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RETRACTED: An empirical study on the behavior of e-commerce strategic planning based on deep learning algorithm (Retracted Article)

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INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT 2023年第SUPPL 1期21卷 9-9页

作者： Ren, Xiaocong He, Jun Huang, Zilong Liaoning Univ Sch Econ Shenyang 110036 Liaoning Peoples R China

On the basis of large-scale literature research, the evaluation and element model for the successful implementation of e-commerce are established, and the key elements (customer, strategy, leadership, technology) and the evaluation elements (system quality, system quality, information quality, service quality) affect the success of e-commerce. First, learn the effective features of the items from the content data through deep learning in advance, and then transform the learned features into the learning task of the collaborative filtering target, and add balance and no relevant constraints to the e-commerce strategic planning behavior values of users and items, using alternating optimization algorithms to learn the value of e-commerce strategic planning behavior and fine-tuning the deep network, and finally get the compact and informative e-commerce strategic planning behavior value of users and items, effectively solving the data sparse problem and cold start in the collaborative filtering algorithm problem. Secondly, the combination of conceptual model and structural equation model has innovated research methods and introduced structural equation model method, which effectively handles the complex relationship between multi-dimensional variables and revises and verifies the hypothetical model. Through path analysis, the interaction and influence between key success factors, success evaluation factors, and successful implementation of e-commerce are explored, and useful attempts are made to expand relevant research data analysis methods.

关键词： E-commerce Key success factors Evaluation Structural equation model deep learning algorithm Data sparse

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A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection

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Biomedical Signal Processing and Control 2020年 62卷

作者： Junlong Cheng Shengwei Tian Long Yu Xiang Ma Yan Xing College of Information Science and Engineering Xinjiang University China Key Laboratory of Software Engineering Technology Xinjiang University China College of Software Engineering Xin Jiang University China The First Affiliated Hospital of Xinjiang Medical University China

Aortic dissection (AD) is one of the most common aortic diseases, where blood enters the aortic wall through the aortic intimal rift and causes separation of the arterial wall. Any delay or misdiagnosis can have severe consequences for patients with aortic dissection and even lead to higher mortality rates. Therefore, rapid and accurate detection of aortic dissection saves patients valuable time and provides assistance for the selection of clinical treatment options. This paper describes a deep learning algorithm that uses contrast-enhanced CT images for segmentation and automatic detection of aortic dissection. First, we construct a U-Net based semantic segmentation architecture and apply it to contrast-enhanced CT images to segment the aortic true lumen. Then, we use the segmentation results for aortic circularity analysis to obtain slice-level detection results. Finally, we aggregated the slice-level results to present patient-level detection results. We tested our algorithm on 20 contrast-enhanced CT datasets, of which 10 were aortic dissections. In terms of temporal performance, we have achieved millisecond prediction on sliced images. At the same time, we achieved 85.00% accuracy, 90.00% sensitivity and 80.00% specificity in patient-level testing.

关键词： deep learning algorithm Contrast-enhanced CT images Segmentation and detection of aortic dissection

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Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation

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REPRODUCTIVE BIOMEDICINE ONLINE 2023年第6期47卷 103408-103408页

作者： Ahlstrom, Aisling Berntsen, Jorgen Johansen, Martin Bergh, Christina Cimadomo, Danilo Hardarson, Thorir Lundin, Kersti LIVIO Gothenburg IVIRMA Global Res Alliance Gothenburg Sweden UNSILO AS Aarhus Denmark Sahlgrens Univ Hosp Reprod Med Gothenburg Sweden GENERA Clin Valle Giulia IVIRMA Global Res Alliance Rome Italy Vitrolife Sweden AB Gothenburg Sweden

