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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Liu, Tengfei Wang, Tingting Li, Ye Zhao, Jinwei Gao, Ruifeng Pan, Jianping School of Information Science and Technology Nantong University Nantong China University of Victoria Faculty of Engineering and Computer Science Victoria Canada

ISBN: (纸本)9798350368369

The growing popularity of Low-Earth Orbit (LEO) satellites, which are increasingly cheaper to manufacture and launch, has revolutionized the Internet market. Given the impact of packet loss on the quality of service (QoS) and optimization strategies of communication systems, it is of great significance to model packet loss in LEO satellite networks. The existing packet loss models considered in the literature have different assumptions and characteristics, but it remains unclear whether they are suitable for LEO satellite networks. This paper aims to evaluate several packet loss models. Through the evaluation of several metrics, the advantages and disadvantages of each model are discussed. We then introduce the Markovian Arrival Process (MAP) as a choice for packet loss modeling, and the experimental results show that the performance is better than that of existing models. © 2025 IEEE.

关键词： Markov processes

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Influence of Feature Extraction Methods using Neighborhood Component Analysis (NCA) to Improve Classification Performance for Hepatitis C Disease

Influence of Feature Extraction Methods using Neighborhood C...

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International Conference on Informatics and Computing (ICIC)

作者： Christiella Abinosy Setiawan Maria Susan Anggreainy Nora Fitriawati Raymasterio Vera Lucky Renatha Dewi Sabrina Steven Matthew Vierdaria Wijayanti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Research in the development of Hepatitis C disease prediction is increasingly developing, especially using machine learning models which is able to make predictions quickly and accurately. In this study, a comparison of several classification methods was carried out by also applying feature reduction, NCA. In this study, a comparison of performance was carried out if the data was entered into the NCA feature extraction method with KNearest Neighborhood (KNN) and Support Vector Machine (SVM) and a comparison of performance if the data did not use the NCA feature extraction method. The performance comparison metrics used in the study were accuracy, sensitivity, specification, Matthews Correlation Coefficient (MCC), and Kappa value. The highest accuracy (99.36%), sensitivity (91.94%), specification (99.67%), MCC $(0.895)$ and the best Kappa value $(0.889)$ were obtained in the KNN-NCA prediction method.

关键词： Support vector machines Accuracy Sensitivity Liver diseases Computational modeling Nearest neighbor methods Predictive models Feature extraction Diseases Testing

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Frequency Bandwidth Reduction of High-Frequency Intra-Body Propagation Characteristics Used for Attacker's Touch Detection in Fingerprint Authentication 12

Frequency Bandwidth Reduction of High-Frequency Intra-Body P...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Tomura, Shunichi Yamada, Koki Yoshida, Takahiro Tokyo University of Science Graduate School of Engineering Department of Electrical Engineering Tokyo Japan Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo University of Science Faculty of Engineering Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350340181

In previous studies, we have proposed the attacker's touch detection method in fingerprint authentication using high-frequency intra-body propagation characteristics to detect attacks in that an attacker holds the user's finger/wrist and presses the user's finger to a fingerprint scanner. However, the previous method used a wide range of frequency bands from 300 kHz to 10 MHz, and this caused problems in the circuit scale, cost, and size of the device in development for practical use. Therefore, we searched effective frequency bands and applied K-nearest neighbor with data augmentation in this study. As results, we achieved a 99.1 % touch detection rate with a single frequency and a 99.9 % touch detection rate with a combination of two frequencies. We achieved a significant reduction in the frequency bandwidth of the propagation characteristics. © 2023 IEEE.

