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arXiv 2023年

作者： Ferdousi, Rahatara Yang, Chunsheng Hossain, M. Anwar Laamarti, Fedwa Hossain, M. Shamim Saddik, Abdulmotaleb El The University of Ottawa OttawaON Canada National Research Council Canada OttawaON Canada School of Computing Queen’s University KingstonON Canada Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia

Recent advancements in cognitive computing, with the integration of deep learning techniques, have facilitated the development of intelligent cognitive systems (ICS). This is particularly beneficial in the context of rail defect detection, where the ICS would emulate human-like analysis of image data for defect patterns. Despite the success of Convolutional Neural Networks (CNN) in visual defect classification, the scarcity of large datasets for rail defect detection remains a challenge due to infrequent accident events that would result in defective parts and images. Contemporary researchers have addressed this data scarcity challenge by exploring rule-based and generative data augmentation models. Among these, Variational Autoencoder (VAE) models can generate realistic data without extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for rail defects, incorporating weight decay regularization and image reconstruction loss to prevent overfitting. The proposed method is applied to create a synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real sam-ples across five classes. Remarkably, 500 synthetic samples are generated with a minimal reconstruction loss of 0.021. A Visual Transformer (ViT) model underwent fine-tuning using this synthetic CPR dataset, achieving high accuracy rates (98%-99%) in classifying the five defect classes. This research offers a promising solution to the data scarcity challenge in rail defect detection, showcasing the potential for robust ICS development in this *** Codes 68T05, 94A08, 90B25 © 2023, CC BY.

关键词： Deep learning

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European Spine Journal 2025年 1-9页

作者： Alhosanie, Tasneem N. Klaib, Ahmad F. Al Hadidi, Fadi Abdelraheem Kanaan, Tareq Mohammad, Khuzama Al-zurgan, Aya Yousef, Noor Tareq Oweis, Liyana Hasan, Salah M. Eftiha, Majed Alkubaisi, Fatima Abdulsattar Tarawneh, Maher Ahmad Altarazi, Abdelrahman Barakat, Motaz AbuHijlah, Iyas Al-Sabbag, Qusai Tarawneh, Emad Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Department of Orthopedic Surgery School of Medicine University of Jordan Amman Jordan Department of Neurosurgery School of Medicine University of Jordan Amman Jordan Psychiatry Department Jordanian Royal Medical Services Amman Jordan Jordanian Royal Medical Services Amman Jordan Department of Radiology School of Medicine University of Jordan Amman Jordan Department of Maxillofacial School of Dentistry University of Jordan Amman Jordan Oral and Maxillofacial Surgeon Head and Neck Department King Hussein Cancer Center Amman Jordan

Lumbar spondylolisthesis (LS) defined as the displacement of a vertebral body relative to the vertebra below it. Moreover, it typically affects the lower lumbar spine, particularly in women above 50 years of age. Many studies showed that the prevalence of LS increases with age and reported a higher number of cases in women. This study aimed to determine the prevalence of lumbar spondylolisthesis in adults and elderly people in the Jordanian community. A total of 2000 patients with low back pain, aged 18–94 years, with records of their age, gender, and lumbar spine X-ray from Jordan University Hospital were included in this retrospective cross-sectional study. The anterior-posterior (AP) and lateral lumbosacral X-ray images of every subject under study were read using SYNAPSE Viewer. Finally, the IBM SPSS Statistics program was used for statistical analysis. Among the 1,458 subjects included in the study, 910 (62.4%) were females and 548 (37.6%) were males. Their mean age was (53.2 ± 16.1) years. A total of 385 subjects had lumbar spondylolisthesis, with a total incidence of 26.4%. The percentage of Spondylolisthesis among males was (16.1%) and (32.6%) among females. The rates of spondylolisthesis (P = 0.00) were significantly higher in females. There was no significant difference between prevalence of males and females in patients under 50 years old. But the prevalence of LS in patients who are over 50 years old increased significantly, compared to other age groups. So our results showed that as the age of the patients increased, the prevalence increased in both males and females (P = 0.01). There were 385 cases that had slipped at different level, sorting in prevalence as follows L4-L5 > L5-S1 > L3-L4 > L2-L3 > L1-L2. We noticed that the L4-L5 in females took the largest proportion, but in males L4-L5 and L5-S1 took almost the same proportion. The majority of our patients had LS only at one level, with a percentage of 94.3% among all cases. The total pr

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Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms

arXiv

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arXiv 2024年

作者： Yang, Haowei Cheng, Zhan Zhang, Zhaoyang Luo, Yuanshuai Huang, Shuaishuai Xiang, Ao University of Houston Cullen College of Engineering Industrial Engineering Houston United States University of California Irvine Donald Bren School of Information & Computer Science Master of Computer Science Irvine United States University of California San Diego Computational Science San Diego United States Southwest Jiaotong University School of Computing and Artificial Intelligence Computer Science and Technology Chengdu China University of Science and Technology of China Department of Software Software system design Hefei China Northern Arizona University Information Security and Assurance AZ United States

As the complexity and dynamism of financial markets continue to grow, traditional financial risk prediction methods increasingly struggle to handle large datasets and intricate behavior patterns. This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction. First, the application and advantages of deep learning and big data algorithms in the financial field are analyzed. Then, a deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets. The experimental results show that this method significantly improves the accuracy of financial risk behavior prediction and provides valuable support for risk management in financial institutions. Challenges in the application of deep learning are also discussed, along with potential directions for future research. © 2024, CC BY.

