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ICRM: An intelligent citation recommendation mechanism based on BERT and weighted BoW models

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10135-10150页

作者： Chang, Chih-Yung Yang, Yu-Ting Zhang, Qiaoyun Lin, Yi-Ti Roy, Diptendu Sinha Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Department of English Tamkang University New Taipei Taiwan Department of Computer Science and Engineering National Institute of Technology Shillong India

With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. © 2024 - IOS Press. All rights reserved.

关键词： Artificial intelligence

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Machine Translation for Morphologically Rich Low-Resourced south African Languages 4

Machine Translation for Morphologically Rich Low-Resourced S...

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4th International Multidisciplinary Information technology and engineering Conference, IMITEC 2024

作者： Masethe, Hlaudi Daniel Mothapo, Lawrence M Ojo, Sunday O. Owolawi, Pius A. Masethe, Mosima Anna Giunchigilia, Fausto Tshwane University of Technology Department of Data Science South Africa Durban University of Technology Department of Information Technology South Africa Tshwane University of Technology Department of Computer Engineering South Africa Sefako Makgatho Health Sciences University Department of Computer Science and Information Technology South Africa University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350387988

In the context of machine translation, many of south Africa's languages are categorized as low-resourced, despite the country's vast linguistic diversity. Creating efficient multilingual translation systems that can support languages like Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, isiXhosa, Xitsonga, siSwati and isiZulu are severely hampered by the lack of high-quality training data. Low translation accuracy and fluency are the result of existing translation models' frequent inability to perform well in these languages. As a result, speakers of these languages encounter difficulties using increasingly accessible online resources for information and services. By utilizing the MarianMT model to create a reliable multilingual machine translation system, this work seeks to address these issues by enhancing the quality of translation for south African languages with limited resources. This study presents a comprehensive approach to multilingual machine translation focused on low-resourced south African languages, utilizing the MarianMT model. Our objective was to enhance translation quality for languages with limited training data, including Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, IsiXhosa, Xitsonga, siSwati and isiZulu. We conducted extensive experiments, resulting in a BLEU score of 62%, indicating a significant improvement in translation. Our evaluation metrics demonstrated strong performance, with precision at 78%, recall at 81%, F1 score at 79%, and overall accuracy at 82%. © 2024 IEEE.

关键词： Neural machine translation

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia university, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2023年第11期42卷 3244-3255页

作者： Wu, Huimin Li, Xiaomeng Lin, Yiqun Cheng, Kwang-Ting The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen518057 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering The Department of Computer Science and Engineering Hong Kong Hong Kong

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervised learning. In this paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality. In contrast to directly using one model's predictions as pseudo labels, our key idea is that high-quality pseudo labels should be generated by comparing multiple confidence maps produced by different networks to select the most confident one (a compete-to-win strategy). To further refine pseudo labels at near-boundary areas, an enhanced version of ComWin, namely, ComWin + , is proposed by integrating a boundary-aware enhancement module. Experiments show that our method can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation, respectively. The source code is now available at https://***/Huiimin5/comwin. © 1982-2012 IEEE.

关键词： Image segmentation

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Aberration-Aware Depth-From-Focus

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年 PP卷 1-11页

作者： Yang, Xinge Fu, Qiang Elhoseiny, Mohamed Heidrich, Wolfgang King Abdullah University of Science and Technology Saudi Arabia Department of Computer Science and Electrical and Computer Engineering King Abdullah University of Science and Technology Saudi Arabia

computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated data, especially for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate the domain gap caused by off-axis aberrations that will affect the decision of the best-focused frame in a focal stack. We then explore bridging this domain gap through aberration-aware training (AAT). Our approach involves a lightweight network that models lens aberrations at different positions and focus distances, which is then integrated into the conventional network training pipeline. We evaluate the generality of network models on both synthetic and real-world data. The experimental results demonstrate that the proposed AAT scheme can improve depth estimation accuracy without fine-tuning the model for different datasets. The code will be available in ***/vccimaging/Aberration-Aware-Depth-from-Focus. Author

