Based on the theory of complex network and gray system, the sugesstion that there exist two types of gray nodes in complex networks, Gray Node I and Gray Node II, is concluded. The first one refers to the existent unk...Based on the theory of complex network and gray system, the sugesstion that there exist two types of gray nodes in complex networks, Gray Node I and Gray Node II, is concluded. The first one refers to the existent unknown gray nodes, and the second the evolution gray nodes. The relevant definitions are also given. Further- more, grayness degree in complex networks is described and divided into two forms--the relative grayness degree （RGD） and the absolute grayness degree （AGD）, which are proved respectively.展开更多
本文通过结合混沌系统与数据编码,提出了一种新型彩色图像加密算法。该算法通过离散型广义Arnold映射对明文像素位置进行非线性置乱,破坏相邻像素相关性;引入广义Gray码变换对置乱后图像颜色分量值实施编码,初步隐藏视觉信息;利用连续...本文通过结合混沌系统与数据编码,提出了一种新型彩色图像加密算法。该算法通过离散型广义Arnold映射对明文像素位置进行非线性置乱,破坏相邻像素相关性;引入广义Gray码变换对置乱后图像颜色分量值实施编码,初步隐藏视觉信息;利用连续型广义Arnold映射生成伪随机密钥流,对编码图像完成扩散运算,进一步破坏明文统计特征。加密算法融合了广义Gray码变换的局部混淆能力和广义Arnold映射的全局扩散特性,构建双重安全机制。一方面,离散型广义Arnold映射和广义Gray编码协同增强像素位置与灰度值的动态扰乱效果;另一方面,连续型广义Arnold映射扩展了加密算法的密钥空间。数值实验表明,该图像加密算法具有优良的加密性能,可以抵御蛮力攻击、统计分析攻击以及差分攻击等。The paper proposes a novel image encryption algorithm by integrating chaotic system with data coding. The algorithm employs a discrete generalized Arnold map to nonlinearly scramble plain image’s pixel positions, effectively disrupting adjacent pixel correlations. A generalized gray code transformation is introduced to perform encoding on color component values of the scrambled image, achieving preliminary visual information concealment. Subsequently, a continuous generalized Arnold map generates pseudo-random keystreams to execute diffusion operations on the encoded image, further eliminating statistical features of the plain image. Combining the local confusion capability of generalized gray code transformation with the global diffusion nature of generalized Arnold map, the encryption algorithm establishes a dual security mechanism. On the one hand, the collaborative effect of discrete generalized Arnold map and generalized gray coding enhances dynamic disruption of pixel positions and grayscale values;on the other hand, the continuous generalized Arnold map significantly expands the key space of the proposed encryption. Numerical experiments demonstrate that the proposed image encryption algorithm exhibits excellent performance and security, showing strong resistance against differential analysis attack, statistical attacks and brute-force attack, etc.展开更多
A pregnant woman underwent fetal brain magnetic resonance imaging(MRI)following ultrasound detection of a posterior fossa cyst at 29 weeks'gestation.She presented with no relevant medical history and underwent a r...A pregnant woman underwent fetal brain magnetic resonance imaging(MRI)following ultrasound detection of a posterior fossa cyst at 29 weeks'gestation.She presented with no relevant medical history and underwent a routine obstetric examination during pregnancy.The fetal head position,fetal cranial development,and limb development remained normal until 29 weeks.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections.Previous studies have demonstrated abnormal b...Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections.Previous studies have demonstrated abnormal brain structures in patients with sleep disturbances who have recovered from coronavirus disease 2019(COVID-19).However,neuroimaging studies on sleep disturbances caused by COVID-19 are scarce,and existing studies have primarily focused on the long-term effects of the virus,with minimal acute phase data.As a result,little is known about the pathophysiology of sleep disturbances in the acute phase of COVID-19.To address this issue,we designed a longitudinal study to investigate whether alterations in brain structure occur during the acute phase of infection,and verified the results using 3-month follow-up data.A total of 26 COVID-19 patients with sleep disturbances(aged 51.5±13.57 years,8 women and 18 men),27 COVID-19 patients without sleep disturbances(aged 47.33±15.98 years,9 women and 18 men),and 31 age-and gender-matched healthy controls(aged 49.19±17.51 years,9 women and 22 men)were included in this study.Eleven COVID-19 patients with sleep disturbances were included in a longitudinal