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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In IEEE 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in IEEE 802.11 broadcast ad-hoc networks on highways. Based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Markov processes

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Development of Buffer Zone into Living Lab-Garuda Smart City Framework: Systematic Review 10

Development of Buffer Zone into Living Lab-Garuda Smart City...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Emmanuel, Gunadi Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Bina Nusantara University Doctor of Computer Science Jakarta Indonesia Univ. Katolik Musi Charitas Fakultas Sains Dan Teknologi Palembang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab-Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019-2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues. © 2023 IEEE.

关键词： Smart city

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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2023 International Conference on science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Akinrotimi, Akinyemi Omololu Ogundokun, Roseline Oluwaseun Mabayoje, Modinat Abolore Oyekunle, Rafiat Ajibade Adebiyi, Marion Olubunmi Augustine University Department of Mathematics Sciences Computer Science Program Ilara-Epe Epe Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Omu Aran Sdg 4. Quality Education Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria University of Ilorin Department of Information and Communication Ilorin Nigeria Landmark University Omu-Aran Department of Computer Science Omu Aran Nigeria

ISBN: (纸本)9798350324785

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection. © 2023 IEEE.

关键词： Forecasting

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Qin, Zheyi Paffenroth, Randy Jayasumana, Anura P. Colorado State University Electrical and Computer Engineering Fort Collins United States Worcester Polytechnic Institute Department of Data Science Program WorcesterMA United States

ISBN: (纸本)9798350324457

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

关键词： graph analytics graph coordinate graph embedding green AI network embedding

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Investigating the User Experience and Identifying User Needs for Kitchen Appliances Using Thematic Analysis

Investigating the User Experience and Identifying User Needs...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Jung, W.C. Lazaro, M.J. Lee, J.H. Kim, G.B. Yang, E.J. Yun, M.H. Seoul National University Department of Industrial Engineering Seoul Korea Republic of Seoul National University Interdisciplianary Program in Cognitive Science Seoul Korea Republic of

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Kitchen appliances are essential to accomplish cooking tasks efficiently. Advancements in technology have led to changes in the needs and expectations of the users of commonly used kitchen appliances. Thus, this qualitative research aims to investigate the user experience and identify the user needs for common kitchen appliances (microwave, cooktop, oven, and dishwasher) by conducting semi-structured interviews with users who frequently use the appliances. Results of the thematic analysis showed that UX factors such as informativeness, comfortability, and safety were prominent sub-themes across all kitchen appliances. Based on the findings, we presented design recommendations on how the product can be improved. © 2022 IEEE.

关键词： Kitchens

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A Trilogy of AI Safety Frameworks: Paths from Facts and Knowledge Gaps to Reliable Predictions and New Knowledge

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Program in Bioinformatics Department of Computer Science Boston University United States

AI Safety has become a vital front-line concern of many scientists within and outside the AI community. There are many immediate and long term anticipated risks that range from existential risk to human existence to deep fakes and bias in machine learning systems [1-5]. In this paper, we reduce the full scope and immense complexity of AI safety concerns to a trilogy of three important but tractable opportunities for advances that have the short-term potential to improve AI safety and reliability without reducing AI innovation in critical domains. In this perspective, we discuss this vision based on several case studies that already produced proofs of concept in critical ML applications in biomedical science. © 2024, CC BY-NC-ND.

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

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

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

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Short Wave Infrared Upconversion Imager with Electron Blocking Layer to Screen Electron Injection into Photosensitive Layer 5

Short Wave Infrared Upconversion Imager with Electron Blocki...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Shin, Chanho Li, Ning Ng, Tse Nga University of California San Diego Material Science and Engineering Program La Jolla United States University of California San Diego Department of Electrical and Computer Engineering La Jolla United States

ISBN: (纸本)9781665457330

In this work, we demonstrated upconversion imagers integrated with shortwave infrared photodetectors paired with an electron blocking layer. The use of electron blocking layer screened charge injection to prevent recombination in photosensitive layer. The characteristics of each electron blocking layer were analyzed in aspects of noise and detectivity. For the optimized device, the parasitic luminance in the dark was efficiently suppressed, and the photon-to-photon efficiency was increased. The electron blocking layer used in this work is generally applicable for upconversion imagers using different absorption and emitting materials. © 2023 IEEE.

关键词： Photodetectors

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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A Comparative Study among Different computer Vision Algorithms for Assisting Users in Picture Password Composition 21

A Comparative Study among Different Computer Vision Algorith...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Constantinides, Christodoulos Constantinides, Argyris Belk, Marios Fidas, Christos Pitsillides, Andreas University of Cyprus Department of Computer Science Cyprus University of Cyprus and Cognitive Ux Ltd Department of Computer Science Cyprus Cognitive Ux GmbH and University of Cyprus Department of Computer Science Germany University of Patras Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9781450383677

Picture gesture authentication (PGA), utilized by millions of users worldwide, is a cued-recall graphical authentication system which requires users to select an image and subsequently draw gestures on that image to create their picture password. A crucial component for enhancing the security of PGA-like schemes is the accurate quantification of the user-chosen passwords through a picture password strength meter. Despite the huge adoption of PGA worldwide, there is rather limited knowledge on the implementation aspects of an accurate picture password strength meter that would assist users in creating secure picture passwords. In this paper, we present the implementation and evaluation of an assistive picture password strength meter system within PGA-like schemes, which is based on image analysis through computer vision techniques. Results of the evaluation study (n=34) revealed that different computer vision approaches perform different across various datasets used during training. These findings could drive the design of intelligent security mechanisms for quantifying the strength of the user-chosen passwords, and ultimately assist end-users towards making better picture password selections by providing feedback about the strength of their passwords, as well as assist service providers in terms of integration of assistive security mechanisms. © 2021 ACM.

关键词： Authentication

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