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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Arabelli, Rajeshwarrao Boddepalli, Eswararao Yadav, Roobal Kanwer, Budesh Wicaksono, Yudi Krisno Dhanraj, Joshuva Arockia Sr University School of Engineering Telanagana Warangal India Virginia Tech Bradely Department of Ece Blacksburg United States G.L. Bajaj Institute Of Technology And Management Greater Noida India Poornima Institute of Engineering and Technology India Uin Sayyid Ali Rahmatullah Social Science Education Program Tulungagung Indonesia Lovely Professional University Research and Development Cell Jalandhar-Delhi Grand Trunk Road Punjab Phagwara144411 India

ISBN: (纸本)9798350350067

The Internet of Things (IoT) has altered many fields because it combines so entirety. But likely that these instruments are so smooth to hack, it has still created plenty safety worries. To hold Wi-Fi IoT networks cautious, Intrusion Detection and Prevention Systems (IDPS) are very main. They look for and fix safety dents. This research focuses on Machine Learning (ML) systems and looks at the various types of IDPS for Wi-Fi IoT networks. There is plenty research on intrusion detection systems (IDS) over the internet of things (IoT) that is look at in this place study. It displays by virtue of what well differing algorithms, containing neural networks, decision trees, random forests, and Support Vector Machines (SVM), act. It further checks machine learning-based IoT safety models, like Support Vector Clustering (SVC) in addition to convolutional neural networks (CNNs), in consideration of label and stop hacking risks in IoT backgrounds. Reviewing secondary evidence and secondary data is part of the study process. These are used to find the basic IDPS for Wireless IoT Networks styles and questions. This research explains that intrusion detection systems (IDS) are essential for consistency the Internet of Things cautious, what plans established machine learning can help find and stop threats more effectively. This study has plenty useful information for scientists and experts that can help them devise better IDPS remedies for Wireless IoT Networks. © 2024 IEEE.

关键词： Network intrusion

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An Improved Particle Swarm Approach for Energy-Aware Location-Aided Routing in Mobile Ad-Hoc Network 7

An Improved Particle Swarm Approach for Energy-Aware Locatio...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, Sandeep Sharma, Khemraj Ashok Kumar, P.M. Jain, Arpit Bhagat, Sukesh Kumar Singh, Pranita Tula's institute U.K Dehradun India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Uttaranchal University Department of Computer Science & Engineering Uttarakhand Dehradun India Roorkee Institute of Technology Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798350350067

A network with no infrastructure, multiple hops, and dispersed nodes called a MANET. Nodes can communicate directly or through intermediaries without the need for permanent infrastructure. In dynamic networks like MANETs, routing procedures enhance the overall appearance of the network by ensuring Quality of Service (QoS). A routing protocol may use a composite routing measure to determine the best path for transferring data. Optimal location-based routing for MANETs based on energy-awareness is presented in this paper. Optimal routes are selected using this fast algorithm. Particle swarm optimization (PSO) is used instead of location-aided routing to select the normal data transmission route. Last but not least, the network is tested by simulating its packet delivery ratio, its energy level, its delay, and its overhead. © 2024 IEEE.

关键词： Adversarial machine learning

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A Social Analysis of Thailand's 2023 Election Through Twitter Feeds

A Social Analysis of Thailand's 2023 Election Through Twitte...

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International Conference on Information technology and Electrical engineering (ICITEE)

作者： Wanaporn Sarapugdi Songphon Namkhun Program in Management of Digital Innovation Faculty of Information Technology Rajabhat Mahasarakham University Maha Sarakham Thailand Program in Computer Engineering Faculty of Engineering Rajabhat Mahasarakham University Maha Sarakham Thailand

