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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Yang, Jie Hubei Light Industry Technology Institute Wuhan China

ISBN: (纸本)9798331527624

This paper studies the design of building energy consumption prediction and optimization control system using deep learning algorithm. First, this paper uses deep learning algorithms, especially recurrent neural networks (RNN), to capture the cyclic characteristics of building energy consumption data. RNNS and their variants Long Short-Term Memory Networks (LSTMS) perform well in processing time series data and can efficiently extract and utilize useful information from historical energy consumption data. A complete building energy consumption forecasting and control system is designed. In order to further improve the prediction accuracy, this paper also introduces the spectrum analysis method, through the frequency domain characteristics of energy consumption data to extract more hidden patterns and correlation information. In order to verify the effectiveness and reliability of the system, detailed simulation experiments are carried out in this paper. The experimental results show that the system has excellent performance in both prediction accuracy and control effect, and can significantly reduce building energy consumption and improve energy utilization efficiency. The design scheme of building energy consumption prediction and optimization control system based on deep learning algorithm proposed in this paper provides a new technical support and solution for realizing building energy saving and emission reduction. © 2024 IEEE.

关键词： cyclic characteristics deep learning algorithm design of building energy consumption prediction and control system spectrum analysis method system simulation

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Self-Trained Robust Adaptive Network Communication (STRANC) Optimization Model Based on deep learning algorithm

Self-Trained Robust Adaptive Network Communication (STRANC) ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Lang, Weiwei Beijing Information Technology College Department of Electronic Information Beijing100070 China

ISBN: (纸本)9798331509828

With the advent of the information age and the continuous development and application of network communications, some nodes or links may be overloaded in high-frequency and high-concurrency network communications, resulting in increased latency, increased packet loss, and other problems. To face with this challenge, this study proposes the novel self-trained robust adaptive network communication optimization model based on the deep learning algorithm. The proposed model is a 3-step approach, and the first step is link communication quality detection model. In order to effectively detect the communication quality of network links, a deep learning algorithm is introduced to establish the network link communication model. Then, network communication error data repair optimization is studied. This model improves the output stability and environmental adaptability of the network communication by adaptively repairing and estimating communication error data. Finally, dynamic network communication resource allocation optimization is proposed. Communication Resource Allocation (CRA) optimization is studied from the perspective of the communication load adaptive balancing. Experiments show that compared with traditional neural network and FCM models, the proposed model has an improved repair accuracy of about 15%. At the same time, the proposed model can effectively reduce communication delay in a test environment with 500 nodes and 200 communication links. © 2025 IEEE.

关键词： Adaptive Network Communication deep learning algorithm Optimization Model Self-trained Model

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Prediction of corneal back surface power - deep learning algorithm versus multivariate regression

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OPHTHALMIC AND PHYSIOLOGICAL OPTICS 2022年第1期42卷 185-194页

作者： Langenbucher, Achim Szentmary, Nora Cayless, Alan Weisensee, Johannes Wendelstein, Jascha Hoffmann, Peter Saarland Univ Dept Expt Ophthalmol Homburg Saar Germany Saarland Univ Dr Rolf M Schwiete Ctr Limbal Stem Cell & Aniridi Homburg Saar Germany Semmelweis Univ Dept Ophthalmol Budapest Hungary Open Univ Sch Phys Sci Milton Keynes Bucks England Johannes Kepler Univ Linz Dept Ophthalmol Linz Austria Augen & Laserklin Castrop Rauxel Castrop Rauxel Germany

Background The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. This study aimed to set up and validate a deep learning algorithm to predict corneal back surface power from the corneal front surface power and biometric measures. Methods This study was based on a large dataset of IOLMaster 700 measurements from two clinical centres. N = 19,553 measurements of 19,553 eyes with valid corneal front (CFSPM) and back surface power (CBSPM) data and other biometric measures. After a vector decomposition of CFSPM and CBSPM into equivalent power and projections of astigmatism to the 0 degrees/90 degrees and 45 degrees/135 degrees axes, a multi-output feedforward neural network was derived to predict vector components of CBSPM from CFSPM and other measurements. The predictions were compared with a multivariate linear regression model based on CFSPM components only. Results After pre-conditioning, a network with two hidden layers each having 12 neurons was derived. The dataset was split into training (70%), validation (15%) and test (15%) subsets. The prediction error (predicted corneal back surface power CBSPP - CBSPM) of the network after training and crossvalidation showed no systematic offset, narrower distributions for CBSPP - CBSPM and no trend error of CBSPP - CBSPM vs. CBSPM for any of the vector components. The multivariate linear model also showed no systematic offset, but broader distributions of the prediction error components and a systematic trend of all vector components vs. CFSPM components. Conclusion The neural network approach based on CFSPM vector components and other biometric measures outperforms the multivariate linear model in predicting corneal back surface power vector components. Modern biometers can supply all parameters required for this algorithm, enabling reliable predictions for corneal back surface data where direct corneal back surface data are unavailabl