analysis.We found that COVID-19 patients with sleep disturbances exhibited brain structural changes in almost all brain lobes.The cortical thicknesses of the left pars opercularis and left precuneus were significantly negatively correlated with Pittsburgh Sleep Quality Index scores.Additionally,we observed changes in the volume of the hippocampus and its subfield regions in COVID-19 patients compared with the healthy controls.The 3-month follow-up data revealed indices of altered cerebral structure(cortical thickness,cortical grey matter volume,and cortical surface area)in the frontal-parietal cortex compared with the baseline in COVID-19 patients with sleep disturbances.Our findings indicate that the sleep disturbances patients had altered morphology in the cortical and hippocampal structures during the acute phase of infection and persistent changes in cortical regions at 3 months post-infection.These data improve our understanding of the pathophysiology of sleep disturbances caused by COVID-19.展开更多
Because the physiological characteristics and melanin regulation mechanism of zebrafish are highly similar with those of humans,it is of high reference value to use zebrafish model in the evaluation of cosmetic whiten...Because the physiological characteristics and melanin regulation mechanism of zebrafish are highly similar with those of humans,it is of high reference value to use zebrafish model in the evaluation of cosmetic whitening efficacy.In this study,zebrafish embryos are used as biological models to evaluate the whitening efficacy of six kinds of cosmetics raw materials,such as antioxidant,preservative and essence,and the formula of facial cleanser and facial mask products,and the limitations of the zebrafish melanin production grayscale detection method in practical application are discussed.The results show that the selection of different types of components can also reduce the production of melanin and show whitening effect.It can be seen that the gray scale method of melanin production in zebrafish is suitable for the evaluation of the efficacy of raw materials.In practical application,due to the complexity of the formula,the toxic effects of different types of ingredients may interfere with the melanin generation of zebrafish,affecting the judgment and evaluation of whitening efficacy.For the detection of whitening efficacy of products,a comprehensive evaluation system should be built together with other methods to accurately evaluate the whitening efficacy.展开更多
Chronic hepatitis B(CHB)remains a significant global health challenge.The natural course of CHB is traditionally divided into four phases:(1)Immune tolerance;(2)Immune activation;(3)Immune control;and(4)Immune escape....Chronic hepatitis B(CHB)remains a significant global health challenge.The natural course of CHB is traditionally divided into four phases:(1)Immune tolerance;(2)Immune activation;(3)Immune control;and(4)Immune escape.However,approximately 20%-30%of patients referred to as the"gray zone"(GZ)do not fit neatly into these categories.These patients often exhibit elevated hepatitis B virus DNA levels alongside normal or mildly elevated alanine aminotransferase levels,placing them at significant risk for liver fibrosis,cirrhosis,and hepatocellular carcinoma.However,current clinical guidelines generally do not recommend antiviral therapy for GZ patients,increasing their vulnerability to adverse outcomes.This mini-review explores the challenges and gaps in CHB management,focusing on GZ patients.It also highlights recent advancements in therapeutic strategies and updates in clinical guidelines,emphasizing the need for a more inclusive,risk-adapted approach to treatment.By leveraging novel biomarkers,noninvasive fibrosis assessment tools,and artificial intelligencedriven predictive models,this article advocates for early intervention to mitigate disease progression and improve clinical outcomes in this overlooked population.展开更多
