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SSRN 2022年

作者： Hu, Linna Wang, Yue Jiang, Yijan Yan, Yinzhou Yang, Lixue Pan, Yongman Zhang, Xuegang Yang, Letian Liu, Qiang Wang, Qiang Faculty of Science Beijing University of Technology Beijing100124 China College of New Materials and Chemical Engineering Beijing Institute of Petrochemical Technology Beijing102617 China Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China School of Printing and Packing Engineer Beijing Institute of Graphic Communication Beijing102627 China College of Chemistry and Chemical Engineering Northeast Petroleum University Daqing163318 China Academy of Artificial Intelligence Beijing University of Technology Beijing100124 China

Here we report a N ion-implanted ZnO microtube fabricated by optical vapor supersaturated precipitation (OVSP) and ion implantation technologies. The N ions with concentration of ~2.4×1023 cm-3 were mainly located within 100 nm of the surface in the acceptor-rich ZnO (A-ZnO) microtubes. Due to the presence of N in ZnO lattice as NO defects, the average crystal size of A-ZnO:N microtubes increased from 77 nm to 95 nm, an ~15-fold enhancement of near-band edge (NBE)/deep-level emission (DLE) intensity ration achieved. The yellow luminescence and the anomalous negative thermal quenching (NTQ) effect further confirmed NO defect level of 1.3 eV above the valence band maximum in A-ZnO:N microtubes. The implantation of N ions could expand the depletion layer between A-ZnO:N detector and atmospheric species, by which fast photoresponse down to 0.1 s was realized in a metal-semiconductor-metal (MSM) UV detector. Compared with the A-ZnO UV detector, the photoresponsivity of the MSM UV detector was enhanced by ~10 folds. This work opens up new opportunities to design effective UV detectors for a variety of practical applications. © 2022, The Authors. All rights reserved.

关键词： Zinc oxide

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Protecting Online Transactions: A Cybersecurity Solution Model

Protecting Online Transactions: A Cybersecurity Solution Mod...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： S. Surya Suvarna Ranjeet Jagtap Ramnarayan Ramnarayan Mankali Priyadarshini Read Khalid Ibrahim Malik Bader Alazzam Sri Ramachandra Faculty of Engineering & Technology(SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai Yashwantrao Mohite College Bharati Vidyapeeth (Deemed to be University) Pune Maharashtra India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Medical Technical College Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Ajloun Jordan

The increasing reliance on online transactions and e-commerce platforms has increased cybersecurity threats, putting businesses and individuals at risk of data breaches, theft, and other malicious attacks. This study proposes a cybersecurity solution model to protect online transactions and mitigate the risks of cyberattacks. The model emphasizes the importance of OTP verification and password protection as protective measures to secure transactions. The study highlights the different types of cybersecurity threats faced by e-commerce platforms, including debit or credit card fraud, data misuse, and vulnerabilities in electronic payment systems. Human errors can also increase the risk of cyberattacks, making it crucial for businesses to implement security measures and strategies to prevent and mitigate cyber threats. The proposed cybersecurity solution model emphasizes the need for a multi-layered security approach, incorporating technologies such as firewalls, antivirus software, and encryption. Additionally, the model recommends regular security audits and employee training to raise awareness of cybersecurity risks and promote best practices. The study concludes that businesses and individuals must take proactive steps to protect their online transactions and safeguard against cybersecurity threats. By implementing the proposed cybersecurity solution model, businesses can mitigate the risks of cyberattacks and ensure secure online transactions, promoting trust and confidence in e-commerce platforms.

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Industrial Smoke Image Segmentation Based on a New Algorithm of Cross-Entropy Model 16th

Industrial Smoke Image Segmentation Based on a New Algorithm...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Huang, Qian-Jing Zou, Le Wu, Zhi-Ze Li, Huan-Yi Wang, Xiao-Feng Chen, Yan-Ping Department of Environmental Engineering Hefei University HefeiAnhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University HefeiAnhui230601 China

ISBN: (纸本)9783030607982

Smoke segmentation from the industrial images is a key concern of environmental monitoring. As the similarities between the gray value of the background and the smoke, the existing segmentation algorithms are difficult to accurately segment the target smoke. In this paper, we construct a cross-entropy based industrial smoke image segmentation by integrating the iterative convolution-thresholding. Specially, we use the iterative convolution-thresholding to implicitly represent the interface of each image domain through a characteristic function. We further perform the combination of a regularization term and a fidelity term in the cross-entropy model. In the proposed algorithm, the fidelity term is first converted into the product of the characteristic function and the cross-entropy function. Then the functional of the characteristic function is used to obtain the regularization term by the approach of thermonuclear convolution approximation. The experimental results demonstrate that our proposal has a more accurate segmentation effect and higher segmentation efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Integrated remote sensing, machine learning and geospatial approach for site selection of sewage treatment plants in the metropolitan city