Research question: Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0?Design: Retrospective observational study (n = 5040 embryos;1786 treatments) conducted at two Swedish assisted reproductive technology centres between 2016 and 2021. Fresh single embryo transfer was carried out on days 2 or 3 after fertilization. Embryo evaluation using iDAScore v2.0 was compared with manual assessment of numbers of cells and grade of fragmentation, analysed by video ***: Data from embryos transferred on days 2 and 3 showed that having three or fewer cells compared with four or fewer cells on day 2, and six or fewer cells versus seven to eight cells on day 3, correlated significantly with a difference in iDAScore (medians 2.4 versus 4.0 and 2.6 versus 4.6 respectively;both P < 0.001). The iDAScore for 0-10% fragmentation was significantly higher compared with the groups with higher fragmentation (P < 0.001). When combining cell numbers and fragmentation, iDAScore values decreased as fragmentation increased, regardless of cell number. iDAScore discriminated between embryos that resulted in live birth or no live birth (AUC of 0.627 and 0.607), compared with the morphological model (AUC of 0.618 and 0.585) for day 2 and day 3, respectively. Conclusions: The iDAScore v2.0 values correlated significantly with cell numbers and fragmentation scored manually for cleavage-stage embryos on days 2 and 3. iDAScore had some predictive value for live birth, conditional that embryo selection was based on morphology.

关键词： deep learning algorithm live birth morphology iDAScore embryo selection time-lapse

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An efficient AI algorithm for fault diagnosis in nuclear power plants based on machine deep learning techniques

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PROGRESS IN NUCLEAR ENERGY 2025年 180卷

作者： Elbordany, Ayman A. Kandil, Magy M. Youness, Hassan A. Abdelaal, Hammam M. Minia Univ Fac Engn Comp & Syst Engn Dept Al Minya Egypt Egyptian Atom Energy Author EAEA Cairo Egypt Luxor Univ Fac Comp & Informat Informat Technol Dept Luxor Egypt

Artificial intelligence (AI) techniques and human intelligence are inextricably linked when it comes to operating Nuclear Power Plants (NPPs) with cutting-edge control rooms that have humans acting as actors. Using AI in fault diagnosis and prediction of accidents is valuable and crucial at NPPs. This research proposes an AI fault diagnosis algorithm for NPPs that uses AI techniques to integrate cutting-edge machine learning (ML) algorithms such as Support Vector Machine, Random Forest, k-Nearest Neighbors, Light Gradient Boosting Machine, and Long Short-Term Memory as deep learning (DL) algorithm to improve decision-making of operators to reduce the human errors in NPPs for increasing their safety. The proposed algorithm seeks to fault diagnosis by predicting and identifying normal and abnormal operations for Turbine trip with Anticipated Transient Without Scram (ATWS), Loss of Flow Accident (LOFA) and Loss of Coolant Accident (LOCA) with different break sizes (1,10,20,30,40,50,60,70,80,90,100 cm2) at hot leg. The dataset is pre-processed and enhanced to get complete and suitable inputs of the used ML and DL algorithms. The results show that the proposed AI fault diagnosis algorithm is effectively capable of anticipating and identifying risk according to predetermined cases since the accuracy is 98%. Moreover, validation of the proposed fault diagnosis algorithm with the previous model results also shows that the outputs are within range.

关键词： Nuclear power plants Loss of coolant accident Neural networks Machine learning algorithms deep learning algorithm Fault diagnosis

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Infrared thermal radiation and deep learning algorithms for evaluating the warm-up effect of sports training: Thermal imaging monitoring model☆

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 58卷

作者： Liu, Yumeng Li, Yunlong Liang, Danqing Li, Cheng Wu, Chuanzhong Jiangsu Univ Sci & Technol Coll Phys Educ Zhenjiang 212100 Peoples R China Shanghai Int Studies Univ Xianda Coll Econ & Humanities Shanghai 202162 Peoples R China Natl Univ Phys Educ & Sports Ukraine UA-02000 Kiev Ukraine

Warm-up activity is an important link to prevent sports injury and improve sports performance. The traditional evaluation methods of warm-up effect mainly rely on the experience judgment of coaches and the subjective feelings of athletes, and lack of objective and quantitative evaluation means. The purpose of this study is to develop an evaluation system based on infrared thermal imaging monitoring model, which can monitor and evaluate the warm-up effect of athletes in real time. The basic principle and acquisition process of infrared thermal imaging technology are introduced. The real-time temperature distribution images of athletes during the warm-up process are obtained by using high-precision infrared thermal imager. The data pre-processing steps include image normalization, enhancement and feature extraction, etc., to ensure that the data quality meets the requirements of model training. In the model training stage, the way of supervised learning is used to train and verify the deep learning model by using the labeled thermal imaging data set. Finally, the performance of the model is evaluated by cross-validation and other methods to ensure that it has good generalization ability. The results show the performance of thermal imaging monitoring model in practical application. The experimental results show that the model can accurately identify the athletes who are under-warmed or over-warmed, and give timely adjustment suggestions. The model can also predict the possible muscle fatigue and potential injury risk during the warm-up process, which provides a scientific basis for coaches to make personalized training plans.

