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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Ambarwari, Agus Suwardhi, Deni Rani, Medria Shekar Husni, Emir Junaidy, Deny Willy Agirachman, Fauzan Alfi Murtiyoso, Arnadi Griess, Verena Christiane Doctoral Program of Electrical Engineering and Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia Faculty of Earth Sciences and Technology Institut Teknologi Bandung Indonesia School of Architecture Planning and Policy Development Institut Teknologi Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia Faculty of Art and Design Institut Teknologi Bandung Indonesia Department of Environmental Systems Science Forest Resources Management Institute of Terrestrial Ecosystems Zurich8092 Switzerland

3D city models are an important cornerstone in the development of digital twin cities, allowing for various analyses and simulations. CityGML, as an open standard for 3D city models, emphasizes five main aspects: scale or level of detail (LoD), semantics, geometry, topology, and appearance. One of the important elements in CityGML 3D city models is representation of vegetation. Vegetation in CityGML, especially individual trees, is still limited in terms of detail and resolution, reducing its effectiveness for specific applications. This paper proposes a graph-based conceptual model for individual trees that conforms to the CityGML v2.0 specification. The model refers to the morphological structure of a tree consisting of roots, trunk, and crown (including branches, twigs, and leaves), and considers the five main aspects of CityGML. By enhancing semantic information and geometric details, the model aims to provide an information-rich and realistic representation of individual trees in a 3D city environment. The 3D tree model created based on this conceptual model may be applicable in the development of digital twin urban forests and virtual forest simulations that utilize immersive technologies such as the metaverse. This research opens up new opportunities in the development of solitary vegetation objects through CityGML ADE by including more detailed semantic and topological information. © Author(s) 2024.

关键词： Digital elevation model

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A Heuristic Approach using Block Chain to Fight Novel COVID-19 during an Election 6

A Heuristic Approach using Block Chain to Fight Novel COVID-...

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6th IEEE International Conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021

作者： Nath, Ira Biswas, Pratnadeep Ganguly, Kankana Singh, Dharmpal Islam, Sardar M.N. Quang Cuong, Ton Pal, Souvik JIS College of Engineering Computer Science Engineering Kalyani India Victoria University ISILC and Decision Sciences and Modeling Program Australia Vietnam National University Hanoi Viet Nam Global Institute of Management and Technology Computer Science Engineering Krisnanagar India

ISBN: (纸本)9781665417846

India is a highly densely populated country. India is facing a very serious challenge due to the novel COVID-I9 outbreak. In this situation, the economic state of the people is very unstable. Blockchain technology proves helpful to subdue the situation. A voting system with ballot papers is quite tuff now. It is very costly for the government. The pandemic may also spread very quickly. The system of developing ledger serves the idea of developments of a new e-voting system that is economical, easy to use, and highly secure. This paper proposes ways and solutions to design an e-voting system using blockchain technology. The paper's primary goal is to develop a highly secure e-voting technique using which people can cast their valuable votes from their home, working place - anywhere from the world. This e-voting technique is least costly, highly secure, and able to prevent further spreading of any infectious disease in the near future. In this paper, an e-voting system using blockchain has been designed to overcome all these difficulties mentioned above during traditional voting using ballot. The significance of the proposed work is to design of a highly safe and least costly e- voting system using blockchain. Using this system the people can cast their vote easily, securely and without wasting any time. Casting vote will be now just from few clicks away and from any place with a stable internet connection. © 2021 IEEE.

关键词： COVID-19

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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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International Seminar on Application for Technology of Information and Communication (ISemantic)

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

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.

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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Artificial Intelligence Identification Model for Chronic Kidney Disease 1

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5th International Conference on Innovative Computing, IC 2022

作者： Cheng, Ya-Fang Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Many people suffer from Chronic Kidney Disease (CKD). Nowadays, CKD is one of the top ten causes of death. CKD should via invasive examination to understand participants health status. If a non-invasive identification model can be established, it can provide users with self-assessment which let users quickly understand their physical condition. This study used machine learning method to establish an identification model of Chronic Kidney Disease. This study found the associated factors with kidney failure from the literature. Selected MJ database as information resources. Used two different factor selection methods to training model. Compared the performance with K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, eXtreme Gradient Boosting and Vote Algorithms, used the better one to establish the model. In this study, the best model used Vote algorithm to establish the model, and can only use 13 non-invasive factors. The accuracy is 88%, the precision is 73%, the sensitivity is 69%, the specificity is 93%, and the AUC is 0.92. The contribution of this study is to use non-invasive factors to identify Chronic Kidney Disease, but it is a preliminary evaluation and ultimately requires doctors to diagnose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence 3

A Study on Medicinal Plant Leaf Recognition Using Artificial...

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Ayumi, Vina Ermatita, Ermatita Abdiansah, Abdiansah Noprisson, Handrie Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019-2020) from IEEE, Springer and science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction;and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier. © 2021 IEEE.

