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3rd International Conference on Robotics and Artificial Intelligence, JCRAI 2023

作者： Anekboon, Khantharat Pratchayatiwat, Nattapat Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9798400707704

The rapid expansion of urban infrastructure and construction projects require efficient monitoring and security measures in surveillance areas. Traditional methods of ground-level surveillance have limitations such as time-consuming, small exploring areas, and requiring a lot of people or ground-level surveillance devices. In response to this challenge, aerial images are valuable and widely used for various reasons in different fields. They can be used to monitor various areas from the top view. This study provides a method for the detection of backhoes in the surveillance area using aerial images with modified Faster-RCNN. Initially, aerial images were sent to the Resnet-101 structured convolution network to automatically extract features from the input image. The outcome will be the input of two sub-networks, a region proposal network and a Fast-RCNN. This proposed method modified the anchor box sizes in the region proposal network to be suitable to detect very small backhoes from aerial images. Additionally, RoI pooling and RoI align were evaluated and compared a result of a backhoe detection. To evaluate the proposed approach, five iterations of hold-out method with RoI pooling and RoI align technique with different anchor box sizes were experimented. The experimental results demonstrate the efficacy of the proposed system, achieving high precision and recall rates in backhoe detection tasks. This study found that RoI align gives the best backhoe detection result, 86.8% of the mean average precision value while the RoI pooling, the original pooling technique from Faster R-RCNN give mean average precision only 76.7%. © 2023 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0770-4/23/07.

关键词： Antennas

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Enhancing data sovereignty to improve intelligent mobility services in smart cities

Urban Governance

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Urban Governance 2025年第1期5卷 20-31页

作者： Anthony, Bokolo Sarshar, Sizarta Department of Applied Data Science Institute for Energy Technology Halden 1777 Norway Department of Computer Science and Communication Østfold University College Halden Norway Department of Risk and Safety Institute for Energy Technology Halden 1777 Norway

Smart cities aim to provide more digitalized, equitable, sustainable, and liveable cities. In smart cities data evolves as an important asset and citizens data in particular is being used to provide data-driven mobility services. Likewise, in smart cities data is produced and used by individuals having no ownership or control over these data thus affecting their security and privacy. Also, actors such as mobility service providers, companies, third parties, etc. often leverage individuals’ data without regarding users’ autonomy. Accordingly, data sovereignty which entails the possibility to govern and keep control over owned data is gaining widespread attention. As a result, stakeholders in the mobility sector need to share data whilst keeping control to manage data access and usage. Although, existing initiatives are promoting sovereign participation using a federated infrastructure. However, achieving an effective governance usage control and access mechanisms can be challenging especially in the transportation sector. It is thus imperative for individuals to have control, ownership, and custody over their data. This has necessitated the notions of data sovereignty. Therefore, this article employs a systematic review to examine how data sovereignty can be achieved for individuals when they use urban mobility services in smart cities by enabling the secure sharing of data by giving control of data over to individuals. Additionally, this article designs a data control scheme that can be applied to realize data sovereign mobility services. Evidence from this study provides technical and non-technical requirements needed in realizing data sovereignty in smart cities. © 2025 The Author(s)

关键词： Data access mechanisms Data governance, Data usage control Data sovereignty Data-driven mobility services Intelligent mobility services Smart cities

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Towards Automatic Generation of Digital Twins: Graph-Based Integration of Smart City Datasets 23rd

Towards Automatic Generation of Digital Twins: Graph-Based ...

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23rd International Conference on Computational science, ICCS 2023

作者： Ernst, Sebastian Kotulski, Leszek Wojnicki, Igor Department of Applied Computer Science AGH University of Science and Technology Al. Mickiewicza 30 Kraków Poland

ISBN: (纸本)9783031359941

This paper presents a graph-based approach to modelling and analysis of spatial (GIS) datasets supporting the deployment of smart city solutions. The presented approach is based on the spatially-triggered graph transformations (STGT) methodology, which allows for materialisation of spatial relationships detected using suitable tools, as well as performing measurements and modifications of geometries. The theory is illustrated using a real-world example which concerns street lighting. It shows how an existing traffic sensor network can be used to enable dynamic dimming of lamps, which can result in significant energy usage savings. Also, network analysis is applied to broaden the coverage of such systems, even in case of sensor sparsity. The presented results have been obtained in a real-world project and are due for larger-scale validation in the near future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy conservation

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Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

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computers, Materials & Continua 2024年第11期81卷 2395-2436页

作者： Deema Alsekait Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni Applied College Department of Computer Science and Information TechnologyPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus ***,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT *** research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis ***,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and *** study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT ***,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested *** research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT ***,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world *** WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT *** summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning *** research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance *** research co

关键词： Obfuscated malware detection IoT devices Wide Residual Network(WRN) malware detection machine learning

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Enhancing Cloud Security: A Comprehensive Review of Machine Learning Approaches 2

Enhancing Cloud Security: A Comprehensive Review of Machine ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Safi, Wassim Ghwanmeh, Sameh Mahfuri, Mahmoud Al-Sit, Waleed T. Higher Institute For Applied Sciences And Technology-HIAST Damascus Syria American University In The Emirates Department Of Computer Science United Arab Emirates International University For Science And Technology Computer And Information Technology Department Damascus Syria College Of Engineering Mu'tah University Department Of Computer Engineering Al-karak Jordan Higher Colleges Of Technology Dubai United Arab Emirates

ISBN: (纸本)9798350394962

Cloud Computing (CC) has ushered in a paradigm shift in the provisioning of IT resources, offering users enhanced cost-efficiency and streamlined infrastructure management. However, this growth in CC adoption has also brought forth a myriad of security challenges, encompassing threats to availability, integrity, and confidentiality. To combat these challenges, Cloud Service Providers (CSPs) are increasingly turning to Machine Learning (ML) to diminish the necessity for human intervention in the identification and resolution of security issues. ML's capacity to process extensive datasets and make highly accurate predictions holds the potential to revolutionize CSPs' approach to security. This paper delves into recent advancements in the realm of ML-based security within Cloud Computing. It assesses the attributes and efficacy of various ML algorithms, underscoring their distinctive strengths and possible limitations. Our objective is to present a comprehensive overview of the current landscape of ML in cloud security, illuminating the promising prospects this emerging field holds. © 2024 IEEE.

关键词： Cloud security

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in Engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Assessing Data Quality: An Approach for the Spread of COVID-19 15th

Assessing Data Quality: An Approach for the Spread of COV...

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15th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2023

作者： Król, Dariusz Bodek, Anna Department of Applied Informatics Wroclaw University of Science and Technology Wroclaw Poland Faculty of Computer Science and Management Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031424298

The work aims to develop a method for assessing the quality of publicly available data collections on the spread of the COVID-19 pandemic with daily infection statistics, recoveries and deaths. The World Health Organization, European Center for Disease Prevention and Control, Johns Hopkins University and Ministry of Health of the Republic of Poland provide this data as proof of concept. Metrics have been proposed that describe the most important quality features for this type of data collection - accuracy, completeness and consistency. Additional measures have also been defined based on anomaly detection, credibility and correlation between sets. A quality assessment method has been developed that uses specific metrics. The effectiveness of measures was tested on original and modified data. The findings showed that the measures were defined correctly. The method assigns lower-quality categories to datasets containing irregularities and higher for data with fewer errors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on Data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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