关键词： biometry corneal back surface power deep learning algorithm feedforward multi-output network neural network posterior corneal astigmatism

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deep learning algorithm Aided E-Commerce Logistics Node Layout Optimization Based on Internet of Things Network

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第4期10卷 1-13页

作者： Li, Lifeng Shenyang Polytechn Coll Shenyang 110045 Liaoning Peoples R China

INTRODUCTION: In recent years, e-commerce has shown a booming trend. Influenced by e-commerce, people's logistics needs have also increased sharply in recent years. OBJECTIVES: Research on the node layout and optimization of e-commerce logistics is conducive to improving the scientificity and rationality of logistics node layout, improving logistics distribution efficiency, reducing logistics distribution costs, and better meeting consumers' logistics needs. However, due to the unreasonable layout of logistics nodes in some areas, it has brought huge logistics cost investment to e-commerce companies, and also laid hidden dangers for the long-term development of e-commerce companies. METHODS: Based on this, this paper studied the node layout and optimization of e-commerce logistics by using IoT big data and deep learning algorithms, and proposed an improved logistics node layout scheme based on IoT big data and deep learning algorithms. The experimental research was carried out from five aspects: the transportation cost of logistics, the efficiency of logistics distribution, the accuracy of logistics information transmission, the location and traffic conditions of logistics nodes, and the evaluation of the plan by e-commerce enterprises. RESULTS: The research results showed that the improved logistics node layout scheme can improve the efficiency of logistics distribution by 3.69% and the accuracy of logistics information transmission by 4.34%, and can reduce the logistics transportation cost of e-commerce enterprises. CONCLUSION: The node locations selected by the improved logistics node layout scheme are more reasonable, and e-commerce companies have higher evaluations of the improved logistics node layout scheme.

关键词： IoT Big Data deep learning algorithm E-commerce Logistics Logistics Node Layout and Optimization Improved Logistics Node Layout Scheme

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External validation of a commercially available deep learning algorithm for fracture detection in children

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DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022年第3期103卷 151-159页

作者： Dupuis, Michel Delbos, Leo Veil, Raphael Adamsbaum, Catherine Bicetre Hosp AP HP Pediat Imaging Dept F-94270 Le Kremlin Bicetre France Bicetre Hosp AP HP Epidemiol & Publ Hlth Dept F-94270 Le Kremlin Bicetre France Paris Saclay Univ Fac Med F-94270 Le Kremlin Bicetre France

Purpose: The purpose of this study was to conduct an external validation of a fracture assessment deep learning algorithm (Rayvolve (R)) using digital radiographs from a real-life cohort of children presenting routinely to the emergency room. Materials and methods: This retrospective study was conducted on 2634 radiography sets (5865 images) from 2549 children (1459 boys, 1090 girls;mean age, 8.5 +/- 4.5 [SD] years;age range: 0-17 years) referred by the pediatric emergency room for trauma. For each set was recorded whether one or more fractures were found, the number of fractures, and their location found by the senior radiologists and the algorithm. Using the senior radiologist diagnosis as the standard of reference, the diagnostic performance of deep learning algorithm (Rayvolve (R)) was calculated via three different approaches: a detection approach (presence/absence of a fracture as a binary variable), an enumeration approach (exact number of fractures detected) and a localization approach (focusing on whether the detected fractures were correctly localized). Subgroup analyses were performed according to the presence of a cast or not, age category (0-4 vs. 5-18 years) and anatomical region. Results: Regarding detection approach, the deep learning algorithm yielded 95.7% sensitivity (95% CI: 94.0-96.9), 91.2% specificity (95% CI: 89.8-92.5) and 92.6% accuracy (95% CI: 91.5-93.6). Regarding enumeration and localization approaches, the deep learning algorithm yielded 94.1% sensitivity (95% CI: 92.1-95.6), 88.8% specificity (95% CI: 87.3-90.2) and 90.4% accuracy (95% CI: 89.2-91.5) for both approaches. Regarding age-related subgroup analyses, the deep learning algorithm yielded greater sensitivity and negative predictive value in the 5-18-years age group than in the 0-4-years age group for the detection approach (P < 0.001 and P = 0.002) and for the enumeration and localization approaches (P = 0.012 and P = 0.028). The high negative predictive value was robust, pers