BACKGROUND Mild cognitive impairment(MCI)is a transitional state between normal aging and Alzheimer's disease(AD),characterized by subtle cognitive decline.Amnestic MCI(aMCI),in particular,is a critical precursor ...BACKGROUND Mild cognitive impairment(MCI)is a transitional state between normal aging and Alzheimer's disease(AD),characterized by subtle cognitive decline.Amnestic MCI(aMCI),in particular,is a critical precursor often progressing to AD.There is growing interest in understanding the neuroanatomical correlates of aMCI,especially the role of gray matter volume(GMV)in cognitive and motor function decline.This study hypothesized that aMCI patients will exhibit reduced GMV,particularly in brain regions associated with cognition and motor control,impacting both cognitive performance and motor abilities.AIM To investigate the association of GMV with cognitive and motor functions in aMCI.METHODS In this cross-sectional study conducted from March 2022 to March 2024,45 aMCI patients and 45 normal controls from our Department of Geratology were enrolled.Voxel-based morphometry was used to compare GMV between groups.Correlation of differential GMV with cognitive scores and gait parameters was assessed via partial correlation analysis.Linear regression was used to assess associations between whole-brain GMV and gait measures.RESULTS GMV of aMCI region of interest(ROI)1 and ROI2 was negatively correlated with Activities of Daily Living(ADL)score.GMV of ROI6 was positively correlated with the total scores of Mini-Mental State Examination and Cambridge Cognitive Examination-Chinese Version(CAMCOG-C)and negatively correlated with ADL score.In the partial correlation analysis of cognitive and motor function parameters,age,gender,educational level,height,and weight were controlled,and the results showed that CAMCOG-C was negatively correlated with Dual Task of Time Up and Go Test(TUG)duration in the aMCI group.The volume of the left occipital gray matter in the aMCI group was negatively correlated with TUG.GMV of the bilateral frontal gyrus,right orbitofrontal gyrus,right occipital cleft,right supraoccipital gyrus,and left anterior central gyrus was positively correlated with walking speed.CONCLUSION GMV reduction in aMCI correlates with impaired cognition and motor function,emphasizing key roles for prefrontal,occipital,and central regions in gait disorders.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
基金Supported by the National Natural Science Foundation of China(71110307023)~~
文摘Based on the theory of complex network and gray system, the sugesstion that there exist two types of gray nodes in complex networks, Gray Node I and Gray Node II, is concluded. The first one refers to the existent unknown gray nodes, and the second the evolution gray nodes. The relevant definitions are also given. Further- more, grayness degree in complex networks is described and divided into two forms--the relative grayness degree （RGD） and the absolute grayness degree （AGD）, which are proved respectively.
文摘本文通过结合混沌系统与数据编码,提出了一种新型彩色图像加密算法。该算法通过离散型广义Arnold映射对明文像素位置进行非线性置乱,破坏相邻像素相关性;引入广义Gray码变换对置乱后图像颜色分量值实施编码,初步隐藏视觉信息;利用连续型广义Arnold映射生成伪随机密钥流,对编码图像完成扩散运算,进一步破坏明文统计特征。加密算法融合了广义Gray码变换的局部混淆能力和广义Arnold映射的全局扩散特性,构建双重安全机制。一方面,离散型广义Arnold映射和广义Gray编码协同增强像素位置与灰度值的动态扰乱效果;另一方面,连续型广义Arnold映射扩展了加密算法的密钥空间。数值实验表明,该图像加密算法具有优良的加密性能,可以抵御蛮力攻击、统计分析攻击以及差分攻击等。The paper proposes a novel image encryption algorithm by integrating chaotic system with data coding. The algorithm employs a discrete generalized Arnold map to nonlinearly scramble plain image’s pixel positions, effectively disrupting adjacent pixel correlations. A generalized gray code transformation is introduced to perform encoding on color component values of the scrambled image, achieving preliminary visual information concealment. Subsequently, a continuous generalized Arnold map generates pseudo-random keystreams to execute diffusion operations on the encoded image, further eliminating statistical features of the plain image. Combining the local confusion capability of generalized gray code transformation with the global diffusion nature of generalized Arnold map, the encryption algorithm establishes a dual security mechanism. On the one hand, the collaborative effect of discrete generalized Arnold map and generalized gray coding enhances dynamic disruption of pixel positions and grayscale values;on the other hand, the continuous generalized Arnold map significantly expands the key space of the proposed encryption. Numerical experiments demonstrate that the proposed image encryption algorithm exhibits excellent performance and security, showing strong resistance against differential analysis attack, statistical attacks and brute-force attack, etc.
