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7th IEEE International Conference on Electrical, Electronics, and System Engineering, ICEESE 2024

作者： Tey, Kok Soon Leong, Wen Chek Rahman, Tuan A.Z. Kunjunni, Baarath Choong, Khong Neng Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia IoT Systems Lab MIMOS Berhad Kuala Lumpur57000 Malaysia

ISBN: (纸本)9798350367447

Sensors such as force and distance sensors are crucial for accurate measurements in many engineering applications. However, sensor data often contains noise that can compromise the reliability of the measurements. This study addresses the problem of noisy sensor data by applying several moving average methods to smooth the data. The methods investigated include simple, exponential, modified, square-root, and triangular moving averages, implemented using MATLAB. The effectiveness of these methods is evaluated using the Root Mean Square Error (RMSE) as a performance metric. The results demonstrate that all moving average methods significantly reduce noise, with the exponential moving average showing the lowest RMSE values, thereby indicating superior performance in improving data quality. © 2024 IEEE.

关键词： MATLAB

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Enhanced Explanations for Knowledge-Augmented Clustering using Subgroup Discovery 10

Enhanced Explanations for Knowledge-Augmented Clustering usi...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Szelazek, MacIej Hudson, Dan Bobek, Szymon Nalepa, Grzegorz J. Atzmueller, Martin Agh University Faculty of Applied Computer Science Kraków Poland Institute of Applied Computer Science Jagiellonian University Jagiellonian Human-Centered Artificial Intelligence Laboratory Mark Kac Center for Complex Systems Research Kraków Poland Osnabrück University Semantic Information Systems Group Osnabrück Germany Osnabrück Germany

ISBN: (纸本)9798350345032

Contemporary machine learning techniques are capable of extracting complex structure from data in a way that complements or exceeds manual examination, yet, as is welldocumented, many of these techniques suffer from a lack of interpretability. This paper extends previous work on explainable and interpretable machine learning, in particular on the 'Knowledge-Augmented Clusters (KnAC)' approach, allowing human users to benefit from uninterpretable 'black box' models to extract structure from datasets by clustering and to make this better understandable. One of the key functions of KnAC is to relate expert-annotated clusters to clusters that have been identified by a machine learning method, and then provide a comprehensible explanation, thus clarifying the relationships that KnAC discovered. Our novel contribution in this paper is to examine the usefulness of subgroup discovery as a way to generate comprehensible explanations within KnAC, and to compare this to the existing approach based on the XAI algorithm Anchors through a detailed evaluation. We find that the approach using subgroup discovery performs equally or better in our extensive experimentation testing this on six different datasets. © 2023 IEEE.

关键词： Machine learning

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Optimizing Connection Weights in Neural Networks Using Ali Baba and the Forty Thieves Algorithm 24

Optimizing Connection Weights in Neural Networks Using Ali B...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Al-Hiary, Heba Braik, Malik Al-Betar, Mohammed Azmi Al-Balqa Applied University Department of Computer Information Systems Al-salt Jordan Al-Balqa Applied University Department of Computer Science Al-salt Jordan Department of Information Technology Ajman United Arab Emirates Al-Balqa Applied University Irbid Jordan

ISBN: (纸本)9798350384307

Learning neural networks (NNs) is one of the most challenging problems in machine learning and has lately drawn the attention of many academics. The nonlinear structure of NNs and the unknown optimal set of key governing parameters (biases and weights) make training NNs particularly challenging. The primary drawbacks of conventional training methods are delayed convergence and local optima stagnation. Due to this, stochastic optimization techniques are dependable substitutes for addressing these problems. This study presents a new training method on the basis of the newly announced ali Baba and the forty thieves (AFT) algorithm. It has been shown that this algorithm can tackle a variety of optimization problems and can outperform many existing algorithms. Our efforts to measure its performance were prompted by this when training NNs. The proposed AFT-based trainer was tested using a bunch of 15 datasets that vary in intricacy. A comparison of AFT-based training NNs with other meta-heuristics presented for the same purpose was conducted to explore the efficiency of this training method. In most datasets, the training method-based AFT converged faster and was more adept at avoiding local optima than other rival methods. © 2023 IEEE.

关键词： AFT algorithm Classification Multilayer perceptron Neural networks

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Combination of Blockchain and Machine Learning for the Improvement of Decision Making and Secure Supply Chain Authentication Management

Combination of Blockchain and Machine Learning for the Impro...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Allahham, Mahmoud Alrashed, Atheer Ahmed Al-Omari, Omaia Almajali, Wasef Ibrahim Business Faculty Research Unit Amman Arab University Amman Jordan Applied Science University Department of Business Administration East Al-Ekir Bahrain College of Computer and Information Sciences Information Systems Department Prince Sultan University Riyadh Saudi Arabia Amman Arab University Faculty of Business Amman Jordan

