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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Oktavia, Petricia Frindo, Muhamad Meky Pujiati, Tri Nugroho, Fajar Agung Yasser Arafat, Muhamad Pratiangga, Aptanta Pamulang University Departement of Informatics Engineering Tangerang Indonesia University of Trunojoyo Madura Department of English Literature Madura Indonesia Rey Department of Computer Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

This paper presents the design and development of an educational game application aimed at introducing transportation vocabulary in English to early childhood education students. The application development follows a five-stage prototype methodology there are communication, quick planning, modeling quick design, prototype construction, and deployment with feedback. Using photoshop, paint for graphic design, unity for programming, and we developed an Android-based offline-accessible application. The application's effectiveness was evaluated via a questionnaire administered to 35 respondents, resulting in a significant improvement in children's interest in learning English, with an average score of 82.9%. These findings indicate that the educational game application is an effective and engaging tool for teaching transportation vocabulary to young learners, potentially enhancing early childhood education methodologies. © 2024 IEEE.

关键词： Game design

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The Evolution of Generative AI: Trends and Applications

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IEEE Access 2025年 13卷 98504-98529页

作者： Trigka, Maria Dritsas, Elias University of West Attica Egaleo Park Campus Department of Informatics and Computer Engineering Athens12243 Greece

Generative artificial intelligence (AI) has revolutionized AI by enabling high-fidelity content creation across text, images, audio, and structured data. This survey explores the core methodologies, advancements, applications, and challenges of generative AI, covering key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformer-based architectures. These innovations have driven breakthroughs in healthcare, scientific computing, Natural Language Processing (NLP), computer vision, and autonomous systems. Despite its progress, generative AI faces challenges in bias mitigation, interpretability, computational efficiency, and ethical governance, necessitating research into scalable architectures, explainability, and AI safety mechanisms. Integrating Reinforcement Learning (RL), multi-modal learning, and self-supervised techniques enhances controllability and adaptability in generative models. Additionally, as AI reshapes industrial automation, digital media, and scientific discovery, its societal and economic implications demand robust policy frameworks. This survey provides a comprehensive analysis of generative AI's current state and future directions, highlighting innovations in efficient generative modelling, AI-driven scientific reasoning, adversarial robustness, and ethical deployment. By consolidating theoretical insights and real-world applications, it offers a structured foundation for researchers, industry professionals, and policymakers to navigate the evolving landscape of generative AI. © 2013 IEEE.

关键词： Reinforcement learning

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

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Soft Computing 2024年第19期28卷 11633-11646页

作者： Amiya, Gautam Murugan, Pallikonda Rajasekaran Ramaraj, Kottaimalai Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fracture

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BLNet:Bidirectional learning network for point clouds

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Computational Visual Media 2022年第4期8卷 585-596页

作者： Wenkai Han Hai Wu Chenglu Wen Cheng Wang Xin Li School of Informatics Xiamen University422 Siming South RoadXiamen 361005China School of Electrical Engineering and Computer Science Louisiana State UniversityBaton RougeLA 70803USA

The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D *** relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second *** convolving kernels with irregular points will result in loss of shape *** paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D *** optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular *** the other hand,explicitly modeling point positions leads to a new feature encoding with increased ***,an attention pooling unit selectively combines important *** bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature *** show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.

关键词： point clouds irregularity shape features bidirectional learning

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(ω1,ω2)-temporal random hyperbolic graphs

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Physical Review E 2024年第2期110卷 024309-024309页

作者： Sofoclis Zambirinis Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics <a href="https://***/05qt8tf94">Cyprus University of Technology</a> 3036 Limassol Cyprus.

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities ω1 and ω2. This extension, while conceptually simple, poses analytical challenges involving the Appell F1 series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The incorporation of ω1 and ω2 enables more flexible tuning of the average contact and intercontact durations, and of the average time-aggregated degree, providing a finer control for exploring the effect of temporal network dynamics on dynamical processes. Overall, our results provide new insights into the analysis of temporal networks and contribute to a more general representation of real-world scenarios.