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Desalination and Water Treatment 2025年 322卷

作者： Rajalakshmi, S. Subathradevi, S. Alghamdi, Abdulaziz G. Alsolai, Hadeel Department of Artificial Intelligence and Data Science Dr.N.G.P. Institute of Technology Coimbatore 641 048 India Department of Electronics and Communication Engineering University College of Engineering BIT Campus Anna University Tiruchirapalli India Department of Soil Sciences College of Food and Agricultural Sciences King Saud University P.O. Box 2460 Riyadh 11451 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia

Effective wastewater management is vital for environmental and public health in rapidly growing urban areas. This study presents an integrated framework combining remote sensing (RS), geographic information systems (GIS), machine learning (ML), and multi-criteria decision analysis (MCDA) to identify suitable sites for sewage treatment plants (STPs). Key geospatial factors such as slope, elevation, soil type, land use/land cover, drainage, proximity to urban areas, and hydrology were standardized and weighted. A Random Forest (RF) algorithm was applied to enhance spatial prediction. The study area (363.57 ***) was categorized into five suitability classes. Only 1.19 *** (0.33 %) was found to be very highly suitable, with 8.03 *** (2.21 %) highly suitable, and the majority (90.71 %) falling into the low or very low suitability classes. These results highlight the scarcity of optimal land, emphasizing the need for strategic planning. This approach offers a scalable, data-driven tool for sustainable and resilient urban wastewater infrastructure development. © 2025 The Authors

关键词： Geospatial analysis Machine learning Remote sensing Sewage treatment Site suitability Urban sustainability

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Double relaxed broad learning system for image classification

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Knowledge-Based Systems 2025年 324卷

作者： Zhenhao Qin Dengxiu Yu Junwei Jin C.L. Philip Chen Unmanned System Research Institute Northwestern Polytechnical University Xi’an 710072 China School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University Xi’an 710072 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China School of Computer Science and Engineering South China University of Technology Guangzhou 510641 China

The Broad Learning System (BLS), as a prominent network paradigm, has garnered significant attention across various domains in machine learning due to its remarkable efficiency and impressive performance. Nevertheless, two deficiencies exist when its variants are applied to supervised image classification tasks. First, they generally rely on rigid binary labels, which restricts the approximation procedure and fails to find the best classification margins. Second, using a single transformation matrix in graph-regularized BLS struggles to mitigate the overfitting problem. In this article, we focus on offering more freedom to the graph regularized BLS to explore appropriate margins between samples. Specifically, we learn the adaptive labels from data, and a marginalized constraint is implemented to enhance the distinguishability of the learned labels simultaneously. Subsequently, two distinct transformation matrices are employed for the graph embedding process, with the addition of a novel matrix designed to capture the similar structure between the transformations. The intra-class compactness of samples can be assured through experimental verification. Hence, a novel method named double relaxed BLS (DRBLS) is proposed. Further, with the help of the alternating direction method of multipliers, an efficient iterative approach is developed to find the closed-form solution. Experiments on diverse image classification tasks are conducted to certify the effectiveness of our method compared to state-of-the-art algorithms.

关键词： Broad learning system Double relaxed Optimization Similar structure Soft labels

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Preparation and Characterization of Micro-Arc Oxidation Biological Coatings on Magnesium Alloys Containing Graphene Oxide

SSRN

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SSRN 2023年

作者： Wang, Jing Fu, Zhanghua Liu, Hao Dou, Jinhe Yu, Huijun Chen, Chuanzhong Shandong Engineering Research Center for Superhard Materials School of Materials Science and Engineering Shandong University Shandong Ji'nan250061 China National Demonstration Center for Experimental Mechanical Engineering Education School of Mechanical Engineering Shandong University Shandong Ji'nan250061 China Shandong Guanyun Technology Co. Ltd. Shandong Ji’nan250003 China College of Artificial Intelligence and Big Data Shandong First Medical University Shandong Academy of Medical Sciences Shandong Ji’nan250117 China

Magnesium (Mg) alloys have been widely investigated as a biodegradable metallic biomaterial. However, the rapid corrosion rate seriously limits their clinical application. The corrosion resistance and biocompatibility of Mg alloys can be improved by micro-arc oxidation (MAO) technology, and can be further improved by introducing graphene oxide (GO) into MAO coatings. The loading of GO provides a better support structure for in-situ nucleation and growth of MAO coating, and also enhances the cell viability through nano effect and reactive oxygen species. The layered microporous structure of GO coating provides a suitable micro-environment for the adhesion and proliferation of osteoblasts. The structures and elements are analyzed by scanning electron microscopy (SEM), X-ray diffractometer (XRD) and Fourier transform infrared spectroscopy (FT-IR). Electrochemical impedance spectroscopy (EIS) and immersion experiments are used to study the degradation properties. CCK-8 assays and cell adhesion are employed for detecting cell viability, proliferation and adhesion. The results show that the embedded GO additive improves the surface roughness, promotes the formation of blocked pores and small size structures. In addition, GO additive can effectively enhance the corrosion resistance and improve the polarization resistance. And G300 sample exhibits excellent cell viability and cell adhesion. © 2023, The Authors. All rights reserved.