Nowadays, it cannot be denied that social media has been in many parts of people's lives, especially in entertainment, business and education. Public figures including politicians frequently use social platforms to connect with their audience, for example Facebook, Instagram, Tiktok and etc. One of the most commonly used is a platform called Twitter or also known as X. It is a platform that people can share their opinions in short messages. Since there is an alphabet limitation, people can deliver and receive message rapidly. It is a perfect platform that we could use to update the real-time situations for example election, emergency alert, natural disasters and etc. According to its characteristic, it can be used as a data source for social media analytics, which involves collecting and analyzing data to perform business decisions. The social media analytics has become commonplace because the number of people using social media tends to increase every second. Specifically, Twitter sentiment analysis offers a quick and inexpensive tool for monitoring and predictions. In this work, Twitter is used as a data source to monitor and predict Thailand Election in 2023 because this election highly attracts attention from Thais and international. Moreover, Twitter is a significant platform that is used to debate about political events in Thailand. The result of this election potentially indicates Thailand's future, including economics, public health, environment, international relations and etc. Our work performs sentiment analysis to categorize the data and mainly focuses on extracting sentiment from text without considering semantic word relations and co-occurrences because it is appropriate and functional for Thai sentence analysis. Based on the result, it can be seen that Twitter plays a significant role in politics, with politicians wielding considerable influence over public perception. Comparing of the actual election result and the predicted result, this analysis dem

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Assessing the Performance of Machine Learning Algorithms for Water Level Prediction in the Chao Phraya River and its Tributaries: A Focus on Low and High Water Levels

SSRN

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SSRN 2024年

作者： Priyasiri, Wilmat D.S.M. Rittima, Areeya Kraisangka, Jidapa Sawangphol, Wudhichart Phankamolsil, Yutthana Talaluxmana, Yutthana Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Thailand Faculty of Information and Communication Technology Mahidol University Thailand Environmental Engineering and Disaster Management Program Mahidol University Kanchanaburi Campus Thailand Department of Water Resources Engineering Faculty of Engineering Kasetsart University Thailand

High precision of hydrological prediction is crucial for real–time operation of flood and drought risk mitigation and strategic planning. This study assessed the predictive performances of three Machine Learning (ML) algorithms;XGBoost, Random Forest (RF), and Multi–Layer Neural Networks (MLNNs) for water level prediction with a focus on low and high water levels. The one–day and one–week water level prediction models for six key gauged stations along the Chao Phraya River and its major tributaries were accordingly developed. Selecting the combination of input features was carried out and structured in the prediction model at all gauged stations based on the physical river–reservoir system using past water level, rainfall, controlled reservoir outflow, and upstream discharges with different travel times. The statistical evaluation indicated that both XGBoost and RF, the tree–based ML models, with rainfall input robustly outperformed than MLNNs, as it strongly achieved higher R2 from 0.957 to 0.999 for model training and from 0.743 to 0.995 for model testing and lower MAE, MSE, and RMSE values for all prediction scenarios. Among these three ML algorithms applied for daily prediction, RF demonstrated the superior performance for low water level prediction exhibiting the smallest percentage error of overestimating lying between +0.0088% and +0.9380%. XGBoost, RF, and MLNNs, demonstrated acceptable performance level for high water level prediction as it shows small percentage error of both overestimating and underestimating lying between –2.2696% and +1.1587%. Additionally, it can capture the entire testing dataset with high precision than weekly model. © 2024, The Authors. All rights reserved.

关键词： Rain

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Design of Optical System of Augmented Reality Near Eye Device 6

Design of Optical System of Augmented Reality Near Eye Devic...

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6th IEEE Eurasian Conference on Educational Innovation, ECEI 2023

作者： Chang, Chao-Kai Yeh, Feng-Ming Lue, Jiann-Hwa Wang, Hsuan-Fu Hsieh, Ya-Hui Chiang, Chun-Yu Chen, Der-Chin Tien, Chuen-Lin Department of Optometry Yuanpei University of Medical Technology Hsinchu Taiwan Department of Optometry Taiwan University of Science and Technology Taichung Taiwan Department of Aeronautical Engineering National Formosa University Yunlin Taiwan Department of Healthcare Information and Management Ming Chuan University Taoyuan Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering Feng Chia University Taichung Taiwan

