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Relationship between land use and user’s behavior along the street:A case study of Da Nang City,Viet Nam

Frontiers of Architectural Research 2024年第1期13卷 144-163页

作者： Duy Thinh Do Duy Thao Do Faculty of Civil Engineering Ho Chi Minh City University of Technology and Education(HCMUTE)Ho Chi Minh City 71307Viet Nam School of Computing Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea

Human activities on street spaces are affected by both physical and non-physical settings on *** two aspects are primarily impacted by land use which results in the uneven distribution of different activities on *** research investigates land use patterns and their characteristics in association to user’s *** using mixed qualitative and quantitative research methods,e.g.,place-centered behavioral map,observation,visual encounter surveys,machine learning,the relationship between user’s behavior and land use characteristics along the street is *** user behaviors along both types of streets were classified into six main categories,with 26 *** observation results show that the residential use of land along the street was transformed into the commercial use for various types of shophouses based on the resident’s *** is a great correlation between land use and user’s *** findings give support to authorities to manage urban streets and develop a sustainable policy for improving street ***,this research contributes useful information to urban designers and planners in creating a successful street space that is appropriate for the Vietnam Community.

关键词： Land use pattern User’s behavior Da Nang Street space Data mining

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Real-Time Disease Classification in Coffee Plants with Coral Developer Board Mini

Real-Time Disease Classification in Coffee Plants with Coral...

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2024 IEEE International Conference on Agrosystem engineering, Technology and Applications, AGRETA 2024

作者： Warsi, Mohammad Hamzah Nugroho, Hermawan Almurib, Haider Abbas Siang, Tay Fei Eswaran, Sivaraman University of Nottingham Malaysia Department of Electrical and Electronic Engineering Semenyih Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Campus Sarawak Malaysia Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350373660

Coffee significantly contributes to the socioeconomic development of numerous countries. However, coffee production faces several challenges, including the detrimental effects of coffee leaf diseases, which impact crop yield and quality. Traditional methods for diagnosing coffee leaf diseases are time-consuming and prone to human error. This paper presents an automated system for coffee leaf disease classification with edge computing using the Coral Developer Board Mini, featuring Google's Edge TPU. The system leverages advanced convolutional neural network (CNN) models, including MobileNetV2, VGG-16, VGG19, and DenseNet121, to achieve high accuracy in disease identification. The MobileNetV2 model achieved the highest training accuracy of 98.9%. Inference time comparisons revealed that the Coral Developer Board Mini, with an inference time of 10 milliseconds, outperformed a traditional GPU-based workstation, which had an inference time of 40 milliseconds. Results highlight the efficiency and suitability of the Coral Developer Board Mini for real-time, on-device machine learning applications in agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Binary ionic liquid electrolyte design for ultrahigh-energy density graphene-based supercapacitors

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Materials Reports(Energy) 2022年第2期2卷 58-67页

作者： Shao Ing Wong Han Lin Tianyi Ma Jaka Sunarso Basil T.Wong Baohua Jia Centre for Translational Atomaterials School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia School of Science RMIT UniversityMelbourneVictoria 000Australia Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang Tiga93350 KuchingSarawakMalaysia

Although room temperature ionic liquids(ILs)have emerged as potential next-generation electrolytes for their wide electrochemical stability window(ESW),the trade-off between this window and viscosity has hindered their widespread use in energy storage ***,we present for the first time that such a trade-off can be balanced by mixing two ILs with the common anion([NTf_(2)]^(-))but different cations([EMIM]^(+) and[N1114]^(+))***[EMIM]cation-based IL possesses low viscosity while the[N1114]cation-based IL exhibits wide *** the concentrations of each IL in the mixtures can result in different electrolyte properties,we demonstrate a systematic approach by exploring the properties of various concentration *** addition,the corresponding cell voltage of their resulting graphene supercapacitors(SCs)accompanied based on the interaction between the binary ionic liquid and the electrodes,and the associated electrochemical performance were studied to determine the optimum electrolyte system for the highest SC energy *** well-balanced viscosity/ESW trade-off is achieved in binary IL consisting 50 vol%[EMIM][NTf_(2)]and 50 vol%[N1114][NTf_(2)]as evident from the extraordinary electrode specific capacitance of 293.1 F g^(-1) and the ultrahigh SC energy density of 177 Wh kg^(-1),which approaches that of a lithium-ion battery.

关键词： Electrolyte Binary ionic liquid Maximum working voltage High capacitance High energy density Supercapacitor

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Research on a New Speech Synthesis Fraud Method 9

Research on a New Speech Synthesis Fraud Method

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9th International Conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Hang Li, Yuhui Kui, Shijian Qin, Zhaoyi Harbin Institute of Technology Faculty of Computing Harbin China School of Automotive Engineering Harbin Institute of Technology Harbin China Harbin Institute of Technology Faculty of New Energy Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350376548

With the rapid development of deep learning technology, deep counterfeiting has also quietly ascended to the stage of history, and voice disguise fraud, as a new type of attack means, has laid a huge hidden danger for people's privacy security. Based on the research of speech synthesis, this paper designs and implements a speech disguise synthesis system based on the Tacotron2 model, and uses the WaveGlow model as a vocoder to replace the original WaveNet module, which is used to simulate experimental research from the direction of the attack to achieve a better synthesis effect. After this experiment, this paper verifies the feasibility of the program and provides technical references for subsequent security technology development. © 2024 IEEE.