关键词： Radiographs deep learning algorithm Artificial intelligence Fractures Pediatric

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Views of deep learning algorithm Applied to Computer Vision Knowledge Discovery

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2022年第6期45卷 561-566页

作者： Jeyaraj, Pandia Rajan Asokan, Siva Prakash Kathiresan, Aravind Chellachi Mepco Schlenk Engn Coll Autonomous Dept Elect & Elect Engn Sivakasi India

During the last few decades, the development of deep learning algorithms for obtaining better performance, accurate classification, extracting valuable information has gained much attention. In recent years, many deep learning architecture methods have been proposed. The main contribution of this research view is to fill the gap between each research by comparing and summarizing the analysis and research findings of reputed deep learning papers. We analysed 20 representative papers published in various reputed publishers like Springer, Elsevier, Nature, IEEE, Sage, Wiley, etc., for a past period of 5 years (June 2016-2021). Also, in this view, we compared the deep learning algorithm used, their architecture, research problem, evaluation metrics, hardware specification, and conclusions. Based on this view, we pointed out future scope and challenges in data analysis research using deep learning algorithms towards computer vision.

关键词： deep learning algorithm Computer vision Pattern recognition Data analysis Feature extraction

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Big data network security defense mode of deep learning algorithm

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OPEN COMPUTER SCIENCE 2022年第1期12卷 345-356页

作者： Yu, Yingle NYU Tandon Sch Engn Brooklyn NY 11201 USA

With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.

关键词： deep learning algorithm big data network security defense mode

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A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study

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JOURNAL OF DIABETES 2022年第2期14卷 111-120页

作者： Li, Na Ma, Mingming Lai, Mengyu Gu, Liping Kang, Mei Wang, Zilong Jiao, Shengyin Dang, Kang Deng, Junxiao Ding, Xiaowei Zhen, Qin Zhang, Aifang Shen, Tingting Zheng, Zhi Wang, Yufan Peng, Yongde Shanghai Jiao Tong Univ Shanghai Gen Hosp Dept Endocrinol & Metab 100 Haining Rd Shanghai 200080 Peoples R China Shanghai Gen Hosp Shanghai Engn Ctr Precise Diag & Treatment Eye Di Shanghai Key Lab Ocular Fundus DisNatl Clin Res Dept OphthalmolShanghai Engn Ctr Visual Sci & Ph 100 Haining Rd Shanghai 200080 Peoples R China Shanghai Jiao Tong Univ Shanghai Gen Hosp Clin Res Ctr Shanghai Peoples R China VoxelCloud Shanghai Peoples R China

Background The aim of our research was to prospectively explore the clinical value of a deep learning algorithm (DLA) to detect referable diabetic retinopathy (DR) in different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, glycosylated hemoglobin (HbA1c), diabetes duration, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) at a real-world diabetes center in China. Methods A total of 1147 diabetic patients from Shanghai General Hospital were recruited from October 2018 to August 2019. Retinal fundus images were graded by the DLA, and the detection of referable DR (moderate nonproliferative DR or worse) was compared with a reference standard generated by one certified retinal specialist with more than 12 years of experience. The performance of DLA across different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, HbA1c, diabetes duration, UACR, and eGFR was evaluated. Results For all 1674 gradable images, the area under the receiver operating curve, sensitivity, and specificity of the DLA for referable DR were 0.942 (95% CI, 0.920-0.964), 85.1% (95% CI, 83.4%-86.8%), and 95.6% (95% CI, 94.6%-96.6%), respectively. The DLA showed consistent performance across most subgroups, while it showed superior performance in the subgroups of patients with type 1 diabetes, UACR >= 30 mg/g, and eGFR < 90 mL/min/1.73m(2). Conclusions This study showed that the DLA was a reliable alternative method for the detection of referable DR and performed superior in patients with type 1 diabetes and diabetic nephropathy who were prone to DR.