基金supported by China Society for Maternal and Child Health Research(Grant/Award Number:2023CAMCHS003A17).
文摘A pregnant woman underwent fetal brain magnetic resonance imaging(MRI)following ultrasound detection of a posterior fossa cyst at 29 weeks'gestation.She presented with no relevant medical history and underwent a routine obstetric examination during pregnancy.The fetal head position,fetal cranial development,and limb development remained normal until 29 weeks.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
基金supported by grants from Major Project of Science and Technology of Guangxi Zhuang Autonomous Region,No.Guike-AA22096018(to JY)Guangxi Key Research and Development Program,No.AB22080053(to DD)+6 种基金Major Project of Science and Technology of Guangxi Zhuang Autonomous Region,No.Guike-AA23023004(to MZ)the National Natural Science Foundation of China,Nos.82260021(to MZ),82060315(to DD)the Natural Science Foundation of Guangxi Zhuang Autonomous Region,No.2021GXNSFBA220007(to GD)Clinical Research Center For Medical Imaging in Hunan Province,No.2020SK4001(to JL)Key Emergency Project of Pneumonia Epidemic of Novel Coronavirus Infection in Hunan Province,No.2020SK3006(to JL)Science and Technology Innovation Program of Hunan Province,No.2021RC4016(to JL)Key Project of the Natural Science Foundation of Hunan Province,No.2024JJ3041(to JL).
文摘Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections.Previous studies have demonstrated abnormal brain structures in patients with sleep disturbances who have recovered from coronavirus disease 2019(COVID-19).However,neuroimaging studies on sleep disturbances caused by COVID-19 are scarce,and existing studies have primarily focused on the long-term effects of the virus,with minimal acute phase data.As a result,little is known about the pathophysiology of sleep disturbances in the acute phase of COVID-19.To address this issue,we designed a longitudinal study to investigate whether alterations in brain structure occur during the acute phase of infection,and verified the results using 3-month follow-up data.A total of 26 COVID-19 patients with sleep disturbances(aged 51.5±13.57 years,8 women and 18 men),27 COVID-19 patients without sleep disturbances(aged 47.33±15.98 years,9 women and 18 men),and 31 age-and gender-matched healthy controls(aged 49.19±17.51 years,9 women and 22 men)were included in this study.Eleven COVID-19 patients with sleep disturbances were included in a longitudinal analysis.We found that COVID-19 patients with sleep disturbances exhibited brain structural changes in almost all brain lobes.The cortical thicknesses of the left pars opercularis and left precuneus were significantly negatively correlated with Pittsburgh Sleep Quality Index scores.Additionally,we observed changes in the volume of the hippocampus and its subfield regions in COVID-19 patients compared with the healthy controls.The 3-month follow-up data revealed indices of altered cerebral structure(cortical thickness,cortical grey matter volume,and cortical surface area)in the frontal-parietal cortex compared with the baseline in COVID-19 patients with sleep disturbances.Our findings indicate that the sleep disturbances patients had altered morphology in the cortical and hippocampal structures during the acute phase of infection and persistent changes in cortical regions at 3 months post-infection.These data improve our understanding of the pathophysiology of sleep disturbances caused by COVID-19.