关键词： Differential privacy

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Modeling uncertainties associated with decision-making algorithms based on similarity measures of possibility belief interval-valued fuzzy hypersoft setting

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Neutrosophic Sets and Systems 2025年 77卷 282-309页

作者： Romdhini, Mamika Ujianita Al-Sharqi, Faisal Al-Obaidi, R.H. Rodzi, Zahari Md. Department of Mathematics Faculty of Mathematics and Natural Science University of Mataram Mataram83125 Indonesia Department of Mathematics Faculty of Education for Pure Sciences University of Anbar Ramadi55431 Iraq Fuel and Energy Techniques Engineering Department College of Engineering and Technologies Al-mustaqbal University Babylon51001 Iraq College of Computing Informatics and Mathematics UiTM Cawangan Negeri Sembilan Kampus Seremban Negeri Sembilan 72000 Malaysia

Hypersoft sets (HSSs) were initiated as an extension of soft sets (SSs) to address real-life scenarios involving multiple disjoint sets with different traits. One such extension is the interval-valued fuzzy hypersoft set (IVFHSS), which has proven effective in decision-making (DM). However, the IVFHSS model lacks a mechanism to incorporate the degree of acceptance of DM opinions, which is crucial for accurate decision-making. To overcome this limitation, our work aims to develop a novel hyperstructure called a possibility interval-valued fuzzy hypersoft set (PIVFHS-set). We begin by introducing essential operations and their properties, such as PIVFHS-subset, PIVFHS-null set, PIVFHS-absolute set, and complement of a PIVFHS-set. These concepts are illustrated through numerical examples to demonstrate their practical applications. Next, we delve into set-theoretic operations of PIVFHS sets, including union, intersection, AND, OR, and relevant laws. These operations are further elucidated through numerical examples, matrix representations, and graphical illustrations. Additionally, we present two algorithms based on AND and OR operations, providing step-by-step explanations and showcasing their effectiveness through illustrative examples. Furthermore, we introduce a similarity measure to facilitate pattern recognition in PIVFHS-sets, aiding users in recruitment processes. Alongside an analytical study of the advantages and disadvantages of this model, we provide suggestions for future research based on the identified limitations. © 2025, University of New Mexico. All rights reserved.

关键词： Fuzzy sets

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Reflecting on a community of practice for engineering education research capacity in Africa: who are we and where are we going?

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Australasian Journal of engineering Education 2023年第1期28卷 74-84页

作者： Matemba, Esther Smith, Lelanie Wolff, Karin Inglis, Helen Mogashana, Disaapele Jansen, Lauren Gwynne-Evans, Alison Campbell, Anita L. Kwuimy, Cedrick Nassar, Shamim Magara, Irene Kloot, Bruce Hattingh, Teresa Raji, Atanda Musa, Tagwa Nyamapfene, Abel School of Civil and Mechanical Engineering Curtin University Perth Australia Faculty of Engineering Built Environment and Information Technology University of Pretoria Pretoria South Africa Faculty of Engineering Stellenbosch University Stellenbosch South Africa Faculty of Engineering and the Built Environment University of Cape Town Cape Town South Africa Department of Engineering and Computing Education University of Cincinnati Cincinnati United States Department of Electrical Electronic and Information Engineering South Eastern Kenya University Kitui Kenya Faculty of Applied Sciences and Technology Mbarara University of Science and Technology Mbarara Uganda Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Faculty of Engineering and the Built Environment Cape Peninsula University of Technology Cape Town South Africa Department of Petroleum Engineering Sudan University of Science and Technology Khartoum Sudan Faculty of Engineering Sciences University College London London United Kingdom

The engineering Education Research Network in Africa (EERN-Africa) was created to enable connections between practitioners and researchers with a shared interest in African engineering education contexts. Recognising the importance of developing an African voice in the engineering education research space, the EERN-Africa community has interacted in a dynamic and dialogic way with our own teaching and research practices across diverse African contexts, with an ethical commitment to democratic and inclusive community-building. The objective of this paper is to reflect on the current status of the Community of Practice (CoP), and the challenges and opportunities in sustaining and growing the CoP. A collaborative analysis of perspectives on this emerging identity is presented, using an Appreciative Inquiry (AI) methodology and drawing on collective written reflections and discussions. Six broad themes on the value that the CoP has for both individuals and the group were identified: networking, capacity development, emotional support, impact on professional identity, social and environmental impact, and breaking borders. This paper contributes an approach for collaborative capacity-building in EER through a virtual CoP, underpinned by the spirit of ubuntu. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Environmental impact

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