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Applying Bayesian belief in LoRa: smart parking case study

Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7857-7870页

作者： Gupta, Sakshi Snigdh, Itu School of Computer Application Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

With the popularity of LoRa, many IoT applications are now being realized with this Low Power Wide Area Network (LPWAN) technology. Apart from ensuring low power and long-range communications, LoRa is also interference-free and hence an obvious choice for applications in urban scenarios. We dedicate our study to analyze and infer the impact of LoRa parameters like SF (Spreading Factor), BW (Bandwidth), CR (Coding Rate) on network performance metrics like delay, throughput for a smart parking scenario. Suited to the smart parking case study, we also present network performance observations for varied LOS settings (Line-of-Sight), NON-LOS, and obstruction. We try to identify the most significant parameters under different network conditions and determine the relationship among them through canonical correlation through our article. We also propose a Bayesian belief network to infer the best possible combination of LoRa parameters for achieving high-quality network efficiency under the environment scenarios. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Predicting Bank Loan Approval with Artificial Intelligence 1

Predicting Bank Loan Approval with Artificial Intelligence

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Ratan, Raj Yadav, Sudeept Singh Malhotra, Ritika Kumar, Avneesh Galgotias University School of Computer Application and Technology Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

In the contemporary financial landscape, banks play a crucial role in facilitating individuals and businesses with their monetary needs. Central to their operations is the assessment of borrower creditworthiness through loan approval processes, ensuring the security of loans extended. However, traditional manual assessment is not only time-consuming but also prone to human error. Therefore, there is an urgent need to develop an automated and reliable credit approval system. In this study, diverse machine learning calculations are compared to decide the ideal show for anticipating whether a bank ought to endrose a advance for a person. Employing a dataset gotten from Kaggle, diverse machine learning methods to a degree logistic reversion, decision trees, K-nearest neighbors (KNN), haphazard woodlands and neural networks are exploited. The animation of these models is evaluated handling measurements to a degree veracity, accuracy, review and F1 score. In particular, the influence of the model is improved by taking into account a number of factors that include the borrower's personal characteristics, such as tax liabilities, purpose of the loan, credit history, loan amount and home ownership, in addition to traditional checking account information. This holistic approach allows for a more accurate estimation of the likelihood of loan default. Furthermore, the logistic regression model proves to be the top performer with an impressive accuracy of 81.89%. Additionally, a trait importance analysis is performed to identify the variables that have the greatest impact on loan approval decisions. The results underline the superiority of ML models over conventional statistical approaches and illustrate their potential to significantly improve the accuracy of loan forecasting. © 2024 IEEE.

关键词： Logistic regression

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth 9th

Predictive Analytics for Future Air Pollution Levels Based o...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Yeshe, Darel Wairooy, Irma Kartika Makalew, Brilly Andro Computer Science Department School of Computer Science Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. © 2024 The Authors.

关键词： Adversarial machine learning

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Development of educational game as a media to raise public awareness of illegal animal trafficking issue in Indonesia 9th

Development of educational game as a media to raise public a...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： William, Jonathan Ahmadi, Khalid Fajar, Muhamad Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The amount of illegal wildlife trade cases increases every year in Indonesia. This causes some unique Indonesian wildlife to become endangered or even extinct. Therefore, the purpose of this research is to raise public awareness to illegal wildlife trade issue by using a game as its media to reduce the number of cases of illegal wildlife trade in Indonesia. The game made in this research will be developed using the GDLC development method and 2021.3.11f1 version of Unity as its game engine. To evaluate the game that has been developed in this research, a comparation to other related games is conducted and resulted in the game that has been developed in this research is significantly better than other related games in Challenge, Learnability and Concentration aspects (p © 2024 The Authors.

关键词： Invertebrates

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Player evaluation and learning outcomes of "NusaVenturas": A serious game for enhancing player knowledge and interest in Indonesian traditional foods 9th

Player evaluation and learning outcomes of "NusaVenturas": A...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Sanjaya, Vito Sebastian Tanoto, Nicholas Valenthinus Tandinata, Nicholas Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The popularity of Indonesian traditional food has been declining due to the increasing influence of foreign cuisine. This study presents the development method that combining Game Development Life Cycle (GDLC) with Experiential Game Learning Design Framework and evaluation of a serious game "NusaVenturas" using usability, learning outcome, and Game Experience Questionnaire (GEQ) that designed to introduce, promote interest, educate, and preserve knowledge of Indonesian traditional foods. "NusaVenturas" is a unique 2D serious game that integrates puzzle-platformer elements with time-management cooking mechanics. The study involved 37 participants aged 18 to 23. Evaluation results indicate that "NusaVenturas" significantly enhances players' knowledge of Indonesian traditional foods, with statistical analysis confirming the significance of the findings. © 2024 The Authors.

