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2nd International Conference on Information technology Research and Innovation, ICITRI 2023

作者： Ariefah, Amelia Widodo Nugraheni, Murien State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia State University of Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798350324945

Curriculum changes have a significant impact on the field of education in Indonesia, especially in universities. This is because the curriculum is used as a means to achieve the goals of educational success as well as guidelines in the implementation of teaching at every level of education. This study aims to determine public sentiment regarding the implementation curriculum of independent learning (MBKM) through Twitter based on Naïve Bayes with Laplace Estimator. Tweet search results use the keywords 'kurikulum mbkm' and 'mbkm'. The data used in this study amounted to 2500 tweet data which will be divided into 2000 data as training data with details of 1000 positive tweets and 1000 negative tweets and 500 data as testing data. The results showed the performance of Naïve Bayes using Laplace Estimator which resulted in an accuracy value of 80.20%, precision value of 82.00%, and recall value of 79.00%. Meanwhile, the performance of Naïve Bayes without Laplace Estimator resulted in an accuracy value of 77.00%, precision value of 80.00%, and recall value of 74.00%. Naive Bayes with Laplace Estimator reached the highest value in all metrics. © 2023 IEEE.

关键词： Curricula

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An Accurate Intelligent Plan Library for Belief-Based Desire Prioritization to Intentions in BDIx Agents

An Accurate Intelligent Plan Library for Belief-Based Desire...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Iacovos Ioannou Andreas Gregoriades Prabagarane Nagaradjane Christophoros Christophorou Vasos Vassiliou Department of Computer Science University of Cyprus and CYENS - Centre of Excellence Cyprus Cyprus University of Technology Department of ECE Sri Sivasubramaniya Nadar College of Engineering Chennai India University of Central Lancashire (UCLAN) Larnaca Cyprus

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The emergence of 6G technology is poised to revolutionize wireless networks through advanced Device-to-Device (D2D) communications, offering ultra-reliable, low-latency, and high-throughput connections. However, the increased complexity and density of 6G D2D networks necessitate autonomous, distributed, and dynamic decision-making capabilities to ensure efficient and reliable operations. Belief-Desire-Intention eXtended (BDIx) agents present a promising solution by enabling intelligent selection and execution of communication strategies based on real-time environmental assessments and internal states. This paper introduces an Plan Library designed to enhance BDIx agents’ ability to prioritize desires grounded in their beliefs, thereby facilitating robust intention formation tailored to the dynamic requirements of 6G D2D communications. Our approach integrates advanced machine learning and fuzzy logic techniques to optimize desire prioritization and intention *** evaluated six models— Adaptive Neuro-Fuzzy Inference System PlanLibrary (ANFIS PL), Random Forest, Support Vector Machine (SVM), Fuzzy Logic, Gradient Boosting, and K-Nearest Neighbors (K-NN)—across metrics including Accuracy, Precision, Recall, F1-Score, Computational Time, Memory Usage, and CPU Utilization. Results demonstrate that ANFIS PL and Random Forest achieve superior performance while maintaining efficient computational resource usage. The proposed Plan Library approach significantly enhances BDIx agents’ capability to prioritize and execute communication strategies autonomously, ensuring optimal performance and adaptability in 6G D2D networks. This advancement underscores the pivotal role of intelligent agent architectures in next-generation wireless communication systems, paving the way for more resilient and efficient network operations.

关键词： 6G mobile communication Fuzzy logic Support vector machines Accuracy Wireless networks Decision making Signal processing Libraries Device-to-device communication Random forests

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Comparative Analysis of Word vs. Character Embedding for Machine Learning Based Detection of Malicious URLs and DGA-Generated Domains 15

Comparative Analysis of Word vs. Character Embedding for Mac...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Ayodele, Oludayo C. Yerima, Suleiman Y. School of Computer Science and Informatics De Montfort University Leicester United Kingdom British University in Dubai Faculty of Engineering and Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350324433

This study presents a comprehensive comparative analysis of the effectiveness of word-level and character-level embeddings in the context of machine learning-based detection of malicious URLs and DGA-generated domains. Utilizing distinct datasets comprising DGA-generated domains and Spam URLs, we systematically evaluate various machine learning models coupled with word-level and character-level tokenization techniques. Our findings indicate that character-level tokenization yields superior results in identifying DGA-generated domains, particularly due to the random character composition of these URLs. Conversely, both word-level and character-level embeddings exhibit comparable success rates in classifying Spam URLs, owing to the non-random nature of their URL structures. The study sheds light on the importance of tailoring tokenization strategies based on the unique characteristics of the data. We recommend character-level embeddings for detecting DGA-generated domains characterized by random characters. In contrast, the choice between word-level and character-level embeddings is less critical when dealing with Spam URLs, as both approaches yield effective results. © 2023 IEEE.

