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Rapid Monitoring for Flood Inundation Detection-Based Sentinel-1 SAR Image Data and Exposure Assessment in Urban Building Area: Makassar City, Indonesia 3rd

Rapid Monitoring for Flood Inundation Detection-Based Sentin...

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3rd International Conference on Radioscience, Equatorial Atmospheric science and Environment, INCREASE 2023

作者： Sekarjati, Dhanu Hanif, Muhammad Komala, Siffa Anastasya Giofandi, Eggy Arya Putri, Febita Safera Bunga, Brigitta Audrynne Rombe Putklang, Wasana Wilis, Ratna Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Geophysics and Meteorology Faculty of Mathematics and Natural Science IPB University Bogor Indonesia Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Department of Geography Faculty of Social Science Universitas Negeri Padang Padang Indonesia

ISBN: (纸本)9789819707393

Real-time monitoring remains a challenge in providing accurate information, especially in the case of urban flood inundation disasters. The accuracy of data for disaster risk reduction has a positive impact on reducing the loss of livelihood or the regional economy. Understanding flood levels and their impact on urban areas can be crucial information for flood mitigation and effective urban planning. The research aims to conduct a rapid monitoring investigation for flood inundation detection in Makassar City, Indonesia, using Sentinel-1 SAR image data and assess the exposure of urban building areas to flooding. By identifying flood inundation areas using backscatter values, it was determined that Tamalanrea District exhibits a high density and is prone to flooding as a result of urban expansion. Additionally, Biringkanaya District, which is adjacent to Tamalanrea, was also impacted by the flood. The most severely affected area was found to be Tamalanrea District, with an inundation area covering 73,114 m2. It also recorded the highest extent of built-up area affected by flooding, reaching 29,475 m2. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floods

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IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmentation Network

IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zheng, Yisen Huang, Guoheng Zhang, Feng Cheng, Lianglun Yuan, Xiaochen Zhong, Guo Luo, Shenghong School of Computer Science and Technology Guangdong University of Technology Guangzhou China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781665410205

In recent years, many Lesion segmentation (LS) models based on UNet have been proposed. However, existing researches rarely consider the influence of illumination change leads to the weak boundary area. Such as melanomas and polyps, the demarcation of the boundary between the diseased area and the surrounding tissue remains particularly challenging. To overcome these challenges, we propose an IlluminationAdaptive Multi-scale Lesion Segmentation Network (IAMS-Net). In IAMS-Net, we integrate Illumination-Adaptive MultiStream Attention (IAMA) and Contour Perception Module (CPM). In the decoding stage, the IAMA is used as a bridge between the encoder and the decoder to solve the adverse effects of illumination changes on the segmentation of weak boundary lesions. In order to further enhance the boundary features lost due to illumination change in the low-contrast lesion area, we introduce the CPM to improve the perception of the integrity of the lesion area. Subsequently, we performed comparison and ablation experiments using the publicly available ISIC2018 dataset and the individually collected data set BoreIllumination(BI). © 2024 IEEE.

关键词： Signal encoding

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An Optimized Deep Learning Model for Emotion Classification in Tweets

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computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques *** main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better *** step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关键词： Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes

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How do programmers Use the Internet? Discovering Domain Knowledge from Browsing and Coding Behaviors 15

How do Programmers Use the Internet? Discovering Domain Know...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Watanabe, Ko Matsuda, Yuki Nakamura, Yugo Arakawa, Yutaka Ishimaru, Shoya Department of Computer Science University of Kaiserslautern and Dfki GmbH Kaiserslautern Germany Nara Institute of Science and Technology Graduate School of Science and Technology Nara Japan Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

The Internet is an effective tool for learners to gain new knowledge. Often, people use search engines (e.g., Google) rather than accessing websites directly. People have their search techniques to find specific information. In particular, people with domain knowledge tend to search more efficiently than novices. By understanding the gap between people with domain knowledge and novices, the novice can understand the path to becoming an expert. Therefore, in this study, we wanted to know what differences exist in search and programming behavior with and without domain knowledge. In this experiment, we asked a group with and without domain knowledge to solve ten programming problems and collected search logs (input knowledge) and compilation logs (output knowledge). Specifically, the first dataset consisted of 13 participants who had taken a university programming class. The second dataset consisted of 20 participants who had not taken a programming class and had no domain knowledge. We examined differences in search and compilation behavior based on participants' domain knowledge from this data. Since we observed a difference between each group when referring to the correlation coefficient, we performed a binary classification of novice and experienced participants using Random Forest, and achieved an average precision of 0.95, indicating that there were different trends in behavior with and without domain knowledge. © 2022 IEEE.

