Smart contract

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Free vibration analysis of a skew sandwich plate with bamboo biocomposite and polylactic acid core under temperature and moisture conditions

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Noise and Vibration Worldwide 2023年第9期54卷 434-459页

作者： Gawande, Atul S Kattimani, Subhaschandra Murigendrappa, S.M. Nguyen-Thoi, T. Kamyab, Hesam Althamer, Saeed Department of Mechanical Engineering National Institute of Technology Karnataka Mangalore Surathkal India Institute of Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Mechanical Engineering King Khalida University Abha Saudi Arabia

This article investigates the impact of moisture and temperature on vibration characteristics of bio-composite skew-laminated composite sandwich (SLCS) plates. The bio-composite SLCS plates with bamboo face sheets and polylactic acid (PLA) cores are biodegradable, radiolucent, lightweight, high strength, and withstand vibrations. The coupled thermo-elastic and hygro-elastic finite element (FE) model of the SLCS plate is derived using the higher-order shear deformation theory (HSDT). An initial stress stiffness matrix is developed using the nonlinear strain-displacement relations to incorporate the effect of temperature and moisture in FE modeling. Temperature and moisture-dependent material properties of bamboo fiber-reinforced biocomposite (BFB) and PLA core are employed for the analysis. A comprehensive investigation has been carried out to study the impact of moisture and temperature situations with different geometrical parameters and various skew angles on the frequency of the SLCS plate. The results indicate that the biocomposite sandwich material has excellent potential for structural applications under different environmental conditions in various applications like food processing, and biomedical applications, including MRI and CT scan beds. © The Author(s) 2023.

关键词： Laminated composites

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A Binary and Multi Classification Model on Tax Evasion: A Comparative Study

A Binary and Multi Classification Model on Tax Evasion: A Co...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Abeer Abdullah Shujaaddeen Fadl Mutaher Ba-Alwi Ammar T. Zahary Ahmed Sultan Alhegami Ayman Alsabry Abdulkader M. Al-Badani Computer Science Faculty of Computer and Information Technology Sana'a University Sana'a Yemen International University of Technology Twintech Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana'a University Sana'a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Department of Computers Faculty of Science and Engineering Aljazeera University Ibb Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

In this paper, a model was built to compare the performance of the following machine learning (ML) models: DT, RF, SVM, and MLP, using two types of classification: binary classification and multi classification. The researchers concluded that the MLP classifier was the most efficient using multi classifications, as the classifier gave an accuracy of 99.77%, a recall of 93.25%, a precision of 92.02%, and an F-score of 92.63%. Using the dataset provided by the Tax Authority of Yemen, which is related to the commercial and industrial profits tax explained in detail in other papers for the same authors, which consists of 1083 record, after the preprocessing of data. Keywords— ML techniques, RF, DT, SVM,MLP techniques, Binary classification, Multi-classification, Dataset of Tax.

关键词： Support vector machines Accuracy Finance Machine learning Internet of Things Classification tree analysis

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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computer Modeling in engineering & Sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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Teaching in student-centred active learning spaces: How relational, pedagogical, spatial, and technological aspects intertwine and affect the learning environment 51

Teaching in student-centred active learning spaces: How rela...

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51st Annual Conference of the European Society for engineering Education, SEFI 2023

作者： Korpås, G.S. Andersen, T.H. Kahrs, M.S. Hansen, G. Røren, K.A.F. Department of Physics Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Section for Teaching and Learning Support Education Quality Division Norwegian University of Science and Technology Trondheim Norway

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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