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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hua, Xiaoqi Chen, Jian Zhang, Peiyun Fu, Zhangjie Zhu, Haibin Lu, Kezhong Ren, Jigang School of Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics of Ministry of Education The School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China

ISBN: (纸本)9781665410205

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio. © 2024 IEEE.

关键词： Blockchain

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Arabic Fake News Detection Using Machine Learning Approach

Arabic Fake News Detection Using Machine Learning Approach

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Duridi, Tasneem Eleyan, Derar Eleyan, Amna Bejaoui, Tarek Palestine Technical University Department of Computer Science Tulkarm Palestine Nablus University for Technical and Vocational Education Faculty of Telecomm. and Information Technology Nablus Palestine Palestine Technical University - Kadoorie Department of Computer Science Tulkarm Palestine Manchester Metropolitan University Department of Computing and Mathematics ManchesterM1 5GD United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798350364910

In the contemporary digital realm, the widespread dissemination of false Arabic news is a significant social concern laden with various risks. Recognizing the seriousness of this issue, our research utilizes cutting-edge technologies, specifically Machine Learning, to distinguish between authentic Arabic news and deceptive counterparts. The consequences of propagating misinformation go beyond compromising social cohesion;they extend to the erosion of digital information's credibility, fostering an atmosphere of mistrust and deception that undermines the very foundations of societal bonds. Through the application of contemporary technologies, this study aims to identify and underscore Arabic news that embodies such risks. The intricate characteristics of Arabic language morphology, marked by words carrying multiple meanings based on inflectional forms and the prevalence of numerous diacritical marks, intensify the challenges of text classification. Despite contending with these linguistic intricacies, modern natural language processing approaches offer practical solutions. Notably, our methodology relies on the preprocessing of two available datasets for training and testing, a crucial step for seamlessly integrating a range of Machine Learning techniques, including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and Multinomial Naive Bayes (NB). Importantly, the Logistic Regression technique emerged as the most effective, achieving an accuracy of 95.92% in the SANAD dataset for discerning nuanced Arabic news © 2024 IEEE.

关键词： Adversarial machine learning

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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

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computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Wireless networked control over lossy uplinks abstracted by finite-state Markov channels

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IFAC-PapersOnLine 2023年第2期56卷 3041-3047页

作者： Yuriy Zacchia Lun Claudia Rinaldi Fortunato Santucci Alessandro D'Innocenzo The Department of Information Engineering Computer Science and Mathematics University of L'Aquila Via Vetoio (Coppito 1) 67100 L'Aquila Italy

Networked control systems using wireless links to convey information among sensors, controllers, and actuators greatly benefit from having an accurate estimate of the communication channel condition. To this end, the finite-state Markov channel abstraction allows for reliable channel state estimation. This paper develops a Markov jump linear system representation for wireless networked control with intermittent channel state observation, message losses, and generalized hold-input dropout compensation. Furthermore, it exploits the emerging structural properties of the system to solve the finite-horizon linear quadratic regulation problem efficiently.

关键词： Telecommunication-based automation systems

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Improving the learning performance by exploiting multimedia in eXtreme apprenticeship

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Multimedia Tools and Applications 2024年 1-25页

作者： Persia, Fabio D’Auria, Daniela Ge, Mouzhi Pilato, Giovanni Department of Information Engineering Computer Science and Mathematics University of L’Aquila Via Vetoio L’Aquila67100 Italy Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani 3 Bozen-Bolzano39100 Italy Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Institute for High Performance Computing and Networking National Research Council of Italy Via Ugo La Malfa 153 Palermo90146 Italy

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between University education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distance education

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Guest Editorial to the Special Issue Public Transport Optimization: From Theory to Practice

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Operations Research Forum 2024年第3期5卷 73页

作者： Cacchiani, Valentina Müller-Hannemann, Matthias Rojas-Marcos, Federico Perea Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Viale Risorgimento 2 Bologna 40136 Italy Institute for Computer Science Martin Luther University Halle-Wittenberg Von-Seckendorff-Platz 1 Halle D 06120 Germany Department of Applied Mathematics II and Institute of Mathematics (IMUS) University of Seville Avda. Reina Mercedes s/n Seville 41012 Spain

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DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3295-3307页

作者： Leilei Li Yansheng Fu Dongjie Zhu Xiaofang Li Yundong Sun Jianrui Ding Mingrui Wu Ning Cao Russell Higgs Artificial Intelligence Academy Wuxi Vocational College of Science and TechnologyWuxi214068China School of Computer Science and Technology Harbin Institute of TechnologyWeihai204209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China College of Information Engineering Shandong Vocational and Technical University of International StudiesRizhao276826China School of Mathematics and Statistics University College DublinDublinD04 V1W8Ireland

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval *** and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge *** the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge *** representation learning method is sig-nificant to the application of multi-modal knowledge *** paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data ***,use distributed technology to classify and store the data in the knowledge graph to improve retrieval ***,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of ***,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image *** paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

关键词： Multi-modal retrieval distributed storage knowledge graph

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science CambridgeCB1 1PT United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory BristolBS16 1QY United Kingdom Hartpury University Department of Animal and Agriculture GloucesterGL19 3BE United Kingdom Aston University College of Engineering and Physical Sciences BirminghamB4 7ET United Kingdom Royal Military College of Canada Department of Mathematics and Computer Science KingstonONK7K 7B4 Canada Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2014 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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A Double Protecting Approach for Data Privacy 10

A Double Protecting Approach for Data Privacy

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Almutairi, Mishaal M. Apostolopoulou, Dimitra Stupples, David Sen, Adnan Ahmed Abi Yamin, Mohammad Halikias, George School of Mathematics Computer Science and Engineering Department of Electrical and Electronic Engineering LondonEC1V 0HB United Kingdom Deanship of Information Technology Islamic University Madinah Saudi Arabia School of Computing The Australian National University Canberra Australia

ISBN: (纸本)9789380544472

Hackers have become more vigilant and malicious. Thus, cases of direct threats and blackmail, which lead to hacking and theft of users' data, have increased over the years. As such, it is necessary to build effective privacy protection methods and techniques that can guarantee users' data privacy even in the presence of a malicious attacker or an unreliable service provider. In this view, this study proposes a double approach for protecting users' data in a crowd. The study uses a systematic literature review method to identify technologies that have been advanced to ensure data privacy. These technologies include encryption, data mining techniques, obfuscation, dummies, and TTP. Unfortunately, all these methods have significant weaknesses that hinder them from effectively achieving data security and protection. As such, the current study proposes a new approach to privacy that can enhance the effectiveness of existing methods without impacting their performance. This approach also supports complex systems such as those used in crowd management and a wide variety of applications within these systems. Ideally, the double-approach method proposed in this study is a collection of various technologies that rely on two primary conditions: doubling the work of traditional data protection methods and using fog computing to enhance the efficiency and overall performance of these methods. This way, the new approach will effectively serve different complex systems and applications used in crowd management. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Personal computing

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