ISBN: (纸本)9798350369106

This justifies the feature of blockchain in decision-making where companies predominantly use data processing and managing methods or have intricate supply chains demanding authenticity and relying on blockchain technology. This study is motivated by increasing convolutions and demands for secure speedy supply chain system as it is important in nurturing trustworthiness and accountability during commercial transactions, so it focuses on showing why current supply chain authentication methods are inefficient and unreliable leading to potential frauds and mistrust. This research starts by reviewing existing supply chain authentication methods and their shortcomings. The Basics of Blockchain refer to How Decentralized and Secure it really is, explaining what blockchain technology is. This blog post discusses how blockchain can be used to enhance decision-making processes, providing an unchangeable, auditable way to keep track of and authenticate the supply chain, and briefly covers various use cases of blockchain in supply chains and explains how they are transforming the security and efficiency of operations. The objective of this study is to suggest a structure that combines blockchain and machine learning for better supply chain decisions and authentication systems. It makes use of decentralized system architecture from methodological point of view by which the immutability aspect of blockchain and machine learning analytics can be integrated together in forecasting of demand while at the same time ensuring safety for transactions. Overall, blockchain technology enables significant disruption in supply chain authentication by helping partner systems and organizations make better decisions and reinforcing security requirements between partners. Combined with blockchain, RFIDs provide improved transparency and cost savings and ensure data is secure and trustworthy, thereby greatly enhancing supply chain networks. Indeed, the findings have shown clearly that coherent u

关键词： Blockchain

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in Engineering & sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Pho

Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Ph...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Pitcha Seripanichkarn Chananart Piyasoranart San Ratanasanya Porawat Visutsak Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper presents the development of an interactive virtual reality (VR) web application for exploring Wat Pho, a UNESCO Memory of the World heritage site in Bangkok, Thailand. Aimed at enhancing cultural tourism and accessibility, the application provides a 360° virtual tour of key locations within the temple grounds. Developed using Unity and Firebase, the application integrates interactive elements such as navigational arrows, Thai audio narration, textual information, and a chatbot for user queries. This immersive VR experience offers a novel way to engage with Wat Pho’s rich history and cultural significance for both local and remote audiences, overcoming barriers of physical access.

关键词： Surveys Headphones Accuracy Navigation Immersive experience User experience Cultural differences History Iterative methods Testing

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Enhancing Creative Problem-Solving with Contextual Stimuli: A Study on Individual Characteristics and Computational Methods 44

Enhancing Creative Problem-Solving with Contextual Stimuli: ...

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44th International Conference on information systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Weber, Sebastian Klein, Hans Christian Kordyaka, Bastian Palombo, Raphael Niehaves, Bjoern Faculty 3 - Mathematics and Computer Science University of Bremen Bremen Germany Department of Information Systems University of Siegen Siegen Germany

ISBN: (纸本)9781713893622

Fostering creative efforts may involve offering stimuli with varying degrees of relatedness to a certain creative task. Nonetheless, the perception of stimulus relatedness is subjective, and understanding how to deliver personalized stimuli remains limited. In our research, we computationally derived concepts with diverse levels of stimulus relatedness and asked 202 individuals to assess the stimulus relatedness. We explored the connections between the participants' unique traits and their perceived stimuli relatedness. Based on these factors, we grouped the participants into clusters. The taxonomy devised in this study reveals the existence of distinct user groups (i.e., four archetypes). Particularly, users have different perceptions of stimulus relatedness based on the Big Two stability, plasticity, and domain knowledge relevant to a task. With our taxonomy, we allow a more nuanced view of the perception of stimulus relatedness, and future creativity support systems can be further individualized delivering user-matching stimuli to foster creativity. Copyright © 2023 Weber et al.

关键词： Taxonomies

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Online Payments Fraud Detection Using Machine Learning Techniques 2

Online Payments Fraud Detection Using Machine Learning Techn...

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2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Atia, Hend Abdelbakey Aboul-Ela, Magdy Reyad, Christina Albert Awad, Nancy Awadallah National Bank of Egypt Auditing department Dakahlia Egypt Sadat Academy for Management Sciences Faculty of Computers & Information Department of computer science Cairo Egypt Sadat Academy for Management Sciences Department of Computer and Information Systems Cairo Egypt

ISBN: (纸本)9798350362633

With the rapid growth of online transactions and e-commerce, concerns about the security of online payment systems have increased. Additionally, the rising incidence of fraudulent activity imposes higher costs on bank transactions, posing a significant threat to businesses, especially credit card fraud. To effectively combat deception, it is crucial to understand the strategies employed by criminals targeting credit card transactions. Consequently, there has been considerable interest in identifying fraudulent operations. In this study, we propose a model based on data mining techniques and machine learning algorithms that outperforms rule-based algorithms for online payment fraud detection. We compare the performance of Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM - LGBM) models. Experimental results demonstrate that the LightGBM model achieves the highest performance metrics. With an accuracy of 0.998, the LightGBM model accurately distinguishes between fraudulent and valid transactions. It also exhibits a high sensitivity (recall) score of 0.989, indicating its ability to detect fraudulent cases. The precision score of 0.346 suggests a possibility of false positives. The F1-score, which provides a balanced measure of recall and precision, is 0.513, representing a fair compromise. The Area Under the Curve (AUC-ROC) score of 0.9933 reflects the model's discriminative capability. The Matthews correlation coefficient (MCC) shows a moderate agreement of 0.584 between actual labels and predictions. © 2024 IEEE.

关键词： Electronic money

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