关键词： Corrosion resistance

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FenceTalk: Exploring False Negatives in Moving Object Detection

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Algorithms 2023年第10期16卷 481-481页

作者： Lin, Yun-Wei Liu, Yuh-Hwan Lin, Yi-Bing Hong, Jian-Chang The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan Faculty of Technology Management China Medical University Taichung404 Taiwan College of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Biomedical Engineering China Medical University Taichung404 Taiwan Miin Wu School of Computing National Cheng Kung University Tainan701 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

Deep learning models are often trained with a large amount of labeled data to improve the accuracy for moving object detection in new fields. However, the model may not be robust enough due to insufficient training data in the new field, resulting in some moving objects not being successfully detected. Training with data that is not successfully detected by the pre-trained deep learning model can effectively improve the accuracy for the new field, but it is costly to retrieve the image data containing the moving objects from millions of images per day to train the model. Therefore, we propose FenceTalk, a moving object detection system, which compares the difference between the current frame and the background image based on the structural similarity index measure (SSIM). FenceTalk automatically selects suspicious images with moving objects that are not successfully detected by the Yolo model, so that the training data can be selected at a lower labor cost. FenceTalk can effectively define and update the background image in the field, reducing the misjudgment caused by changes in light and shadow, and selecting images containing moving objects with an optimal threshold. Our study has demonstrated its performance and generality using real data from different fields. For example, compared with the pre-trained Yolo model using the MS COCO dataset, the overall recall of FenceTalk increased from 72.36% to 98.39% for the model trained with the data picked out by SSIM. The recall of FenceTalk, combined with Yolo and SSIM, can reach more than 99%. © 2023 by the authors.

关键词： Internet of things

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Jointly Subspace Hashing for Medical Image Retrieval 8

Jointly Subspace Hashing for Medical Image Retrieval

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Xiaoqin Chen, Chen Huang, Jiayu Lu, Linfa Li, Ji Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Chiang Mai University Department of Computer Science Faculty of Science Chiang Mai Thailand Guilin University of Electronic Technology Institute for Artificial Intelligence Interdisciplinary Research Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin China

ISBN: (纸本)9781728192345

The explosive growth of medical images brings about massive amounts of high-dimensional images. The medical image retrieval technique is effective in selecting some similar medical images for medical analysis. A vast majority of existing medical image retrieval methods aim to extract a single feature to represent a medical image. Although these methods have improved the performance, they ignore that different characteristics express different information and they are an indispensable part of content information. Moreover, conventional features consume much storage, which is not conducive to data storage. Therefore, to reduce the loss of information and save storage space, we propose a novel jointly subspace hashing (JSSH) method for medical image retrieval. We solve the high-dimensionality of medical by the simplest image segmentation technology, and then study different subspace projection matrices. Finally, we integrate these matrices into the hash learning model, and build an objective function and learn a series of discriminative binary codes. By conducting comprehensive experiments on three medical image benchmark datasets, we demonstrate the effectiveness of our proposed JSSH. © 2020 IEEE.

关键词： Image segmentation

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Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking

arXiv

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arXiv 2022年

作者： Cui, Yubo Shan, Jiayao Gu, Zuoxu Li, Zhiheng Fang, Zheng The Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China The Science and Technology Near-Surface Detection Laboratory Wuxi214000 China The National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China The The Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China

3D single object tracking is a key task in 3D computer vision. However, the sparsity of point clouds makes it difficult to compute the similarity and locate the object, posing big challenges to the 3D tracker. Previous works tried to solve the problem and improved the tracking performance in some common scenarios, but they usually failed in some extreme sparse scenarios, such as for tracking objects at long distances or partially occluded. To address the above problems, in this paper, we propose a sparse-to-dense and transformer-based framework for 3D single object tracking. First, we transform the 3D sparse points into 3D pillars and then compress them into 2D bird's eye view (BEV) features to have a dense representation. Then, we propose an attention-based encoder to achieve global similarity computation between template and search branches, which could alleviate the influence of sparsity. Meanwhile, the encoder applies the attention on multi-scale features to compensate for the lack of information caused by the sparsity of point cloud and the single scale of features. Finally, we use set-prediction to track the object through a two-stage decoder which also utilizes attention. Extensive experiments show that our method achieves very promising results on the KITTI and NuScenes datasets. Code is available at https://***/3bobo/smat. © 2022, CC BY.

关键词： Signal encoding

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