关键词： Evolving networks Networks & random structures Random graphs Evolving network models

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Designing an Advanced Situational Awareness Platform Using Intelligent Multi-Drones for Smart Farming Towards Agriculture 5.0 11

Designing an Advanced Situational Awareness Platform Using I...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

With regard to Agriculture 5.0, the goal of this research is to create an intelligent platform for multi-drone collaboration that will improve situational awareness. This study examines the crucial phases in the Sensing, Understanding, and Action processes by fusing cutting-edge drone technology with creative coordination methods. Creating the architecture and foundation for drone collaboration, utilizing artificial intelligence for real-time data processing, and creating user interfaces that work are all part of the study technique. Area coverage, data quality, processing speed, and decision-making effectiveness are some of the performance evaluation measures. The goal of the research is to identify strategies for enhancing large-scale farming methods' resource management and operational effectiveness. The results will greatly enhance Agriculture 5.0 technologies and create avenues for the deployment of multi-drone collaboration platforms in a variety of high-situational awareness businesses. © 2024 IEEE.

关键词： Data quality

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Open-Source Online Mission-Planning in Emergent Environments with PDDL for Multi-robot Applications 13th

Open-Source Online Mission-Planning in Emergent Environments...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Michailidis, Iakovos T. Michailidis, Panagiotis Kosmatopoulos, Elias Bassiliades, Nick Information Technologies Institute Thessaloniki Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031632266

The current study focuses on the development of an open-source framework which is outsourcing the lack of expressivity of the standardized Planning Domain Definition Language – PDDL, leveraging the capacity and flexibility of the hosting environment in Python 3.8. The implementation was based on the definition and logic of a PDDL domain modeling an object stacking/collecting problem, in specified order and locations, by a fleet of autonomous synergetic vehicles by a centralized management agent. A PDDL-parser was utilized to instantiate the solver. Random changes were imposed in the fully-observable environment during mission execution. The proposed framework considered dynamic replanning of the currently valid mission, every time the state space – and consequently the corresponding state graph representation – is stochastically changed. Appropriate test scenarios were defined, to validate the capacity of the implemented framework and establish that it could serve as an interoperable and extensible foundation for other add-ons and graph-searching tools. The evidence from the simulation results indicates the speed and flexibility of the implemented environment for emulating the dynamic replanning problems in highly emergent scenario cases where the state space changes stochastically. © IFIP International Federation for Information Processing 2024.

关键词： Autonomous agents

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of computer Science 2025年第2期8卷 303-333页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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DLMetaChain: An IoT Data Lake Architecture Based on the Blockchain 13

DLMetaChain: An IoT Data Lake Architecture Based on the Bloc...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Pingos, Michalis Christodoulou, Panayiotis Andreou, Andreas Cyprus University of Technology Computer Engineering and Informatics Department of Electrical Engineering Limassol Cyprus Neapolis University Department of Computer Science Pafos Cyprus

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

Nowadays, the IoT ecosystem is evolving rapidly, with multiple heterogeneous sources producing high volumes of data and processes transforming this data into meaningful or 'smart' information. These volumes of data, including IoT data, need to be stored in repositories that can host raw, unprocessed, relational and non-relational types of data, such as Data Lakes. Due to the weakness of metadata management, security & access control is one of the main challenges of Big Data storage architectures as Data Lakes can be replaced without oversight of the contents. Recently, the Blockchain technology has been introduced as an effective solution to build trust between different entities, where trust is either nonexistent or unproven, and to address security and privacy concerns. In this paper we introduce DLMetaChain, an extended Data Lake metadata mechanism that consists of data from heterogeneous data sources which interact with IoT data. The extended mechanism mainly focuses on developing an architecture to ensure that the data in the Data Lake is not modified or altered by taking into advantage the capabilities of the Blockchain. © 2022 IEEE.

关键词： Smart contract

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