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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Günaydın, Yusuf Şevki Mintemur, Ömer Ankara Yıldırım Beyazıt University Computer Engineering Faculty of Engineering and Natural Sciences Ankara Turkey Ankara Yıldırım Beyazıt University Software Engineering Faculty of Engineering and Natural Sciences Ankara Turkey

ISBN: (纸本)9783031509193

One of the most basic human needs is the need for shelter. Since ancient times, people have been looking for a house that is both safe and affordable. However, in modern times, although safety is no longer an issue, the definition of an affordable house has changed. In the past, affordability did not depend on many parameters as it does today. However, today, this definition depends on different features, such as the location of the house, the year of construction, the number of rooms, etc. These features affect the level of affordability, and consequently the price of the house. Since houses are also used as an investment option, correct estimation of house prices is an important issue. The determination of features that have a significant impact on the price of a house is a subjective notion and, therefore, requires an objective approach. Thanks to technological developments, Artificial Intelligence algorithms remove the human factor in most of the decision-making processes. In this study, a naive approach was proposed to estimate house prices by selecting the most effective features of a house (https://***/OmerMintemur/Feature-Elimination-Using-GA.). To select the most effective features, Genetic Algorithm approach was utilized. For estimation, LightGBM was used. The AmesHouse data set was used for the experiments. The results suggested that the proposed method both reduced the features and produced lower estimation errors than other proposed methods that used the same dataset. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Reduction Genetic Algorithm House Prices Prediction

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An Implementation of DNS Operator and Network Guideline for Migrating Virtual Machine to KubeVirt 26

An Implementation of DNS Operator and Network Guideline for ...

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26th International computer Science and engineering Conference, ICSEC 2022

作者： Trakulmaiphol, Supakorn Piromsopa, Krerk Chulalongkorn University Software Engineering Program Department of Computer Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665491983

We created a customize DNS operator to address the shortcoming of CoreDNS in Kubernetes. While Kubernetes is gaining popularity in orchestrating containers, many organizations with Virtual Machine (VM) based legacy applications still struggle to containerize. Rehost strategy seems to be a faster method. On Kubernetes, KubeVirt can be used to manage rehosted VM. However, there is a huge difference between the long-term use of the VMs and containers. For example, container IP addresses change over time is problematic for VMs that need a static IP. In addition, some VM-based applications may require multiple network interfaces or a specific domain name for service discovery. These issues prevent some VMs from functioning properly after the migration to KubeVirt. We developed an operator framework to solve network problems that arise in the application layer such as DNS queries. In addition, we provide a guideline on how to use open-source projects like the Multus Container Network Interface, NMState to enable the migration of VMs with applications related to network function properly. Our operator and guideline allow most VMs to function properly with few modifications (to none) after migrating to KubeVirt. This facilitated the adoption of Kubernetes in more organizations. © 2022 IEEE.

关键词： Containers

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Performance Improvement in Blood Cell Segmentation with Deep Learning-based Image Fusion Approach 8

Performance Improvement in Blood Cell Segmentation with Deep...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Dogu, Fatma Tuana Dogan, Hulya Dogan, Ramazan Ozgur Sezen, F. Sena Karadeniz Technical University Faculty of Engineering Department of Electrical and Electronics Engineering Trabzon Turkey Karadeniz Technical University Faculty of Engineering Department of Software Engineering Trabzon Turkey Trabzon University Faculty of Computer and Information Sciences Department of Artificial Intelligence Engineering Trabzon Turkey Karadeniz Technical University Faculty of Pharmacy Department of Pharmacology Trabzon Turkey

ISBN: (纸本)9798331531492

To overcome the limitations of examining blood cells under a microscope, several investigations have been carried out using artificial intelligence techniques. These investigations aim to automate the processes of detecting, counting, segmenting, and classifying blood cells. Typically, these investigations employ two-dimensional (2D) images to apply artificial intelligence and image processing techniques. Nevertheless, these 2D images occasionally lack optimal focus as a result of the microscope's depth of focus (DOF). Inadequate achievement of optimally focused imaging has a detrimental impact on the efficiency of automated processes. In this context, the objective of this study is founded around two primary roles. The first of these is the performance improvement of blood cell segmentation by developing a deep learning-based image fusion approach. The second is the preparation of new data sets containing images with different focuses for optimally focused imaging of blood cells. In the study, four different performance evaluation criteria, namely Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), and Jaccard, are utilized to demonstrate the efficacy of the deep learning-based image fusion approach in blood cell segmentation. Both the experimental results of the performance evaluation criteria and the visual findings obtained in the study indicate that the performance in blood cell segmentation is improved with a deep learning-based approach. © 2024 IEEE.

关键词： Image segmentation

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Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

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computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

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Evaluating BERT-based language models for detecting misinformation

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Neural Computing and Applications 2025年第16期37卷 9937-9968页

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Information Technology and Data Science Faculty Sebelas Maret University Ir Sutami 36A Central Java Surakarta57126 Indonesia Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further. © The Author(s) 2025.