关键词： deep learning algorithm diabetic retinopathy referable DR retinal fundus images

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deep learning algorithm for breast masses classification in mammograms

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IET IMAGE PROCESSING 2020年第12期14卷 2860-2868页

作者： Gnanasekaran, Vaira Suganthi Joypaul, Sutha Meenakshi Sundaram, Parvathy Chairman, Durga Devi Sethu Inst Technol Kariapatti Tamil Nadu India AAA Coll Engn & Technol Sivakasi Tamil Nadu India Madurai Med Coll Madurai Tamil Nadu India

A mammogram is an image of a breast used to detect and diagnose breast cancer. This paper emphases a Computer-Aided Detection system based on convolutional neural network (CNN) that uses the concept of deep learning to classify the mammogram images into benign, malignant and normal. The proposed CNN model consists of eight convolutional, four max-pooling and two fully connected layers and achieved better results compared to the pre-trained nets, AlexNet and VGG16. The proposed model demonstrates the feasibility of using CNNs on medical image processing techniques for the classification of breast masses. The results are also compared with the state-of-the-art machine learning algorithm like kNN classifier. Experimentation is done with three datasets. Among them, two are publicly available, Mammographic Image Analysis Society (MIAS), digital database for screening mammography (DDSM) and an internally collected dataset. The proposed model achieved accuracies of 92.54, 96.47 and 95 and the Area under the ROC curve (AUC) score of 0.85, 0.96 and 0.94 for MIAS, DDSM and the internally collected dataset respectively. Furthermore, the images of the three datasets are merged to build one large set and used to fine tune the proposed CNN model and produced accuracy of 98.32 and AUC of 0.98.

关键词： mammography neural nets image classification cancer learning (artificial intelligence) medical image processing max-pooling fully connected layers pre-trained nets medical image processing techniques kNN classifier Mammographic Image Analysis Society internally collected dataset CNN model deep learning algorithm breast masses classification breast cancer Computer-Aided Detection system convolutional neural network mammogram images automatic classification

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Intelligent detection of hidden distresses in asphalt pavement based on GPR and deep learning algorithm

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CONSTRUCTION AND BUILDING MATERIALS 2024年 416卷

作者： Liu, Wenchao Luo, Rong Xiao, Manzhe Chen, Yu Shandong High Speed Engn Inspection Co Ltd Jinan Shandong Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Peoples R China Hubei Highway Engn Res Ctr Wuhan Peoples R China

Hidden distresses of asphalt pavement are hidden dangers in the service of expressways. Efficient and accurate detection of hidden distresses is of great importance for maintaining and managing the expressways suffering from them. In this study, the intelligent detection technology of asphalt pavement hidden distresses was studied using the object detection algorithm and ground penetrating radar (GPR) image data. A GPR image data set was established to train and test the object detection model. And the distresses samples in the data set include settlement, hidden cracks and loosening. According to the distribution of hidden distresses in GPR image data, a two-way object detection model for targets of different scales was proposed, and each part of the two-way model was optimized and improved based on the basic model. At the same time, a copy-and-paste data enhancement scheme was designed to expand and balance the number of distress data samples. The final model greatly improved the detection accuracy of asphalt pavement hidden distresses. The results of the model test showed that the two-way object detection model proposed in this paper can effectively improve the detection accuracy of small-sized distress objects. Compared to the basic model, the final model had an increase of 17.9% in the average precision (AP) of small objects and a 9.9% increase in the comprehensive index AP (0.5: 0.95). The results of the ablation experiment show that the optimization methods for each part of the model improved the comprehensive detection effect of the model. When compared with other object detection models, the final model was superior to other models in all AP indicators, and had obvious advantages in the detection accuracy of the three types of hidden distresses. The confusion matrix of the test results of the final model showed that the recall rate of the final model for the three types of hidden distresses was above 96%. Thus, applying the final model established in this s

关键词： Asphalt pavement Hidden distress Ground penetrating radar (GPR) deep learning algorithm Object detection

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