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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan Ahmed I.Taloba Amr Abozeid Ahmed Ibrahim Alzahrani Ali H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia information system Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen institute of Artificial Intelligence and robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

Quick and automatic detection of co-seismic landslides with multifeature deep learning model

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Science China Earth Sciences 2024年第7期67卷 2311-2325页

作者： Wenchao HUANGFU Haijun QIU Peng CUI Dongdong YANG Ya LIU Bingzhe TANG Zijing LIU Mohib ULLAH College of Urban and Environmental Sciences Northwest UniversityXi'an 710127China institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu 610041China Shaanxi Key Laboratory of Earth Surface system and Environmental Carrying Capacity Northwest UniversityXi'an 710127China institute of Earth Surface system and Hazards Northwest UniversityXi'an 710127China

Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake ***,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and ***,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic *** ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and *** conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,*** accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference *** findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.

关键词： Co-seismic landslide automatic detection Deep learning Landslide gain index PlanetScope images

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Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

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Computers, Materials & Continua 2024年第8期80卷 1889-1905页

作者： Abdulraqeb Alhammadi Zaid Ahmed Shamsan Arijit De Center for Artificial Intelligence and robotics(CAIRO) Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Electrical Engineering College of EngineeringImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of information Engineering Electronics and TelecommunicationsSapienza UniversityRome00185Italy

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation *** complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational *** localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online *** networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user *** networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to *** article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user *** proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s *** operates in both online and offline *** the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s *** results unequivocally showcase the superior performance of the proposed model when compared to other existing models and *** proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing ***,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

关键词： Localization positioning Bayesian fingerprinting received signal strength(RSS)

A novel frequency-dependent hysteresis model based on improved neural Turing machine

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Science China(information Sciences) 2023年第1期66卷 330-331页

作者： Yinan WU Yongchun FANG Zhi FAN Cunhuan LIU institute of robotics and automatic information system College of Artificial IntelligenceNankai University Tianjin Key Laboratory of Intelligent robotics Nankai University

Dear editor,The inherent hysteresis of a piezoelectric actuator(PEA) results in intricate nonlinearity between the output displacement and input voltage, which restricts positioning accuracy of the actuator [1, 2]. Hysteresis behavior appears as a coupling of nonlinearity, frequency-dependence and memory characteristic, which makes it difficult to comprehensively characterize hysteresis [3]. To eliminate the effect of hysteresis on the positioning accuracy of PEAs,

关键词： ATOMIC-FORCE MICROSCOPY

Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances

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Autonomous Intelligent systems 2022年第1期2卷 1-15页

作者： Tianci Wen Yongchun Fang Biao Lu institute of robotics and automatic information system College of Artificial IntelligenceNankai UniversityTianjin300353China

To improve transportation capacity,dual overhead crane systems(DOCSs)are playing an increasingly important role in the transportation of large/heavy cargos and ***,when trying to deal with the control problem,current methods fail to fully consider such factors as external disturbances,input dead zones,parameter uncertainties,and other unmodeled dynamics that DOCSs usually suffer *** a result,dramatic degradation is caused in the control performance,which badly hinders the practical applications of *** by this fact,this paper designs a neural network-based adaptive sliding mode control(SMC)method for DOCS to solve the aforementioned issues,which achieves satisfactory control performance for both actuated and underactuated state variables,even in the presence of matched and mismatched *** asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based ***,extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

关键词： Asymptotic stability Sliding mode control Neural networks Unmatched disturbances Underactuated system

A fast scanning strategy based on trajectory shaping for atomic force microscopy

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Nano Research 2022年第7期15卷 6438-6446页

作者： Yinan Wu Yingao Chang Yongchun Fang Zhi Fan institute of robotics and automatic information system College of Artificial IntelligenceNankai UniversityTianjin 300350China Tianjin Key Laboratory of Intelligent robotics Nankai UniversityTianjin 300350China

To improve the scanning speed of an atomic force microscopy(AFM),a smooth scanning pattern is elaborately devised via trajectory shaping in this paper,so as to achieve fast imaging without hardware ***,in the proposed scanning method,the piezoelectric actuator tracks a well-designed smooth periodic signal in x-direction,and simultaneously tracks a step signal in *** advantage of the proposed method is that it does not require additional data reprocessing to construct the morphology of the sample surface,while significantly increasing the scanning bandwidth restricted by the raster scanning ***,to directly utilize the height data collected by scanning to produce the sample morphology,the forward process in the common raster scanning mode is retained in the proposed method,the tracking signal in the forward process is thus set to a ramp function in *** addition,to ensure the continuity and smoothness of the entire tracking signal in x-direction,a segment of a sine curve is uniquely determined as the backward tracking signal by position and acceleration constraints,so as to ensure that the forward and backward curves are continuous and acceleration-continuous at the intersection ***,the frequency spectrum analysis of the designed smooth signal is carried out to exhibit the depressed amplitudes of high-frequency components,which demonstrates that the proposed method is able to reduce the resonance in AFM high-speed scanning,so as to improve the capacity of rapidly generating high-quality ***,convincing comparison experiments are implemented to verify the imaging performance of the designed scanning algorithm.

关键词： atomic force microscopy fast scanning smooth signal trajectory shaping

Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network

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Transportation Safety and Environment 2024年第2期6卷 24-35页

作者： Jiahao Huang Steffen Jung inger Hui Liu Kerstin Thurow Center for Life Science Automation(CELISCA) University of RostockRostock 18119Germany institute of Automation University of RostockRostock 18119Germany institute of Artificial Intelligence&robotics(IAIR) School of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075HunanChina

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory ***,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are *** can compromise the accuracy of the robot’s localization,which is crucial for the safety of staff,robots and the laboratory.A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory *** proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile *** experimental results demonstrate the effectiveness of this proposed method.

关键词： mobile robots laboratory automation indoor localization neural network

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

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Advances in Atmospheric Sciences 2024年第7期41卷 1342-1363页

作者： Yunqing LIU Lu YANG Mingxuan CHEN Linye SONG Lei HAN Jingfeng XU Faculty of information Science and Engineering Ocean University of ChinaQingdao 266100China institute of Urban Meteorology China Meteorological AdministrationBeijing 100089China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast *** present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation *** this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the institute of Urban Meteorology(IUM)with a lead time of 1 to 6 *** determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm ***,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a *** analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing ***,the performance of TG-TransUnet is compared with other *** results show that TG-TransUnet has the best prediction results at 1–6 *** IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

关键词： thunderstorm gusts deep learning weather forecasting convolutional neural network transformer