ISBN: (纸本)9798350334289

Augmented reality (AR) is a technology that instantly merges digital pictures with the real scene. The perspective near-eye display is a key module of AR, through which computer-generated pictures or videos can be displayed in the existing environment. The purpose of this research is to establish the system structure of an augmented reality near-eye display device and to design the off-axis two-piece reflective aspherical mirror. Because AR glasses are wearable and are worn close to the eyes, not only the optical performance of the display but also the clear vision of the user needs to be considered. In this study, an off-axis two-piece reflective aspherical mirror is used as the optical system of the AR for displaying the digital image. The micro-display is placed at the focal point of the optical system to form a digital image, and the horizontal movement of the micro-display along the optical axis is controlled by a precise displacement mechanism so that the patient with uncorrected myopia or hyperopia can see the digital picture and real scene. Because the right and left micro-displays move horizontally independently, the user can see the images on the AR device, which becomes the biggest feature of this study. © 2023 IEEE.

关键词： Augmented reality

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A Review on Digital Simulation Strategies and Its Applications for Advanced Compression Garments 16

A Review on Digital Simulation Strategies and Its Applicatio...

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16th Textile Bioengineering and Informatics Symposium, TBIS 2023

作者： Youn, Seonyoung Darden, Ryan Mathur, Kavita Fiber and Polymer Science Ph.D. Program North Carolina State University RaleighNC27606 United States Department of Textile Engineering Chemistry and Sciences M.S. Textile Engineering Program North Carolina State University RaleighNC27606 United States Department of Textile and Apparel Technology and Management North Carolina State University RaleighNC27606 United States

ISBN: (纸本)9781713879473

This paper presents a comprehensive review on the current state of digital simulation technology and its strategic application usages with a key focus on advanced compression garments. Although three-dimensional (3D) digital garment simulators have significantly impacted the apparel and textile industry, their use has been limited to visual prototyping, quality checking, or marketing. The physical properties of garment simulators have been overlooked in advanced functional manufacturing. This literature review investigates the possibility of strategically utilizing virtual physical properties to enhance garment functions, specifically in wearables and medical-grade compression fields. The first phase of this paper gives an overview of the current state of 3D garment simulation tools and their measurement features. This is followed by a gap analysis between simulated and physical property evaluations, focusing on minimizing the gaps. The literature is thoroughly examined to identify appropriate studies that uses digital tools for compressional garments and add value to wearables and healthcare applications. This paper provides valuable insights for apparel and textile engineers, enabling them to better understand the limitations and capabilities of garment simulators in improving advanced features for specific end users. © TBIS 2023. All rights reserved.

关键词： Physical properties

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A Novel CSAHP Approach to Assess the Priority of Maintenance Work Outsourced by a Metro Company

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Processes 2023年第1期11卷

作者： Peng, Sung-Neng Huang, Chien-Yi Liu, Hwa-Dong Department of Industrial Engineering and Management National Taipei University of Technology Taipei106 Taiwan Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan

To lower maintenance costs and improve a metro company’s competitiveness, this research came up with an innovative technique using a considering sensitivity and analytic hierarchy process (CSAHP). Along with interviews with managers and workers at the Taipei Rapid Transit Corporation, this study was able to undertake quantitative analysis. To determine which subsystems and metro lines should be prioritized for outsourcing based on the CSAHP framework, we used the criterium decision plus (CDP) program. This research adds to the existing body of knowledge by advancing the existing analytic hierarchy process (AHP) technique and recommending the CSAHP strategy for assessment. According to the findings, the power supply system was the most in need of outsourcing, followed by air conditioning, firefighting, and elevator systems. When considering which of the four metro lines to outsource first, the blue line came out on top, followed by the red, green, and brown lines. By prioritizing the outsourcing of the power supply system as a result of this research, the Taipei Rapid Transit Corporation may cut the system’s maintenance expenditures from USD 1.57 million to USD 1.33 million, saving 15% on maintenance costs. Applying these findings can improve the economic benefits of outsourced maintenance for the Taipei Rapid Transit Corporation. © 2022 by the authors.

