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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Zhang, Yadong Zheng, Yujuan Yin, Chen Sun, Lixia Shandong Vocat Univ Foreign Affairs Sch Informat Engn Weihai 264504 Shandong Peoples R China Shandong Drug & Food Vocat Coll Informat Ctr Weihai 264210 Shandong Peoples R China

ISBN: (纸本)9798400709784

With the increase of campus education scale and the improvement of work quality, the demand for school safety work is also increasing. In the process of school safety construction, there are many problems such as weak student safety awareness, need to strengthen safety management, and incomplete systems. This article applies the concept of deep learning (DL) to the analysis and prevention of safety accident modes, based on DL algorithms, with the goal of achieving target tasks within the monitoring area and detecting their motion status. In the motion target trajectory prediction module, the motion target trajectory prediction module is based on the principle of DL algorithms to monitor the surrounding environment of the monitored person in real-time, obtain information such as the position, speed, and direction of the monitored person, and predict their movement direction information. The accuracy of classification tasks includes the accuracy of student behavior recognition, teacher behavior recognition, and item leaving recognition. The accuracy of behavior recognition for test students is 97.8%. The accuracy rate of teacher behavior recognition is 96.3%, and the accuracy rate of item leaving identification is 95.6%. This article helps to improve the level of campus safety management.

关键词： Intelligent Campus Security Monitoring Security Management deep learning algorithms Recognition Accuracy

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Electrocardiogram Classification Problem Solving using deep learning algorithms Fully connected Neural Networks 13

Electrocardiogram Classification Problem Solving using Deep ...

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13th International Conference on Information and Communication Systems (ICICS)

作者： Gharaibeh, Anwaar Quwaider, Muhannad Jordan Univ Sci & Technol Dept Comp Engn Irbid Jordan

ISBN: (纸本)9781665480970

Heart Electrocardiogram is an example of physiological instruments. It can be extracted from some natural sensors with high accuracy. In simple, Biomedical Signal processing is the process that extracts the useful information and analyses the signals. deep learning algorithms is the most common way that can be used to predict the correct useful information. In this paper we are interested in Electrocardiogram classification problem using deep learning algorithms. In addition, we are going to presented a comparison study of the present ECG databases in one dimension. In this paper we will present a state-of-the-art deep learning frameworks for one dimensional Electrocardiogram. The collected data using fully connected neural network is trained using standard database. Then, we studied three parameters for fully connected neural network, the training and validation iteration and the validation accuracy. Then, we compared several trials to find the most possible optimal solution with the highest accuracy. The result was enhanced the training accuracy about 1.39% to reach 100%. While, the max enhanced value of validation accuracy of 0.89%.

关键词： deep learning algorithms ECG databases data analysis classification supervised learning

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deep learning algorithms SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS

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JOURNAL OF EVIDENCE-BASED DENTAL PRACTICE 2022年第4期22卷 101772-101772页

作者： Hegde, Shwetha Gao, Jinlong Univ Sydney Sydney Dent Sch Dentomaxillofacial Radiol Chalmers St Surry Hills NSW 2010 Australia Univ Sydney Fac Med & Hlth Sydney Dent Sch Chalmers St Surry Hills NSW 2010 Australia

Article Title and Bibliographic Information Mohammad-Rahimi H, Reza Motamedian S, Hossein Rohban M, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F, deep learning for caries detection: A systemat... 详细信息

关键词： deep learning algorithms Dental caries Dental images Diagnosis Accuracy Sensitivity Specificity

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Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

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Chinese Medical Journal 2021年第15期134卷 1828-1837页

作者： Tai-Hui Xia Man Tan Jing-Hua Li Jing-Jing Wang Qing-Qing Wu De-Xing Kong Department of Ultrasound Beijing Obstetrics and Gynecology HospitalCapital Medical UniversityBeijing 100026China The School of Mathematical Sciences Zhejiang UniversityHangzhouZhejiang 310058China

Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is *** study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing ***:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this *** were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory *** images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 *** were 3323,2142,and 1548 images in each class,***,we performed a pre-processing algorithm to remove irrelevant information from each ***,a convolutional neural network was designed to identify different categories of fetal lung ultrasound ***,we used ten-fold cross-validation to validate the performance of our *** new machine learning algorithm automatically extracted and classified lung ultrasound image information related to *** was used to establish a grading *** performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic ***:A normal fetal lung GA grading model was established and *** sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,*** specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,*** total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,*** micro-average AUC was 0.957,and the macro-average AUC was ***:The normal fetal lung GA grading model could accurately identify ultraso

关键词： Convolutional neural network deep learning algorithms Grading model Normal fetal lung Fetal lung maturity Gestational age Artificial intelligence

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Highly Dense FBG Temperature Sensor Assisted with deep learning algorithms

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SENSORS 2021年第18期21卷 6188-6188页

作者： Kokhanovskiy, Alexey Shabalov, Nikita Dostovalov, Alexandr Wolf, Alexey Novosibirsk State Univ 1 Pirogova St Novosibirsk 630090 Russia RAS SB Inst Automat & Electrometry 1 Acad Koptyug Ave Novosibirsk 630090 Russia

In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500-1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

关键词： fiber bragg grating optical fiber sensor distributed temperature sensor deep learning algorithms fully connected neural network convolutional neural network

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Federated recommender systems based on deep learning: The experimental comparisons of deep learning algorithms and federated learning aggregation strategies

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Liu, Yang Lin, Tao Ye, Xin Dalian Univ Technol Sch Econ & Management Dalian 116024 Peoples R China Dalian Univ Technol Inst Adv IOlligence Dalian 116024 Peoples R China

Due to the requirements of privacy protection and data asset ownership, recommender systems (RSs) based on centralized training process may be infeasible in some practical situations. In these situations, federated recommender systems (FedRec) based on deep learning are regarded as the substitutes with extensive application backgrounds and enormous potential business values. However, which deep learning algorithm and federated learning aggregation strategy perform best in the training of FedRec is still an unsolved question. To answer this question, experiments are conducted for comparing the performances of four popular deep learning algorithms and three federated learning aggregation strategies in FedRec. The experiments are conducted on 8 public datasets to obtain 96 FedRec precisions (4 deep learning algorithms x 3 federated learning aggregation strategies x 8 datasets). Then, the Wilcoxon test is used to verify whether there is a significant difference between the FedRec combinations and to derive the best combination. The result shows that, in terms of recommendation precision, the best FedRec is the combination of neural FM and federated averaging. In terms of execution iterations, the best FedRec is the combination of deep crossing and attentive federated aggregation. The obtained results would be valuable for further promoting the development of RSs under the situations that a centralized training process is infeasible.

关键词： Federated recommender system Experimental comparison deep learning algorithms Federated learning aggregation strategies

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Damages Detection of Aeroengine Blades via deep learning algorithms

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Li, Shuangbao Yu, Jingyi Wang, Hao Civil Aviat Univ China Res Inst Sci & Technol Tianjin 300300 Peoples R China Civil Aviat Univ China Coll Aeronaut Engn Tianjin 300300 Peoples R China Civil Aviat Univ China Engn Technol Training Ctr Tianjin 300300 Peoples R China