关键词： Bandwidth

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QSVG: Quaternion Vision Graph Neural Network with Structure Inference for Texture Classification 16

QSVG: Quaternion Vision Graph Neural Network with Structure ...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Wen, Haiyuan Huang, Guoheng Cheng, Lianglun Liao, Xianglian Wu, Shiting Yuan, Xiaochen Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Faculty of Applied Sciences Macao Polytechnic University China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

In the realm of texture classification, while traditional approaches like Bag of Words (BoW) and Convolutional Neural Networks (CNNs) are commonly used, recent research highlights the remarkable flexibility offered by Graph Neural Networks (GNNs). Specifically, GNNs can effectively represent image blocks as one-dimensional multi-channel node features from three-dimensional data, interconnecting node feature channels. However, existing methods tend to process channels separately during graph convolution, overlooking critical inter-channel connections that are integral to capture intricate relationships between texture features, potentially limiting the effectiveness of GNNs in texture classification. Moreover, textures contain vital global information essential for classification performance, but GNNs tend to focus on information from similar neighboring nodes, which can restrict their capability to capture the global feature of the entire image during data processing. To address the aforementioned challenges, we propose a novel GNN architecture called Quaternion Structure inference Vision Graph neural network (QSVG) for texture classification, which combines quaternions and structure inference. Unlike previous approaches, QSVG comprises two key components: first, Quaternion Max-related convolution (Qmax), which uses the Hamilton transform to enhance node characterization by exploiting inter-channel connections;second, Structure Inference Block (SIF Block), which enriches node information through Bidirectional Gated Recurrent Unit (Bi-GRU)-based structure inference, fusing graph neural network node features with global features. Experimental results on three standard texture datasets demonstrate the competitive classification performance of our method. © 2025 SPIE.

关键词： Graph neural networks

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Diabetic Retinopathy Detection: A PySpark-Driven Approach with VGG 16 Feature Extraction and MLP Classification

Diabetic Retinopathy Detection: A PySpark-Driven Approach wi...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Toka Nabil Sama Ayman Samaa Ahmed Mawada Nagy Muhammad Megahed Abdulmoniem Elsaeed Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Infromation Technology and Computer Science Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and precise medical diagnostics. Three processes were involved in the development of a Diabetic Retinopathy (DR) diagnosis tool: feature extraction, feature reduction, and image classification. The research employed Apache Spark, a distributed computing framework, to manage large datasets and enhance the performance of the multilayer perceptron (MLP) model via hyperparameter tuning and cross validation. Utilizing resources more effectively and achieving faster training times were made possible by Apache Spark. To support data-driven decision-making, the study also emphasized the significance of distributed platforms for analyzing large amounts of real-time diabetic data. To produce discriminative features for classification, the VGG16 architecture was employed for feature extraction. In the last epoch, the MLP model performed remarkably well, with an accuracy of 97%. The study also underlined the value of distributed platforms for data-driven decision-making by analyzing substantial volumes of real-time diabetes data.

关键词： Diabetic retinopathy Computational modeling Distributed databases Cluster computing Machine learning Multilayer perceptrons Feature extraction Retina Real-time systems Medical diagnosis

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Understanding the Definitions of Microcredentials in Higher Education: Systematic Literature Review

Understanding the Definitions of Microcredentials in Higher ...

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TVET Excellence & Development (ICTeD), International Conference on

作者： Wihendro Harco Leslie Hendric Spits Warnars Harjanto Prabowo Sfenrianto Information Systems Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information Systems Management Department BINUS Graduate Program–Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331516413

ISBN: (纸本)9798331516420

The growing focus on microcredentials emphasizes the urgent need for precise and widely accepted definitions, as existing uncertainties hinder their effective implementation. This research aims to investigate the comprehension of microcredentials definitions in the context of higher education by conducting a systematic literature review. The goal is to identify current definitions of microcredentials to facilitate standardization initiatives. Following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to systematically search in Scopus database within 2019-2024 resulting $\mathbf{9 4 0}$ articles, by removing the duplicates, $\mathbf{2 8 0}$ articles were initially reviewed, with 29 selected for detailed thematic analysis based on specific inclusion and exclusion criteria. By adapting the six phases of thematic analysis, selected articles were identified analyzed, organized, described, and themes found the data set were reported. Key findings reveal that, while a universally accepted definition is lacking, common themes emerge: microcredentials are competency-based, modular, portable, stackable, and often include quality assurance. These attributes highlight microcredentials as valuable for upskilling and reskilling in a flexible manner. The study concludes that establishing standard definitions can increase the recognition and utility of microcredentials across educational and professional sectors and recommends further research to strengthen core elements for consistency.

