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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Fact-in-a-Box: Hiding Educational Facts in Short Stories for Implicit Learning 15

Fact-in-a-Box: Hiding Educational Facts in Short Stories for...

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15th International Conference on computer Supported Education, CSEDU 2023

作者： El Bolock, Alia Sabty, Caroline Awad, Nour Eldin Abdennadher, Slim Informatics and Computer Science German International University Cairo Egypt Media Engineering and Technology German University in Cairo Cairo Egypt

ISBN: (纸本)9789897586415

Generating stories on-demand is one of the covered tasks in Natural Language Generation. Stories are being used in every culture by any age. They have been used for different purposes, such as entertainment and the education of children. They are an effective way of indirectly providing students with valuable facts that are easier embedded in their memory. We propose an approach for embedding facts into existing or automatically generated stories, given a specific target audience and a story context. As proof of concept, we implemented a framework called Fact-in-a-Box to hide facts in existing stories or a human-like generated text through a customized user. The framework is based on a fine-tuned model for children as the target audience and fixes the story context to animals. Instructors can apply the approach to deliver facts to the learner in an exciting yet informative way. The framework is composed of two modules, one for selecting the most relevant story and the other one to embed the fact in it. We tested the proposed approach using an experiment to test the learning gain of children and a survey for adults to evaluate the language of the resulting stories and the concept itself. The performance was relatively good in hiding facts inside an existing story where children could correctly re-convey 50% of the complex facts and 80% of the simpler tasks. Copyright © 2023 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Natural language processing systems

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Compiler-based Web Services code conversion model for different languages of mobile application 1

Compiler-based Web Services code conversion model for differ...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Mahmoud, Amira T. Muhammad, Ahmad A. Yousef, Ahmed H. Medhat, Walaa Zayed, Hala H. Selim, Sahar Nile University School of Information Technology and Computer Science Giza Egypt Egypt University of Informatics School of Engineering Cairo Egypt

ISBN: (纸本)9798350335569

Mobile applications are very popular nowadays. The two most common platforms depend on different languages to have native performance. Most trans-compiler-based converters between those languages didn't take webservices conversion into consideration. However, in recent years, mobile applications developers have used web services for lighter and more dynamic mobile applications. Therefore, it becomes crucial to include web services mapping to the trans-compiler-based code converters for mobile applications code. In this paper, we present a model for web service trans-compiler code mapper from swift for iOS to java for Android to be integrated to previous trans-pilers. The testing for the conversion accuracy is done for six different web services functions. The average accuracy of converting webservice calling from iOS to Android is 89.3%. In addition, due to the rise of generative language models and their capability to generate code, we compare OpenAI's chatGPT to our tool, evaluating its capabilities for code translation and exploring the potential of prompt engineering for seamless integration into our trans-compiler tool. © 2023 IEEE.

关键词： Web services

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Putra, Qori Alfatah Sari, Christy Atika Rachmawanto, Eko Hari Cahyo, Nur Ryan Dwi Mulyanto, Edy Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers. © 2023 IEEE.

关键词： Image segmentation

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Genetic Programming and Coevolution to Play the Bomberman™ Video Game 26th

Genetic Programming and Coevolution to Play the Bomberman...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Gold, Robert Branquinho, Henrique Hemberg, Erik O’Reilly, Una-May García-Sánchez, Pablo ALFA MIT CSAIL Cambridge United States Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering University of Coimbra Coimbra Portugal Department of Computer Architecture and Computer Technology University of Granada Granada Spain

ISBN: (纸本)9783031302282

The field of video games is of great interest to researchers in computational intelligence due to the complex, rich and dynamic nature they provide. We propose using Genetic Programming with coevolution and lexicographic fitness to generate an agent that plays the Bomberman™game. We investigate two sets of Genetic Programming building blocks: one contains conditions relative to movement, and the other does not. We aim to see whether the benefits of these movement-related conditions outweigh the negatives caused by increased search space size. We show that the benefits gained do not outweigh the increase in search space size. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic programming

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Analysis of YouTube Video Comments with NLP Methods 16

Analysis of YouTube Video Comments with NLP Methods

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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Leveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models

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Expert Systems 2025年第7期42卷

作者： Nelson, Jordan Pavlidis, Michalis Fish, Andrew Polatidis, Nikolaos Manolopoulos, Yannis School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Informatics University of Nicosia Nicosia Cyprus

The growing popularity of generative AI and large language models (LLMs) has sparked innovation alongside debate, particularly around issues of plagiarism and intellectual property law. However, a less-discussed concern is the quality of code generated by these models, which often contains errors and encourages poor programming practices. This paper proposes a novel solution by integrating LLMs with automated machine learning (AutoML). By leveraging AutoML's strengths in hyperparameter tuning and model selection, we present a framework for generating robust and reliable machine learning (ML) algorithms. Our approach incorporates natural language processing (NLP) and natural language understanding (NLU) techniques to interpret chatbot prompts, enabling more accurate and customisable ML model generation through AutoML. To ensure ethical AI practices, we have also introduced a filtering mechanism to address potential biases and enhance accountability. The proposed methodology not only demonstrates practical implementation but also achieves high predictive accuracy, offering a viable solution to current challenges in LLM-based code generation. In summary, this paper introduces a new application of NLP and NLU to extract features from chatbot prompts, feeding them into an AutoML system to generate ML algorithms. This approach is framed within a rigorous ethical framework, addressing concerns of bias and accountability while enhancing the reliability of code generation. © 2025 The Author(s). Expert Systems published by John Wiley & Sons Ltd.

关键词： Expert systems

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Optimizing Power Allocation and Beamforming in mmWave-NOMA Downlink with Deep Reinforcement Learning

Optimizing Power Allocation and Beamforming in mmWave-NOMA D...

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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Abbas Akbarpour Kasgari Chrysostomos Chrysostomou Department of Electrical Engineering Computer Engineering and Informatics Frederick University and Frederick Research Center Nicosia Cyprus

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

The high demand for data rates in the fifth generation (5G) and beyond of wireless communication can be met by the Non-Orthogonal Multiple Access (NOMA) approach in the millimeter-wave (mmWave) frequency band. Joint power allocation and beamforming for 5 G and beyond mmWave-NOMA systems are essential, which can be achieved through optimization approaches. To this end, we have employed a Deep Reinforcement Learning (DRL) approach for policy generation, leading to an optimized sum-rate for users. Unlike existing methods, our approach is not susceptible to channel impulse response (CIR) estimation errors. The actor-critic framework is utilized to measure the immediate reward and provide new actions to maximize the overall Q-value of the network. The immediate reward is defined based on the summation of the rates of two users, considering the minimum guaranteed rate for each user and the total consumed power as constraints. The simulation results demonstrate the superiority of the proposed approach compared to state-of-the-art counterparts, including Time-Division Multiple Access (TDMA) and NLOS-NOMA optimization strategies, in terms of sum-rate of users, while considering both perfect and imperfect channel state information (CSI) at the receiver. This work can also be beneficial for communications in vehicular networks, particularly in applications involving Collective Perception Messages (CPMs) dissemination for autonomous driving.

关键词： Wireless communication NOMA Time division multiple access Array signal processing 5G mobile communication Deep reinforcement learning Resource management Millimeter wave communication Channel impulse response Optimization

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Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

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30th IEEE International Symposium for Design and technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (Department of Electronics technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

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