关键词： Infrared thermal radiation deep learning algorithm Physical training Evaluation of warm-up effect Thermal imaging monitoring model

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and deep learning algorithms for Neck Movement Monitoring

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ACS APPLIED MATERIALS & INTERFACES 2025年第8期17卷 12862-12874页

作者： Qu, Bingbing Mou, Qirui Zhou, Zelong Xie, Yiyuan Li, Yudong Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Xian Shaangu Power CO LTD Xian 710075 Peoples R China

Prolonged use of digital devices and sedentary lifestyles have led to an increase in the prevalence of cervical spondylosis among young people, highlighting the urgent need for preventive measures. Recent advancements in triboelectric nanogenerators (TENGs) have shown their potential as self-powered sensors. In this study, we introduce a novel, flexible, and stretchable TENG for neck movement detection. The proposed TENG utilizes a glycerol/poly(vinyl alcohol) (GL/PVA) hydrogel and silicone rubber (GH-TENG). Through optimization of its concentration and thickness parameters and the use of environmentally friendly dopants, the sensitivity of the GH-TENG was improved to 4.50 V/kPa. Subsequently, we developed a smart neck ring with the proposed sensor for human neck movement monitoring. By leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) algorithm, sensor data can be efficiently analyzed in both spatial and temporal dimensions, achieving a promising recognition accuracy of 97.14%. Additionally, we developed a neck motion monitoring system capable of accurately identifying and recording neck movements. The system can timely alert users if they maintain the same neck posture for more than 30 min and provide corresponding recommendations. By deployment on a Raspberry Pi 4B, the system offers a portable and efficient solution for cervical health protection.

关键词： glycerol/PVA hydrogel electrode self-powered triboelectricsensor deep learning algorithm smart neck ring neck movements monitoring system

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Accuracy and robustness evaluation of deep learning algorithms in facial recognition systems

SYSTEMS AND SOFT COMPUTING

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SYSTEMS AND SOFT COMPUTING 2025年 7卷

作者： Zhang, Jing Hu, Ningyu Xinzhou Normal Univ Comp Dept Xinzhou 034000 Peoples R China

To solve the high cost and low accuracy in facial recognition system, a facial recognition system based on deep learning algorithm is designed in this paper. First, the YOLO model is improved by introducing the EfficientNet to enhance the performance of the facial detection model. Second, a feature extraction model based on the loss function of the improved FaceNet is constructed. In the medium test dataset validation, the proposed facial detection model improved the detection accuracy by an average of 26.30 % compared with the YOLOv3 series models. The LFW dataset validation showed that the model achieved 99.54 % accuracy after 90,000 iterations, which was 1.59 % higher than the average of other models. In the mixed dataset, the proposed facial recognition system improved the accuracy by 4.76 % and 8.64 % compared with the existing mainstream systems, respectively. The system shows strong robustness in diverse scenarios with different skin colors, ages, facial occlusions, and expressions. The designed facial detection method has high detection efficiency, and the feature extraction model has superior recognition results. The system can provide real-time recognition in complex scenes such as facial occlusion, meeting real-time requirements.

