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Pothole Detection: A Study of Ensemble Learning and Decision Framework

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 67-79页

作者： Gorro, Ken D. Ranolo, Elmo B. Ilano, Anthony S. Balijon, Deofel P. Center for Cloud Computing Big Data and Artificial Intelligence Philippines College of Computing Artificial Intelligence and Sciences of Cebu Normal University Philippines

This study investigates the potential use of ensemble learning (YOLOv9 and Mask R-CNN) and Multi-Criteria Decision Making for pothole detection system. A series of experiments were conducted, including variations in confidence thresholds, IoU thresholds, dynamic weight configurations, camera angles and MCDM criteria, to assess their effects on detection performance. The YOLOv9 model achieved a mean Average Precision (mAP) of 0.908 at 0.5 IoU and an F1 score of 0.58 at a confidence threshold of 0.282, indicating a strong balance between precision and recall. However, adjusting IoU thresholds showed that lower thresholds improved recall but resulted in false positives, while higher thresholds improved precision but reduced recall. Dynamic weight configurations were explored, with balanced weights (wY = 0.5, wM = 0.5) yielding the best overall performance, while uneven weights allowed trade-offs between precision and recall based on specific application needs. The MCDM framework refined detection outputs by evaluating pothole features such as size, position, depth, and shape. The proposed algorithm has the potential to be widely used in practical applications. Overfitting is the main drawback of the proposed algorithm, but this is dependent on the use case where the pothole detection will be used. © (2025), (Science and Information Organization). All rights reserved.

关键词： Decision making

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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Computer Science Undergraduate Programs in Australia

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ACM Inroads 2024年第1期15卷 18-26页

作者： Hamilton, Margaret Hol, Ana Richardson, Joan McGovern, Jim School of Computing Technologies RMIT University Australia School of Computer Data and Mathematical Sciences Western Sydney University Australia

Australian CS programs generally provide good problem-solving skills, depth in the foundations of computing theory, hardware and software operating environments and technologies, and complexity, together with a strong focus on programming and software development. Increasingly, programs are focusing on addressing the emerging areas of Artificial Intelligence, Cyber Security and data Science. Australian CS graduates should be in demand in a growing, and increasingly digitized economy. Greater focus on application with industry tools and frameworks, exposure to other disciplines and ways of working, recognition of diversity and development of non-technical competency areas of working may be the best avenues to enhance the capabilities and career prospects of CS graduates. There are challenges in developing more diverse graduates, in funding more effective learning environments, and in building awareness of CS in the community, particularly those that influence the choices of potential students. Overall, the Australian CS discipline is well placed to underpin the developing knowledge economy. © 2024 Copyright held by owner/author(s)

关键词： Computer aided instruction

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Fuzzy knowledge based intelligent decision support system for ground based air defence

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Journal of Ambient Intelligence and Humanized computing 2024年第4期15卷 2317-2340页

作者： Ahmad, Adnan Amjad, Rawan Basharat, Amna Farhan, Asma Ahmad Abbas, Ali Ezad Department of Computer Science School of Computing National University of Computer and Emerging Sciences Islamabad Pakistan Department of Data Science School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Islamabad Pakistan

This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A Fire Control Officer is responsible for assessing threats and assigning the most appropriate weapon to neutralize them. However, the decision-making process can be prone to errors, risking resource wastage and endangering DA protection. To address this problem, this research proposes a hybrid approach that combines a knowledge-driven fuzzy inference system with machine learning models to optimize resource allocation while incorporating expert knowledge in the decision-making process. Since sensory data obtained from multiple radars may be incomplete or incorrect, a fuzzy knowledge graph-based system is used for data fusion and providing it to the connected modules. Feature selection is optimized by including the most important parameters, such as the vitality of defended assets and threat score, in the threat evaluation. The results from these subsystems are visualized using a Geographical Information System, allowing for real-time mapping of the GBAD environment and displaying the results in a user-friendly web interface. The proposed system has undergone rigorous testing and evaluation, resulting in an efficient and accurate weapon assignment model with a low RMSE value of 0.037. Overall, this Intelligent Decision Support System provides an effective solution for optimizing decision-making processes in GBAD environments and can significantly improve DA protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graphic methods

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Enhancing Recommendation with Denoising Auxiliary Task

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Journal of Computer Science & Technology 2024年第5期39卷 1123-1137页

作者： Peng-Sheng Liu Li-Nan Zheng Jia-Le Chen Guang-Fa Zhang Yang Xu Jin-Yun Fang College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The historical interaction sequences of users play a crucial role in training recommender systems that can accurately predict user ***,due to the arbitrariness of user behaviors,the presence of noise in these sequences poses a challenge to predicting their next actions in recommender *** address this issue,our motivation is based on the observation that training noisy sequences and clean sequences(sequences without noise)with equal weights can impact the performance of the *** propose the novel self-supervised Auxiliary Task Joint Training(ATJT)method aimed at more accurately reweighting noisy sequences in recommender ***,we strategically select subsets from users’original sequences and perform random replacements to generate artificially replaced noisy ***,we perform joint training on these artificially replaced noisy sequences and the original *** effective reweighting,we incorporate the training results of the noise recognition model into the recommender *** evaluate our method on three datasets using a consistent base *** results demonstrate the effectiveness of introducing the self-supervised auxiliary task to enhance the base model’s performance.