关键词： Aberrations

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

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computers, Materials & Continua 2023年第5期75卷 2741-2757页

作者： Muhammad Usman Sana Zhanli Li Tayybah Kiren Hannan Bin Liaqat Shahid Naseem Atif Saeed College of Computer Science and Technology Xi’an University of Science and TechnologyXi’anShaanxi710054China Department of Computer Science(RCET Campus) University of Engineering and TechnologyLahore39161Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54700Pakistan Department of Computer Science COMSATS UniversityIslamabadLahore54700Pakistan

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being *** cloud computation,data processing,storage,and transmission can be done through laptops andmobile *** Storing in cloud facilities is expanding each day and data is the most significant asset of *** important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s *** have to be dependent on cloud service providers for assurance of the platform’s *** security and privacy issues reduce the progression of cloud computing and add ***;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured *** this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of *** implementation of a modified RSA algorithm results guaranteed the security of data in the cloud *** enhance the user data security level,a neural network is used for user authentication and ***;the proposed technique develops the performance of detection as a loss function of the bounding *** Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.

关键词： Cloud computing data security RSA algorithm Faster R-CNN

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The use of multi-task learning in cybersecurity applications: a systematic literature review

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Neural Computing and Applications 2024年第35期36卷 22053-22079页

作者： Ibrahim, Shimaa Catal, Cagatay Kacem, Thabet Department of Computer Science and Engineering Qatar University 2713 Doha Qatar Department of Computer Science and Information Technology University of the District of Columbia WashingtonDC20008 United States

Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field. © The Author(s) 2024.

关键词： Cybersecurity

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White shark optimizer via support vector machine for video-based gender classification system

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Multimedia Tools and Applications 2025年 1-17页

作者： Oyediran, Mayowa Oyedepo Ajagbe, Sunday Adeola Ojo, Olufemi Samuel Alshahrani, Reem Awodoye, Olufemi O. Adigun, Matthew O. Department of Computer Engineering Ajayi Crowther University Oyo Nigeria Department of Computer Science University of Zululand Kwadlangezwa3886 South Africa Department of Computer Engineering Abiola Ajimobi Technical University Ibadan200255 Nigeria Department of Computer Science College of Computers and IT Taif University P.O.Box 11099 Taif21944 Saudi Arabia Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria

Gender identification from videos is a challenging task with significant real-world applications, such as video content analysis and social behavior research. In this study, we propose a novel approach, the White Shark Optimizer-Support Vector Machine (WSO-SVM), tailored specifically for gender identification from video data. The WSO-SVM integrates the White Shark Optimizer, a bio-inspired optimization algorithm mimicking the hunting behavior of white sharks, with the Support Vector Machine, a powerful machine learning technique for classification. By combining these two methods, we aim to exploit the advantages of both algorithms and enhance gender identification accuracy. To evaluate the performance of the WSO-SVM in gender identification, the work conducted extensive experiments using a diverse dataset of video clips containing individuals of various genders and backgrounds. The work compared the results with conventional SVM-based gender identification and state-of-the-art methods. The findings demonstrate that the WSO-SVM achieves superior accuracy in gender identification compared to traditional SVM-based approaches. The WSO-SVM's ability to efficiently explore the solution space and select optimal SVM parameters contributes to its improved performance. Moreover, the WSO-SVM exhibits robustness in handling variations in lighting conditions, poses, and facial expressions, making it well-suited for real-world video-based gender identification tasks. The outcomes derived from the SVM approach demonstrate that WSO-SVM produced an average FPR of 7.14%, Sensitivity of 93.06%, Specificity of 92.86%, Precision of 91.0%, and overall accuracy of 93.00% in 45.83 s with a recognition time of 45.83 s. © The Author(s) 2025.

关键词： Support vector machines

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