文摘Because the physiological characteristics and melanin regulation mechanism of zebrafish are highly similar with those of humans,it is of high reference value to use zebrafish model in the evaluation of cosmetic whitening efficacy.In this study,zebrafish embryos are used as biological models to evaluate the whitening efficacy of six kinds of cosmetics raw materials,such as antioxidant,preservative and essence,and the formula of facial cleanser and facial mask products,and the limitations of the zebrafish melanin production grayscale detection method in practical application are discussed.The results show that the selection of different types of components can also reduce the production of melanin and show whitening effect.It can be seen that the gray scale method of melanin production in zebrafish is suitable for the evaluation of the efficacy of raw materials.In practical application,due to the complexity of the formula,the toxic effects of different types of ingredients may interfere with the melanin generation of zebrafish,affecting the judgment and evaluation of whitening efficacy.For the detection of whitening efficacy of products,a comprehensive evaluation system should be built together with other methods to accurately evaluate the whitening efficacy.
文摘Chronic hepatitis B(CHB)remains a significant global health challenge.The natural course of CHB is traditionally divided into four phases:(1)Immune tolerance;(2)Immune activation;(3)Immune control;and(4)Immune escape.However,approximately 20%-30%of patients referred to as the"gray zone"(GZ)do not fit neatly into these categories.These patients often exhibit elevated hepatitis B virus DNA levels alongside normal or mildly elevated alanine aminotransferase levels,placing them at significant risk for liver fibrosis,cirrhosis,and hepatocellular carcinoma.However,current clinical guidelines generally do not recommend antiviral therapy for GZ patients,increasing their vulnerability to adverse outcomes.This mini-review explores the challenges and gaps in CHB management,focusing on GZ patients.It also highlights recent advancements in therapeutic strategies and updates in clinical guidelines,emphasizing the need for a more inclusive,risk-adapted approach to treatment.By leveraging novel biomarkers,noninvasive fibrosis assessment tools,and artificial intelligencedriven predictive models,this article advocates for early intervention to mitigate disease progression and improve clinical outcomes in this overlooked population.
基金Supported by Zhejiang Province Traditional Chinese Medicine Science and Technology Plan Project,No.2023ZL460Zhejiang Province Traditional Chinese Medicine Modernization Special Project,No.2021ZX011。
文摘BACKGROUND Mild cognitive impairment(MCI)is a transitional state between normal aging and Alzheimer's disease(AD),characterized by subtle cognitive decline.Amnestic MCI(aMCI),in particular,is a critical precursor often progressing to AD.There is growing interest in understanding the neuroanatomical correlates of aMCI,especially the role of gray matter volume(GMV)in cognitive and motor function decline.This study hypothesized that aMCI patients will exhibit reduced GMV,particularly in brain regions associated with cognition and motor control,impacting both cognitive performance and motor abilities.AIM To investigate the association of GMV with cognitive and motor functions in aMCI.METHODS In this cross-sectional study conducted from March 2022 to March 2024,45 aMCI patients and 45 normal controls from our Department of Geratology were enrolled.Voxel-based morphometry was used to compare GMV between groups.Correlation of differential GMV with cognitive scores and gait parameters was assessed via partial correlation analysis.Linear regression was used to assess associations between whole-brain GMV and gait measures.RESULTS GMV of aMCI region of interest(ROI)1 and ROI2 was negatively correlated with Activities of Daily Living(ADL)score.GMV of ROI6 was positively correlated with the total scores of Mini-Mental State Examination and Cambridge Cognitive Examination-Chinese Version(CAMCOG-C)and negatively correlated with ADL score.In the partial correlation analysis of cognitive and motor function parameters,age,gender,educational level,height,and weight were controlled,and the results showed that CAMCOG-C was negatively correlated with Dual Task of Time Up and Go Test(TUG)duration in the aMCI group.The volume of the left occipital gray matter in the aMCI group was negatively correlated with TUG.GMV of the bilateral frontal gyrus,right orbitofrontal gyrus,right occipital cleft,right supraoccipital gyrus,and left anterior central gyrus was positively correlated with walking speed.CONCLUSION GMV reduction in aMCI correlates with impaired cognition and motor function,emphasizing key roles for prefrontal,occipital,and central regions in gait disorders.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