关键词： auxiliary task learning recommender system sequence denoising

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Enhancing the Efficiency of Systems with Overlapping Coalition Formation 21st

Enhancing the Efficiency of Systems with Overlapping Coal...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Valizadeh, Jaber Zhang, Dongmo Mubin, Omar School of Computing Data and Mathematical Sciences Western Sydney University Sydney Australia

ISBN: (纸本)9789819601240

We studied the decision-making process of autonomous agents within the Electric Vehicle (EV) charging domain, where EV charging suppliers, acting as autonomous agents, interact to maximize their utilities in a shared environment. Initially, we modeled the suppliers’ utilities in an EV charging scenario as a non-cooperative game aimed at achieving Pareto-optimal outcomes. We then expanded this model to include two cooperative game approaches: Non-Overlapping Coalition Formation and Overlapping Coalition Formation. Our results indicate that allowing suppliers to form partial coalitions through the overlapping coalition formation model significantly enhances outcomes compared to both the non-overlapping coalition formation and non-cooperative models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi agent systems

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Bangalore Coming Closer to It's Satellite Town : Overview of Urban Sprawl in the City

Bangalore Coming Closer to It's Satellite Town : Overview of...

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2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024

作者： Dasgupta, Anindita Kumar, Uttam Spatial Computing Laboratory Center for Data Sciences Bangalore560100 India

ISBN: (纸本)9798350358582

Urbanization is a worldwide phenomenon is much evident in 21st century. This widespread occurrence is mostly caused by infrastructure and economic growth followed by population expansion. Rapid urbanization with the changes in land use patterns has resulted in urban expansion that happens linearly in certain directions or radially around the city. The extent of urbanization, which is a global phenomenon mainly driven by population growth and large scale migration is referred as urban sprawl. Spatial metrics are used to quantify urban sprawl based on patch size, density, shapes etc. Spatial metrics quantified is computed on the entire built-up area of the city as metrics here is computed on the entire image and can be analyzed for any part of the city to be studied in details. The human induced urban heat island (UHI) is a well-known phenomenon in urban environment which is more evident in the cases of urban sprawl. City-heat interacts with rising summer temperatures and heat-waves, placing additional pressure on infrastructure. Landsat 8 dataset of 2014 and 2021 was used for the spatio-temporal analysis. This study focuses on summer season when UHI effect is apparent. Multiple ring buffer of 1 km radius was overlaid from the centre towards the periphery of the city to analyze the variations in Land Surface Temperature (LST) and sprawling pattern. It was found that the as we go away from the centre LST also increased that can be attributed to unplanned urbanization. © 2024 IEEE.

关键词： Atmospheric temperature

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A Novel Competition-Based Coordination Model With Dynamic Feedback for Multi-Robot Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2029-2031页

作者： Bo Peng Xuerui Zhang Mingsheng Shang Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China

Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, a novel competition-based coordination model is proposed to solve the multi-robot task allocation problem and applied to a multi-robot object tracking scenario.

关键词： robot Robot Dynamic

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Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-27页

作者： Liu, Jiao Gupta, Abhishek Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Mechanical Sciences Indian Institute of Technology Goa Goa India

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tradeoff solutions is sought under tight evaluation budgets. In this article, we introduce a novel concept of inverse transfer in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification. Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi-and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modeling accuracy of the invTrEMO relative to state-of-The-Art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://***/LiuJ-2023/invTrEMO. © 2024 Copyright held by the owner/author(s).

关键词： Budget control

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Online Incremental Machine Learning Model For Multi-Sensor and Multi-Temporal Satellite data: A Case Study of Bangalore 3

Online Incremental Machine Learning Model For Multi-Sensor a...

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3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Mittal, Yash Kumar, Uttam Aggarwal, Deeksha Spatial Computing Laboratory Center for Data Sciences Bangalore India

ISBN: (纸本)9798350325591

Once a machine learning model has been trained and deployed, training it again on new and different data is time and compute expensive. In this research, we propose a novel approach that enables machine learning models to continue learning on multi-sensor and multi-temporal data. A comparative analysis between traditional learning and incremental learning approaches, focusing on their performances and effectiveness in handling large and evolving datasets is presented in the context of land use classification. The online incremental approach effectively integrated information from multiple sources and time periods, enhancing the overall predictive power and accuracy of the models. Over single and multiple years, incremental learning performed better as compared to traditional learning. © 2023 IEEE.

关键词： Classification (of information)

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