关键词： BERT-based models Comparative analysis Rumour detection Text embeddings

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256 Metaverse Records Dataset 24

256 Metaverse Records Dataset

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32nd ACM International Conference on Multimedia, MM 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. Faculty of Mathematics and Computer Science University of Hagen Hagen Germany Faculty of Business Computing and Software Engineering Pfh University of Applied Sciences Göttingen Germany School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9798400706868

The metaverse is a developing field that crosses several areas of multimedia research. In this paper, we introduce the 256-MetaverseRecords dataset, a novel and extensive collection of annotated screen recordings in the form of videos from various virtual worlds of the metaverse. We describe the process of creating the dataset, the quality criteria for the annotations, and the exploration of the dataset. We also show four experiments to evaluate the performance of different feature extraction methods for Metaverse Recordings (MVRs): MVR segmentation, audio event detection, and object and interaction detection based on this dataset. Our results demonstrate that existing methods have limitations and leave challenges in dealing with the diversity and complexity of metaverse data, and that more research is needed to develop metaverse-specific techniques. Our dataset can serve as a valuable resource for the research community and foster the development of new applications and solutions for the metaverse. © 2024 Owner/Author.

关键词： dataset

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study 4

UVM Based Verification Framework for Deep Learning Hardware ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Aboudeif, Randa Awaad, Tasneem A. Abdelsalam, Mohamed Ismail, Yehea The American University in Cairo Siemens Digital Industries Software Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Siemens Digital Industries Software Department of Computer and Systems Engineering Faculty of Engineering Cairo Egypt Siemens Digital Industries Software Cairo Egypt The American University in Cairo Electronics and Communications Engineering Department Cairo Egypt

ISBN: (纸本)9798350395914

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous Systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study. © 2024 IEEE.

关键词： CNN Deep Learning Accelerators NVDLA UVM veri-fication

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Local Weighted Neighborhood Rough Sets for Weakly Labeled Data

Local Weighted Neighborhood Rough Sets for Weakly Labeled Da...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Guo, Yanting Hu, Meng Liu, Qiang Tsang, Eric C.C. Wang, Xizhao College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Innovation Engineering China

ISBN: (纸本)9798350303780

Local rough set models are efficient rough data analysis methods for weakly labeled data in the rough set field. However, the existing local models do not distinguish the importance of different attributes to decisions when describing and defining binary relations, resulting in inaccurate information granules or classes, which affects the accuracy of concept approximation. In this paper, we choose local neighborhood rough sets as the representative model, and then propose Local Weighted Neighborhood Rough Sets (LWNRS). First, the importance of attributes to decisions is quantified by combining the supervised information of labeled data and the structural information of unlabeled data, and an attribute weighted method based on semi-supervised dynamic optimization is proposed. Meanwhile, the weighted neighborhood relation and neighborhood classes of objects are defined under weakly labeled data, and the WLNRS model is constructed. In addition, the weighted dependency is proposed to measure the data uncertainty, and the relationships among WLNRS and other relevant local and global models are deeply explored. Finally, the efficiency and accuracy of WLNRS in concept approximation are verified by comparison. © 2023 IEEE.

关键词： Rough set theory

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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An Efficient Routing Schema for Internet of Underwater Things/Ocean of Things

An Efficient Routing Schema for Internet of Underwater Thing...

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2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

作者： Mohammadi, Reza Nazari, Amin Daneshmand, Behrooz Bu-Ali Sina University Computer Engineering Faculty Hamedan Iran S. Petersburg Natl. Research University of Information Technologies Mechanics and Optics University Faculty of Software Engineering and Computer Engineering ITMO Saint-Petersburg Russia

ISBN: (纸本)9798350348293

Recently, Internet of Underwater Things (IoUT) is a new technology used by researchers to explore underwater phenomena. Information is collected by underwater sensors and sent to the Ocean of Things (OoT) network, which is located on the water surface. OoT floaters then collect the data and send it to coastal stations via radio signals. Since the underwater environment is harsh for telecommunication, one of the current challenges in this architecture is the collected data transmission through the routes that can provide quality of service parameters (e.g., delay and packet loss). Hence, an optimal method is proposed in this article to reduce end-to-end delay and increase packet delivery rate in the hybrid networks of the internet of things and the ocean of things. In the proposed method, the network controller collects the underwater things data and creates the network graph using the capabilities of software-Defined Network (SDN) architectures. Then, the routing problem is modeled in the form of an Integer Linear Programming (ILP) model to improve the quality of service. The created model is then solved using the Golden Eagle Optimization (GEO) metaheuristic algorithm to find the optimal routes. Finally, the network controller installs the calculated routes on the underwater things to send information to the ocean of things floaters through these optimal routes. In the proposed method, the ocean of things floaters acts as fog nodes and send the received data to the coastal stations after preprocessing operations for analysis. Simulation results show that the proposed method can significantly improve the quality-of-service parameters. © 2023 IEEE.

关键词： Quality of service

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