关键词： Data accuracy

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Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Feedback Recommendation System Based on Structured Feedback Acquisition 4

Feedback Recommendation System Based on Structured Feedback ...

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4th International Conference on Advanced Technology and Applied Sciences, ICaTAS 2019

作者： Hassan, E. Sherief, N. Abdelmoez, W.M. Saad, A.A. Department of Computer Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Egypt Department of Software Engineering College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Egypt

Users' feedback are increasingly becoming a vital factor during the software evolution process. Users' feedback are widely considered to be an indication of how satisfied are the system's users. It was found that large percentage of feedback represents problems reported by users who face problems in using the system. Users' feedback are usually communicated in an ad-hoc manner with no defined structure. The ad-hoc nature of the feedback makes it difficult to study and analyse feedback. Also, it usually happens that similar problems are reported by different users, so engineers spend wasted time and effort in duplicate issues. In this paper we propose a recommendation system which recommends to the user reported problems that are similar to his from prevoius feedback threads to reuse existing solutions. We based our work on a structured feedback system to ensure we get better results. Structured feedback ensures minimum level of useful and meanigful information that helps enhance the analysis results. Initial evaluation was conducted and the results are promising. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Blockchain as a decentralized communication tool for sustainable development 2

Blockchain as a decentralized communication tool for sustain...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Hatamleh, Amer Aseh, Khairi Altrad, Abdullah Amayreh, Khalid Thaher Hilles, Shadi Mohamed, Rajina R. Ravindran Pathmanathan, P. Aledinat, Lowai Saleh UniSZA Faculty of Informatics and Computing Malaysia Irbid National University Faculty of Financial and Business Science Information System Department Irbid Jordan International Institute of Applied Science Swiss School of Management Netherlands Al Madinah International University Faculty of Computer and Info. Tech Malaysia Irbid National University Faculty of Financial and Business Science Administration Department Irbid Jordan Istanbul Okan University Department of Software Engineering Turkey Universiti Tenaga Nasional College of Computing Dan Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia

ISBN: (纸本)9781665432221

This research introduces the distributed ledger technology (blockchain) in terms of sustainable development in the future. This study is based on private, public and global communications in various industries. The growing demands for secure and decentralized transactions around the world demonstrate the need for Blockchain technology as a medium of operations and communications. The blockchain system is completely decentralized and allows users to exchange messages in an efficient and secure manner. The highlight of the paper is the anticipated future use of blockchain networks as a communication tool in every business and digital economy for a safe and sustainable development, while presenting its advantages and limitations that have been confirmed based on the experiences of various experts. Moreover, the article provides recommendations for the use of blockchain to greatly complicate its identification in future communications. The paper also concludes that the use of the blockchain system could be beneficial and enhance future communications and digital technologies. © 2021 IEEE.

关键词： Blockchain

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Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach

arXiv

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arXiv 2023年

作者： Hong, Haokai Jiang, Min Lin, Qiuzhen Tan, Kay Chen Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China College of Computer Science and Software Engineering Shenzhen University China Department of Computing Hong Kong Polytechnic University Hong Kong

We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of real-world scenarios necessitating the optimization of hundreds of thousands, if not millions, of variables. However, the larger dimension in VLSMOPs intensifies the curse of dimensionality and poses significant challenges for existing large-scale evolutionary multiobjective algorithms, rendering them more difficult to solve within the constraints of practical computing resources. To overcome this issue, we propose a novel approach called the very large-scale multiobjective optimization framework (VMOF). The method efficiently samples general yet suitable evolutionary directions in the very large-scale space and subsequently fine-tunes these directions to locate the Pareto-optimal solutions. To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations. Furthermore, a technique is designed for fine-tuning directions specific to tracking Pareto-optimal solutions. To understand the designed framework, we present our analysis of the framework and then evaluate VMOF using widely recognized benchmarks and real-world problems spanning dimensions from 100 to 1,000,000. Experimental results demonstrate that our method exhibits superior performance not only on LSMOPs but also on VLSMOPs when compared to existing algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Mustafa A. Al Sibahee Chengwen Luo Jin Zhang Yijing Huang Zaid Ameen Abduljabbar National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen PR China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen PR China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

关键词： Privacy Data privacy Cloud computing Memory Search problems Cryptography Servers

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