To solve the problem of detecting the damages of aeroengine blades in harsh environments and reduce the aviation safety hazards caused by visual reasons, such as careless observation and delayed reporting of blade damages, the detection model of damages for aeroengine blades via deep learning algorithms is proposed in this article. First, the gamma correction method is used to process the dataset captured by the borescope to enhance the characterization ability. Second, the improved convolutional block attention module (CBAM) is embedded into the head and the end of backbone network of the YOLOv7 model. Meanwhile, a branch is added to the channel attention module of CBAM to optimize its network structure. Finally, in order to improve the accuracy and convergence speed, complete intersection over union (CIOU) is replaced by Alpha - GIOU as a coordinate loss function in the YOLOv7 model, and a new flowchart of detection for aeroengine blade damages is proposed. Detection experiment results demonstrate that the mean average precision (mAP) of the improved YOLOv7 model in this article is 96.1%, which is 1.0% higher than the original model. The improved YOLOv7 module has remarkable effects compared with YOLOv5s, YOLOv4, single shot multibox detector (SSD), and Faster region-convolutional neural network (R-CNN) models. Meanwhile, the improved YOLOv7 model has better generalization performance, which provides a more reliable support for the real-time and visualization of damages detection of aeroengine blades.

关键词： Aeroengine blades convolutional block attention module (CBAM) damages detection deep learning algorithms YOLOv7

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The impact of step sizes on pressure prediction in fracturing treatment via deep learning algorithms

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PETROLEUM SCIENCE AND TECHNOLOGY 2024年

作者： Liu, Yuxuan Peng, Ziyi Liu, Zhong-Hui Wu, Liansong Wu, Yutong Guo, Jianchun Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu Peoples R China Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu Peoples R China

Hydraulic fracturing relies on accurate pressure prediction for effective risk management and treatment evaluation. deep learning models such as artificial neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, gated recurrent units, and CNN-LSTM offer promising avenues for this prediction. However, the choice of time step size significantly influences the models' predictive power and engineering relevance. Understanding how different models respond to varying time step sizes remains a challenge. This study systematically compares these models for both single-step and multistep pressure predictions in hydraulic fracturing scenarios. By examining the impact of time step size on prediction accuracy and lag, the research sheds light on the tradeoffs between model complexity, dataset size, and prediction performance. The integration of multiple models aims to leverage their individual strengths while mitigating weaknesses, potentially enhancing overall prediction accuracy. This comprehensive analysis aims to inform practitioners and researchers about the optimal selection and deployment of deep learning models for pressure prediction, thus advancing the effectiveness and reliability of hydraulic fracturing operations.

关键词： deep learning algorithms hydraulic fracturing pressure prediction screen-out warning time step

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Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms

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SUPERCONDUCTOR SCIENCE & TECHNOLOGY 2022年第2期35卷

作者： Ke, Zhihao Liu, Xiaoning Chen, Yining Shi, Hongfu Deng, Zigang Southwest Jiaotong Univ State Key Lab Tract Power Chengdu Peoples R China Southwest Jiaotong Univ Leeds Joint Sch Chengdu Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China

Due to the merits of self-stability and low energy consumption, high-temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes five different neural network models (radial basis function, deep neural network (DNN), convolution neural network, recurrent neural network, long short-term memory neural network (LSTM)) to predict HTS maglev guiding force and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptive iterative algorithms for the parameter matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. According to the results, it is revealed that the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90 000, respectively. Moreover, the K-fold cross-validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-tim

关键词： high-temperature superconducting maglev guiding force prediction deep learning algorithms neural network

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Wildfire Susceptibility Mapping Using deep learning algorithms in Two Satellite Imagery Dataset

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FORESTS 2023年第7期14卷 1325页

作者： Bahadori, Nazanin Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Al-Kindi, Khalifa M. Abuhmed, Tamer Nazeri, Behrokh Choi, Soo-Mi KN Toosi Univ Technol Fac Geodesy & Geomat Engn Geoinformat Technol Ctr Excellence Tehran 163171419 Iran Sejong Univ XR Res Ctr Dept Comp Sci & Engn & Convergence Engn Intelligen Seoul 05006 South Korea Univ Nizwa UNESCO Aflaj Studies Archaeohydrol Nizwa 616 Oman Sungkyunkwan Univ Coll Comp & Informat Suwon 16419 South Korea Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

关键词： wildfire satellite imagery spatial modeling deep learning algorithms

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