关键词： Food ingredients

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Comparative usability analysis across different driving control scenarios in a driving simulation game 9th

Comparative usability analysis across different driving cont...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： David Davis, Juan Lewis, Leon Oliver, Christian Fajar, Muhamad Pratama, Galih Dea Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study aims to address the gap in the evaluation of driving simulation games by further assessing and developing ProjectStir (a Driving Simulation Game) through its adaptation to two different driving control devices: a steering wheel and an arcade stick. Despite the growing popularity of simulation games, limited research has been conducted on how different control devices impact user experience and usability. Usability was evaluated through variables including Easiness of Game Control, Learnability of Gameplay, Remembrance, Challenge, Efficiency, Tension, Familiarity, and Satisfaction. Involving 30 respondents from the computer Science program at BINUS University, the results revealed no significant differences in usability between the two scenarios (p © 2024 The Authors.

关键词： Automobile driver simulators

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Multi-Objective Defect Detection Method for Transmission Lines Based on Improved YOLOv8

Multi-Objective Defect Detection Method for Transmission Lin...

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

作者： Wang, Jingdong Ding, Xu Meng, Fanqi Zhou, Lina School of Computer Science Northeast Electric Power University Jilin China Beijing Institute of Computer Technology and Application in China China

ISBN: (纸本)9781665410205

Aiming at the problem that the traditional detection method has low efficiency and accuracy due to the fact that there are many key component targets to be inspected by unmanned aerial vehicle (UAV) during power inspection, and the shape difference is large, and the image quality is not high, a transmission line abnormal target detection method based on improved YOLOv8 and super-resolution reconstruction is proposed. Firstly, the super-resolution reconstruction algorithm is used to reconstruct the abnormal image to improve the clarity and enrich the characteristic information contained in the image. On this basis, the improved YOLOv8 network is used to detect the defects in the inspection image. The CBAM attention mechanism is fused in the Bottleneck part of the C2f module to strengthen the model's ability to locate the target;In order to further improve the detection ability of small targets in patrol inspection, a small target detection layer is added to make the network pay more attention to the detection of small targets. Finally, in order to be deployed to the edge devices, the original convolution in the network is modified to a lightweight convolution GhostConv to reduce the number of model parameters. The experimental results show that the proposed method can accurately detect abnormal defects of transmission line components on the basis of improving the quality of inspection images. mAP is improved by 3.7% and the number of model parameters and calculation amount are greatly reduced, which reflects the effectiveness of the algorithm, and it has stronger extraction ability and robustness for subtle defect targets, meeting the detection requirements of power inspection. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Development of "Peter's First-Aid Adventure" Virtual Reality-Based Serious Game in First-Aid Education: Usability Analysis of Virtual Reality Interaction Tools 9th

Development of "Peter's First-Aid Adventure" Virtual Reality...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Rainer, Alexander Setiono, Angeline Leonardrich, Kevin Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Incidents that threaten lives can occur unexpectedly, highlighting the importance of immediate first aid. Studies show that Virtual Reality (VR)-based Serious Games (SG) are excellent alternatives for learning, especially for physical training, as they provide immersive and realistic scenarios where users can learn and practice their skills in a safe environment. Therefore, this research proposes the design of a VR-based SG for learning first aid procedures. A total of 31 participants tested the game's usability using both VR controllers and hand tracking, playing the game with both tools in random order, and then completing the XR-GUQ and Interaction Categories questionnaire. The results indicate that controllers offer higher usability than hand tracking. However, the game can still effectively utilize both interaction tools, given their satisfactory usability ratings. © 2024 The Authors.

关键词： Virtual environments

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Extracting Aspect Sentiment from Users' Book Reviews for Recommendation

Extracting Aspect Sentiment from Users' Book Reviews for Rec...

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2024 International Conference on Computational Linguistics and Natural Language Processing, CLNLP 2024

作者： Cui, Yachao Zhou, Peng Li, Kang Seng, Bingfeng Cao, Han Shaanxi Normal University School of Computer Science Xi'an China Qinghai University Department of Computer Technology and Application Xining China

ISBN: (纸本)9798350392098

When shopping for books on e-commerce platforms, other users' evaluations of books contain a large number of users' intuitive feelings, from which useful information can be mined to provide buyers with ideas for shopping. In terms of recommendation algorithms, although the existing research work can use various methods to model fine-grained user preferences to improve the recommendation effect. However, we believe there are still shortcomings. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools and are not necessarily applicable to the recommendation domain. Second, the features extracted from reviews are still potential features when incorporated into recommendation algorithms, and they cannot be fully utilized to characterize user embeddings. For this reason, this paper proposes a new user comment extraction scheme for personalized recommendation. We first use text summarization techniques in the field of NLP to preprocess and refine the comment text to reduce data noise and obtain high-quality text information. Subsequently, in order to more accurately characterize user embeddings and item features, we use advanced aspect-based sentiment analysis techniques to extract fine-grained aspectual items and corresponding sentiment polarity from the review text. Finally, experimental validation on a real book dataset is conducted to demonstrate the effectiveness of our model. © 2024 IEEE.

关键词： Embeddings

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