关键词： deep learning algorithm Facial recognition technology Robustness Facial detection Feature extraction

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Automatic recognition of different 3D soliton wave types using deep learning methods

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NONLINEAR DYNAMICS 2025年第2期113卷 1577-1590页

作者： Aksoy, Abdullah Yigit, Enes Bursa Uludag Univ Fac Engn Dept Elect Elect Engn Bursa Turkiye

In this study, deep learning (DL) techniques are used for automatic recognition of soliton wave types. Accurate characterization of soliton wave species has the potential to improve their precise and effective use in various fields such as optics, electronics, telecommunications. In addition, the accuracy of the results obtained in the equation solutions will be demonstrated due to the determined wave type. Therefore, soliton analyses were performed at the beginning of the study using equations such as Korteweg-de Vries and nonlinear Schrodinger. These analyses led to the creation of 3D visual representations for eight distinct soliton types, including breather, kink, anti-kink, cusp, loop, lump, multi-peak, and rogue soliton. Following the generation of these images, we proceeded with a rigorous labeling process to prepare the data for the subsequent deep-learning phase. For this phase, we explored the performance of three prominent DL architectures: ResNet50V2, InceptionV3, and DenseNet169. Each architecture underwent separate training, validation, and testing procedures. Among these architectures, ResNet50V2 emerged as the top performer, consistently achieving high accuracies throughout the training, validation, and testing stages. Specifically, ResNet50V2 achieved training, validation, and testing accuracies of 0.9979, 1.00, and 1.00, respectively. Additionally, precision, recall, f1-score, weighted average, and macro average metrics all demonstrated perfect scores of 1.00. After completing the model training and evaluation process, we further assessed the model's performance by testing it on diverse 3D images, all of which resulted in predictions with 100% accuracy. Subsequently, we applied the ResNet50V2 architecture to test datasets representing six distinct soliton types documented in existing literature, successfully achieving accurate predictions for all instances. Through experiments conducted using both internally generated dataset pools and literature-d

关键词： Soliton wave deep learning algorithm Soliton wave types Soliton equations CNNs

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Application of deep learning algorithms in the Design of Interactive Chinese Corpus System

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Procedia Computer Science 2025年 261卷 336-344页

作者： Yuee Yang Tieling Normal College Tieling 112000 Liaoning China

With the rapid growth of AI technology, deep learning algorithms have made remarkable achievements in the field of natural language processing, especially showing great potential in processing Chinese, the world’s most widely spoken language. The design and growth of Chinese corpus systems are of vital importance to promoting Chinese teaching, research and application. However, traditional corpus systems lack interactivity and are unable to meet users’ urgent needs for instant feedback and personalized learning. To address this challenge, this paper explores the application of deep learning algorithms in the design of an interactive Chinese corpus system to improve the system’s interactive performance and user experience. This paper first outlines the basic concepts of deep learning algorithms and interactive corpus systems, and then analyzes the limitations and shortcomings of current research. On this basis, this paper elaborates in detail the design logic, implementation path and experimental verification results of the proposed interactive Chinese corpus system based on deep learning. Experimental data show that the system can greatly improve the user’s interactive operation experience in the process of learning Chinese, and its operation time is between 0-2s, bringing users a more efficient new learning experience. At the same time, this paper also conducts an in-depth discussion of the experimental results, points out the limitations of the system, and proposes future improvement strategies and growth directions.

关键词： deep learning algorithm Interactive Corpus System Chinese Teaching Natural Language Processing

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Feature extraction and quantitative analysis of steel corrosion in reinforced concrete components based on XCT scanning and deep learning model

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JOURNAL OF BUILDING ENGINEERING 2025年 106卷

作者： Miao, Xu Wang, Yuzhou Peng, Ligang Zhao, Yuxi Zhejiang Univ Inst Struct Engn Hangzhou 310058 Zhejiang Peoples R China

Accurate quantification and analysis of steel corrosion is crucial for reliability assessment studies of in-service reinforced concrete structures. However, the pixel-level cross-sectional data provided by X-ray computed tomography (XCT) proves difficult to quantify, especially for the amorphous corrosion products filled in mortar, due to the absence of robust feature extraction methods. In this study, multiple deep learning models were trained to automatically identify corrosion products and calculate corrosion-related parameters from a large number of XCT images. The database comprised XCT images obtained from a RC component subjected to chloriderich environment for four years. The results indicate that deep learning models can segment different regions of XCT images with high accuracy. Among the models, the K-Net model performed the best on this dataset, achieving an accuracy of 94.60 %, and a mean Precision (mPrecision) of 88.21 %. This advance makes it possible to automatically extract parameters that characterize steel corrosion and to assess the damage to RC structures caused by corrosion.

关键词： Steel corrosion Reinforced concrete XCT deep learning algorithm Semantic segmentation

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