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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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N-Body Simulation Inspired by Metaheuristics Optimization

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Computer Systems Science & Engineering 2022年第6期41卷 1143-1155页

作者： Muhammad Ali Ismail Maria Waqas Farah Sadiq National Center in Big Data and Cloud Computing Department of Computer and Info Systems EngineeringNED University of Engineering and TechnologyKarachiPakistan

The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists andmathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been themajor hurdles in solving this problem for large and complex galaxies. Adventof high performance computational machines have mitigated the problem to muchextent, but still for large number of bodies it consumes huge amount of resourcesand days for computation. Conventional algorithms have been able to reduce thecomputational complexity from O n2 ð Þ to O nlogn ð Þ by splitting the space into atree or mesh network, researchers are still looking for improvements. In thisresearch work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. Theresults are compared with that of conventional algorithms. The outcomes showabout 50% time saving with almost no loss in accuracy. The proposed approachbeing a metaheuristics optimization technique, attempts to find optimal solution tothe problem, searching the entire space in a unique and efficient manner in a verylimited amount of time.

关键词： N-body problem metaheuristics optimization particle swarm optimization heavenly bodies

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DeepFake Detection Using Deep Learning 1st

DeepFake Detection Using Deep Learning

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Rehman, Mariam Rasool, Mehran Safder, Sadaf Government College University Faisalabad Pakistan National Center in Big Data and Cloud Computing University of Agriculture Faisalabad Pakistan

ISBN: (纸本)9789819979684

A deepfake is a computer-generated video or image where one person's face is replaced with another face of a person which uses a generative adversarial network (GAN) to create and alter images that are practically impossible for humans to distinguish from authentic ones. The development of GAN technology has led to significant improvements in image generation. This progress has made it difficult for humans to differentiate between generated images and raised concerns about their misuse and also creating security threats for the society. Deepfake image detection is an essential challenge because the current methods often face inaccuracy and time-consuming issues. The research work employed GAN discriminators to address these issues. The experiment used the CelebA dataset, which contained diverse celebrity images. Moreover, the proposed model consists of components i.e., generator and discriminator. The generator creates fake data from the training images, while the discriminator distinguishes real from the fake images. The result shows that the proposed approach is out-performing with an accuracy of 95.8% in identifying deep fake images, even in the presence of various manipulations. The proposed model is helpful for enhancing trust and security in the society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Generative adversarial networks

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data Catalogs with Artificial Intelligence and Active Metadata: A Case Study of China Mobile 6

Data Catalogs with Artificial Intelligence and Active Metada...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Wang, Jibin Guo, Yanmei Jiang, Yong Shang, Jing Chen, Zhuo Liu, Yu Hu, Qingyuan Yin, Zheng China Mobile Information Technology Center Beijing China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9798350388374

In the context of the rapid development of artificial intelligence, telecommunications operators have accumulated an increasing amount of data. China Mobile has developed a set of practical methods of enhanced data catalog in response to current issues such as difficulties in scheduling heterogeneous data from multiple sources, lack of macroscopic views of data usage, unintelligent data operation methods, and high risk of data leakage. By leveraging data virtualization, active metadata management, machine learning, along with other large-scale model technologies, China Mobile has crafted an enhanced data catalog, improving the data management efficiency and business decision-making process, further strengthening its competitiveness in the telecommunication industry. © 2024 IEEE.

关键词： Information leakage

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An insider user authentication method based on improved temporal convolutional network

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High-Confidence computing 2023年第4期3卷 87-95页

作者： Xiaoling Tao Yuelin Yu Lianyou Fu Jianxiang Liu Yunhao Zhang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational *** the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal *** of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature *** do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication *** order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for *** results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural *** the final authentication,the Area Under the Curve(AUC)can reach 96%.

关键词： Insider users Mouse dynamics Feature extraction Temporal convolutional network Efficient channel attention mechanism Authentication

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Research on Blockchain-based Decentralized Federated Learning

Research on Blockchain-based Decentralized Federated Learnin...

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2023 International Conference on Computer Applications Technology, CCAT 2023

作者： Han, Boyuan Liang, Zhihong Qin, Mingming Jiang, Rongxin Dai, Wei Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Big Data and Cloud Computing Center Kunming China Yunnan Pu'er Technical School Yunnan Pu'er Technical School Kunming China

ISBN: (纸本)9798350301540

The integration of blockchain and federated learning exhibits a highly synergistic relationship. Blockchain, with its assurance of trustworthiness in computational processes and data, combined with federated learning's ability to make data available yet invisible, mutually complement each other and have shown initial advancements across various domains. Consequently, research and analysis of blockchain-based federated learning technology have garnered considerable *** blockchain enhances the federated learning process, it also poses a number of problems, such as increased communication overhead and lack of computational *** article aims to provide a comprehensive examination of the subject. Firstly, it introduces the fundamental concept of federated learning technology, along with privacy protection techniques and the existing threat landscape. Secondly, it analyzes the core principles of blockchain-based federated learning technology, delving into current research status, progress, and outlining the various research directions involved. Furthermore, it conducts an in-depth analysis and summarization of the integration between federated learning and blockchain technology, identifying numerous challenges that need to be addressed within the relevant domains. Finally, it recognizes and analyzes the future development of this combination, emphasizing the need to overcome existing *** summary, this article highlights the amalgamation of federated learning and blockchain technology. It identifies and analyzes the many challenges that require resolution within the pertinent fields, while also offering insights into the future development of this integration. © 2023 IEEE.

关键词： Blockchain

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Privacy Protection Multi-copy Provable data Possession Supporting data Reliability 5th

Privacy Protection Multi-copy Provable Data Possession Suppo...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Zhang, Feng Wen, Bin Yan, Yifei Zeng, Zhaowu Zhou, Wei School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789819981007

With the development of technology, a massive amount of data continues to emerge, and there is still a strong demand for data storage. Outsourcing data to cloud service providers has become a common practice. Although cloud storage offers the advantage of location-independent data access and reduces burdens such as hardware and software maintenance, this service still faces some challenges in terms of privacy and data reliability. To address this issue, we present Privacy-preserving Multi-copy Provable data Possession supporting data Reliability (PMPDP-R). Our proposal ensures the integrity, reliability, and freshness of data by designing Timestamp-based Double-layer Merkle Hash Tree (TD-MHT), a novel dynamic structure for multicopy storage. Furthermore, our approach uses random masking techniques to develop unique replica blocks and safeguard privacy. We’ve also ensured data reliability by enabling PMPDP-R to locate damaged data blocks to aid recovery. The experimental analysis proves that our scheme is both practical and effective. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： cloud storage

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Blockchain-Based Multi-cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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