关键词： Analytic hierarchy process

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Editorial engineering and technology management: A Growing Field

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IEEE Transactions on engineering management 2020年第3期67卷 514-515页

作者： Daim, Tugrul U Technology Management Doctoral Program Department of Engineering and Technology Management Maseeh College of Engineering and Computer Science Portland State University PortlandOR United States

As the world has struggled against a virus, technology enabled our survival in many dimensions. In many cases adoptions of technologies which would have lasted years happened in weeks if not days. For example, remote health care adoption was accelerated immensely to deal with the challenged health system. We are about to see similar technological innovations ramp up the hard hit economies through many different sectors. As always said, challenges create opportunities. Our field of engineering and technology management is growing. IEEE technology and engineering management Society (TEMS) just finished the first virtual conference: TEMSCON 2020. As a part of it we held an editors’ panel. Holding the conference on line enabled many editors of the leading journals in the field attend the event.

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Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

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aBIOTECH 2024年第3期5卷 281-297页

作者： Sishi Chen Xuqi Lu Hongda Fang Anand Babu Perumal Ruyue Li Lei Feng Mengcen Wang Yufei Liu College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China State Key Laboratory of Rice Biology and Breeding Zhejiang UniversityHangzhou 310058China Computational Modeling and Nanoscale Processing Unit National Institute of Food TechnologyEntrepreneurship and Management-ThanjavurMinistry of Food Processing IndustriesThanjavur 613005India College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Global Education Program for AgriScience Frontiers Graduate School of AgricultureHokkaido UniversitySapporo 060-8589Japan

Bakanae disease,caused by Fusarium fujikuroi,poses a significant threat to rice production and has been observed in most rice-growing *** disease symptoms caused by different pathogens may vary,including elongated and weak stems,slender and yellow leaves,and dwarfism,as *** disease is likely to cause necrosis of diseased seedlings,and it may cause a large area of infection in the field through the transmission of ***,early disease surveillance plays a crucial role in securing rice *** monitoring methods are both time-consuming and labor-intensive and cannot be broadly *** this study,a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae *** data were obtained on the 9th,15th,and 21st day after rice infection to explore the physiological and biochemical performance,which helps to deepen the research on the disease *** data were obtained over these same periods of infection,and a deep learning model,named Rice Bakanae Disease-Visual Geometry Group(RBD-VGG),was established by leveraging hyperspectral imaging technology and deep learning *** on this model,an average accuracy of 92.2%was achieved on the 21st day of *** also achieved an accuracy of 79.4%as early as the 9th *** characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field ***,the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease,thereby providing an efficient avenue for disease prevention and control.

关键词： Bakanae disease Hyperspectral imaging Deep learning Early surveillance Disease monitoring

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CAFO: Feature-Centric Explanation on Time Series Classification

arXiv

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arXiv 2024年

作者： Kim, Jaeho Hahn, Seok-Ju Hwang, Yoontae Lee, Junghye Lee, Seulki Ulsan Korea Republic of Ulsan Korea Republic of Technology Management Economics and Policy Program Graduate School of Engineering Practice Institute of Engineering Research Seoul Korea Republic of Ulsan Korea Republic of

In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimensional nature of MTS data, intricate temporal dynamics, and the necessity for domain-specific interpretations. Current explanation methods for MTS mostly focus on time-centric explanations, apt for pinpointing important time periods but less effective in identifying key features. This limitation underscores the pressing need for a feature-centric approach, a vital yet often overlooked perspective that complements time-centric analysis. To bridge this gap, our study introduces a novel feature-centric explanation and evaluation framework for MTS, named CAFO (Channel Attention and Feature Orthgonalization). CAFO employs a convolution-based approach with channel attention mechanisms, incorporating a depth-wise separable channel attention module (DepCA) and a QR decomposition-based loss for promoting feature-wise orthogonality. We demonstrate that this orthogonalization enhances the separability of attention distributions, thereby refining and stabilizing the ranking of feature importance. This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS. Furthermore, we develop metrics to evaluate global and class-specific feature importance. Our framework’s efficacy is validated through extensive empirical analyses on two major public benchmarks and real-world datasets, both synthetic and self-collected, specifically designed to highlight class-wise discriminative features. The results confirm CAFO’s robustness and informative capacity in assessing feature importance in MTS classification tasks. This study not only advances the understanding of feature-centric explanations in MTS but also sets a foundation for future explorations in feature-centric explanations. The codes are available at https://***/eai-lab/CAFO. © 2024, CC BY-SA.

关键词： Time series

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