关键词： Quality assurance Electronic learning Uncertainty Limiting Databases Education Lead Stakeholders Standards Systematic literature review

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A Node-Merging based Approach for Generating iStar Models from User Stories 34

A Node-Merging based Approach for Generating iStar Models fr...

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34th International Conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Wu, Chao Wang, Chunhui Li, Tong Zhai, Ye College of Computer Science and Technology Inner Mongolia Normal University Hohhot China Faculty of Information Technology Beijing University of Technology Bejing China

ISBN: (纸本)1891706543

User story is a widely adopted requirement notation in agile development. Generally, user stories are written by customers or users in natural language with limited form to describe user's needs for the software system from their perspectives. However, since user stories are generally presented in a flat list, the relations derived from the user stories are difficult to capture. It reduces the understanding of the system as a whole. One solution to this problem is to build goal-oriented models that provide explicit relations among user stories. But extracting concepts and relationships from a large number of discrete user stories often take a lot of time for the agile development team. This paper proposes an iStar model generating approach based on node-merging from user stories. The method first extracts the iStar nodes from the semi-structured user stories, then uses a BERT (Bidirectional Encoder Representations from Transformers) model to measure the similarity between the nodes, and then nodes to be merged are identified and the edges between the iStar nodes are connected. Experiments are designed to illustrate the effectiveness of the proposed approach. © 2022 Knowledge Systems Institute graduate School. All rights reserved.

关键词： Merging

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Explainable Artificial Intelligence in Malignant Lymphoma Classification: Optimized DenseNet121 Deep Learning Approach with Particle Swarm Optimization and Genetic Algorithm.

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IEEE Access 2025年 13卷 98639-98655页

作者： Elwahsh, Haitham Bakhiet, Ali Alirr, Omar Ibrahim Khalifa, Tarek Alsabaan, Maazen Ibrahem, Mohamed I. El-Shafeiy, Engy Applied Science Private University Faculty of Information Technology Amman 11931 Jordan Kafrelsheikh University Faculty of Computers and Information Department of Computer Science Kafrelsheikh 33516 Egypt Badya University Faculty of Computing and Data Science Egypt American University of the Middle East College of Engineering and Technology Egaila 54200 Kuwait King Saud University College of Computer and Information Sciences Department of Computer Engineering P.O.Box 51178 Riyadh 11543 Saudi Arabia Augusta University School of Computer and Cyber Sciences Augusta 30912 GA United States University of Sadat City Faculty of Computers and Artificial Intelligence Department of Computer Science Sadat City 32897 Egypt

One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence. © 2013 IEEE.

关键词： Artificial intelligent Deep Learning Genetic Algorithm malignant lymphoma Optimized DenseNet121 Particle Swarm Optimization

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p, q-Spherical fuzzy sets and their aggregation operators with application to third-party logistic provider selection

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Journal of Intelligent and Fuzzy Systems 2024年第1期46卷 505-528页

作者： Rahim, Muhammad Amin, Fazli Tag Eldin, ElSayed M. Abd El-Wahed Khalifa, Hamiden Ahmad, Sadique Department of Mathematics and Statistics Hazara University KP Mansehra Pakistan Future University in Egypt New Cairo Egypt Department of Mathematics College of Science and Arts Qassim University Al-Badaya Saudi Arabia Department of Operations and Management Research Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt EIAS: Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today's business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q-SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q-spherical fuzzy (p, q-SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. © 2024 - IOS Press. All rights reserved.

关键词： Decision making

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A Lightweight CNN-Based Pothole Detection Model for Embedded Systems Using Knowledge Distillation 21

A Lightweight CNN-Based Pothole Detection Model for Embedded...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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