关键词： Embeddings

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Federated Digital Twin 27

Federated Digital Twin

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Vergara, Christian Bahsoon, Rami Theodoropoulos, Georgios Yanez, Wendy Tziritas, Nikos School of Computer Science University of Birmingham Birmingham United Kingdom Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece

ISBN: (纸本)9798350337846

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence;and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems. © 2023 IEEE.

关键词： Metadata

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A Framework for Detection of MitM Cyberattacks in Smart Grid Networks Based on the Application of the Ensemble Process for Feature Selection 13

A Framework for Detection of MitM Cyberattacks in Smart Grid...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Nicheporuk, Andrii Savenko, Oleg Sachenko, Anatoliy Nicheporuk, Anastasiia Khmelnytskyi National University Department of Computer Engineering and Information Technologies Khmelnytskyi Ukraine Kazimierz Pulaski University of Technology and Humanities Department of Informatics and Tele infomatics Radom Poland

ISBN: (纸本)9798350396119

Recent years have shown an increase in the number and complexity of cyberattacks not only on traditional IT infrastructures, but also on smart energy supply networks. Cybercriminals and cyberspies are becoming more sophisticated and persistent in their actions, which requires increased cyber security measures and the development of new technologies to protect infrastructure. In this work we present a framework for detection of Man-in-the-Middle cyberattacks in smart grids based on the application of the ensemble process for feature selection. The workflow of the framework consists of two main stages: model creation and performance evaluation. To create the model, a study was carried out, which involved the selection of the optimal set of features by applying various methods of feature selection. In addition to the sets of features that were formed by feature selection methods, another set of features was formed, which is the result of the process of assembling features that were formed by feature selection methods. A feature score was proposed, which allows weighting of each feature depending on its frequency of selection in all selection methods and the relative number of selected features in the entire set. To check the effectiveness of detecting MitM cyberattacks, SVM and Decision Tree classifiers were used, the test data for which were generated according to the obtained sets of features. © 2023 IEEE.

关键词： Smart power grids

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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SecureSIoTChain: A relationship enhanced Blockchain Operational Security Framework for the Social Internet of Things 2

SecureSIoTChain: A relationship enhanced Blockchain Operatio...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, K S Santhosh Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill Department of Studies in Computer Science University of Mysore Karnataka Mysuru570006 India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru570006 India Scientist Co-evolutionary Artificial Intelligence Paphos Cyprus

The Social Internet of Things (SIoT) is a fast growing technology that autonomously connects things, allowing for exchange of information depending on relationships. However, the open nature of SIoT exposes it to various security challenges, such as default passwords, brute-forcing attacks, data privacy breaches, and an increasing number of IoT botnets. To address these vulnerabilities, a novel operational security framework for SIoT, leveraging the robust properties of blockchain technology. The primary objectives of the operational security model are to establish a secure and trusted environment in SIoT, protect sensitive data, ensure reliable device authentication, and enable secure and encrypted communication in SIoT operations. The proposed model combines key features of blockchain, including decentralization, immutability, transparency, and cryptographic mechanisms, with SIoT security operations. Techniques like encryption, anonymization and differential privacy are employed to preserve privacy in SIoT devices and safeguard sensitive data. An empirical assessment of this operational security framework yields promising outcomes in the pursuit of its defined objectives. It furnishes a decentralized and tamper-proof foundation for device authentication, thereby cementing the trustworthiness of SIoT devices. Moreover, the proposed Graph Neural Network (GNN) model eclipses the performance of traditional models, boasting a 95% accuracy, a 96% precision, a 95% recall, and a 95% F1-score. This achievement can be attributed to the incorporation of computations that encapsulate both transitivity and composability among devices. In tandem, the suggested framework emerges triumphant when compared to other methodologies, spanning diverse metrics such as throughput, latency, security score, privacy score, and trust score. Notably, this framework not only secures communication but also upholds data integrity and confidentiality, ensuring exclusive access to SIoT devices and

关键词： Internet of things

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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