关键词： Image processing

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Recover energy and materials from agricultural waste via thermalchemical conversion (vol 5, pg 1200, 2022)

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ONE EARTH 2022年第12期5卷 1404-1404页

作者： Lee, Kuan-Tieng Chen, Wei-Hsin Sarles, Paul Park, Young-Kwon Ok, Yong Sik Department of Aeronautics and Astronautics National Cheng Kung University Tainan 701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung 407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung 411 Taiwan International Master Degree Program on Energy National Cheng Kung University Tainan 701 Taiwan School of Environmental Engineering University of Seoul Seoul 02504 Republic of Korea Korea Biochar Research Center APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering Korea University Seoul Republic of Korea

Every year, at least 1,300 million tons of agricultural waste is produced, and the amount is likely to expand further in a world with a growing population to feed. Our current linear approach to handling this vast amount of waste via landfills and incineration has already contributed to about 3% of global greenhouse gas emissions and also created various toxic pollutants. There is an urgent need to sustainably manage agricultural waste that can benefit both the environment and humans. Thermochemical conversion emerges as a promising technology that can convert agricultural waste into a variety of valuable products, creating circularity opportunities. In this primer, we explain the thermochemical conversion of agricultural waste by illustrating four mainstreaming processes. We further discuss the potential tradeoffs of these processes in terms of their environmental externalities and reflect upon the potential toward a circular and net-zero future.

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Implementation of Multi-process Automatic Loading and Unloading Production System

Implementation of Multi-process Automatic Loading and Unload...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Li-Chiuan Tung Shu-Hung Chen Yu-Cheng Ye Guan-Zhi Liu Lin-Yin Chen Yu-Rou Tu Yen-Wen Lai Jeng-Dao Lee Department of Automation Engineering National Formosa University Yunlin Taiwan Department of Electronic Engineering Ming Chuan University Taoyuan City Taiwan Doctoral Program in Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan

This research develops an intelligent automatic production loading and unloading solution for a multi-process part. According to the posture required for each processing operation of each part, a novel gripper and carrier that can implement multi-position clamping are designed. The Beckhoff controller is adopted as the host controller in the control system to integrate the robotic arm (including the vision system), the automated guided vehicle (AGV), and the computer numerical control (CNC) machine. The main topic of this research is to meet the requirements of automatic loading and unloading in the processing of the entire production line. It will significantly reduce the workforce consumption of the processing production line and avoid human error, thereby improving processing reproducibility and product production stability.

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Pursuing overall welfare in federated learning through sequential decision making 24

Pursuing overall welfare in federated learning through seque...

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Proceedings of the 41st International Conference on Machine Learning

作者： Seok-Ju Hahn Gi-Soo Kim Junghye Lee Department of Industrial Engineering Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Department of Industrial Engineering and Artificial Intelligence Graduate School Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Technology Management Economics and Policy Program and Graduate School of Engineering Practice and Institute of Engineering Research Seoul National University (SNU) Seoul South Korea

In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation scheme for updating the global model to an adaptive one, in response to the local signals of the participating clients. Our work reveals that existing fairness-aware aggregation strategies can be unified into an online convex optimization framework, in other words, a central server's sequential decision making process. To enhance the decision making capability, we propose simple and intuitive improvements for suboptimal designs within existing methods, presenting AAggFF. Considering practical requirements, we further subdivide our method tailored for the cross-device and the cross-silo settings, respectively. Theoretical analyses guarantee sublinear regret upper bounds for both settings: O(√T log K) for the cross-device setting, and O(K log T) for the cross-silo setting, with K clients and T federation rounds. Extensive experiments demonstrate that the federated system equipped with AAggFF achieves better degree of client-level fairness than existing methods in both practical settings. Code is available at https://***/vaseline555/AAggFF.

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Using AI Algorithm to Establish the CVD Risk Assessment Model 1

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5th International Conference on Innovative Computing, IC 2022

作者： Chen, Yin-Chen Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

In Taiwan, diseases with cardiovascular include heart disease and cerebrovascular disease among the top ten causes of death. With the development of data mining in the medical field, it can be used to establish the risk prediction models of disease as a tool to assist physicians in decision-making in diagnosis. The purpose of this study is taking Taiwanese health examination data as an example, to identify the significant risk factors of cardiovascular disease and to establish the risk assessment model of cardiovascular disease using data mining. Using Chi-square test and Information Gain identify the correlation between various factors and cardiovascular diseases. Using eight algorithms such as decision tree, random forest, XGBoost, neural network, logistic regression, support vector machine, K nearest neighbor algorithm and voting algorithm to establish the risk assessment model of cardiovascular disease and using confusion matrix and AUC as model evaluation. Compare model performance with different factor combinations. Experiment result shows that it finds 22 questionnaires and biochemical variable risk factors affecting cardiovascular disease and 10 questionnaire factors. ANN and VOTE with 22 factors of Information Gain (threshold > 0.01) are the best models. Both models have the same accuracy (0.88) and AUC (0.90). The best model of questionnaire variable is ANN with the accuracy rate is 0.89, and the AUC is 0.91. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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