关键词： Domain Knowledge

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Sustainable Learning of computer programming Languages Using Mind Mapping

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Intelligent Automation & Soft Computing 2023年第5期36卷 1687-1697页

作者： Shahla Gul Muhammad Asif Zubair Nawaz Muhammad Haris Aziz Shahzada Khurram Muhammad Qaiser Saleem Elturabi Osman Ahmed Habib Muhammad Shafiq Osama E.Sheta Department of Computer Science National Textile University Faisalabad37610Pakistan Department of Data Science University of the PunjabLahore54590Pakistan Department of Mechanical Engineering University of SargodhaSargodha40100Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea College of Science Zagazig UniversityZagazig44511Egypt

In the current era of information technology,students need to learn modern programming languages efﬁ*** art of teaching/learning program-ming requires many logical and conceptual *** it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efﬁ*** mapping is a useful visual tool for establishing ideas and connecting them to solve *** research proposed an effective way to teach programming languages through visual *** experimental study uses a mind mapping tool to teach two programming environments:Text-based programming and Blocks-based *** performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Paciﬁc *** different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this *** results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.

关键词： Text programming blocks programming novice programmer

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ACO with Reinforcement Learning Applied to Rescues Operations on Urban Forests 13

ACO with Reinforcement Learning Applied to Rescues Operation...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Alves, Cláudio Mendonça, Israel De Almeida Guimarães, Vanessa González, Pedro Henrique System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Coppead Graduate Business School Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm. © 2024 IEEE.

关键词： Ant colony optimization

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ICT-Based Conceptual Model for Local Culture Communities using Soft System Methodology 7

ICT-Based Conceptual Model for Local Culture Communities usi...

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7th International Conference on information Management and technology, ICIMTech 2022

作者： Nindito, Hendro Prabowo, Harjanto Sfenrianto Hendric, Spits Warnars Harco Leslie BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The problems that exist in the field of art and culture preservation experienced by the arts and culture community side are the limitations on physical facilities for disseminating works, exchanging information between arts and culture communities, as well as the need to increase professionalism so that they can be independent in order to help improve the economy, where the role of regulation in this regard also affects this condition. Based on the facts above, this community requires an interaction model that is integrated with the use of information technology. This study uses a Soft System Methodology approach to create a model that is suitable for the arts and cultural community. The result is an ICI-based conceptual model for a local culture community. (Abstract) © 2022 IEEE.

关键词： information use

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Proposed IT Governance Model for Smart Tourism Destinations based on COBIT 2019 Framework 7

Proposed IT Governance Model for Smart Tourism Destinations ...

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7th International Conference on information Management and technology, ICIMTech 2022

作者： Hiererra, Siti Elda Gaol, Ford Lumban Ranti, Benny Supangkat, Suhono Harso BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Indonesia Faculty of Computer Science Computer Science Department Depok16424 Indonesia Institute of Technology Bandung Smart Cities and Communities Innovation Centre Faculty of Digital Communication and Hotel & Tourism Bandung40116 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Tourism is one of Indonesia's main economic drivers because it can absorb many workers and bring in foreign exchange through tourism activities. Research related to Smart Tourism Destinations (STDs) and the technology that supports them has gained popularity and has received special attention in the last ten years. STDs show that various technologies are applied to a tourist destination, one of which aims to increase added value for the surrounding community and tourists. This study employs the systematic literature review (SLR) method to investigate smart tourism destinations and governance topics. This research aims to identify and analyze the main components of Smart Tourism Destinations Governance. Two research questions were developed and incorporated into the search term. This research resulted in 24 research papers on smart tourism destinations and governance. The result of this study is a proposed IT Governance Model, which can be a guideline for tourism's DMOs (Destination Management Organizations), tourism enterprises, IT companies that support smart tourism concepts, and policymakers (government) to improve their service to society/community. The model can be used as a guide to governing the IT implementation in smart tourism destinations projects. Limitations of this study regarding the scalability of database and keyword usage and their methodologies. Theoretical implication for scholars in the next research is that research can be conducted on how to implement this model and whether this model can be easily adopted. This future work also regarding the IT Governance Maturity Model or the measurement of IT Gov readiness in smart tourism destinations. © 2022 IEEE.

关键词： Tourism

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE 4

Air Quality Prediction Based on Air Pollution Emissions in t...

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4th IEEE International Conference of computer science and information technology, ICOSNIKOM 2022

作者： Nababan, Adli A Sutarman Zarlis, Muhammad Nababan, Erna B Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350399073

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction. © 2022 IEEE.

关键词： Air quality

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Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN Network with Self Attention Model 48

Audio-Driven Facial Landmark Generation in Violin Performanc...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Lin, Ting-Wei Liu, Chao-Lin Su, Li Taiwan International Graduate Program Social Networks and Human-Centered Computing Taiwan Academia Sinica Institute of Information Science Taipei Taiwan National Chengchi University Department of Computer Science Taipei Taiwan

ISBN: (纸本)9781728163277

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians' facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://***/kevinlin91/icassp-music2face). © 2023 IEEE.

关键词： Music

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